Journal of Modern Power Systems and Clean Energy

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    • 1  Compound Compensation Control for Improving Low-voltage Ride-through Capability of Virtual Synchronous Generators
    Zhiyuan Meng ,  Xiangyang Xing ,  Xiangjun Li ,  Jiadong Sun
    2025, 13(3):1064-1077. DOI: 10.35833/MPCE.2024.000404
    [Abstract](96) [HTML](47) [PDF 11.27 M](170)
    Abstract:
    The virtual synchronous generator (VSG), utilized as a control strategy for grid-forming inverters, is an effective method of providing inertia and voltage support to the grid. However, the VSG exhibits limited capabilities in low-voltage ride-through (LVRT) mode. Specifically，the slow response of the power loop poses challenges for VSG in grid voltage support and increases the risk of overcurrent, potentially violating present grid codes. This paper reveals the mechanism behind the delayed response speed of VSG control during the grid faults. On this basis, a compound compensation control strategy is proposed for improving the LVRT capability of the VSG, which incorporates adaptive frequency feedforward compensation (AFFC), direct power angle compensation (DPAC), internal potential compensation (IPC), and transient virtual impedance (TVI), effectively expediting the response speed and reducing transient current. Furthermore, the proposed control strategy ensures that the VSG operates smoothly back to its normal control state following the restoration from the grid faults. Subsequently, a large-signal model is developed to facilitate parameter design and stability analysis, which incorporates grid codes and TVI. Finally, the small-signal stability analysis and simulation and experimental results prove the correctness of the theoretical analysis and the effectiveness of the proposed control strategy.
    2  Smallest Eigenvalues Based Logarithmic Derivative Method for Computing Dominant Oscillation Modes in Large-scale Power Systems
    Linguang Wang ,  Xiaorong Xie ,  Wenkai Dong ,  Yong Mei ,  Aoyu Lei
    2025, 13(3):747-756. DOI: 10.35833/MPCE.2024.000630
    [Abstract](256) [HTML](47) [PDF 3.00 M](409)
    Abstract:
    With the rapid integration of renewable energy, wide-band oscillations caused by interactions between power electronic equipment and grids have emerged as one of the most critical stability issues. Existing methods are usually studied for local power systems with around one hundred nodes. However, for a large-scale power system with tens of thousands of nodes, the dimension of transfer function matrix or the order of characteristic equation is much higher. In this case, the existing methods such as eigenvalue analysis method and impedance-based method have difficulty in computation and are thus hard to utilize in practice. To fill this gap, this paper proposes a novel method named the smallest eigenvalues based logarithmic derivative (SELD) method. It obtains the dominant oscillation modes by the logarithmic derivative of the k-smallest eigenvalue curves of the sparse extended nodal admittance matrix (NAM). An oscillatory stability analysis tool is further developed based on this method. The effectiveness of the method and the tool is validated through a local power system as well as a large-scale power system.
    3  Multi-temporal Optimization of Virtual Power Plant in Energy-frequency Regulation Market Under Uncertainties
    Wenping Qin ,  Xiaozhou Li ,  Xing Jing ,  Zhilong Zhu ,  Ruipeng Lu ,  Xiaoqing Han
    2025, 13(2):675-687. DOI: 10.35833/MPCE.2024.000118
    [Abstract](62) [HTML](87) [PDF 3.30 M](287)
    Abstract:
    The virtual power plant (VPP) facilitates the coordinated optimization of diverse forms of electrical energy through the aggregation and control of distributed energy resources (DERs), offering as a potential resource for frequency regulation to enhance the power system flexibility. To fully exploit the flexibility of DER and enhance the revenue of VPP, this paper proposes a multi-temporal optimization strategy of VPP in the energy-frequency regulation (EFR) market under the uncertainties of wind power (WP), photovoltaic (PV), and market price. Firstly, all schedulable electric vehicles (EVs) are aggregated into an electric vehicle cluster (EVC), and the schedulable domain evaluation model of EVC is established. A day-ahead energy bidding model based on Stackelberg game is also established for VPP and EVC. Secondly, on this basis, the multi-temporal optimization model of VPP in the EFR market is proposed. To manage risks stemming from the uncertainties of WP, PV, and market price, the concept of conditional value at risk (CVaR) is integrated into the strategy, effectively balancing the bidding benefits and associated risks. Finally, the results based on operational data from a provincial electricity market demonstrate that the proposed strategy enhances comprehensive revenue by providing frequency regulation services and encouraging EV response scheduling.
    4  Consideration on Present and Future of Battery Energy Storage System to Unlock Battery Value
    Ji-Soo Kim ,  Jin-Sol Song ,  Chul-Hwan Kim ,  Jean Mahseredjian ,  Seung-Ho Kim
    2025, 13(2):622-636. DOI: 10.35833/MPCE.2023.000723
    [Abstract](137) [HTML](91) [PDF 1.50 M](507)
    Abstract:
    To address environmental concerns, there has been a rapid global surge in integrating renewable energy sources into power grids. However, this transition poses challenges to grid stability. A prominent solution to this challenge is the adoption of battery energy storage systems (BESSs). Many countries are actively increasing BESS deployment and developing new BESS technologies. Nevertheless, a crucial initial step is conducting a comprehensive analysis of BESS capabilities and subsequently formulating policies. We analyze the current roles of BESS and review existing BESS policies worldwide, which focuses on key markets in Asia, Europe, and the U.S.. Using collected survey data, we propose a comprehensive three-phase framework for policy formulation, providing insights into future policy development directions.
    5  A Mix-integer Programming Based Deep Reinforcement Learning Framework for Optimal Dispatch of Energy Storage System in Distribution Networks
    Shengren Hou ,  Edgar Mauricio Salazar ,  Peter Palensky ,  Qixin Chen ,  Pedro P. Vergara
    2025, 13(2):597-608. DOI: 10.35833/MPCE.2024.000391
    [Abstract](155) [HTML](92) [PDF 2.09 M](524)
    Abstract:
    The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.
    6  DR-MMC Hub Based Hybrid AC/DC Collection and HVDC Transmission System for Large-scale Offshore Wind Farms
    Wang Xiang ,  Mingrui Yang ,  Jinyu Wen
    2025, 13(2):452-461. DOI: 10.35833/MPCE.2024.000229
    [Abstract](88) [HTML](40) [PDF 3.23 M](572)
    Abstract:
    Conventional offshore wind farm (OWF) integration systems typically employ AC cables to gather power to a modular multilevel converter (MMC) platform, subsequently delivering it to onshore grids through high-voltage direct current (HVDC) transmission. However, scaling up the capacity of OWFs introduces significant challenges due to the high costs associated with AC collection cables and offshore MMC platforms. This paper proposes a diode rectifier (DR)-MMC hub based hybrid AC/DC collection and HVDC transmission system for large-scale offshore wind farms. The wind farms in proximity to the offshore converter platform utilize AC collection, while distant wind farms connect to the platform using DC collection. The combined AC/DC power is then transmitted to the offshore DR-MMC hub platform. The topology and operation principle of the DR-MMC hub as well as the integration system are presented. Based on the operational characteristics, the capacity design method for DR-MMC hub is proposed. And the control and startup strategies of the integration system are designed. Furthermore, an economic comparison with the conventional MMC-HVDC based offshore wind power integration system is conducted. Finally, the technical feasibility of the proposed integration scheme is verified through PSCAD/EMTDC simulation with the integration scale of 2 GW.
    7  Two-stage Optimal Scheduling of Community Integrated Energy System Considering Operation Sequences of Hydrogen Energy Storage Systems
    Wei Kong ,  Kai Sun ,  Jinghong Zhao
    2025, 13(1):276-288. DOI: 10.35833/MPCE.2023.001027
    [Abstract](31) [HTML](26) [PDF 3.35 M](585)
    Abstract:
    The hydrogen energy storage system (HESS) integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty. To address the above issues, a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems (CIESs) with power to hydrogen and heat (P2HH) capability. It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads. First, the refined operation model of HESS is established, and its operation model is linearized according to the operation domain of HESS, which simplifies the difficulty of solving the optimization problem under the premise of maintaining high approximate accuracy. Next, considering the flexible start-stop of alkaline electrolyzer (AEL) and the avoidance of multiple energy conversions, the operation sequences of HESS are formulated. Finally, a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established, and the model is simulated and verified using the source-load prediction data of typical days in each season. The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14% while maintaining similar operating cost to the optimal day-ahead economic optimization scheduling. Furthermore, by formulating the operation sequences of HESS, the operating cost of CIES is reduced by up to about 4.4%.
    8  Matching Synchronous Machine Control for Improving Active Support of Grid-forming PV Systems with Enhanced DC Voltage Dynamics
    Zizhen Guo ,  Wenchuan Wu
    2025, 13(1):179-189. DOI: 10.35833/MPCE.2023.000624
    [Abstract](112) [HTML](93) [PDF 3.67 M](919)
    Abstract:
    With photovoltaic (PV) sources becoming more prevalent in the energy generation mix, transitioning grid-connected PV systems from grid-following (GFL) mode to grid-forming (GFM) mode becomes essential for offering self-synchronization and active support services. Although numerous GFM methods have been proposed, the potential of DC voltage control malfunction during the provision of the primary and inertia support in a GFM PV system remains insufficiently researched. To fill the gap, some main GFM methods have been integrated into PV systems featuring detailed DC source dynamics. We conduct a comparative analysis of their performance in active support and DC voltage regulation. AC GFM methods such as virtual synchronous machine (VSM) face a significant risk of DC voltage failure in situations like alterations in solar radiation, leading to PV system tripping and jeopardizing local system operation. In the case of DC GFM methods such as matching control (MC), the active support falls short due to the absence of an accurate and dispatchable droop response. To address the issue, a matching synchronous machine (MSM) control method is developed to provide dispatchable active support and enhance the DC voltage dynamics by integrating the MC and VSM control loops. The active support capability of the PV systems with the proposed method is quantified analytically and verified by numerical simulations and field tests.
    9  A Systematic Small-signal Analysis Procedure for Improving Synchronization Stability of Grid-forming Virtual Synchronous Generators
    Francisco Jesús Matas-Díaz ,  Manuel Barragán-Villarejo ,  José María Maza-Ortega
    2025, 13(1):102-114. DOI: 10.35833/MPCE.2024.000316
    [Abstract](101) [HTML](66) [PDF 3.37 M](867)
    Abstract:
    The integration of converter-interfaced generators (CIGs) into power systems is rapidly replacing traditional synchronous machines. To ensure the security of power supply, modern power systems require the application of grid-forming technologies. This study presents a systematic small-signal analysis procedure to assess the synchronization stability of grid-forming virtual synchronous generators (VSGs) considering the power system characteristics. Specifically, this procedure offers guidance in tuning controller gains to enhance stability. It is applied to six different grid-forming VSGs and experimentally tested to validate the theoretical analysis. This study concludes with key findings and a discussion on the suitability of the analyzed grid-forming VSGs based on the power system characteristics.
    10  Proportion of Grid-forming Wind Turbines in Hybrid GFM-GFL Offshore Wind Farms Integrated with Diode Rectifier Unit Based HVDC System
    Yanqiu Jin ,  Zheren Zhang ,  Zheng Xu
    2025, 13(1):87-101. DOI: 10.35833/MPCE.2024.000432
    [Abstract](73) [HTML](97) [PDF 6.88 M](1075)
    Abstract:
    This study analyzes the stability and reactive characteristics of the hybrid offshore wind farm that includes grid-forming (GFM) and grid-following (GFL) wind turbines (WTs) integrated with a diode rectifier unit (DRU) based high-voltage direct current (HVDC) system. The determination method for the proportion of GFM WTs is proposed while considering system stability and optimal offshore reactive power constraints. First, the small-signal stability is studied based on the developed linear model, and crucial factors that affect the stability are captured by eigenvalue analysis. The reactive power-frequency compensation control of GFM WTs is then proposed to improve the reactive power and frequency dynamics. Second, the relationship between offshore reactive power imbalance and the effectiveness of GFM capability is analyzed. Offshore reactive power optimization methods are next proposed to diminish offshore reactive load. These methods include the optimal design for the reactive capacity of the AC filter and the reactive power compensation control of GFL WTs. Third, in terms of stability and optimal offshore reactive power constraints, the principle and calculation method for determining the proportion of GFM WTs are proposed, and the critical proportion of GFM WTs is determined over the full active power range. Finally, case studies using a detailed model are conducted by time-domain simulations in PSCAD/EMTDC. The simulations verify the theoretical analysis results and the effectiveness of the proposed determination method for the proportion of GFM WTs and reactive power optimization methods.
    11  Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee
    Hang Shuai ,  Buxin She ,  Jinning Wang ,  Fangxing Li
    2025, 13(1):79-86. DOI: 10.35833/MPCE.2023.000882
    [Abstract](87) [HTML](117) [PDF 2.47 M](892)
    Abstract:
    This study investigates a safe reinforcement learning algorithm for grid-forming (GFM) inverter based frequency regulation. To guarantee the stability of the inverter-based resource (IBR) system under the learned control policy, a model-based reinforcement learning (MBRL) algorithm is combined with Lyapunov approach, which determines the safe region of states and actions. To obtain near optimal control policy, the control performance is safely improved by approximate dynamic programming (ADP) using data sampled from the region of attraction (ROA). Moreover, to enhance the control robustness against parameter uncertainty in the inverter, a Gaussian process (GP) model is adopted by the proposed algorithm to effectively learn system dynamics from measurements. Numerical simulations validate the effectiveness of the proposed algorithm.
    12  DC Voltage Control with Grid-forming Capability for Enhancing Stability of HVDC System
    Ghazala Shafique ,  Johan Boukhenfouf ,  François Gruson ,  Frédéric Colas ,  Xavier Guillaud
    2025, 13(1):66-78. DOI: 10.35833/MPCE.2024.000822
    [Abstract](91) [HTML](69) [PDF 7.71 M](1010)
    Abstract:
    Grid-forming (GFM) converters are recognized for their stabilizing effects in renewable energy systems. Integrating GFM converters into high-voltage direct current (HVDC) systems requires DC voltage control. However, there can be a conflict between GFM converter and DC voltage control when they are used in combination. This paper presents a rigorous control design for a GFM converter that connects the DC-link voltage to the power angle of the converter, thereby integrating DC voltage control with GFM capability. The proposed control is validated through small-signal and transient-stability analyses on a modular multilevel converter (MMC)-based HVDC system with a point-to-point (P2P) GFM-GFM configuration. The results demonstrate that employing a GFM-GFM configuration with the proposed control enhances the stability of the AC system to which it is connected. The system exhibits low sensitivity to grid strength and can sustain islanding conditions. The high stability limit of the system with varying grid strength using the proposed control is validated using a system with four voltage source converters.
    13  Grid Strength Assessment Method for Evaluating Small-signal Synchronization Stability of Grid-following and Grid-forming Converters Integrated Systems
    Qianhong Shi ,  Wei Dong ,  Guanzhong Wang ,  Junchao Ma ,  Chenxu Wang ,  Xianye Guo ,  Vladimir Terzija
    2025, 13(1):55-65. DOI: 10.35833/MPCE.2024.000759
    [Abstract](120) [HTML](81) [PDF 4.23 M](945)
    Abstract:
    Oscillations caused by small-signal instability have been widely observed in AC grids with grid-following (GFL) and grid-forming (GFM) converters. The generalized short-circuit ratio is commonly used to assess the strength of GFL converters when integrated with weak AC systems at risk of oscillation. This paper provides the grid strength assessment method to evaluate the small-signal synchronization stability of GFL and GFM converters integrated systems. First, the admittance and impedance matrices of the GFL and GFM converters are analyzed to identify the frequency bands associated with negative damping in oscillation modes dominated by heterogeneous synchronization control. Secondly, based on the interaction rules between the short-circuit ratio and the different oscillation modes, an equivalent circuit is proposed to simplify the grid strength assessment through the topological transformation of the AC grid. The risk of sub-synchronization and low-frequency oscillations, influenced by GFL and GFM converters, is then reformulated as a semi-definite programming (SDP) model, incorporating the node admittance matrix and grid-connected device capacities. The effectiveness of the proposed method is demonstrated through a case analysis.
    14  Hybrid Frequency-domain Modeling and Stability Analysis for Power Systems with Grid-following and Grid-forming Converters
    Ni Liu ,  Hong Wang ,  Weihua Zhou ,  Jie Song ,  Yiting Zhang ,  Eduardo Prieto-Araujo ,  Zhe Chen
    2025, 13(1):15-28. DOI: 10.35833/MPCE.2023.000842
    [Abstract](98) [HTML](79) [PDF 3.61 M](1040)
    Abstract:
    With the increase of the renewable energy generator capacity, the requirements of the power system for grid-connected converters are evolve, which leads to diverse control schemes and increased complexity of systematic stability analysis. Although various frequency-domain models are developed to identify oscillation causes, the discrepancies between them are rarely studied. This study aims to clarify these discrepancies and provide circuit insights for stability analysis by using different frequency-domain models. This study emphasizes the limitations of assuming that the transfer function of the self-stable converter does not have right half-plane (RHP) poles. To ensure that the self-stable converters are represented by a frequency-domain model without RHP poles, the applicability of this model of grid-following (GFL) and grid-forming (GFM) converters is discussed. This study recommends that the GFM converters with ideal sources should be represented in parallel with the P / Q - θ / V admittance model rather than the V - I impedance model. Two cases are conducted to illustrate the rationality of the P / Q - θ / V admittance model. Additionally, a hybrid frequency-domain modeling framework and stability criteria are proposed for the power system with several GFL and GFM converters. The stability criteria eliminates the need to check the RHP pole numbers in the non-passive subsystem when applying the Nyquist stability criterion, thereby reducing the complexity of stability analysis. Simulations are carried out to validate the correctness of the frequency-domain model and the stability criteria.
    15  Dynamic Analysis of Uniformity and Difference for Grid-following and Grid-forming Voltage Source Converters Using Phasor and Topological Homology Methods
    Haiyu Zhao ,  Hongyu Zhou ,  Wei Yao ,  Qihang Zong ,  Jinyu Wen
    2025, 13(1):3-14. DOI: 10.35833/MPCE.2024.000722
    [Abstract](168) [HTML](92) [PDF 3.02 M](1009)
    Abstract:
    Grid-following voltage source converter (GFL-VSC) and grid-forming voltage source converter (GFM-VSC) have different dynamic characteristics for active power-frequency and reactive power-voltage supports of the power grid. This paper aims to clarify and recognize the difference between grid-following (GFL) and grid-forming (GFM) frequency-voltage support more intuitively and clearly. Firstly, the phasor model considering circuit constraints is established based on the port circuit equations of the converter. It is revealed that the voltage and active power linearly correspond to the horizontal and vertical axes in the phasor space referenced to the grid voltage phasor. Secondly, based on topological homology, GFL and GFM controls are transformed and mapped into different trajectories. The topological similarity of the characteristic curves for GFL and GFM controls is the essential cause of their uniformity. Based on the above model, it is indicated that GFL-VSC and GFM-VSC possess uniformity with regard to active power response, type of coupling, and phasor trajectory. They differ in synchronization, power coupling mechanisms, dynamics, and active power-voltage operation domain in the quasi-steady state. Case studies are undertaken on GFL-VSC and GFM-VSC integrated into a four-machine two-area system. Simulation results verify that the dynamic uniformity and difference of GFL-VSC and GFM-VSC are intuitively and comprehensively revealed.
    16  Towards Renewable-dominated Energy Systems: Role of Green Hydrogen
    Sheng Chen ,  Jingchun Zhang ,  Zhinong Wei ,  Hao Cheng ,  Si Lv
    2024, 12(6):1697-1709. DOI: 10.35833/MPCE.2023.000887
    [Abstract](120) [HTML](86) [PDF 1.67 M](460)
    Abstract:
    Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strengthened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.
    17  Digital Twin Empowered PV Power Prediction
    Xiaoyu Zhang ,  Yushuai Li ,  Tianyi Li ,  Yonghao Gui ,  Qiuye Sun ,  David Wenzhong Gao
    2024, 12(5):1472-1483. DOI: 10.35833/MPCE.2023.000351
    [Abstract](158) [HTML](202) [PDF 2.66 M](1968)
    Abstract:
    The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.
    18  Two-stage Transient-stability-constrained Optimal Power Flow for Preventive Control of Rotor Angle Stability and Voltage Sags
    Jorge Uriel Sevilla-Romero ,  Alejandro Pizano-Martínez ,  Claudio Rubén Fuerte-Esquivel ,  Reymundo Ramírez-Betancour
    2024, 12(5):1357-1369. DOI: 10.35833/MPCE.2023.000461
    [Abstract](156) [HTML](96) [PDF 4.96 M](1378)
    Abstract:
    In practice, an equilibrium point of the power system is considered transiently secure if it can withstand a specified contingency by maintaining transient evolution of rotor angles and voltage magnitudes within set bounds. A novel sequential approach is proposed to obtain transiently stable equilibrium points through the preventive control of transient stability and transient voltage sag (TVS) problems caused by a severe disturbance. The proposed approach conducts a sequence of non-heuristic optimal active power re-dispatch of the generators to steer the system toward a transiently secure operating point by sequentially solving the transient-stability-constrained optimal power flow (TSC-OPF) problems. In the proposed approach, there are two sequential projection stages, with the first stage ensuring the rotor angle stability and the second stage removing TVS in voltage magnitudes. In both projection stages, the projection operation corresponds to the TSC-OPF, with its formulation directly derived by adding only two steady-state variable-based transient constraints to the conventional OPF problem. The effectiveness of this approach is numerically demonstrated in terms of its accuracy and computational performance by using the Western System Coordinated Council (WSCC) 3-machine 9-bus system and an equivalent model of the Mexican 46-machine 190-bus system.
    19  Optimal Operation Control Strategies for Active Distribution Networks Under Multiple States: A Systematic Review
    Jingtao Zhao ,  Zhi Wu ,  Huan Long ,  Huapeng Sun ,  Xi Wu ,  Chingchuen Chan ,  Mohammad Shahidehpour
    2024, 12(5):1333-1344. DOI: 10.35833/MPCE.2023.000372
    [Abstract](259) [HTML](114) [PDF 1.12 M](1369)
    Abstract:
    With the large-scale integration of distributed renewable generation (DRG) and increasing proportion of power electronic equipment, the traditional power distribution network (DN) is evolving into an active distribution network (ADN). The operation state of an ADN, which is equipped with DRGs, could rapidly change among multiple states, which include steady, alert, and fault states. It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs. In this paper, the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states. The multi-state identification indicators and identification methods are summarized in detail. The multi-state regulation capacity quantification methods are analyzed considering controllable resources, quantification indicators, and quantification methods. A detailed survey of optimal operation control strategies, including multiple state operations, is presented, and key problems and outlooks for the expansion of ADN are discussed.
    20  Small-signal Stability Criteria in Power Electronics-dominated Power Systems: A Comparative Review
    Qifan Chen ,  Siqi Bu ,  Chi Yung Chung
    2024, 12(4):1003-1018. DOI: 10.35833/MPCE.2023.000526
    [Abstract](279) [HTML](165) [PDF 3.80 M](2615)
    Abstract:
    To tackle emerging power system small-signal stability problems such as wideband oscillations induced by the large-scale integration of renewable energy and power electronics, it is crucial to review and compare existing small-signal stability analysis methods. On this basis, guidance can be provided on determining suitable analysis methods to solve relevant small-signal stability problems in power electronics-dominated power systems (PEDPSs). Various mature methods have been developed to analyze the small-signal stability of PEDPSs, including eigenvalue-based methods, Routh stability criterion, Nyquist/Bode plot based methods, passivity-based methods, positive-net-damping method, lumped impedance-based methods, bifurcation-based methods, etc. In this paper, the application conditions, advantages, and limitations of these criteria in identifying oscillation frequencies and stability margins are reviewed and compared to reveal and explain connections and discrepancies among them. Especially, efforts are devoted to mathematically proving the equivalence between these small-signal stability criteria. Finally, the performance of these criteria is demonstrated and compared in a 4-machine 2-area power system with a wind farm and an IEEE 39-bus power system with 3 wind farms.
    21  Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning
    Jie Xu ,  Hongjun Gao ,  Renjun Wang ,  Junyong Liu
    2024, 12(3):886-899. DOI: 10.35833/MPCE.2023.000213
    [Abstract](219) [HTML](90) [PDF 4.37 M](756)
    Abstract:
    The increasing integration of intermittent renewable energy sources (RESs) poses great challenges to active distribution networks (ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multi-agent deep reinforcement learning (MADRL), which harnesses the regulating function of switch state transitions for the real-time voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources (DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing (PS) and prioritized experience replay (PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118-bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.
    22  Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size
    Zhoujun Ma ,  Yizhou Zhou ,  Yuping Zheng ,  Li Yang ,  Zhinong Wei
    2024, 12(3):852-862. DOI: 10.35833/MPCE.2023.000204
    [Abstract](154) [HTML](88) [PDF 2.80 M](725)
    Abstract:
    This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.
    23  A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles
    Abdelfatah Ali ,  Hossam H. H. Mousa ,  Mostafa F. Shaaban ,  Maher A. Azzouz ,  Ahmed S. A. Awad
    2024, 12(3):675-694. DOI: 10.35833/MPCE.2023.000107
    [Abstract](336) [HTML](220) [PDF 7.11 M](2112)
    Abstract:
    Electric vehicles (EVs) are becoming more popular worldwide due to environmental concerns, fuel security, and price volatility. The performance of EVs relies on the energy stored in their batteries, which can be charged using either AC (slow) or DC (fast) chargers. Additionally, EVs can also be used as mobile power storage devices using vehicle-to-grid (V2G) technology. Power electronic converters (PECs) have a constructive role in EV applications, both in charging EVs and in V2G. Hence, this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications. It examines PECs from the point of view of their classifications, configurations, control approaches, and future research prospects and their impacts on power quality. These can be classified into various topologies: DC-DC converters, AC-DC converters, DC-AC converters, and AC-AC converters. To address the limitations of traditional DC-DC converters such as switching losses, size, and high-electromagnetic interference (EMI), resonant converters and multiport converters are being used in high-voltage EV applications. Additionally, power-train converters have been modified for high-efficiency and reliability in EV applications. This paper offers an overview of charging topologies, PECs, challenges with solutions, and future trends in the field of the EV charging station applications.
    24  Virtual Transmission Solution Based on Battery Energy Storage Systems to Boost Transmission Capacity
    Matías Agüero ,  Jaime Peralta ,  Eugenio Quintana ,  Victor Velar ,  Anton Stepanov ,  Hossein Ashourian ,  Jean Mahseredjian ,  Roberto Cárdenas
    2024, 12(2):466-474. DOI: 10.35833/MPCE.2023.000729
    [Abstract](619) [HTML](125) [PDF 4.50 M](1401)
    Abstract:
    The increasing penetration of variable renewable energy (VRE) generation along with the decommissioning of conventional power plants in Chile, has raised several operational challenges in the Chilean National Power Grid (NPG), including transmission congestion and VRE curtailment. To mitigate these limitations, an innovative virtual transmission solution based on battery energy storage systems (BESSs), known as grid booster (GB), has been proposed to increase the capacity of the main 500 kV corridor of the NPG. This paper analyzes the dynamic performance of the GB using a wide-area electromagnetic transient (EMT) model of the NPG. The GB project, composed of two 500 MVA BESS units at each extreme of the 500 kV corridor, allows increasing the transmission capacity for 15 min during N - 1 contingencies, overcoming transmission limitations under normal operation conditions while maintaining system stability during faults. The dynamic behavior of the GB is also analyzed to control power flow as well as voltage stability. The results show that the GB is an effective solution to allow greater penetration of VRE generation while maintaining system stability in the NPG.
    25  Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators
    Xiao Xu ,  Ziwen Qiu ,  Teng Zhang ,  Hui Gao
    2024, 12(2):440-453. DOI: 10.35833/MPCE.2023.000742
    [Abstract](878) [HTML](150) [PDF 3.53 M](1171)
    Abstract:
    The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.
    26  Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits
    Jing Bian ,  Yuheng Song ,  Chen Ding ,  Jianing Cheng ,  Shiqiang Li ,  Guoqing Li
    2024, 12(2):427-439. DOI: 10.35833/MPCE.2023.000707
    [Abstract](676) [HTML](145) [PDF 3.46 M](1331)
    Abstract:
    Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.
    27  Optimal Offering of Energy Storage in UK Day-ahead Energy and Frequency Response Markets
    Makedon Karasavvidis ,  Andreas Stratis ,  Dimitrios Papadaskalopoulos ,  Goran Strbac
    2024, 12(2):415-426. DOI: 10.35833/MPCE.2023.000737
    [Abstract](925) [HTML](121) [PDF 595.82 K](1015)
    Abstract:
    The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.
    28  Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems
    Pavitra Sharma ,  Krishna Kumar Saini ,  Hitesh Datt Mathur ,  Puneet Mishra
    2024, 12(2):381-392. DOI: 10.35833/MPCE.2023.000761
    [Abstract](1130) [HTML](180) [PDF 4.42 M](2064)
    Abstract:
    The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These prosumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.
    29  Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy
    Jianlin Li ,  Zhijin Fang ,  Qian Wang ,  Mengyuan Zhang ,  Yaxin Li ,  Weijun Zhang
    2024, 12(2):359-370. DOI: 10.35833/MPCE.2023.000345
    [Abstract](898) [HTML](198) [PDF 2.13 M](2012)
    Abstract:
    As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.
    30  Low-carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value
    Hongchao Gao ,  Tai Jin ,  Guanxiong Wang ,  Qixin Chen ,  Chongqing Kang ,  Jingkai Zhu
    2024, 12(2):346-358. DOI: 10.35833/MPCE.2023.000762
    [Abstract](892) [HTML](144) [PDF 3.23 M](2250)
    Abstract:
    The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, wherein energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear reconstruction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
    31  Storing Freshwater Versus Storing Electricity in Power Systems with High Freshwater Electric Demand
    Mubarak J. Al-Mubarak ,  Antonio J. Conejo
    2024, 12(2):323-333. DOI: 10.35833/MPCE.2023.000306
    [Abstract](884) [HTML](238) [PDF 2.79 M](1907)
    Abstract:
    We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.
    32  Electricity Theft Detection Method Based on Ensemble Learning and Prototype Learning
    Xinwu Sun ,  Jiaxiang Hu ,  Zhenyuan Zhang ,  Di Cao ,  Qi Huang ,  Zhe Chen ,  Weihao Hu
    2024, 12(1):213-224. DOI: 10.35833/MPCE.2022.000680
    [Abstract](523) [HTML](62) [PDF 2.16 M](627)
    Abstract:
    With the development of advanced metering infrastructure (AMI), large amounts of electricity consumption data can be collected for electricity theft detection. However, the imbalance of electricity consumption data is violent, which makes the training of detection model challenging. In this case, this paper proposes an electricity theft detection method based on ensemble learning and prototype learning, which has great performance on imbalanced dataset and abnormal data with different abnormal level. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are employed to obtain abstract feature from electricity consumption data. After calculating the means of the abstract feature, the prototype per class is obtained, which is used to predict the labels of unknown samples. In the meanwhile, through training the network by different balanced subsets of training set, the prototype is representative. Compared with some mainstream methods including CNN, random forest (RF) and so on, the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5% and 1.25% of normal data. The results show that the proposed method outperforms other state-of-the-art methods.
    33  Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks
    Bingjing Yan ,  Pengchao Yao ,  Tao Yang ,  Boyang Zhou ,  Qiang Yang
    2024, 12(1):41-51. DOI: 10.35833/MPCE.2022.000524
    [Abstract](569) [HTML](58) [PDF 2.57 M](741)
    Abstract:
    Electric power grids are evolving into complex cyber-physical power systems (CPPSs) that integrate advanced information and communication technologies (ICTs) but face increasing cyberspace threats and attacks. This study considers CPPS cyberspace security under distributed denial of service (DDoS) attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources. Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks. Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free, failed attack, and successful attack, which lead to a corresponding consumption of resources. A multidimensional node value analysis is designed to introduce physical and cybersecurity indices. Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.
    34  Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems
    Xiaoxue Zhang ,  Fang Zhang ,  Wenzhong Gao ,  Jinghan He
    2024, 12(1):22-33. DOI: 10.35833/MPCE.2022.000766
    [Abstract](839) [HTML](87) [PDF 2.95 M](1663)
    Abstract:
    The subsynchronous oscillations (SSOs) related to renewable generation seriously affect the stability and safety of the power systems. To realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method (MPM), this paper proposes an improved MPM-based parameter identification with synchrophasors. The MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coefficients of the matrix pencil to ensure the accuracy of the identified parameters, since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation modes. Then, the identification and recovery of bad data are proposed by utilizing the difference in temporal continuity of the synchrophasors before and after noise reduction. The proposed parameter identification is verified with synthetic, simulated, and actual measured phase measurement unit (PMU) data. Compared with the existing MPM, the improved MPM achieves better accuracy for parameter identification of each component in SSOs, better real-time performance, and significantly reduces the effect of bad data.
    35  Exploiting Flexibility of Integrated Demand Response to Alleviate Power Flow Violation During Line Tripping Contingency
    Tong Cheng ,  Zhenfei Tan ,  Haiwang Zhong
    2023, 11(6):1971-1981. DOI: 10.35833/MPCE.2021.000535
    [Abstract](277) [HTML](87) [PDF 2.00 M](993)
    Abstract:
    Multi-energy integrations provide great opportunities for economic and efficient resource utilization. In the meantime, power system operation requires enough flexible resources to deal with contingencies such as transmission line tripping. Besides economic benefits, this paper focuses on the security benefits that can be provided by multi-energy integrations. This paper first proposes an operation scheme to coordinate multiple energy production and local system consumption considering transmission networks. The integrated flexibility model, constructed by the feasible region of integrated demand response (IDR), is then formulated to aggregate and describe local flexibility. Combined with system security constraints, a multi-energy system operation model is formulated to schedule multiple energy production, transmission, and consumption. The effects of local system flexibility on alleviating power flow violations during N-1 line tripping contingencies are then analyzed through a multi-energy system case. The results show that local system flexibility can not only reduce the system operation costs, but also reduce the probability of power flow congestion or violations by approximately 68.8% during N-1 line tripping contingencies.
    36  A Review on Self-healing in Modern Power Distribution Systems
    Seyed Ali Arefifar ,  Md Shahin Alam ,  Abdullah Hamadi
    2023, 11(6):1719-1733. DOI: 10.35833/MPCE.2022.000032
    [Abstract](645) [HTML](134) [PDF 1.33 M](1236)
    Abstract:
    The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.
    37  Multi-energy Management of Interconnected Multi-microgrid System Using Multi-agent Deep Reinforcement Learning
    Sichen Li ,  Di Cao ,  Weihao Hu ,  Qi Huang ,  Zhe Chen ,  Frede Blaabjerg
    2023, 11(5):1606-1617. DOI: 10.35833/MPCE.2022.000473
    [Abstract](438) [HTML](88) [PDF 2.51 M](1096)
    Abstract:
    The multi-directional flow of energy in a multi-microgrid (MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.
    38  A Review on Challenges in DC Microgrid Planning and Implementation
    Kolampurath Jithin ,  Puthan Purayil Haridev ,  Nanappan Mayadevi ,  Raveendran Pillai Harikumar ,  Valiyakulam Prabhakaran Mini
    2023, 11(5):1375-1395. DOI: 10.35833/MPCE.2022.000053
    [Abstract](824) [HTML](149) [PDF 5.27 M](1431)
    Abstract:
    DC microgrids are gaining more attention with the increased penetration of various DC sources such as solar photovoltaic systems, fuel cells, batteries, etc., and DC loads. Due to the rapid integration of these components into the existing power system, the importance of DC microgrids has reached a salient point. Compared with conventional AC systems, DC systems are free from synchronization issues, reactive power control, frequency control, etc., and are more reliable and efficient. However, many challenges need to be addressed for utilizing DC power to its full potential. The absence of natural current zero is a significant issue in protecting DC systems. In addition, the stability of the DC microgrid, which relies on inertia, needs to be considered during system design. Moreover, power quality and communication issues are also significant challenges in DC microgrids. This paper presents a review of various value streams of DC microgrids including architectures, protection schemes, power quality, inertia, communication, and economic operation. In addition, comparisons between different microgrid configurations, the state-of-the-art projects of DC microgrid, and future trends are also set forth for further studies.
    39  Stability Comparison Between Grid-forming and Grid-following Based Wind Farms Integrated MMC-HVDC
    Rongcai Pan ,  Dong Liu ,  Shan Liu ,  Jie Yang ,  Longze Kou ,  Guangfu Tang
    2023, 11(4):1341-1355. DOI: 10.35833/MPCE.2022.000158
    [Abstract](462) [HTML](53) [PDF 3.53 M](894)
    Abstract:
    Grid-forming (GFM) control based high-voltage DC (HVDC) systems and renewable energy sources (RESs) provide support for enhancing the stability of power systems. However, the interaction and coordination of frequency support between the GFM-based modular multilevel converter based HVDC (MMC-HVDC) and grid-following (GFL) based RESs or GFM-based RESs have not been fully investigated, which are examined in this study. First, the detailed AC- and DC-side impedances of GFM-based MMC-HVDC are analyzed. The impedance characteristics of GFL- and GFM-based wind turbines are next analyzed. Then, the influences of GFL- and GFM-based wind farms (WFs) on the DC- and AC-side stabilities of WF-integrated MMC-HVDC systems are compared and evaluated. The results show that the GFM-based wind turbine performs better than the GFL-based wind turbine. Accordingly, to support a receiving-end AC system, the corresponding frequency supporting strategies are proposed based on the GFM control for WF-integrated MMC-HVDC systems. The GFM-based WF outperforms the GFL-based WF in terms of stability and response time. Simulations in PSCAD/EMTDC demonstrate the DC- and AC-side stability issues and seamless grid support from the RESs, i.e., WFs, to the receiving-end AC system.
    40  An Improved Perturb and Observed Maximum Power Point Tracking Algorithm for Photovoltaic Power Systems
    Rasool Kahani ,  Mohsin Jamil ,  M. Tariq Iqbal
    2023, 11(4):1165-1175. DOI: 10.35833/MPCE.2022.000245
    [Abstract](436) [HTML](101) [PDF 6.33 M](1231)
    Abstract:
    This paper aims to improve the performance of the conventional perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. As the oscillation around the maximum power point (MPP) is the main disadvantage of this technique, we introduce a modified P&O algorithm to conquer this handicap. The new algorithm recognizes approaching the peak of the photovoltaic (PV) array power curve and prevents the oscillation around the MPP. The key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous cycle. If a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction, an increase in array terminal power is observed after a decrease in the previous cycle; it means we are at the peak of the power curve, so the duty cycle of the boost converter should remain the same as the previous cycle. Besides, an optimized duty cycle is introduced, which is adjusted based on the operating point of PV array. Furthermore, a DC-DC boost converter powered by a PV array simulator is used to test the proposed concept. When the irradiance changes, the proposed algorithm produces an average ηMPPT of nearly 3.1% greater than that of the conventional P&O algorithm and the incremental conductance (InC) algorithm. In addition, under strong partial shading conditions and drift avoidance tests, the proposed algorithm produces an average ηMPPT of nearly 9% and 8% greater than that of the conventional algorithms, respectively.
    41  Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique
    Wenlong Liao ,  Shouxiang Wang ,  Birgitte Bak-Jensen ,  Jayakrishnan Radhakrishna Pillai ,  Zhe Yang ,  Kuangpu Liu
    2023, 11(4):1100-1114. DOI: 10.35833/MPCE.2022.000632
    [Abstract](461) [HTML](50) [PDF 3.35 M](1192)
    Abstract:
    Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To represent the uncertainty of wind power, this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network (GNN) and an improved Bootstrap technique. Specifically, adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective. Then, the graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) are proposed to capture spatiotemporal features between nodes in the graph. To obtain high-quality prediction intervals (PIs), an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively. Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph, and the prediction results outperform popular baselines on two real-world datasets, which implies a high potential for practical applications in power systems.
    42  Modeling and Simulation of Hydrogen Energy Storage System for Power-to-gas and Gas-to-power Systems
    Jianlin Li ,  Guanghui Li ,  Suliang Ma ,  Zhonghao Liang ,  Yaxin Li ,  Wei Zeng
    2023, 11(3):885-895. DOI: 10.35833/MPCE.2021.000705
    [Abstract](628) [HTML](105) [PDF 3.74 M](1325)
    Abstract:
    By collecting and organizing historical data and typical model characteristics, hydrogen energy storage system (HESS)-based power-to-gas (P2G) and gas-to-power systems are developed using Simulink. The energy transfer mechanisms and numerical modeling methods of the proposed systems are studied in detail. The proposed integrated HESS model covers the following system components: alkaline electrolyzer (AE), high-pressure hydrogen storage tank with compressor (CM & H 2 tank), and proton-exchange membrane fuel cell (PEMFC) stack. The unit models in the HESS are established based on typical U-I curves and equivalent circuit models, which are used to analyze the operating characteristics and charging/discharging behaviors of a typical AE, an ideal CM & H 2 tank, and a PEMFC stack. The validities of these models are simulated and verified in the MicroGrid system, which is equipped with a wind power generation system, a photovoltaic power generation system, and an auxiliary battery energy storage system (BESS) unit. Simulation results in MATLAB/Simulink show that electrolyzer stack, fuel cell stack and system integration model can operate in different cases. By testing the simulation results of the HESS under different working conditions, the hydrogen production flow, stack voltage, state of charge (SOC) of the BESS, state of hydrogen pressure (SOHP) of the HESS, and HESS energy flow paths are analyzed. The simulation results are consistent with expectations, showing that the integrated HESS model can effectively absorb wind and photovoltaic power. As the wind and photovoltaic power generations increase, the HESS current increases, thereby increasing the amount of hydrogen production to absorb the surplus power. The results show that the HESS responds faster than the traditional BESS in the microgrid, providing a solid theoretical foundation for later wind-photovoltaic-HESS-BESS integration.
    43  A Review on Cybersecurity Analysis, Attack Detection, and Attack Defense Methods in Cyber-physical Power Systems
    Dajun Du ,  Minggao Zhu ,  Xue Li ,  Minrui Fei ,  Siqi Bu ,  Lei Wu ,  Kang Li
    2023, 11(3):727-743. DOI: 10.35833/MPCE.2021.000604
    [Abstract](612) [HTML](121) [PDF 2.72 M](2108)
    Abstract:
    Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.
    44  Sharing Economy in Local Energy Markets
    Zhaoyuan Wu ,  Jianxiao Wang ,  Haiwang Zhong ,  Feng Gao ,  Tianjiao Pu ,  Chin-Woo Tan ,  Xiupeng Chen ,  Gengyin Li ,  Huiru Zhao ,  Ming Zhou ,  Qing Xia
    2023, 11(3):714-726. DOI: 10.35833/MPCE.2022.000521
    [Abstract](730) [HTML](89) [PDF 1.80 M](1232)
    Abstract:
    With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.
    45  What May Future Electricity Markets Look Like?
    Pierre Pinson
    2023, 11(3):705-713. DOI: 10.35833/MPCE.2023.000073
    [Abstract](556) [HTML](124) [PDF 703.70 K](1978)
    Abstract:
    Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
    46  Reliability Assessment of Interconnected Power Systems with HVDC Links Considering Frequency Regulation Process
    Chengjin Ye ,  Libang Guo ,  Yi Ding ,  Ming Ding ,  Peng Wang ,  Lei Wang
    2023, 11(2):662-673. DOI: 10.35833/MPCE.2021.000491
    [Abstract](588) [HTML](129) [PDF 2.60 M](1772)
    Abstract:
    With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.
    47  Parameter Estimation for Hot-spot Thermal Model of Power Transformers Using Unscented Kalman Filters
    Miguel Ángel González-Cagigal ,  José Antonio Rosendo-Macías ,  Antonio Gómez-Expósito
    2023, 11(2):634-642. DOI: 10.35833/MPCE.2022.000439
    [Abstract](493) [HTML](47) [PDF 2.64 M](1202)
    Abstract:
    This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.
    48  Bi-level Energy Trading Model Incorporating Large-scale Biogas Plant and Demand Response Aggregator
    Hanyu Yang ,  Canbing Li ,  Ruanming Huang ,  Feng Wang ,  Lili Hao ,  Qiuwei Wu ,  Long Zhou
    2023, 11(2):567-578. DOI: 10.35833/MPCE.2021.000632
    [Abstract](506) [HTML](76) [PDF 2.88 M](1163)
    Abstract:
    Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.
    49  Comprehensive Optimization-based Techno-economic Assessment of Hybrid Renewable Electricity-hydrogen Virtual Power Plants
    James Naughton ,  Shariq Riaz ,  Michael Cantoni ,  Xiao-Ping Zhang ,  Pierluigi Mancarella
    2023, 11(2):553-566. DOI: 10.35833/MPCE.2022.000324
    [Abstract](505) [HTML](87) [PDF 1.92 M](2115)
    Abstract:
    Hydrogen is being considered as an important option to contribute to energy system decarbonization. However, currently its production from renewables is expensive compared with the methods that utilize fossil fuels. This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant (VPP) that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost. Additionally, multiple possible investment options are considered. Case studies of VPP placement in a renewable-rich, congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen. This highlights the importance of value stacking for driving down the cost of cleaner hydrogen. Due to the participation in multiple markets, all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD$/kg (2.25 USD$/kg), which has been identified as a threshold value for Australia to export hydrogen at a competitive price. Additionally, if the high price volatility that has been seen in gas prices in 2022 (and by extension electricity prices) continues, the flexibility of hybrid VPPs will further improve their business cases.
    50  Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes
    Shengyuan Liu ,  Yicheng Jiang ,  Zhenzhi Lin ,  Fushuan Wen ,  Yi Ding ,  Li Yang
    2023, 11(2):523-533. DOI: 10.35833/MPCE.2021.000196
    [Abstract](559) [HTML](51) [PDF 2.71 M](1221)
    Abstract:
    In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted K-nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.
    51  Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids
    Alejandro Latorre ,  Wilmar Martinez ,  Camilo A. Cortes
    2023, 11(2):511-522. DOI: 10.35833/MPCE.2021.000359
    [Abstract](521) [HTML](51) [PDF 3.71 M](1253)
    Abstract:
    Among hybrid energy storage systems (HESSs), battery-ultracapacitor systems in active topology use DC/DC power converters for their operations. HESSs are part of the solutions designed to improve the operation of power systems in different applications. In the residential microgrid applications, a multilevel control system is required to manage the available energy and interactions among the microgrid components. For this purpose, a rule-based power management system is designed, whose operation is validated in the simulation, and the performances of different controllers are compared to select the best strategy for the DC/DC converters. The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters, allowing voltage regulation values close to 1%. The results are validated using real-time hardware-in-the-loop (HIL). These systems can be easily adjusted for other applications such as electric vehicles.
    52  Data-driven Approach for State Prediction and Detection of False Data Injection Attacks in Smart Grid
    Haftu Tasew Reda ,  Adnan Anwar ,  Abdun Mahmood ,  Naveen Chilamkurti
    2023, 11(2):455-467. DOI: 10.35833/MPCE.2020.000827
    [Abstract](545) [HTML](100) [PDF 4.49 M](2049)
    Abstract:
    In a smart grid, state estimation (SE) is a very important component of energy management system. Its main functions include system SE and detection of cyber anomalies. Recently, it has been shown that conventional SE techniques are vulnerable to false data injection (FDI) attack, which is a sophisticated new class of attacks on data integrity in smart grid. The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model, which is different from the traditional weighted least square based SE model. This SE model has a number of unique advantages compared with traditional SE models. First, the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors. Second, the proposed SE model can learn the actual power system states. Finally, this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors. The proposed FDI attack detection technique is evaluated on a number of standard bus systems. The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-of-the-art techniques. Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.
    53  Time-domain Dynamic State Estimation for Unbalanced Three-phase Power Systems
    Martin Pfeifer ,  Felicitas Mueller ,  Steven de Jongh ,  Frederik Gielnik ,  Thomas Leibfried ,  Sören Hohmann
    2023, 11(2):446-454. DOI: 10.35833/MPCE.2021.000761
    [Abstract](732) [HTML](90) [PDF 1.94 M](1168)
    Abstract:
    In this paper, we present a time-domain dynamic state estimation for unbalanced three-phase power systems. The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network, i.e., the dynamics of the electrical lines. This enables our approach to release the assumption of the network being in quasi-steady state. Initially, based on the line dynamics, we derive a graph-based dynamic system model. To handle the large number of interacting variables, we propose a port-Hamiltonian modeling approach. Based on the port-Hamiltonian model, we then follow an observer-based approach to develop a dynamic estimator. The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents. The design and implementation of the estimator are illustrated through the IEEE 33-bus system. Numerical simulations verify the estimator to produce asymptotic exact estimates, which are able to detect harmonic distortion and sub-second transients as arising from converter-based resources.
    54  Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System
    Jun Mo ,  Hui Yang
    2023, 11(2):421-433. DOI: 10.35833/MPCE.2021.000318
    [Abstract](582) [HTML](75) [PDF 4.29 M](1358)
    Abstract:
    Considering a variety of sampled value (SV) attacks on busbar differential protection (BDP) which poses challenges to conventional learning algorithms, an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper. The healthy SV data of BDP are defined as self-data composed of spheres of the same size, whereas the SV attack data, i.e., the nonself data, are preserved in the nonself space covered by spherical detectors of different sizes. To avoid the confusion between busbar faults and SV attacks, a self-shape optimization algorithm is introduced, and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives. Based on the difficulty of boundary coverage in traditional negative selection algorithms, a self-data-driven detector generation algorithm is proposed to enhance the detector coverage. A testbed of differential protection for a 110 kV double busbar system is then established. Typical SV attacks of BDP such as amplitude and current phase tampering, fault replays, and the disconnection of the secondary circuits of current transformers are considered, and the delays of differential relay operation caused by detection algorithms are investigated.
    55  Impact of Cascade Disconnection of Distributed Energy Resources on Bulk Power System Stability: Modeling and Mitigation Requirements
    Fabricio Andrade Mourinho ,  Tatiana Mariano Lessa Assis
    2023, 11(2):412-420. DOI: 10.35833/MPCE.2022.000365
    [Abstract](552) [HTML](92) [PDF 3.14 M](2481)
    Abstract:
    This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency（RoCoF） protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.
    56  Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators
    Tannan Xiao ,  Ying Chen ,  Jianquan Wang ,  Shaowei Huang ,  Weilin Tong ,  Tirui He
    2023, 11(2):401-411. DOI: 10.35833/MPCE.2022.000099
    [Abstract](629) [HTML](77) [PDF 2.51 M](1347)
    Abstract:
    With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators.
    57  Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging
    Zixuan Jia ,  Jianing Li ,  Xiao-Ping Zhang ,  Ray Zhang
    2023, 11(2):389-400. DOI: 10.35833/MPCE.2021.000777
    [Abstract](1514) [HTML](42) [PDF 1.75 M](1289)
    Abstract:
    The rapid development of electric vehicles (EVs) has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection. This paper focuses on the optimization of EV charging, which cannot be ignored in the rapid development of EVs. The increase in the penetration of EVs will generate new electrical loads during the charging process, which will bring new challenges to local power systems. Moreover, the uncoordinated charging of EVs may increase the peak-to-valley difference in the load, aggravate harmonic distortions, and affect auxiliary services. To stabilize the operations of power grids, many studies have been carried out to optimize EV charging. This paper reviews these studies from two aspects: EV charging forecasting and coordinated EV charging strategies. Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models. At the end of this paper, recommendations are given to address the challenges of EV charging and associated charging strategies.
    58  A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems
    Yuzhou Zhou ,  Qiaozhu Zhai ,  Lei Wu ,  Moammad Shahidehpour
    2023, 11(1):254-266. DOI: 10.35833/MPCE.2021.000382
    [Abstract](350) [HTML](34) [PDF 3.28 M](887)
    Abstract:
    This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment (TCUC). Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are popular approaches to solving TCUC. However, with many binary unit commitment variables, LR suffers from slow convergence and MILP presents heavy computation burden. The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems. A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible (ASF) approaches, which is then leveraged to efficiently solve new TCUC instances online. On/off statuses of considerable units can be fixed in the online calculation according to the database, which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances. A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables, which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes. Numerical tests illustrate the efficiency of the proposed approach.
    59  Reconfiguration of Active Distribution Networks Equipped with Soft Open Points Considering Protection Constraints
    Ali Azizi ,  Behrooz Vahidi ,  Amin Foroughi Nematollahi
    2023, 11(1):212-222. DOI: 10.35833/MPCE.2022.000425
    [Abstract](588) [HTML](77) [PDF 1.69 M](895)
    Abstract:
    The purpose of active distribution networks (ADNs) is to provide effective control approaches for enhancing the operation of distribution networks (DNs) and greater accommodation of distributed generation (DG) sources. With the integration of DG sources into DNs, several operational problems have drawn attention such as overvoltage and power flow alteration issues. These problems can be dealt with by utilizing distribution network reconfiguration (DNR) and soft open points (SOPs). An SOP is a power electronic device capable of accurately controlling active and reactive power flows. Another significant aspect often overlooked is the coordination of protection devices needed to keep the network safe from damage. When implementing DNR and SOPs in real DNs, protection constraints must be considered. This paper presents an ADN reconfiguration approach that includes DG sources, SOPs, and protection devices. This approach selects the ideal configuration, DG output, and SOP placement and control by employing particle swarm optimization (PSO) to minimize power loss while ensuring the correct operation of protection devices under normal and fault conditions. The proposed approach explicitly formulates constraints on network operation, protection coordination, DG size, and SOP size. Finally, the proposed approach is evaluated using the standard IEEE 33-bus and IEEE 69-bus networks to demonstrate the validity.
    60  Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties
    Ruoxuan Leng ,  Zhengmao Li ,  Yan Xu
    2023, 11(1):120-131. DOI: 10.35833/MPCE.2022.000510
    [Abstract](808) [HTML](79) [PDF 3.68 M](887)
    Abstract:
    This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.
    61  Multi-stage Co-planning Model for Power Distribution System and Hydrogen Energy System Under Uncertainties
    Qirun Sun ,  Zhi Wu ,  Wei Gu ,  Pengxiang Liu ,  Jingxuan Wang ,  Yuping Lu ,  Shu Zheng ,  Jingtao Zhao
    2023, 11(1):80-93. DOI: 10.35833/MPCE.2022.000337
    [Abstract](754) [HTML](37) [PDF 2.03 M](1056)
    Abstract:
    The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system (PDS) and hydrogen energy system (HES). Considering that power to hydrogen (PtH) has great potential to facilitate the usage of renewable energy sources (RESs), the coordination of PDS and HES is important for planning purposes under high RES penetration. To this end, this paper proposes a multi-stage co-planning model for the PDS and HES. For the PDS, investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs. For the HES, capacities of PtHs and hydrogen storages (HSs) are optimally determined to satisfy hydrogen load considering the hydrogen production, tube trailer transportation, and storage constraints. The overall planning problem is formulated as a multi-stage stochastic optimization model, in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods. Case studies validate that the proposed co-planning model can reduce the total planning cost, promote RES consumption, and obtain more flexible decisions under long-term load growth uncertainties.
    62  Two-stage Optimal Dispatching of AC/DC Hybrid Active Distribution Systems Considering Network Flexibility
    Yi Su ,  Jiashen Teh
    2023, 11(1):52-65. DOI: 10.35833/MPCE.2022.000424
    [Abstract](694) [HTML](38) [PDF 3.35 M](1202)
    Abstract:
    The increasing flexibility of active distribution systems (ADSs) coupled with the high penetration of renewable distributed generators (RDGs) leads to the increase of the complexity. It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation. This study establishes an alternating current (AC)/direct current (DC) hybrid ADS model that considers the dynamic thermal rating, soft open point, and distribution network reconfiguration (DNR). Moreover, it transforms the optimal dispatching into a second-order cone programming problem. Considering the different control time scales of dispatchable resources, the following two-stage dispatching framework is proposed. ① The day-ahead dispatch uses hourly input data with the goal of minimizing the grid loss and RDG dropout. It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system. ② The intraday dispatch uses 15 min of input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching. To eliminate error between the actual operation and dispatching plan, the first 15 min is divided into three 5-min step-by-step executions. The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost. The measured data are used as feedback input for the rolling-plan dispatch after each step is executed. A case study shows that the comprehensive cooperative ADS model can release the line capacity, reduce losses, and improve the penetration rate of RDGs. Further, the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.
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