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This paper captures an engaging—and at times heated—Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “smart grid” over the past two decades, as highlighted in the spirited PG debate. “Smart grids” have sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “smart grid” in terms of raw technical functionalities, applications, and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions.
Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis high-lights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.
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.
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.
The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These pro-sumer 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.
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.
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.
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.
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.
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Om Malik, Jay Liu, Marcelo Simões, Chris Dent, Kai Strunz, Jeffrey Wischkaemper, Vladimiro Miranda, Trevor Gaunt, Math Bollen, Mladen Kezunovic, Daniel Kirschen, Antonio Gomez-Exposito, Robin Podmore, Harold Kirkham, Panayiotis Moutis, Anjan Bose, Ian Hiskens, Gene Preston, Canbing Li, Hasala Dharmawardena, Alexandra von Meier, Leigh Tesfatsion, Paulo Ribeiro
2025,13(6):1845-1853, DOI: 10.35833/MPCE.2025.000807
Abstract:
This paper captures an engaging— and at times heated—Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “ smart grid” over the past two decades, as highlighted in the spirited PG debate. “ Smart grid” has sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “ smart grid” in terms of raw technical functionalities, applications, and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions. - 1
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Ziyang Zhang, Ning Zhang, Ershun Du, Fei Teng, Goran Strbac, Chongqing Kang
2025,13(6):1854-1870, DOI: 10.35833/MPCE.2024.000745
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Variable renewable energy (VRE) integrated via non-synchronous inverters exhibits low inertia and fluctuating output, posing substantial frequency security challenges for future power systems. When frequency security constraints are omitted from generation planning, the resulting low-inertia generation portfolios often fail to meet critical frequency requirements. To address this issue, this paper proposes a novel frequency security constrained generation planning (FSCGP) model that leverages the frequency support potential of diverse power sources, including conventional thermal generators (CTGs), VRE units, concentrating solar power (CSP) units, and energy storage systems (ESSs). A physics-data hybrid-driven method is introduced to formulate frequency security constraints, enabling accurate representation of diverse frequency regulation characteristics, particularly the fast frequency support capabilities of inverter-based generators (IBGs). To further enhance the computational efficiency, several acceleration techniques are incorporated into the proposed FSCGP model. Case studies based on a modified IEEE RTS-79 system validate the effectiveness of the proposed FSCGP model. The numerical results identify the primary contributors to frequency security under different renewable energy penetration (REP) levels and demonstrate the cost-effectiveness of coordinating various frequency support sources, especially CSP units and IBGs, in mitigating challenges in low-inertia grids.
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Xiaoyu Peng, Feng Liu, Peng Yang, Peixin Yu, Kui Luo, Zhaojian Wang
2025,13(6):1871-1883, DOI: 10.35833/MPCE.2024.001297
Abstract:
This two-part paper presents a generic methodology for measuring the short-term voltage stability (STVS) of power systems dominated by inverter-based resources (IBRs), which introduces the concept of generalized voltage damping (GVD) for quantifying STVS from both global and local perspectives. It leads to a model-independent approach to assessing the voltage stability, the system strength, and the capability of dynamic devices to support voltage during transient process. Part I of this paper focuses on deriving the system-wise generalized voltage damping (sGVD) index and its applications. The sGVD index is defined as the decay rate of voltage-related transient energy (VTE) dissipated on the (aggregated) buses of the power system, which can be obtained using the maximum Lyapunov exponent (MLE) technique. The proposed sGVD index is theoretically demonstrated to capture the actual voltage damping of devices and to be strongly linked with STVS. These unique properties enable a model-independent approach to measuring STVS and system strength, even in the presence of heterogeneous and strongly nonlinear dynamics of IBRs. We verify the theoretical results by conducting simulations on the modified IEEE 39-bus system and two large-scale practical power systems with integration of massive renewable resources, demonstrating the effectiveness and practicality of the methodology.
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Huihuang Cai, Huan Long, Zhi Wu, Wei Gu, Jingtao Zhao
2025,13(6):1884-1895, DOI: 10.35833/MPCE.2024.001036
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As the scale of power system continues to grow, a fast and accurate distributed optimal power flow solver becomes crucial for the effective dispatch of power system. This paper presents a learning to optimize (L2O) approach to accelerating the distributed optimal power flow solving. The final convergence values of global variables and Lagrange multipliers of the alternating direction method of multipliers (ADMM) are estimated as its warm-start solution. A long short-term memory-variational auto-encoder (LSTM-VAE) model is developed as the core for estimating the convergence value, and the LSTM-VAE assisted ADMM is proposed. The LSTM generates low-dimensional representations of global variables and Lagrange multipliers, while the decoder part of VAE reconstructs the high-dimensional asymptotic convergence values. A novel loss function is designed in the form of a quadratic sum penalty term to incorporate the constraint violations of the Lagrange multipliers. Additionally, a two-stage training data generation strategy is proposed to efficiently generate substantial data within a limited amount of time. The effectiveness of the LSTM-VAE assisted ADMM is evaluated using the modified IEEE 123-bus system, a synthetic 500-bus system, and a 793-bus system.
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Quan Zhang, Jiajie Ling, Wei Luo, Rong Yan, Guangchao Geng, Quanyuan Jiang
2025,13(6):1896-1908, DOI: 10.35833/MPCE.2024.000972
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In this paper, policy-assisted graph reinforcement learning (PAGRL) is proposed for real-time economic dispatch (RTED). RTED is presented as a sequential decision problem formulated by Markov decision process (MDP). PAGRL employs a graph convolutional network to extract grid operation features containing topological information and then an agent that performs power dispatch is trained through proximal policy optimization. Moreover, the adaptiveness of agent to more hard-to-learn scenarios is enhanced by difficulty sampling, and policy-assisted action post-processing mechanism is designed to reduce search space and improve decision quality, which provides a general performance enhancement scheme for reinforcement learning in power system applications. Comparative studies on modified IEEE 118-bus system and real-world provincial grid demonstrate the flexible and reliable performance of the proposed PAGRL for RTED.
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Xianchao Liu, Guoqing Li, Tao Huang, Tao Jiang, Soheil Saadatmandi, Graduate, Gianfranco Chicco
2025,13(6):1909-1920, DOI: 10.35833/MPCE.2024.001177
Abstract:
Future power systems will be characterized by low levels of inertia and limited frequency regulation capacity due to the widespread use of renewable energy sources. Furthermore, the response of different types of loads to system disturbances significantly affects the frequency dynamics. To address these issues, this paper proposes an aggregated system frequency response model considering load dynamics. Initially, a dynamic model of different loads is established, followed by the derivation of a small-signal load model that affects the active power imbalance of the system. The active power variations of loads are categorized into three components: load voltage dynamics, load frequency dynamics, and load inertia contribution. These components are incorporated into the system frequency response model, which accounts for load active power dynamics. The final output is an aggregated reduced-order system frequency response model, where the aggregation is primarily weighted by the primary frequency regulation capability of the load, load capacity, and rotor kinetic energy. Finally, the accuracy and effectiveness of the proposed model are validated using the WECC 9-bus test system with power electronic sources, and the influence of load parameters on the frequency stability indicators is analyzed.
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Yida Yang, Hongjun Gao, Yingmeng Xiang, Minghao Guo, Jiye Wang, Junyong Liu
2025,13(6):1921-1932, DOI: 10.35833/MPCE.2024.000705
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As the share of renewable generations (RGs) in power systems grows, the demand for peak regulation has increased, leading to higher associated costs. In this paper, we propose a mechanism for allocating peak regulation cost among RGs and distributing compensation among peak regulation resources (PRRs). This mechanism is integrated into a coordinated generation scheduling model to enhance the economic efficiency of independent system operators (ISOs) and incentivize PRRs. First, we propose a model for peak regulation cost of diverse PRRs. Next, we develop a method for constructing an RG output curve that facilitates peak regulation. The waveform difference between this constructed curve and the RG forecasted output curve is then calculated. In addition, we create a mechanism for peak regulation cost allocation and compensation distribution that incorporates the waveform difference, the peak regulation contribution of PRRs, and participant satisfaction as key indicators. We then establish a coordinated generation scheduling model using this mechanism, which is solved through the column-and-cut generation algorithm and rolling optimization. Finally, we conduct case studies based on an improved IEEE 30-bus test system and perform several comparative analyses to validate the effectiveness of the proposed mechanism and coordinated generation scheduling model.
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Hwang Goh Hui, Yuxin Ou, Wei Dai, Hui Liu, Tonni Agustiono Kurniawan, Jun Xu
2025,13(6):1933-1944, DOI: 10.35833/MPCE.2024.000976
Abstract:
To maintain the frequency stability of power systems integrated with large-scale renewable energy sources (RESs), a frequency-constrained unit commitment (FCUC) model is proposed, which incorporates a coordinated frequency control strategy of wind turbines and energy storage system (WT-ESS), a vital component for enhancing frequency regulation capacity of wind farms. Analytical formulations for the maximum rate of change of frequency (RoCoF) and steady-state frequency deviation are derived for both serial control and parallel control, accounting for the output-limited state of energy storage under serial control. To address the problem of solution slowness caused by the strong nonlinear frequency nadir constraints, a model-based multi-directional bilayer solution method is proposed. This method employs the simulation model to detect whether the frequency nadir constraint is active and generates parallel optimized cuts in three directions. Simulation results on the IEEE 39-bus test system demonstrate that the proposed FCUC model and solution method could accurately reflect the primary frequency regulation (PFR) characteristics of WT-ESS. Furthermore, the coordinated frequency control strategy effectively reduces overall operating costs while ensuring frequency security.
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Eduardo Resende, Robson Pires, Lamine Mili
2025,13(6):1945-1954, DOI: 10.35833/MPCE.2024.000951
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This work presents a new topological observability algorithm to strengthen the performance of the hybrid power system static state estimation, assuming that the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are recorded at the same time intervals. The observability of each estimated state variable is assessed by the value assigned to its least local redundancy index. The algorithm has been specifically developed to enhance the observability of an existing wide-area monitored system and exempts its expansion from critical sets and critical measurements. These objectives are achieved by building the incidence matrices of the measurements for the nodes and branches. The performance of the proposed algorithm is evaluated using the IEEE test systems and the SIN test systems of Brazilian equivalent systems.
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Xueyong Jia, Xiaoming Dong, Chengfu Wang, Ming Yang, Tianguang Lu
2025,13(6):1955-1965, DOI: 10.35833/MPCE.2024.000997
Abstract:
Power transfer limit (PTL) calculation plays an important role in assessing power network capability under certain constraints of system security and stability. However, the impact of ambient factors, which is different due to variations in time and space, is ignored in traditional methods to obtain PTL, thus inducing errors. Furthermore, system operation based on traditional PTL results may increase system security risks, particularly in the case of power flow congestion under heavy loads. Therefore, this paper proposes a decentralized PTL calculation method with improved optimal power flow model, which allows for the effect of the ambient factors characterized by the balance of heat absorption and dissipation for overhead conductors. The ambient factors of overhead transmission lines and the temperature of overhead conductors are involved as independent variables and state variables, respectively. Moreover, the sequential optimization problem is decomposed into several subproblems by the optimal conditional decomposition to deal with the temporal coupling constraints, and a parallel decomposition framework is used to solve multiple subproblems in parallel. Finally, the proposed method is implemented on two test systems under varying ambient factors, demonstrating the efficiency of the proposed method and the significant impacts of spatial and seasonal differences on PTL results.
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Kangyi Sun, Hongyu Zhou, Wei Yao, Bitao Xiao, Jinyu Wen
2025,13(6):1966-1979, DOI: 10.35833/MPCE.2024.000975
Abstract:
This paper presents a comprehensive control of modular multilevel converter-based high-voltage direct current (MMC-HVDC) integrated offshore wind farm (OWF) system, which is aimed at enhancing the fast frequency support capability. The comprehensive control consists of the active energy control (AEC), the optimized energy control (OEC), and the OWF control. The proposed OEC decouples the voltage of MMC submodule (SM) capacitor and the DC-link voltage of the MMC-HVDC. A mathematical model of the energy release process in an MMC and onshore frequency dynamics is developed, and a sinusoidal-function-based energy utilization preset curve is derived to theoretically achieve optimized frequency support, while ensuring the constrained energy usage. Upon the occurrence of a frequency event, MMC SMs may adjust their responses according to the energy utilization preset curve. The MMC then provides onshore frequency support by releasing energy. The coordination process with the OWF is also explored to further enhance the frequency support performance. Finally, case studies are conducted on the PSCAD/EMTDC platform through employing actual engineering parameters. The frequency support performance of various controls under both load increase and load decrease conditions are compared, demonstrating the effectiveness of the proposed OEC.
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Jing Liu, Xiandong Xu, Longfei Liu, Hongjie Jia
2025,13(6):1980-1989, DOI: 10.35833/MPCE.2024.000942
Abstract:
The high share of intermittent wind power jeopardizes system frequency security in isolated offshore field microgrids (IOFMs). Existing scheduling strategies, mainly focusing on stable energy supply and demand, fail to ensure frequency security due to the limited flexible and dispatchable resources in the IOFM. Thus, this paper proposes an optimal scheduling model of wind power generators with unified frequency response and spinning reserve constraints to assist operators in efficiently managing turbine generators. Frequency security indices are introduced to quantify the impact of both sudden wind power shortages and continuous wind power fluctuations on the frequency dynamics under different control modes. Based on these indices, unified frequency response and spinning reserve constraints are analytically derived to support the optimization of the control mode and on/off status of wind power generators. These highly nonlinear unified constraints are then reformulated as mixed-integer linear constraints, which are integrated into the scheduling model with operating costs as the objective. The proposed model is tested using a modified real-world IOFM. The results demonstrate that the proposed model not only ensures system frequency security but also reduces operating costs and carbon emissions.
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Yini Wang, Yang Hu, Yueli Zhao, Yunzhi Li, Fang Fang, Jizhen Liu
2025,13(6):1990-2001, DOI: 10.35833/MPCE.2024.000869
Abstract:
Optimal capacity configuration (OCC) of large-scale energy bases with multi-timescale operation characteristics presents a critical challenge. To address the problem, this study proposes an OCC approach of large-scale energy bases considering external multi-stochastic scenarios and interactive multi-timescale objectives. Firstly, guided by the system theory, the nonlinear state-space description is presented for systematic analysis of a general large-scale energy base. Due to interactive multi-timescale objectives between annual and daily cumulative objectives, a nested optimization structure is established. Then, considering the external multi-stochastic scenarios caused by the variables such as wind speed, solar irradiance, electric load, and thermal load, a multi-step optimization strategy is proposed including pre-configuration in regular scenarios and re-configuration by introducing micro-incremental scenarios. The multi-step optimization strategy and nested optimization structure jointly achieve the OCC of the large-scale energy base. In each step, the nested optimization structure is executed once. Finally, while ensuring the balance between thermal supply and load demand, the imbalances between electric power supply and the load demand are eliminated, significantly showing the efficiency of the proposed OCC approach.
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Yujing Li, Pengfei Hu, Liqun Qian, Dong Wang, Yanxue Yu, Zaixin Yang
2025,13(6):2002-2013, DOI: 10.35833/MPCE.2024.001158
Abstract:
The large-scale integration of renewable energy sources, such as wind power and solar power, into the power system has significantly transformed its characteristics. The issue of sub-synchronous oscillation (SSO) becomes increasingly prominent, severely impacting the system stability. As the wind turbines vary in structures and parameters, existing model-based SSO suppression approaches do not fully consider wind turbine differences and multi-mode oscillation frequencies. To address these issues, this paper proposes a decentralized SSO suppression controller for doubly-fed induction generator (DFIG)-based wind farms using periodic updating data-enabled predictive control (PUDeePC) approach. Firstly, to better adapt to the time-varying system and external disturbance, a periodic updating algorithm is proposed incorporating anomaly detection. The stability of the PUDeePC approach is theoretically validated, and its robustness to variations and disturbances is qualitatively analyzed. Finally, the effectiveness of the PUDeePC approach is revealed through numerical simulations under various conditions, including compensation level variation, wind power output variation, number of online DFIG variations, multi-mode SSOs, and asynchronous PUDeePC approach.
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Youze Fu, Yandong Chen, Zili Wang, Zhiwei Xie, Xuyang Li, Song Zhang, Huijie Yu
2025,13(6):2014-2026, DOI: 10.35833/MPCE.2025.000032
Abstract:
Grid-connected systems with multiple self-synchronizing voltage source inverters (SSVSIs) (referred to as multi-SSVSI grid-connected systems for simplicity) are exposed to low-frequency oscillations (LFOs) and synchronous frequency resonance (SFR). However, the synergistic suppression of these two oscillation modes has not been achieved to date. Considering the dynamic characteristics of transmission circuits and power coupling characteristics, an improved power-frequency (
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Yuchong Huo, Zaiyu Chen, Qun Li, Qiang Li, Minghui Yin
2025,13(6):2027-2039, DOI: 10.35833/MPCE.2024.000886
Abstract:
This paper introduces a machine learning (ML) based model predictive control (MPC) with piecewise-affine approximation (PWA) structure for maximizing wind energy capture for an individual wind turbine operating in wind farms with low-quality wind resources. While MPC has the capability to systematically consider the stochasticity of wind speed and the dynamic process of wind turbine, its real-time implementation in a hardware controller of wind turbine has not been successful due to its high online computational burden and stringent execution time requirement in practice. To address this long-standing issue, this paper proposes a two-phase ML-based method consisting of linear regression and clustering to construct a PWA of the optimal law for original MPC scheme. The two-phase ML-based method is tunable with computational complexity, which can be adjusted to meet the hardware limitation of the given controller of wind turbine to enable real-time implementation, while preserving the optimality of linearized full-fidelity MPC as much as possible. We conduct simulations and experiments to demonstrate the effectiveness of the two-phase ML-based method.
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Mengyuan Wang, Xiaoyuan Xu, Shuai Fan, Zheng Yan, Bo Yang, Xinping Guan
2025,13(6):2040-2050, DOI: 10.35833/MPCE.2024.000779
Abstract:
The electrical array reconfiguration (EAR) method has become a promising solution to enhance photovoltaic (PV) system performance under partial shading conditions. Existing studies focus on maximizing single-period PV generation but neglect the impact of power fluctuation on grid stability. To address this, we propose a multi-period EAR method for multi-PV systems considering net power fluctuation mitigation. First, we design a multi-period EAR model to maximize total revenue by balancing electricity sales and net power fluctuation penalties, formulated as a stochastic mixed-integer quadratic programming problem. The model incorporates constraints on the average number of switching actions per unit time to ensure practical implementation. Then, to handle the unpredictability of partial shading conditions, we develop a Lyapunov optimization-based online algorithm to decouple the time-coupling constraints involving state transitions. Additionally, we propose a reduced set of EAR strategies to improve the computational efficiency. Numerical studies demonstrate that the proposed method significantly reduces net power fluctuations in distribution networks with high PV penetration rate and enhances total revenue compared with conventional methods.
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Yi Yang, Ping Tang, Can Wang, Nan Yang, Zhuoli Zhao
2025,13(6):2051-2062, DOI: 10.35833/MPCE.2024.001263
Abstract:
The integrated energy cyber-physical system (IECPS), a typical cyber-physical system (CPS), demonstrates tight interaction between cyber and physical spaces across time and space, exhibiting inherent spatial-temporal properties. Effective IECPS modeling requires the simultaneous consideration of both temporal and spatial properties, which remains a significant challenge. This paper proposes a hierarchical spatial-temporal event modeling method for IECPS based on hybrid automata (HA). Different event model layers are defined, representing events as functions of attributes, time, and space, with attributes characterized by system states. An integration method for multi-layer events is introduced, enabling accurate reflection of the system spatial-temporal characteristics and the current operating state of energy units. The modeling is applied to the optimal regulation of IECPS. Numerical simulations demonstrate that the proposed HA-based modeling method achieves precise regulation of IECPS while reducing system operating costs.
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Alessandro Bosisio, Francesca Soldan, Matteo Pisani, Enea Bionda, Federico Belloni, Andrea Morotti
2025,13(6):2063-2073, DOI: 10.35833/MPCE.2024.000528
Abstract:
Distribution networks have been experiencing significant changes under the pressure of the energy transition. The high integration of renewable energy sources combined with the electrification process introduces new challenges in managing distribution networks. Innovative solutions aimed at optimizing the control of complex problems, starting from historical data instead of a detailed system model, have been growing due to rapid development in artificial intelligence and machine learning. This paper proposes a Q-learning algorithm to control the tap setting of the on-load tap changer installed in primary substation transformers. The ultimate goal is to maintain voltage magnitudes at all busses of the medium-voltage distribution network within a safe range, simultaneously optimizing on-load tap changer operations. As a case study, the effectiveness of the proposed algorithm is assessed using a real medium-voltage distribution network with high penetration of renewable energy sources that supplies more than 2500 users/prosumers. The ability of the proposed algorithm to control bus voltages is tested in several scenarios characterized by significant variability and uncertainty. Outcomes show that the proposed algorithm is suitable for optimizing voltage control in distribution networks using a data-driven approach.
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Zhengbo Li, Youbo Liu, Yue Xiang, Haolan Yang, Lingfeng Wang, Junyong Liu
2025,13(6):2074-2085, DOI: 10.35833/MPCE.2024.000884
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Feeder routing and reliability assessment are essential for effective distribution network planning. However, excessively long feeders can lead to increased costs and decreased reliability. To enhance economic and reliability performance, this paper proposes a reliability-centered planning method for feeder routing and conductor sizing. Specifically, a graph-based fictitious power flow model is constructed within the geographic graph. Overlapping feeder routes powered by fictitious power flows from multiple sources are designated as line connection. These feeder routes, constrained by the geographic graph, are interconnected via line connection to form a mesh network structure. To meet the requirements of reliability-centered optimization, the affiliation variables are introduced. Based on the affiliation variables, the algebraic formula is embedded into the fictitious power flow model to enable the calculation of reliability during the optimization process. By incorporating customized reliability-related constraints in the model, the specific reliability objectives can be achieved. In addition, the non-convex terms in the fictitious power flow model are relaxed into convex forms, and certain variable products are replaced with auxiliary variables, allowing the problem to be solved by an off-the-shelf solver. Finally, the proposed method is tested on two case studies, demonstrating its effectiveness.
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Shengyuan Wang, Fengzhang Luo, Chengshan Wang, Yunqiang Lyu, Ranfeng Mu, Jiacheng Fo, Lukun Ge
2025,13(6):2086-2097, DOI: 10.35833/MPCE.2024.001279
Abstract:
To address the limitations of traditional planning methods in handling complex scenarios such as multi-feeder or substation cluster supply under high photovoltaic (PV) penetration, this paper proposes a collaborative configuration optimization method of soft open points (SOPs) and distributed multi-energy stations with spatiotemporal coordination and complementarity to reduce renewable energy curtailment. First, a shared strategy of multiple types of resources is proposed based on an SOP-enabled flexible distribution network. Second, a distributed hydrogen-based multi-energy coupling system (DHMECS) is developed. Then, a DHMECS siting model considering inter-feeder resource sharing is formulated. Finally, a configuration model of SOP and DHMECS is proposed, incorporating a partitioned autonomous operation strategy that considers spatiotemporal coordination and complementarity. The proposed method is validated on the improved Portugal 54-node and 219-node distribution networks, and the results demonstrate that it mitigates severe voltage violations and PV curtailment, enhances partitioned autonomous operation capabilities, and addresses the challenges of complex planning scenarios involving multi-feeder or substation cluster supply.
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Chengwei Lou, Ran Zhao, Hangxing Zhang, Lu Zhang, Wei Tang, Jin Yang, Linjuan Zhang
2025,13(6):2098-2110, DOI: 10.35833/MPCE.2024.001114
Abstract:
This paper proposes an advanced voltage source converter (VSC)-driven model for soft open points (SOPs) and battery energy storage systems (BESSs) to actively balance three-phase distribution networks. The proposed model addresses the phase imbalance caused by the increasing integration of renewable energy and distributed generation. Unlike traditional models, which mainly focus on AC capacity constraints, the proposed model explores the complexities of the DC-link. This allows for a thorough examination of the interactions between active and reactive power, as well as the voltage levels on both the AC and DC sides of VSCs. The relationship between pulse width modulation (PWM) control configurations and VSC power outputs is discussed, enhancing control on both sides of the converters. This improvement also facilitates better cross-phase power transfer through SOPs and enhances the overall balance among the three phases. In addition, the proposed model incorporates the cooperative functionality of VSC-driven BESSs to sustain the phase balance. To further optimize the load distribution, phase-specific dispatching (PSD) is introduced, allowing for the flexible allocation of individual loads to distinct phases. Together, these coordinated technical solutions constitute a systematic optimization strategy. An algorithm is developed to harmonize the VSC-driven modeling for SOPs and BESSs with PSD, thereby improving the computational efficiency in managing power flow and phase balance. The results show that the proposed model significantly reduces losses and enhances the phase balance.
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Shubhankar Kapoor, Adrian G. Wills, Johannes Hendriks, Lachlan Blackhall
2025,13(6):2111-2119, DOI: 10.35833/MPCE.2024.000910
Abstract:
This paper proposes a method for obtaining nonlinear models of distribution grid based on available measurements from the power grid. We formulate a maximum likelihood estimation (MLE) problem that estimates unknown line parameters—specifically, the impedance between nodes—using measured voltage magnitudes and net active and reactive power injections at each node. The nonlinear model for the distribution grid uses a nonlinear approximation of the DistFlow model, which includes line losses and is parameterized by the unknown line impedances. We solve the resulting MLE problem using an expectation maximization (EM) algorithm, tailored for the nonlinear model, and provide a numerically robust implementation. The proposed method is demonstrated on the IEEE 37-node test network, and we compare it with the state-of-the-art methods. The proposed method achieves a 70% reduction in voltage error and an error for state variables that is more than 10000 times smaller. A final comparison uses data from a real network, and the proposed method achieves parameter estimates with errors 100 times smaller than competing methods.
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Ruidong Xu, Zhongxue Chang, Guobing Song, Ke Jia, Jiayi Yang
2025,13(6):2120-2130, DOI: 10.35833/MPCE.2024.001248
Abstract:
The transformer-less configuration is regarded as a preferred solution for soft-open-point (SOP)-based flexible interconnected distribution networks (FIDNs). This study proposes a reliable and cost-effective method for handling single-line-to-ground (SLG) faults in transformer-less SOP-based FIDNs. Firstly, the zero-sequence fault equivalent circuit of FIDNs is established, and the fault propagation characteristics between the grids interconnected by the SOP are analyzed. Secondly, a zero-sequence current isolation strategy based on proportional-resonant control is proposed to prevent fault propagation toward the healthy-side grid. An active injection-based grounding parameter identification (GPI) approach is then proposed, enabling accurate calculation of the compensation current for arc suppression. Finally, the selection of injected signal parameters is discussed, and the complete timing flow of the SLG fault handling method is presented. A 10 kV SOP-based FIDN model configured with a transformer-less topology is developed in PSCAD/EMTDC. The performance of the SLG fault handling method is verified under transition resistances ranging from 10 Ω to 10 kΩ. The results reveal that the average relative error in GPI is less than 4%. Furthermore, the compensated currents of faulty branch remain below 10 A across various fault conditions.
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Congyue Zhang, Xiaobo Dou, Jianfeng Zhao, Yongqing Lv, Zaijun Wu, Wei Gu
2025,13(6):2131-2143, DOI: 10.35833/MPCE.2024.001239
Abstract:
For providing a reliable power supply in an islanded AC microgrid, it is crucial to ensure that the operating states meet the safety constraints. However, conventional distributed secondary control methods often struggle to handle voltage and frequency constraints, limiting their practical applicability. To address these limitations, this paper proposes a novel prescribed performance control (PPC) based distributed secondary coordination method for islanded AC microgrids. The proposed method introduces a systematic framework that integrates bijective transformation with distributed secondary control. In this framework, the constrained distributed voltage and frequency control problems can be transformed into unconstrained ones, enabling strict adherence to predefined performance boundaries. Additionally, Lyapunov-Krasovskii functional analysis is employed in this paper to ensure asymptotic stability and calculate the stable boundaries in the transformed error space under time-delay conditions. The effectiveness of stability recovery and prescribed performance is tested in both MATLAB/Simulink and RTLab-based hardware-in-the-loop (HIL) experimental environments.
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Masoud Zare Shahabadi, Hajar Atrianfar, Hossein A. Abyaneh
2025,13(6):2144-2156, DOI: 10.35833/MPCE.2024.000495
Abstract:
This study introduces a distributed specified-time control mechanism (DSTCM) for secondary control in islanded microgrids (MGs) operating under directed switching communication topologies. The proposed mechanism ensures convergence properties that are independent of initial conditions, enabling the design of an exact offline settling time to reduce power losses and limit the upper bound of convergence time. By employing a piecewise function-based communication approach and directed switching graphs, the proposed mechanism effectively reduces computational and communication demands on the system. Moreover, the proposed mechanism significantly enhances power system performance while minimizing adjustment costs, delivering precise control actions under various operating conditions. The accuracy and effectiveness of the proposed mechanism are validated through extensive MATLAB simulations, demonstrating its ability to regulate MG voltages and frequencies, achieve accurate proportional active power sharing, and maintain state-of-charge (SoC) balancing. Its superiority over previously established mechanisms is also confirmed by a comparative analysis.
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Zhongkai Yi, Zihao Zhao, Ying Xu, Yuhao Zhou, Lun Yang
2025,13(6):2157-2167, DOI: 10.35833/MPCE.2024.000744
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With the increasing number of distributed flexible resources with energy storage capabilities in virtual power plants (VPPs), the traditional market clearing model that only includes quantity and price bids cannot fully unlock their potential flexibility. In light of this, we propose a market clearing model for energy-constrained virtual power plants (EC-VPPs) based on distributionally robust chance-constrained optimization (DRCCO) with moment information. Furthermore, to address the uncertainty of EC-VPPs in the electricity market, a pricing strategy for EC-VPPs is proposed. This strategy helps quantify the impact of uncertainty in EC-VPPs on the system economy. The proposed market clearing model is reformulated as a tractable mixed-integer second-order cone programming (MISOCP) problem via a two-sided distributionally robust chance-constrained convex reformulation method. Numerical simulations verify that the proposed pricing strategy offers fair incentives for both reserve providers and uncertain sources, and delivers an effective market mechanism for the EC-VPPs.
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Junzhou Wang, Xingyu Lin, Junjie Tang, Yuzhi Wang, Guodong Huang, Dan Xu
2025,13(6):2168-2179, DOI: 10.35833/MPCE.2024.000784
Abstract:
High proportion of renewable energies and the installation of power electronic devices (PEDs) pose tough challenges to the operation of power systems. In this paper, the remote coordination adjustment (RCA) of PEDs in stochastic scenarios is studied. The steady-state model for the AC/DC system with PEDs is first established, and the alternate iteration method based on linearization (AIML) is adopted, especially for efficient deterministic power flow calculation. Then, the RCA is proposed using a modular local sensitivity method combined with AIML, which can adjust the electrical variables by diverse PEDs with high efficiency. Additionally, the probabilistic power flow calculation using the quasi-Monte Carlo method with the adaptive sampling number (ASN-QMC) is introduced to keep the balance between the computational efficiency and accuracy, as well as demonstrating the positive impact of RCA by the PEDs in stochastic scenarios. The effectiveness of the proposed RCA is validated by a series of modified IEEE test systems.
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Hao Lin, Liang Liang, Haiqiong Yi, Xiangjun Kong
2025,13(6):2180-2191, DOI: 10.35833/MPCE.2024.000949
Abstract:
Sending-end multi-terminal high-voltage direct current (MT-HVDC) systems are well-suited for large-scale renewable energy collection and transmission. However, the capacity planning for converter stations (CSs), which is directly correlated with their ability to convert renewable energy, remains a critical issue. In this paper, an optimal capacity planning method for CSs is proposed to maximize the converted energy (CE). The proposed method considers the uncertainties of photovoltaic (PV) generation and derives analytical formulas for stochastic CEs. The equal incremental rate (EIR) principle is employed to calculate the optimal capacity planning scheme, and then a general guideline for the capacity planning in stochastic scenarios is presented. Case studies are conducted to validate the effectiveness of the proposed method and the proposed guideline. The results demonstrate that the proposed method converts more renewable energy than the deterministic method.
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Haoxiang Zong, Chen Zhang, Marta Molinas
2025,13(6):2192-2202, DOI: 10.35833/MPCE.2024.0001027
Abstract:
The dynamics of diverse synchronization control, such as grid-following (GFL) and grid-forming (GFM) control, are complicating the oscillatory behaviors in multi-converter systems. In this context, the impedance network (IN) based frequency-domain modal analysis (FMA) method is useful for diagnosing oscillations. However, since the conventional impedance model retains only electric nodes, the FMA primarily reflects the circuit-related information, e.g., node participation factor, making it less intuitive for probing the synchronous dynamics. To address this issue, this paper proposes an augmented impedance network (AIN) modeling method by explicitly characterizing synchronous control loops for stability assessment. First, a four-port augmented impedance model (AIM) with an additional synchronization port is proposed for a generic AC/DC converter, and the corresponding AIN model of a generic AC/DC multi-converter system is formulated in a scalable approach. Then, the FMA method is generalized by simultaneously incorporating the electric nodes (including AC and DC nodes) and synchronous nodes. Finally, the AIN model and its associated FMA method are comprehensively validated in a typical point-to-point high-voltage direct current (HVDC) system and a modified IEEE 9-bus system, both with GFM and GFL converters.
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Volume 13, Issue 6, 2025
>Views
>Original Paper
More+Virtual Issue
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Zhiyuan Meng, Xiangyang Xing, Xiangjun Li, Jiadong Sun
2025,13(3):1064-1077, DOI: 10.35833/MPCE.2024.000404
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.
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Linguang Wang, Xiaorong Xie, Wenkai Dong, Yong Mei, Aoyu Lei
2025,13(3):747-756, DOI: 10.35833/MPCE.2024.000630
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.
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Wenping Qin, Xiaozhou Li, Xing Jing, Zhilong Zhu, Ruipeng Lu, Xiaoqing Han
2025,13(2):675-687, DOI: 10.35833/MPCE.2024.000118
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.
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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:
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.
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Shengren Hou, Edgar Mauricio Salazar, Peter Palensky, Qixin Chen, Pedro P. Vergara
2025,13(2):597-608, DOI: 10.35833/MPCE.2024.000391
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.
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Wang Xiang, Mingrui Yang, Jinyu Wen
2025,13(2):452-461, DOI: 10.35833/MPCE.2024.000229
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.
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Wei Kong, Kai Sun, Jinghong Zhao
2025,13(1):276-288, DOI: 10.35833/MPCE.2023.001027
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%.
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2025,13(1):179-189, DOI: 10.35833/MPCE.2023.000624
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.
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Francisco Jesús Matas-Díaz, Manuel Barragán-Villarejo, José María Maza-Ortega
2025,13(1):102-114, DOI: 10.35833/MPCE.2024.000316
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.
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Yanqiu Jin, Zheren Zhang, Zheng Xu
2025,13(1):87-101, DOI: 10.35833/MPCE.2024.000432
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.
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Hang Shuai, Buxin She, Jinning Wang, Fangxing Li
2025,13(1):79-86, DOI: 10.35833/MPCE.2023.000882
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.
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Ghazala Shafique, Johan Boukhenfouf, François Gruson, Frédéric Colas, Xavier Guillaud
2025,13(1):66-78, DOI: 10.35833/MPCE.2024.000822
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.
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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:
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.
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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:
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
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Haiyu Zhao, Hongyu Zhou, Wei Yao, Qihang Zong, Jinyu Wen
2025,13(1):3-14, DOI: 10.35833/MPCE.2024.000722
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.
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Sheng Chen, Jingchun Zhang, Zhinong Wei, Hao Cheng, Si Lv
2024,12(6):1697-1709, DOI: 10.35833/MPCE.2023.000887
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.
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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:
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.
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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:
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.
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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:
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.
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Qifan Chen, Siqi Bu, Chi Yung Chung
2024,12(4):1003-1018, DOI: 10.35833/MPCE.2023.000526
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.
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Jie Xu, Hongjun Gao, Renjun Wang, Junyong Liu
2024,12(3):886-899, DOI: 10.35833/MPCE.2023.000213
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.
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Zhoujun Ma, Yizhou Zhou, Yuping Zheng, Li Yang, Zhinong Wei
2024,12(3):852-862, DOI: 10.35833/MPCE.2023.000204
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.
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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:
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.
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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:
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
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Xiao Xu, Ziwen Qiu, Teng Zhang, Hui Gao
2024,12(2):440-453, DOI: 10.35833/MPCE.2023.000742
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.
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Jing Bian, Yuheng Song, Chen Ding, Jianing Cheng, Shiqiang Li, Guoqing Li
2024,12(2):427-439, DOI: 10.35833/MPCE.2023.000707
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.
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Makedon Karasavvidis, Andreas Stratis, Dimitrios Papadaskalopoulos, Goran Strbac
2024,12(2):415-426, DOI: 10.35833/MPCE.2023.000737
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.
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Pavitra Sharma, Krishna Kumar Saini, Hitesh Datt Mathur, Puneet Mishra
2024,12(2):381-392, DOI: 10.35833/MPCE.2023.000761
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.
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Jianlin Li, Zhijin Fang, Qian Wang, Mengyuan Zhang, Yaxin Li, Weijun Zhang
2024,12(2):359-370, DOI: 10.35833/MPCE.2023.000345
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.
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Hongchao Gao, Tai Jin, Guanxiong Wang, Qixin Chen, Chongqing Kang, Jingkai Zhu
2024,12(2):346-358, DOI: 10.35833/MPCE.2023.000762
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.
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Mubarak J. Al-Mubarak, Antonio J. Conejo
2024,12(2):323-333, DOI: 10.35833/MPCE.2023.000306
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.
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- 15 Papers Awarded as "MPCE Best Papers"
- 2025 MPCE Editorial Board Meeting Held in Austin, TX, USA
- 2024 MPCE Editorial Board Meeting Held in Seattle, WA, USA
- [Extended to July 31] Special Section on Dynamic Performance and Flexibility Enhancement of RES-dominated Power Systems with Grid-forming Converters
- Report Abstracts for Invited Speakers on MPCE 10th Anniversary Forum
- MPCE 10th Anniversary Forum on Resilience and Flexibility of Modern Power Systems will be held on Dec. 15, 2023
- 2023 MPCE Editorial Board Meeting Held in Orlando, FL, USA
- JCR Q1! MPCE 2022 Impact factor is 6.3
- [Extended to September 30] Special Section on Battery Energy Storage Systems for Net-zero Power Systems and Markets
- MPCE 10th Anniversary Forum will be held on Feb. 27, 2023
