Abstract:The increasing penetration of renewable energy resources and reduced system inertia pose risks to frequency security of power systems, necessitating the development of fast frequency regulation (FFR) methods using flexible resources. However, developing effective FFR policies is challenging because different power system operating conditions require distinct regulation logics. Traditional fixed-coefficient linear droop-based control methods are suboptimal for managing the diverse conditions encountered. This paper proposes a dynamic nonlinear P-f droop-based FFR method using a newly established meta-reinforcement learning (meta-RL) approach to enhance control adaptability while ensuring grid stability. First, we model the optimal FFR problem under various operating conditions as a set of Markov decision processes and accordingly formulate the frequency stability-constrained meta-RL problem. To address this, we then construct a novel hierarchical neural network (HNN) structure that incorporates a theoretical frequency stability guarantee, thereby converting the constrained meta-RL problem into a more tractable form. Finally, we propose a two-stage algorithm that leverages the inherent characteristics of the problem, achieving enhanced optimality in solving the HNN-based meta-RL problem. Simulations validate that the proposed FFR method shows superior adaptability across different operating conditions, and achieves better trade-offs between regulation performance and cost than benchmarks.

Abstract:Energy equity refers to the condition in which access to the cleaner energy required by individuals is equally available to all. To relieve the energy expenditures-the key component in the concept of energy equity–of low-income communities, governments worldwide have imposed caps on soaring energy prices. However, the inherent mechanisms within the operational schedule remain undiscussed. This paper innovatively provides guidelines for operators to embed energy burden policies into the bulk power system model, by answering two critical questions. ①What is the impact on system price pattern when embedding the locational price constraints? ② How to reformulate the tie-line schedule to meet the equity thresholds? Consequently, a novel bi-level energy equity-constrained tie-line scheduling model is proposed. The conventional economic dispatch is solved at the upper level, and then a preliminary operational schedule is given to the lower level, where we propose an energy equity slackness component variable to evaluate the gap between preliminary and desired equity-satisfied operational schedules. The implicit constraints on the price are converted into explicit feasibility cuts with dual theory. Case studies on test systems demonstrate the reduced energy expenditure for underserved communities, and the optimal tie-line schedule is also validated.

Abstract:As the proportion of renewable energy sources continues to increase, the local damping contributions of sources in power system decrease, posing a challenge to the power system stability. Therefore, online tracking of the damping contributions of each source is crucial for the prevention of low-frequency oscillations. This paper proposes an online tracking method of local damping under ambient data. The proposed method is based on dissipation energy spectrum analysis (DESA) and the energy dissipation factor (EDF). First, the feasibility of using frequency-domain analysis for the dissipation energy of generator is analyzed. The frequency spectral function of dissipation energy of generator is then derived by integrating with Parseval’s theorem, and the EDF is defined. Second, the generator energy dissipation factor (GEDF) for the dominant oscillation mode frequency is established. The modal information of the dominant oscillation in the power system is obtained through DESA. The relationship between the frequency spectral function and eigenvalues is also established. Finally, an online tracking method of local damping is proposed based on DESA and GEDF. The effectiveness of the proposed method is validated through simulations on a four-machine 11-bus power system and an actual power system in Northwest China.

Abstract:This paper studies the problem of multi-stage robust unit commitment with discrete load shedding. In the day-ahead phase, the on-off status of thermal units is scheduled. During each period of real-time dispatch, the output of thermal units and the action of load shedding are determined, and the discrete choice of load shedding corresponds to the practice of tripping substation outlets. The entire decision-making process is formulated as a multi-stage adaptive robust optimization problem with mixed-integer recourse, whose solution takes three steps. First, we propose and apply partially affine policy, which is optimized ahead of the day and restricts intertemporal dispatch variables as affine functions of previous uncertainty realizations, leaving remaining continuous and binary dispatch variables to be optimized in real time. Second, we demonstrate that the resulting model with partially affine policy can be reformulated as a two-stage robust optimization problem with mixed-integer recourse. Third, we modify the standard nested column-and-constraint generation algorithm to accelerate the inner loops by warm start. The modified algorithm solves the two-stage problem more efficiently. Case studies on the IEEE 118-bus system verify that the proposed partially affine policy outperforms conventional affine policy in terms of optimality and robustness; the modified nested column-and-constraint generation algorithm significantly reduces the total computation time; and the proposed method balances well optimality and efficiency compared with state-of-the-art methods.

Abstract:Maintaining a continuous power balance is crucial for ensuring operational feasibility in power systems. However, due to forecasting difficulties and computational limitations, economic dispatch often relies on discrete interval horizons, which fail to guarantee feasibility within each interval. This paper introduces the concept of a continuous operating envelope for managing intra-interval fluctuations, delineating the range within which fluctuations remain manageable. We propose a parametric programming model to construct the envelope, represented as a polytope that accounts for both timescale and fluctuation dimensions. To address the computational challenges inherent in the parametric programming model, we develop a fast solution method to provide an approximated polytope. The approximated polytope, initially derived from lower-dimensional projections, represents a subset of the exact polytope that ensures operational feasibility. Additionally, we apply a polytope expansion strategy in the original dimensions to refine the approximated polytope, bringing the approximation closer to the exact polytope. Case studies on an illustrative 5-bus and a utility-scale 661-bus system demonstrate that the method effectively and stably provides a continuous operating envelope, particularly for high-dimensional problems.

Abstract:External disturbances can induce torsional oscillation with weak damping in the shaft system of permanent magnet synchronous generators (PMSGs) based wind generation system, thereby inducing low-frequency oscillations. However, the influence of electromagnetic torque on the shaft system damping and corresponding parameter laws have been scarcely explored. We define the electrical damping coefficient as a quantitative measure for the influence of electromagnetic torque on the shaft system damping. The torsional oscillation damping characteristics of the shaft system under vector control are analyzed, and the transfer function for electromagnetic torque and speed is derived. Additionally, we elucidate the mechanism by which the electromagnetic torque influences the shaft system damping. Simultaneously, laws describing the influence of wind speed, system parameters, and control parameters on the torsional oscillation damping are analyzed. Accordingly, the optimal damping angle of the shaft system a torsional oscillation suppression strategy is proposed to compensate for with uncertainty in the parameters affecting damping. The studied system is modeled using MATLAB/Simulink, and the simulation results validate the effectiveness of the theoretical analysis and proposed torsional oscillation suppression strategy.

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.

Abstract:In this paper, a dynamic equivalent method applicable to the direct-drive permanent magnet synchronous generator (PMSG) based wind farms under asymmetrical faults is proposed. Firstly, PMSGs are clustered based on their different active power characteristics under asymmetrical faults. Further, single-machine equivalent models (SMEMs) are constructed for different clusters of PMSGs. In particular, an SMEM with multi-segmented slope recovery is introduced for PMSGs with ramp recovery characteristics. Further, a collector network equivalent method for wind farms applicable to both symmetrical and asymmetrical faults is presented. Moreover, an iterative simulation method is used to gain the required clustering indicators before the fault actually occurs. Eventually, the effectiveness of the proposed dynamic equivalent method is verified on a modified IEEE 39-bus system.

Abstract:The gradual replacement of gasoline vehicles with electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) in recent years has provided a growing incentive for the collaborative optimization of power distribution network (PDN), urban transportation network (UTN), and hydrogen distribution network (HDN). However, an appropriate collaborative optimization framework that addresses the prevalent privacy concerns has yet to be developed, and a sufficient pool of system operators that can competently operate all three networks has yet to be obtained. This study proposes a differentiated taxation-subsidy mechanism for UTNs, utilizing congestion tolls and subsidies to guide the independent traffic flow of EVs and HFCVs. An integrated optimization model for this power-hydrogen-transportation network is established by treating these vehicles and the electrolysis equipment as coupling bridges. We then develop a learning-aided decoupling approach to determine the values of the coupling variables acting among the three networks to ensure the economic feasibility of collaborative optimization. This approach effectively decouples the network, allowing it to operate and be optimized independently. The results for a numerical simulation of a coupled system composed of a IEEE 33-node power network, 13-node Nguyen-Dupuis transportation network, and 20-node HDN demonstrate that the proposed learning-aided approach provides nearly equivalent dispatching results as those derived from direct solution of the physical models of the coupled system, while significantly improving the computational efficiency.

Abstract:The variable and unpredictable nature of renewable energy generation (REG) presents challenges to its large-scale integration and the efficient and economic operation of the electricity network, particularly at the distribution level. In this paper, an operational coordination optimization method is proposed for the electricity and natural gas networks, aiming to overcome the identified negative impacts. The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants. A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption. This method allows for the full consideration of unbalanced operational scenarios. Meanwhile, the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines. The sequential symmetrical second-order cone programming (SS-SOCP) method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process. The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network. The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost, the curtailment of installed REG, the voltage imbalance of three-phase power system, and the overall carbon emissions.

Abstract:Lossy power flow naturally extends lossless linear power flow to lossy distribution networks, further improving the accuracy of approximate computation and analysis. However, these enhanced versions are only applicable at the alternating current (AC) transmission level, and the accuracy is limited in distribution networks, especially in hybrid AC-direct current (DC) distribution networks. In this paper, we revisit the lossy power flow model and extend it to hybrid AC-DC distribution networks with multi-terminal voltage source converters. The proposed lossy power flow model can be reformulated as an iteration problem with node power injection as the fixed point. For this purpose, a node power injection modification model based on direct derivation is proposed by exploiting the negligibility of the phase angle differences, and iteratively solving lossy power flows for both AC and DC sub-networks. For coupling devices, to guarantee that the power flow is matched on both AC and DC sides, we formulate a rigorous fixed-point problem to solve the lossy power flow of voltage source converters. Finally, the high accuracy and computational efficiency of the proposed model are verified on multiple test cases.

Abstract:In the context of large-scale photovoltaic integration, flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks (DNs). Recently, deep reinforcement learning (DRL) has been widely applied to scheduling problems. However, most methods neglect the vulnerability of DRL to state adversarial attacks such as load redistribution attacks, significantly undermining its security and reliability. To this end, a flexibility scheduling method is proposed based on robust graph DRL (RoGDRL). A flexibility gain improvement model considering temperature-dependent resistance is first proposed, which considers weather factors as additional variables to enhance the precision of flexibility analysis. Based on this, a state-adversarial two-player zero-sum Markov game (SA-TZMG) model is proposed, which converts the robust DRL scheduling problem into a Nash equilibrium problem. The proposed SA-TZMG model considers the physical constraints of state attacks that guarantee the maximal flexibility gain for the defender when confronted with the most sophisticated and stealthy attacker. A two-stage RoGDRL algorithm is proposed, which introduces the graph sample and aggregate (GraphSAGE) driven soft actor-critic to capture the complex feature about the neighbors of nodes and their properties via inductive learning, thereby solving the Nash equilibrium policies more efficiently. Simulations based on the modified IEEE 123-bus system demonstrates the efficacy of the proposed method.

Abstract:Although power grids have become safer with increased situational awareness, major extreme events still pose reliability and resilience challenges, primarily at the distribution level, due to increased vulnerabilities and limited recovery resources. Information and communication technologies (ICTs) have introduced new vulnerabilities that have been widely investigated in previous studies. These vulnerabilities include remote device failures, communication channel disturbances, and cyberattacks. However, only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat. This paper proposes a communication-aware restoration approach of smart distribution grids, which leverages power-telecom interdependencies to determine the optimal restoration strategies. The states of grid-energized telecom points are tracked to provide the best restoration actions, which are enabled through the resilience resources of repair, manual switching, remote reconfiguration, and distributed generators. As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies, different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations. System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming. Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources. The existing disparity between overhead and underground power line configurations is also quantified.

Abstract:Peer-to-peer (P2P) energy trading in active distribution networks (ADNs) plays a pivotal role in promoting the efficient consumption of renewable energy sources. However, it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests. Hence, this paper proposes a soft actor-critic algorithm incorporating distributed trading control (SAC-DTC) to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers. First, the soft actor-critic (SAC) algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost, and the primary environmental information of the ADN at this point is published to prosumers. Then, a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues. Subsequently, the results of trading are encrypted based on the differential privacy technique and returned to the ADN. Finally, the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning. Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost, boosts the P2P market revenue, maximizes the social welfare, and exhibits high computational accuracy, demonstrating its practical application to the operation of power systems and power markets.

Abstract:The flexible interconnection of microgrids (MGs) adopting back-to-back converters (BTBCs) has emerged as a new development trend in the field of MGs. This approach enables larger-scale integration and higher utilization of distributed renewable energy sources (RESs). However,their stability characteristics are very different from single MG due to the control characteristics of flexible interconnection. Meanwhile, the uncertainty and stochastic dependence structures of RESs and loads create challenges for stability analysis and cooperative control. In this paper, a probabilistic small-signal stability assessment and cooperative control framework is proposed for interconnected MGs via BTBCs. First, a cooperative control architecture for MGs is constructed. Then, a small-signal model of interconnected MGs via BTBCs containing primary control and secondary control is developed. This model facilitates the analysis of the impacts of BTBCs and various control strategies on the system stability. Subsequently, Copula functions and polynomial chaos expansion (PCE) are combined to achieve the probabilistic small-signal stability assessment. On this basis, the parameters of the cooperative control are optimized, enhancing the robustness of interconnected MGs via BTBCs. Finally, a case of interconnected MGs via BTBCs are built in MATLAB/Simulink to verify the accuracy and effectiveness of the proposed framework.

Abstract:Traditional virtual synchronous generator (VSG) suffers from frequency steady-state deviation in islanded microgrids, which negatively affects the frequency-sensitive loads. Moreover, similar to the synchronous generator, VSG introduces active power oscillation, especially under the condition of multiple parallel VSGs, which may cause overload or damage to the VSG because of its low overcurrent capability as a power electronic inverter. To address these issues, a decentralized frequency restoration and power oscillation damping control method is proposed in this paper, in which the global variable characteristic of the microgrid frequency is considered to restore it to the rated value while ensuring precise active power sharing. Moreover, the proposed control method can dampen the power oscillation during load disturbance without affecting the steady-state characteristics. In addition, the fully decentralized manner obviates the requirement for communication networks, thereby considerably reducing the communication burden and improving system reliability. Finally, simulations and experiments are conducted to validate the effectiveness of the proposed control method.

Abstract:Contingencies, such as behavior shifts of microgrid operators (MGOs) and abrupt weather fluctuations, significantly impact the economic operations of multi-microgrids (MMGs). To address these contingencies and enhance the economic and autonomous performance of MGOs, a self-organizing energy management modeling approach is proposed. A second-order stochastic dynamical equation (SDE) is developed to accurately characterize the self-organizing evolution of the operating cost of MGO incurred by contingencies. Firstly, an operating model of MMG relying on two random graph-driven information matrices is constructed and the order parameters are introduced to extract the probabilistic properties of variations in operating cost. Subsequently, these order parameters, which assist individuals in effectively capturing system correlations and updating state information, are incorporated as inputs into second-order SDE. The second-order SDE is then solved by using the finite difference method (FDM) within a loop-structured solution framework. Case studies conducted within a practical area in China validate that the proposed self-organizing energy management model (SEMM) demonstrates spontaneous improvements in economic performance compared with conventional models.

Abstract:The variable nature of solar power has hitherto been regarded as a major barrier preventing large-scale high-penetration solar energy into the power grid. Based on decades of research, particularly those advances made over the recent few years, it is now believed that dispatchable solar power is no longer a conception but will soon become techno-economically feasible. The policy-driven information exchange among the weather centers, grid operators, and photovoltaic plant owners is the key to realizing dispatchable solar power. In this paper, a five-step forecasting framework for enabling dispatchable solar power is introduced. Among the five steps, the first three, namely numerical weather prediction (NWP), forecast post-processing, and irradiance-to-power conversion, have long been familiar to most. The last two steps, namely hierarchical reconciliation and firm forecasting, are quite recent conceptions, which have yet to raise widespread awareness. The proposed framework is demonstrated through a case study on achieving effectively dispatchable solar power generation at plant and substation levels.

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.

Abstract:The distinctive fault characteristics of battery energy storage stations (BESSs) significantly affect the reliability of conventional protection methods for transmission lines. In this paper, the three-dimensional (3D) data scattergrams are constructed using current data from both sides of the transmission line and their sum. Following a comprehensive analysis of the varying characteristics of 3D data scattergrams under different conditions, a 3D data scattergram image classification based protection method is developed. The depth-wise separable convolution is used to ensure a lightweight convolutional neural network (CNN) structure without compromising performance. In addition, a Bayesian hyperparameter optimization algorithm is used to achieve a hyperparametric search to simplify the training process. Compared with artificial neural networks and CNNs, the depth-wise separable convolution based CNN (DPCNN) achieves a higher recognition accuracy. The 3D data scattergram image classification based protection method using DPCNN can accurately separate internal faults from other disturbances and identify fault phases under different operating states and fault conditions. The proposed protection method also shows first-class tolerability against current transformer (CT) saturation and CT measurement errors.

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.

Abstract:With the development of the carbon markets (CMs) and electricity markets (EMs), discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies (GenCos). Motivated by the carbon option and Black-Scholes (B-S) model, GenCos are given the right but not the obligation to trade carbon emission allowances (CEAs) and use instruments to hedge against price risks. To model the strategic behaviors of GenCos that capitalize on these cross-market and cross-time opportunities, a multi-market trading strategy that incorporates option-jointed daily trading and reinforcement learning-jointed weekly continuous trading are modeled. The daily trading is built with a bi-level structure, where a profit-oriented bidding model that jointly considers both the optimal CEA holding shares and the best bidding curves is developed at the upper level. At the lower level, in addition to market clearing models of the day-ahead EM and auction-based CM, a B-S model that considers carbon trading asynchronism and option pricing is constructed. Then, by expanding the daily trading, the weekly continuous trading is modeled and solved using reinforcement learning. Binary expansion and strike-to-spot price ratio are utilized to address the nonlinearity. Finally, case studies on an IEEE 30-bus system are conducted to validate the effectiveness of the proposed trading strategy. Results show that the proposed trading strategy can increase GenCo profits by influencing market prices and leveraging carbon options.

Abstract:The spot flexibility markets are before the real-time energy exchange, allowing demand-side management to reduce energy consumption during peak periods. In these markets, demand aggregators must quickly choose the customers ’reduction bids that fulfill grid requirements. This clearing procedure is challenging due to the computational complexity of selecting the optimal bids. Therefore, developing a clearing mechanism that avoids searching the entire flexibility bid space while respecting grid constraints is essential for the smooth operation of the spot flexibility market. This paper presents a clearing mechanism with reduced computational complexity of the winner determination problem in spot flexibility market for demand aggregators carrying out reductions in energy consumption. The proposed approach transforms customers’flexibility bids into a reward-based function. Afterward, the gradient-based optimization solves the bid selection problem. This approach helps demand aggregators achieve satisfactory energy reductions within an appropriate delay for spot flexibility markets. A comparative study presents the effectiveness of the proposed approach against commonly used approaches: hybrid particle swarm optimization genetic algorithm and combinatorial search.

Abstract:Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.

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.

Abstract:In this study, a novel parallel converter-based hybrid high-voltage direct current (HVDC) system is proposed for the integration and delivery of large-scale renewable energy. The rectifier uses the line commutated converter (LCC) and low-capacity modular multilevel converter (MMC) in parallel, while the inverter uses MMC. This configuration combines the economic advantages of LCC with the flexibility of MMC. Firstly, the steady-state control strategies are elaborated. The low-capacity MMC operates in the grid-forming mode to offer AC voltage support. It also provides active filtering for the LCC and maintains the reactive power balance of the sending-end system. The LCC efficiently transmits all active power at the rectifier side, fully exploiting its bulk-power transmission capability. Secondly, the fault ride-through strategies of both the AC faults at two terminals and the DC fault are proposed, in which the MMCs at both terminals can remain unblocked under various faults. Thus, the proposed system can mitigate the impact of the faults and ensure continuous voltage support for the sending-end system. Finally, simulations in PSCAD/EMTDC verify the effectiveness and performance of the proposed system.

Abstract:For doubly-fed induction generator (DFIG)-based wind farms connected to flexible DC transmission system, the oscillation suppression after fault clearance proves very difficult. Addressing this problem, this paper constructs the dynamic energy model of the interconnected system, reveals the mechanism of oscillation instability after fault clearance, and designs an oscillation suppression strategy. First, by considering the dynamic characteristics of the control links in grid-connected DFIG-based wind farms via voltage source converter based high-voltage direct current (VSC-HVDC) transmission system, the interconnected system is divided into several subsystems, and the energy model of each subsystem is constructed. Furthermore, the magnitudes and directions of different interaction energy items are quantitatively analyzed, so that the key control links that transmit and magnify the system energy can be identified. On this basis, the corresponding supplementary control links are designed to suppress the system oscillation. Finally, the accuracy and effectiveness of the proposed oscillation suppression strategy are verified by hardware-in-loop tests. The results prove that the d-axis subsystem of DFIG grid-side converter (GSC) current inner loop, phase-locked loop (PLL), and q-axis subsystem of VSC-HVDC voltage outer loop are the key links that induce the oscillation to occur, and the proposed strategy shows promising results in oscillation suppression.

Abstract:Innovative dynamic models for the DC modular multilevel converter (DC-MMC) in rotating dq frame are presented in this paper, which are specifically designed to enhance converter design and stability analysis. Open-loop and closed-loop models are developed using three dq frames, providing a detailed examination of the impact of 2 ^nd and 3 ^rd harmonic components on the model accuracy. A novel contribution of this paper is the integration of a 2 ^nd harmonic current suppression controller (SHCSC) within the closed-loop model, offering new insights into its effects on system stability. The DC-MMC model is further extended by coupling it with high-voltage direct current (HVDC) cables on each side, forming an interconnected system model that accurately represents a more authentic scenario for future DC grids. The proposed model is rigorously validated against PSCAD benchmark model, confirming their precision and reliability. The interconnected system model is then utilized to analyze the influence of cable length on system stability, demonstrating practical applications. The closed-loop model is subsequently employed for stability assessment of the interconnected system, showcasing its applicability in real-world scenarios. Additionally, a damping controller is designed using participation factor and residue approaches, offering a refined approach to oscillation damping and stability optimization. The effectiveness of the controller is evaluated through eigenvalue analysis, supported by simulation results, underscoring its potential for enhancing system stability.

Abstract:The negative-sequence voltage is often caused by the asymmetrical fault in the AC system, as well as the harmonics after the symmetrical fault at the AC side of inverter in line commutated converter based high-voltage DC (LCC-HVDC). The negative-sequence voltage affects the phase-locked loop (PLL) and the inverter control, thus the inverter is vulnerable to the subsequent commutation failure (SCF). In this paper, the analytical expression of the negative-sequence voltage resulting from the symmetrical fault with the commutation voltage is derived using the switching function and Fourier decomposition. The analytical expressions of the outputs of the PLL and inverter control with respect to time are derived to quantify the contribution of the negative-sequence voltage to the SCF. To deal with the AC component of the input signals in the PLL and the inverter control due to the negative-sequence voltage, the existing proportional-integral controls of the PLL, constant current control, and constant extinction angle control are replaced by the linear active disturbance rejection control against the SCF. Simulation results verify the contributing factors to the SCF. The proposed control reduces the risk of SCF and improves the recovery speed of the system under different fault conditions.

Abstract:DC series-parallel power flow controller (SP-PFC) is a highly efficient device to solve the problem of uncontrolled line current in the bipolar DC distribution system. However, its potential in fault current limiting is not fully explored. In this paper, a self-adaptive action strategy (SAAS) and a parameter optimization method of SP-PFC in bipolar DC distribution systems are proposed. Firstly, the common- and different-mode (CDM) equivalent circuits of the bipolar DC distribution system with SP-PFC in different fault stages are established, which avoids the line coupling inductance. Based on this, the influence of different parameters and line coupling inductance on the fault current limiting capability are investigated. It is found that the SP-PFC has the best fault current limiting capability when the capacitance and inductance of filter are inversely proportional. To realize the adaptability of fault current limiting capability under different fault severities, the SAAS of SP-PFC is proposed. The validity of the CDM equivalent circuits and parameter optimization method, and the effectiveness of the SAAS are verified by simulations and experiments.