智能电网
智能电网
- The Paper
Optimal Operation Method of Electric-Hydrogen Hybrid Energy Storage Microgrid System Based on Typical Commercial Operation Mode
Abstract：ObjectivesAiming at the low-carbon transformation of micro-grid, an optimal scheduling method for electric-hydrogen hybrid energy storage micro-grid was proposed to address the scheduling challenges under different business models.MethodsFirstly, a mathematical model of micro-grid with electro-hydrogen hybrid energy storage was developed. Based on two typical business models of multi-party cooperative energy supply and multi-party independent energy supply were analyzed. Based on these models, the corresponding multi-objective optimization scheduling models and these constraints were constructed. Then, the non-dominated sorting genetic algorithm II (NSGA-II) was introduced, which was combined with variance crowding distance method and normal distribution crossover operator to improve optimization efficiency and solution accuracy. Finally, the simulation experiments were conducted using an operational electro-hydrogen hybrid energy storage micro-grid system in a southeastern coastal area to validate the effectiveness of the proposed method.ResultsCompared with before optimization, the economy of the multi-party cooperative energy supply business model is improved by about 4.1%, the wind-solar energy abandon rate is reduced by about 19%, and the annual carbon emission is reduced by about 47.42 t.ConclusionsThe multi-party cooperative energy supply business model aligns better with the current power market conditions of China’s , and the optimized system performance is significantly enhanced. This study demonstrates that the proposed optimization scheduling method effectively supports the low-carbon transition of electro-hydrogen hybrid energy storage micro-grid under various business models.Keywords：microgrid;power system;renewable energy;energy storage system;reversible conversion of electric hydrogen;typical business model;non-dominated sorting genetic algorithm II (NSGA-II);multi-objective optimization algorithm78|724|0
<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Robust Optimal Scheduling Strategy for Virtual Power Plant Participation in Electric Energy and Demand Response Markets Under Multiple Uncertainties
Abstract：ObjectivesTo address the uncertainties of renewable energy output and load faced by virtual power plant (VPP) when participating in electric energy and demand response markets, a robust optimal scheduling strategy considering multiple uncertainties was proposed to reduce the conservativeness of robust optimization and improve the economic benefits of VPP.MethodsA polyhedral uncertainty set based on conditional value at risk (CVaR) was constructed. On this basis, considering the uncertainties of wind power, photovoltaic output and load, a day-ahead two-stage robust optimization model of VPP participating in electric energy and demand response markets was established. Then, using a column-and-constraint generation (C&CG) algorithm and Lagrangian dual theory, the model was divided into a master problem and a sub-problem that can be solved by a solver. Finally, Monte Carlo method was used to generate a large number of wind power, photovoltaic and load data. The proposed strategy was simulated and analyzed, and compared with the optimization results of other schemes.ResultsThe proposed strategy adopting a polyhedral uncertainty set based on CVaR can make full use of historical data. Compared with the scheme using traditional uncertainty set, the total cost of VPP is reduced by about 2%.ConclusionsThe proposed strategy can significantly reduce the conservativeness of robust optimization results and enhance the economy of VPP participation in the market under multiple uncertainties.Keywords：virtual power plant (VPP);wind power;photovoltaic;electric energy;demand response market;condition value at risk (CVaR);robust optimization;scheduling strategy103|130|0<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Low-Carbon and Economic Synergy Optimization Configuration for Microgrid With Hydrogen Energy Storage
Abstract：ObjectivesTraditional electric energy storage has limitations in scale, duration, and environmental impact.Moreover, the renewable energy absorption capacity in the microgrid is low, and low-carbon and economy cannot be taken into account in planning. In order to solve the above problems, based on the basic working principle of hydrogen energy storage, hydrogen energy storage was incorporated into the microgrid instead of traditional electric energy storage, and a low-carbon and economic synergy bi-level optimization configuration model of microgrid with hydrogen energy storage was established.MethodsThe upper-level planning model aimed at minimizing the comprehensive equivalent annual value of the microgrid, based on the joint operation of electricity and hydrogen. The carbon trading mechanism was introduced to plan the capacity of various power generation equipments in the microgrid, which can enhance the low-carbon of the system. The lower-level operation model aimed to minimize the sum of the absolute values of the difference between the new energy output and the load demand. The model also encouraged users to adopt diversified demand-side response behaviors with the goal of accurately tracking the new energy output curve, and feeded back the user’s energy consumption behavior to the upper-level model to optimize the load curve. On the basis of improving the absorption capacity of new energy, the system economy is further improved, deeply exploring the synergy between the low-carbon and economic characteristics of microgrids.ResultsThe simulation results of microgrid in a certain industrial park show that the proposed method yields a planning scheme with excellent low-carbon and economy. Compared with the traditional planning method, the low-carbon and economy are improved by 53.6% and 37.1%, respectively.ConclusionsThe model presented in this paper not only enhances the capacity for new energy absorption but also further improves the system economic performance. It achieves a synergistic enhancement of the microgrid low-carbon and economy.Keywords：microgrid;hydrogen energy storage;new energy;low-carbon;carbon trading;capacity planning;demand response;optimization configuration73|133|12<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Research on Reconfiguration Strategy of Distributed Distribution Network With Self-Healing Performance Under High-Proportion Renewable Energy Access
Abstract：ObjectivesAs the proportion of renewable energy in power grids increases year by year, the volatility and uncertainty of the grid are significantly heightened, posing challenges to the safe operation of distribution networks. To address the issue of distributed network reconfiguration in high-proportion renewable energy grids, this paper proposed an online rolling optimization framework.MethodsThe framework utilized a distributed consensus protocol to obtain network topology and node operation information. It can enable automatic reconfiguration in the event of N-1 and N-2 line failures, allowing the distribution network to automatically restore normal operation without the need for additional external triggering signals, thus ensuring economic operation of the grid. Additionally, a rolling optimization method was employed to handle grid fluctuations caused by the high proportion of renewable energy, and generative adversarial network (GAN) technology was used to generate new data, which combined with historical data. It can help to achieve high-precision forecasting of grid operation data.ResultsThe proposed method can achieve automatic economic optimization and self-healing in normal, single-point failure, and two-point failure scenarios.ConclusionsThis method provides an effective solution for ensuring the safe operation of distributed networks in high-proportion renewable energy grids.Keywords：renewable energy;distributed generator;distribution network reconstruction;distribution network self-healing;high proportion of new energy;distributed optimization;look-ahead optimization65|89|17<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15A Frequency Regulation Method of Energy Storage System Based on Adaptive Adjustment of Virtual Synchronous Generator Control Parameters
Abstract：ObjectivesThe large-scale penetration of wind power has reduced the frequency regulation capability of the power system to a certain extent.As a relatively mature and effective technical means, the energy storage system is widely used in power grid frequency regulation. Therefore, the response process and optimal configuration of energy storage system (ESS) participating in power grid frequency regulation under the control of virtual synchronous generator were studied.MethodsBased on the simulation software DIgSILENT/PowerFactory, an ESS control model and typical power system were constructed to analyze the frequency response characteristics of the grid before and after the ESS participation. Furthermore, considering the reserve capacity of wind turbines under different output modes, the configuration results of the ESS were optimized by dividing the wind speed range and determining the wind turbine power reserve coefficient, so as to realize the adaptive adjustment of the frequency regulation coefficient of the ESS.ResultsThe investment of the ESS can effectively improve the frequency response and reduce wind curtailment of the system. By reserving the frequency regulation capacity of wind turbines reasonably, the ESS can provide reliable power support for the power grid.ConclusionsThe self-adaptive adjustment method of the frequency regulation coefficient of the ESS based on the wind speed and the output power of fans can effectively reduce the overshoot and the output power of the ESS while meeting the frequency regulation requirements, thereby extending the working time.Keywords：double carbon;energy storage;new energy;wind power;virtual synchronous generator;frequency response;frequency regulation93|316|12<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Research on Operation Strategy of Virtual Power Plant and Distributed Control of DG
Abstract：ObjectivesVirtual power plant is usually composed of distributed generator (DG). When the system lacks power grid support, practical and reasonable comprehensive operation strategies and accurate control methods are needed to ensure the smooth operation of virtual power plant.MethodsAn operation strategy based on system fluctuation value was proposed. Then, aiming at the problem of generation control of distributed generators in the system, a distributed finite-time controller was designed. The amplitudes of voltage and frequency were adjusted to nominal values within a finite time, and the active power was distributed among each DG unit. Different from the traditional centralized control strategy, the proposed control method is based on distributed junction, which only requires information exchange between adjacent DG units through sparse communication network.ResultsThe feasibility of the proposed control method and its robustness to the uncertainty of model parameters and load variation were verified by simulation experiments.ConclusionsCompared with previous studies, the proposed operation strategy divides the system operating conditions in more detail, effectively improving the stability and security of the system.Keywords：virtual power plant;operation strategy;primary control;secondary control;finite-time control114|132|5<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Renewable Energy Distribution Network Overcurrent Protection Based on Positive-Sequence Sudden-Change Component Locus Identification
Abstract：ObjectivesWith the widespread integration of renewable energy, the uncertainty of power flow in distribution networks with neutral points grounded through small resistors has made the setting of traditional three-stage current protection increasingly complex, and reduced its sensitivity and reliability. To solve the above issues, a new type of current protection based on positive sequence sudden-change variable current phasor trajectory identification was proposed.MethodsFirstly, the magnitude and distribution characteristics of the positive sequence sudden-change variable current upstream and downstream of the fault point were analyzed. Secondly, using the affinity clustering algorithm, the defined “main fault path” based on the clustering results was identified. Finally, a novel searching method was proposed to identify the source of the positive sequence sudden-change variable, which was the last level feeder located on the main fault path, as the faulted feeder.ResultsSimulation research based on PSCAD shows that the proposed protection method can adapt to various fault conditions, is immune to the integration of renewable energy, and has high sensitivity and reliability.ConclusionsThe research findings are helpful to enhance the operational safety of distribution networks with neutral points grounded through small resistors, and improve the efficiency of fault detection.Keywords：renewable energy;distribution network;relay protection;positive-sequence sudden-change component;affinity clustering algorithm72|357|8<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Research on Temperature Situation Awareness and Auxiliary Decision-Making System Scheme of Substation Equipment
Abstract：ObjectivesTo enhance the intelligent management of substation equipment maintenance, timely identify and mitigate the risks of failures caused by device overheating, and ensure the safe and stable operation of the power grid, the temperature situation awareness and auxiliary decision-making scheme of substation equipment were proposed.MethodsThe research was carried out from four aspects: the perception layer, the understanding layer, the prediction layer, and the auxiliary decision-making layer. In the perception layer, the K-nearest neighbor (KNN) classification algorithm was used to analyze the correlation of multi-class temperature data. In the understanding layer, a BP neural network was employed to construct a historical data transmission model to address missing historical data issues. In the prediction layer, a temperature prediction model combining autoregressive integrated moving average (ARIMA) and support vector machine (SVM) was designed to handle nonlinear data and noise. In the auxiliary decision-making layer, the grey relational analysis was applied to analyze the relationship between equipment temperature changes and fault risks.ResultsThe verification results of numerical examples based on the proposed scheme show that the scheme realizes the effective perception of the future temperature variation trends of the equipment and provides a basis for the identification of equipment defects.ConclusionsThrough multi-dimensional and deep-level temperature data analysis, the proposed scheme reveals the potential correlation between equipment temperature and fault risk, realizes the prediction of the operational trend of substation equipment, and provides a reference for the optimization of operational mode and the formulation of equipment maintenance plan.Keywords：power system;substation;temperature state awareness;auxiliary decision-making;autoregressive integrated moving average (ARIMA) model;BP neural network;support vector machine (SVM)110|245|5<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Voltage Hierarchical Control Strategy of Active Distribution Network Based on Deep Reinforcement Learning
Abstract：ObjectivesThe randomness and volatility of distributed power generation poses significant challenges for the voltage control in active distribution network (AND). In this context, there is an urgent need for an efficient voltage control strategy to ensure the safe operation of ADN.MethodsBased on the deep reinforcement learning method, a voltage control strategy for double-layer regional distribution networks was proposed. First, based on the adjustment characteristics of voltage regulating equipment and the complexity of controllable elements, a regional coordinated control area and a local autonomous control area were designed for the radiating grid structure of the ADN, and the voltage control model of each area was constructed. Then, the model was solved by deep Q-Network (DQN) algorithm and deep deterministic policy gradient (DDPG) algorithm to achieve the purpose of tracking voltage changes in real time, and effectively solve the voltage control problem during the operation of the ADN. Finally, the method was verified by IEEE 33-bus simulation examples.ResultsThe DQN algorithm and the DDPG algorithm were used to solve the control variables in the coordinated control region and the local autonomous region respectively, realizing real-time decision-making of voltage regulation in the ADN system, and solving the problems of bidirectional flow of ADN power flow and complex and changeable voltage.ConclusionsThe proposed control strategy has obvious effect on controlling voltage deviation, and has strong accuracy and practicality.Keywords：active distribution network (ADN);regional coordination control;local autonomous control;deep reinforcement learning;voltage control strategy73|810|12<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15Summary and Prospect of Comprehensive Evaluation Methods of Power Quality
Abstract：ObjectivesWith the continuous development of China's new power system, there is a growing integration of new energy sources such as photovoltaic and wind power into the grid. However, this integration has also led to an increase in intelligent control equipment and nonlinear loads, resulting in more complex power quality issues. In order to enhance the power supply quality of the entire power grid and achieve economic operation and efficient development, it is crucial to conduct a scientific and rational quantitative evaluation of power quality.MethodsStarting from three aspects of power quality evaluation indicators, weighting methods and evaluation models, the comprehensive power quality evaluation methods were systematically summarized and compared, and the research status of comprehensive power quality evaluation methods was summarized. On this basis, the shortcomings and problems that need to be solved of various existing comprehensive power quality assessment methods were summarized, and the possible research directions in this field were prospected.ConclusionsAlthough many results have been achieved in the research on comprehensive evaluation of power quality at home and abroad, each method has its own advantages and disadvantages, there are still a series of important neglected problems that need to be solved in this field.Keywords：new power system;power quality;comprehensive evaluation;indicator;weight;evaluation model79|426|12<HTML><L-PDF><WORD><Meta-XML>Updated：2025-06-15
