To enhance the adoption of new energy sources, expand green hydrogen production and use, and achieve low-carbon, cost-effective operation in multi-energy cogeneration systems, this study proposes a wind-hydrogen multi-energy storage (WHMES) system that incorporates power-to-hydrogen (P2H) technology. A multi-objective optimization model for the integrated energy system and a two-stage energy cascade utilization control strategy are developed. The annual wind power and load data are categorized into three typical scenarios, with the optimization goals focused on economy, energy supply, and carbon emissions. The problem is addressed using a decomposition-based multi-objective evolutionary algorithm. Among the resulting set of non-inferior solutions, a fuzzy membership function is applied to filter and select the optimal configuration. The results demonstrate the model’s scientific validity and effectiveness, showing strong environmental protection and energy supply reliability across various typical daily scenarios. This provides a valuable reference for optimizing integrated energy systems with wind-hydrogen coupling.

To explore flexible resources on both the supply and demand sides of integrated energy systems (IES) and optimize their low-carbon and economic performance, this paper proposes an optimal scheduling strategy for IES that considers the bilateral response of supply and demand. First, to enhance the flexibility of IES energy supply, an electric boiler and an organic Rankine cycle power generation device were introduced. A supply-side response model was developed to accommodate the flexible output of electric and thermal energy from a combined heat and power (CHP) unit. A flexibility indicator—the ratio of electric to thermal energy output—was proposed to measure the operational flexibility of the CHP unit. Second, based on the response characteristics of multiple loads (electricity, heat, and cold), a demand-side response model was constructed, incorporating orderly charging and discharging strategies for electric vehicles to explore the schedulable potential of the demand side. Finally, an IES supply-demand collaborative optimization model was established, aiming to minimize the sum of energy purchase costs, wind abandonment costs, response compensation costs, operation and maintenance costs, and carbon trading costs. The results demonstrate that the proposed strategy effectively utilizes the coordination and complementarity of flexible resources between supply and demand, enhancing the low-carbon performance, economic efficiency, and flexibility of IES operation while promoting the grid-connected consumption of wind power.

For receiving-end power grids with high proportions of new energy stations, the dynamic response characteristics of direct current (DC) and new energy are increasingly interlinked. The configuration of new energy stations is closely related to the voltage support strength of the grid, raising the risks to the system’s safe and stable operation. To address this issue, a novel method for the coordinated planning of new energy station siting and grid network is proposed, incorporating the multi-infeed short circuit ratio (MISCR) and multiple renewable energy station short circuit ratio (MRSCR). First, a research framework is developed that considers the uncertainty of new energy output and the short circuit ratio (SCR) index. The K-Means clustering method is used to identify typical scenarios. An expanded expression for MRSCR is derived by analogy to the MISCR derivation, accounting for variations due to different factors. A two-level model is then created for the coordinated planning of new energy stations and grid expansion, incorporating SCR constraints. The Benders decomposition algorithm is applied to iteratively solve the model. Simulations and tests are performed on a modified IEEE-39 bus system using the PSD-BPA platform. The results show that the proposed method simultaneously meets the requirements for both MISCR and MRSCR, reduces the risks associated with new energy stations disconnecting from the grid, and moderately enhances the voltage support strength of the receiving-end power grid.

A mathematical model for power line faults under extreme weather conditions is developed by examining how extreme weather impacts grid line branches. The ACCF model is employed to quantify actual power loss resulting from random transmission line failures in the power system. Metrics such as normalized grid load-shedding, node voltage, and branch power overrun are used to characterize the multiple fault risks of the grid under extreme weather. Monte Carlo simulations are utilized to account for load uncertainty, wind and PV output variability, and to evaluate node voltage and branch power crossing risks. An example analysis shows that the proposed method effectively and accurately assesses operational risks under various scenarios. This approach aids grid risk professionals in making early warning decisions and implementing systematic risk prevention and control measures.

In order to improve the energy efficiency of the multi-microgrid system and the benefits of ESS, this paper proposes a multi-microgrid system interaction and operation method containing microgrid operators,and establishes an integrated model of microgrid planning and operation under the multi-agent cooperation mode based on quantifying agent’s benefits and demand response. Among them, the upper layer is a planning model targeting the integrated benefits of SESS-MG alliance, and the lower layer is an operation model targeting the costs of the MG alliance. In addition, to ensure the feasibility of the proposed cooperation method, the improved nucleolus based on satisfaction equalization is used to share the costs of the MG alliance. Simulation results show that the proposed model can effectively exploit the complementarity between multiple microgrids, reduce the operating cost of multiple microgrids and protect information privacy, and improve the economic efficiency and utilization of shared energy storage, which is mutually beneficial to both of them. At the same time, the improved nucleolus has less comprehensive deviation than the nucleolus, and the solution efficiency is greatly improved, which can maximize the subjective satisfaction equilibrium.

In the electricity market environment, the charging stations can reduce electricity costs by coordinating the charging of electric vehicle (EV) to form friendly loads to optimize the market bidding strategies. This paper proposes a dynamic pricing strategy for EV charging stations considering the uncertainty of electricity prices in the electricity market and the price response characteristics of user charging. Firstly, the uncertainty of market electricity price is portrayed based on the stochastic programming method to obtain the typical electricity price scenarios. Secondly, from the perspective of behavioral economics, the charging utility model of EV is established based on prospect theory to analyze the charging economy and charging convenience of EV users. Then, a two-tier optimal scheduling model of charging station taking into account the user’s charging response is established considering the charging station and the electric vehicle as the upper and lower problem subjects. In the upper model, the charging station proposes the market trading strategy and dynamic pricing strategy with the goal of maximizing the operational benefits, and EVs make a charging decision with maximizing the charging utility in the lower model. Finally, the case analysis shows that the proposed strategy can set reasonable charging prices based on market price changes and user response potential, which effectively guides user charging behavior and improves the operational efficiency of charging stations.

With the widespread adoption of electric vehicles (EVs), the charging behavior of EV users has become a critical focus area. However, EV users often exhibit low enthusiasm for participating in vehicle-to-grid (V2G) interactions, making it difficult to effectively motivate their involvement in load balancing and frequency regulation. Moreover, user behavior data are complex and limited, posing challenges for the accurate analysis of user behavior. This study proposes a model for identifying the charging behavior characteristics of EV users based on limited information to formulate differentiated incentive strategies. First, it outlines the fundamental characteristics of user charging behavior and proposes incentive strategies tailored to different user types. Subsequently, a classification model for user charging behavior is developed. It then details the steps for identifying user charging behavior and designs a model for recognizing these characteristics based on a cloud model and fuzzy Petri nets. Finally, the model is validated through a case study using limited user data from a specific charging facility. The results of the case study demonstrate that the proposed model successfully categorizes EV users into different types, thereby achieving the goals of targeted incentive strategies. This model offers an effective tool to better understand user behavior, optimize energy management, and provide personalized incentive strategies, thereby encouraging more active participation in V2G interactions and energy scheduling. It further promotes the sustainable development of electric vehicles.

The large number of accesses of distributed resources leads to more and more complex distribution network operation mechanism, as well as multiple types of undesirable data, the expansion of the grid scale and other factors bring new technical challenges to the accurate state estimation of the new distribution system. This paper proposes a new distribution system robust state estimation model based on adaptive partitioning and Spatiotemporal feature variational mode decomposition-Long Short-term Memory (SFVMD-LSTM) pseudo-measurement modeling. On the basis of taking into account the electrical sensitivity of the nodes, considering the distribution characteristics of the poor data, overcoming the shortcomings of the traditional Girvan and Newman (GN) algorithm in adapting to the changes in the quality of the measurement data by improving GN partitioning method, and utilizing the multi-source load data of the nodes in the subregion, the pseudo-measurement modeling method based on the SFVMD-LSTM is proposed, which improves the weighted least square (WLS) estimation. The pseudo-measurement modeling method based on SFVMD-LSTM is proposed to improve the measurement redundancy of WLS estimation, and to solve the problem of low accuracy and insufficient tolerance of traditional state estimation. The estimation accuracy and efficiency of the proposed method are higher than those of the traditional WLS and the Fast decoupling state estimation through example simulation and result comparison analysis.

When a modular multilevel converter (MMC) is connected to an islanded renewable power system, voltage-frequency control must be adopted to provide stable voltage support. This study focuses on the calculation of the short-circuit current of an MMC in the double closed-loop voltage-frequency mode after a three-phase symmetrical short-circuit fault occurs. Considering the circuit and control system characteristics, this study first analyzes the output characteristics of a MMC after a fault occurs and proposes that the MMC can be represented as a current source or a voltage source. Then, calculation methods are proposed for the short-circuit current overshoot when the MMC is represented as a current source and for the short-circuit current DC component when the MMC is represented as a voltage source. Simulations are conducted using PSCAD to verify the accuracy of the proposed method.

Aiming at the problems of increasing power supply demand of important loads and incomplete consideration of energy storage life model, an optimal economic configuration method of photovoltaic-storage microgrid considering energy storage capacity attenuation is proposed and its reliability evaluation is carried out. In order to improve the reliability of the microgrid on the basis of ensuring economy. Firstly, considering the increasing density of important loads and the increasingly urgent of power supply demand, the idea and method of using multi-microgrid system to meet the load power supply demand are proposed. Secondly, based on the energy storage life model, the replacement cost and loss cost of energy storage are updated. The optimal economic planning model is established by taking the maximum net income in the whole life cycle of the photovoltaic-storage microgrid as the objective function, and using the gurobi solver to optimize the photovoltaic and energy storage capacity of the microgrid. Then, according to the topological characteristics of parallel, series and hybrid multi-microgrid systems, considering the influence of faults on the reliability of microgrid, establishing the reliability index system from three dimensions. Finally, the improved RBTS Bus6 F4 system is taken as an example to optimize the configuration of photovoltaic and energy storage, and then evaluate the reliability of the configured multi-microgrid system. The results show that the configuration results of microgrid considering energy storage capacity attenuation are more reasonable, and the reliability of parallel multi-microgrid system is higher in the example designed in this paper, which provides reasonable and effective suggestions for the planning and configuration of power supply microgrid.

The large-scale access of wind power in the new power system leads to the lack of power grid regulation capacity and the problem of wind curtailment. It is imperative to develop load-side resources. As a temperature control load, the regulating capacity of the regenerative electric boiler is constrained by the heat demand of the building. In order to improve the load capacity of large-scale regenerative electric boilers, this paper proposes an operation control strategy and a combination optimization method based on the heat storage constraint of electric boilers. Firstly, the load model of regenerative electric boiler based on heat balance principle is constructed, and the adjustable characteristics of single regenerative electric boiler load are analyzed. Then, the mechanism and combined control method of regenerative electric boiler participating in wind curtailment are studied. Finally, a large-scale regenerative electric boiler load wind power consumption model and control strategy are proposed. The example analysis shows that compared with the control strategy constrained by the heat demand of the building, the control strategy proposed in this paper can not only make the load of the large-scale regenerative electric boiler consume more abandoned wind power, but also have better long-term economic benefits.

Carbon-electricity coupling is an important guarantee for promoting the green and low-carbon transformation of China’s energy and power industry and helping to achieve the "dual carbon" goals. A coupling framework for the electricity market, carbon emission trading market (CET), and certified emission reduction market (CER) is designed and the optimization model for bidding strategies for power generators is constructed to collaborate in the electricity market, CET market, and CER market. In response to the problems of incomplete information and insufficient explanation of complex multi-agent interactions in traditional game theory methods, a deep deterministic policy gradient algorithm is adopted to solve the aforementioned problem. In addition, it can also overcome the problems of discrete low dimensional state space, action space and convergence instability in traditional multi-agent reinforcement learning algorithms. Taking four typical power generators as examples, this study investigates the impact of the introduction of CET and CER markets on the bidding strategies of power generators. Sensitivity analysis and algorithm convergence verification are then conducted around carbon market prices, carbon emission benchmark values, and certified emission reduction offset ratios, demonstrating the effectiveness and superiority of the designed bidding strategy model for power generators under the coupling of carbon-electricity market.

With increasing attention being paid to global environmental protection and sustainable development, reducing carbon emissions has become a pressing issue in the power generation industry. Therefore, exploring the decision-making behavior of generation companies in the electricity market under the “dual carbon” goal is of immense practical significance for promoting energy conservation and emission reduction. A non-cooperative game model of competition among generation companies was constructed based on the bounded rational decision-adjustment mechanism and considering the carbon emissions. The existence and stability of equilibrium points were discussed when generation companies with different degrees of rationality compete in the market. A variable feedback control method was introduced for the destabilized system. Through simulation examples, in-depth analysis of the dynamic behavior of generation companies’ competition in the market was performed. The results show that the strategic adjustment of each generation company and the change in carbon prices affect market stability. When the strategy adjustments of generation companies and carbon pricing are unreasonable, the system enters a chaotic state. The proposed variable feedback control method can stabilize the system in the Nash equilibrium state, thereby reducing the profit uncertainty for each generation company.

With the promotion of power system reforms and development of the power market, the transaction mechanism and bidding strategies of retailers in the retail power market composed of various types of resources needs investigation. Considering this, a bi-level optimization-based active and reactive power collaborative bidding model for power retailers in the retail market environment is proposed. This model considers the respective interests of the retail market operators and power retailers and also the secure and economic operation constraints of power systems. At the upper level, the power retailer aims to develop an optimal bidding strategy to maximize the operational benefits by considering various controllable resources. At the lower level, the market owner conducts the retail market-clearing process to minimize the overall system cost by considering the network topology and power flow security. The simulation results, validated through real-world case studies, show that the model successfully aligns with the operational goals of both the retail market and the aggregators. Unlike traditional bidding methods that focus solely on the active power, this approach significantly improves the operational efficiency of the system. These findings offer important insights into the development of retail market operation models and bidding strategies for power aggregators.

To address the low inertia issues in power systems with high renewable energy penetration, various provinces in China have implemented rotating inertia compensation methods. However, a single compensation mechanism alone cannot accurately reflect the true value of rotating inertia compensation nor stimulate market vitality. This paper aims to develop a commercial rotational inertia ancillary service, including the main components of the rotational inertia market, the division of rotational inertia obligation services and commercial services, and the compensation method. Additionally, since the current market for rotational inertia ancillary services lacks a market clearing mechanism, this paper proposes introducing the Vickrey-Clarke-Groves (VCG) settlement mechanism. This approach avoids strategic pricing by suppliers while ensuring incentive compatibility, minimum cost, and individual rationality. Finally, the feasibility of the proposed improvement scheme to the existing rotational inertia support compensation model and the advantages of the VCG mechanism over marginal settlement are validated through case analysis.