In order to improve the grid-connected ability and response speed of virtual synchronous generator (VSG), a finite-time control strategy of pre-synchronous VSG based on angular frequency feedback is proposed in this paper. Firstly, the basic principle of VSG control strategy is analyzed, and the voltage amplitude pre-synchronization module and the voltage phase pre-synchronization module are added on the basis of traditional VSG control, so as to realize the pre-synchronization tracking of power grid information. Then, based on the principle of finite-time stability, a finite-time controller is designed based on angular frequency feedback, and the finite-time stability of the control strategy is verified in theory to ensure the system has a faster response speed. Finally, the simulation results show that the designed control strategy can make the output voltage of VSG track the power grid information quickly, and effectively improve the tracking ability of active and reactive power and the stability of frequency and phase angle.

A multi-inverter-fed power system is susceptible to small-signal instability owing to weak grid influence. Using the impedance-frequency (IF) method to analyse system stability at engineering sites has the following limitations: 1) The internal information of a grid-connected inverter is unknown because of technical confidentiality. Thus, its impedance model should fit in a grey box. 2) The IF method defaults to the subsystem aggregation impedance without right-half-plane (RHP) poles, which may result in incorrect stability analysis. 3) The data used for drawing the IF curve should be calculated per frequency, which is time-consuming. To address these limitations, this study proposes a grey box method for small-signal stability analysis of a multi-inverter-fed power system based on Bode diagrams. First, grey box impedance fitting for a single grid-connected inverter and impedance aggregation for a multi-inverter-fed power system are discussed. Second, the principle of the proposed method is elaborated. Using the Bode diagram, the number of RHP poles of the aggregated impedance and actual number of circles for Nyquist curves are identified. Third, a multi-inverter-fed power system is constructed, and the effectiveness of the proposed method is verified using case analysis and hardware-in-the-loop real-time experiments based on RT-LAB. Finally, the advantages of the proposed method are revealed by comparing it to the IF method.

Low-carbon development of integrated energy systems is achieved via the sharing of multiple energy interactions by park-level integrated energy systems (PIESs). However, coordinating profit distribution conflict between complex interactive stakeholders under stochastic scenarios is challenging. Accordingly, this study proposes a novel tri-layer framework that aggregates different game mechanisms to investigate the interactions between PIESs and coupled energy markets. First, a linkage trading mechanism is proposed by integrating carbon emissions trading and green certificate trading , which establishes a coupled electricity-carbon-green certificate market. Consequently, a park aggregation operator acts as an intermediary between PIESs and the coupled market, setting purchase and sale prices to guide unit generation in each PIES using the master-slave game theory. Then, the Nash game theory is applied to realize a cooperative bargaining among PIESs for fair revenue distribution. Further, the impact of uncertain environments has been considered using stochastic scenario methods and the conditional value-at-risk theory. Furthermore, to protect the privacy of each participating agent while improving convergence speed, a differential evolutionary method is combined with analysis target cascading to solve the framework. Finally, the proposed scheduling method is verified using a typical case to optimize conflicting PIES interests in multiple scenarios and realize decarbonization transformation.

To both enhance the flexibility of the power system and absorption capacity of renewable energy connected to a grid and achieve the efficient use and cooperation of multi-side complex type energy storage resources on the source, grid, and load sides, a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) considering multiple energy storage types was constructed. Latin hypercube sampling and sample reduction based on k-medoids clustering were adopted to generate the SGLS scenarios, considering the emergency circumstances of the power system. The flexibility of the multi-scenario power system was evaluated, and the battery energy storage stations, pumped storage, and electric vehicles with sufficient capacity were configured on the source, grid, and load sides, respectively, to participate in the scheduling. A multi-scenario SGLS cooperative optimisation scheduling model that considers multiple energy storage capacity configuration types was constructed for economic and environmental protection. Based on data-driven, a multi-objective optimisation algorithm was proposed by using the Gaussian process regression algorithm and non-dominated sorting genetic algorithm II, combined with the manifold interpolation batch evolution mechanism. Finally, using actual regional power grid data for verification, the proposed strategy effectively reduces system operating cost, enhances energy storage battery life, and improves the renewable energy consumption capacity.

With the development of smart distribution network and the proposal of dual carbon target, the importance of demand side management in improving the flexible operation of power system is becoming more and more prominent. In order to solve the problems of excessive load peak valley difference, insufficient utilization of demand side resources, and unreasonable pricing of aggregators, this paper propose an economic optimization scheme for aggregators based on electric vehicle three-stage dispatching. First, the loss aversion analysis is conducted on the willingness of electric vehicle users to participate in dispatching. The contract signing methods between aggregators and electric vehicles are divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Next, the response model of electric vehicle users based on the improved cloud model is obtained. Then, the aggregators conduct three-phase optimal dispatching for electric vehicles according to the bid winning peak shaving capacity. Phase 1 according to the time of regional differences, the dispatching of reward power set rewards and punishment mechanism, phase 2 to determine the full freedom dispatching of electric vehicles, three kinds of dispatching and dynamic load capacity of electricity, phase 3 according to the phase 1 and 2, the amount of information and user loss aversion, the user response model of final rolling rewards and punishment mechanism, get the aggregators final pricing schemes, Finally, a numerical example is given to verify the feasibility of the proposed method.

With the proposal of the carbon peaking and carbon neutrality goals, the number of electric vehicles is increasing day by day. Besides, the insufficient number of charging piles leads to the increase of the queuing time of electric vehicles, the pricing of aggregators and other issues becoming increasingly prominent. To solve the above problems, the aggregator pricing method of two-stage charging station allocation for electric vehicles is proposed. First, the contract signing mode between aggregators and electric vehicles is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Considering the impact of external factors on the energy consumption of electric vehicles, a road network model is established. Then, the improved A-star algorithm is used to solve the shortest path, in which the time factor is introduced into the evaluation function of A-star algorithm. At the same time, the evaluation function is improved according to traffic energy consumption, uninterrupted driving time, traffic light waiting time and other factors. The aggregator obtains the attraction of the charging station to the electric vehicle based on the charging and discharging demand of the electric vehicle and the Coulomb’s law, and then establishes the grid aggregator electric vehicle supply chain, and conducts reasonable pricing and charging station allocation in two stages. Finally, an example shows that this scheme can significantly improve the peak shaving efficiency, improve the profits of aggregators, and reduce the queuing time and traffic energy consumption of electric vehicles in terms of the distribution of electric vehicle charging stations and the pricing of aggregators.

The multi-scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system.This paper first considered the interaction mechanism of multi-type storage peak regulation time sequences based on the Euclidian distance, Dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) was constructed. To attain a low-carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time-series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K-means clustering and dual-structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.