As the prosumer group with distributed resources becomes a prominent player in the power market, and the ratio of distributed power access is increasing, the question of how to effectively utilize these resources beyond meeting the self-sufficiency of the prosumer group has become crucial. This paper begins by analyzing the current state of distributed power development. It then proposes a distributed trading framework for the prosumer group. Within this framework, a model for the prosumer group is established taking into account its real situation, and a power trading method featuring two stages, namely day-ahead and real-time, is proposed. In the day-ahead stage, the alternating direction method of multipliers (ADMM) facilitates electricity trading for the prosumer group. The transaction price is updated through the Lagrange multiplier, which iteratively adjusts according to the power supply and demand relationship. This approach not only addresses the bargaining issue of the prosumer group but also enhances the utilization of renewable resources. Concerning the real-time stage, the prosumer group is enabled to bid reasonably in power auctions based on the generalized second-price (GSP) auction rule. This resolves problems such as the susceptibility of photovoltaic power generation to weather conditions and the power deviation caused by inaccurate load forecast. Additionally, it meets the time constraints for trading among multiple prosumer groups in real-time electricity trading. Finally, the effectiveness of the proposed method is validated through calculations based on examples.

With electric vehicles (EVs) gradually replacing fueled vehicles, the impact of their charging load on the power grid is increasing. Therefore, this study proposes a spatial-temporal distribution prediction method for the charging load of EVs that considers travel demand and a guidance strategy. First, a semi-dynamic traffic network model that divides functional areas was developed based on a road travel time model. Furthermore, an energy-consumption model of EVs was established, and the charging demand, semi-dynamic transportation network model, energy consumption model, and traditional travel chain were revised according to the influence of the electricity price, climate, and season on the travel demand of vehicle owners. Considering the limited rationality of vehicle owners based on the influence of external factors, a charging load prediction method for private cars and taxis based on a guidance strategy is proposed. Finally, the modified trip chain and OD matrix were used to simulate the travel behavior of private cars and taxis, respectively, in the semi-dynamic traffic network model during the study period, and the validity of the proposed prediction method was verified through a simulation experiment of the semi-dynamic traffic network in the divided regions. The results show that the spatial-temporal distribution of the charging load for EVs is consistent with the analysis of external influencing factors, and the proposed guidance strategy can improve the satisfaction of vehicle owners.

With the proposal of a dual-carbon target, the potential of distributed energy resources (DERs) has been explored. Virtual power plants (VPPs) that effectively aggregate DERs have received attention. VPPs promote the balance between supply and demand through market trading. To further improve the power supply capability, the output deviation of VPPs should be reduced, and the risk control level should be improved. In this study, the characteristics of DERs aggregated in VPPs were analyzed. Then, based on the theories of conditional value-at-risk and cooperative game, a profit distribution method was designed by improving the Shapley value method considering the output deviations and risk control of DERs in VPPs. Finally, a case study was conducted to verify the rationality of the proposed profit distribution strategy. This study also quantitatively analyzed the revenue allocation mechanism of VPPs under different risk levels, output deviations, and contribution scenarios of DERs using the conditional value-at-risk theory. The case study demonstrates that the proposed cooperative trading strategies can improve the revenue of DERs and effectively control their output deviations. It can also suppress the risk of DERs participating in the electricity market, thereby improving the supply capacity of the power system in a win-win situation for all parties.

The three-phase equilibrium state of a distribution network is broken owing to the large number of distributed power sources and electric vehicles connected to the distribution network, resulting in the traditional distribution network protection scheme illegally adapting to current development needs. Therefore, this study presents a two-stage fault recovery method for imbalanced distribution networks based on electric vehicle charging stations using an existing balancing system fault recovery technique. The island operation strategy after the fault is determined in the first stage to reduce the fault-recovery cost. In the second stage, a collaborative optimization model is established to determine the output of each charging station when a fault occurs, with the goal of maximizing the operating income of the distribution network, considering the cost of depreciation, operation and maintenance, and the load abandonment penalty of electric vehicle charging stations and distributed generation units. Finally, the enhanced IEEE 33 node system validates the effectiveness of the two-stage fault self-healing model and the optimal operating method suggested in this study. The results reveal that the proposed technique has a lower load rejection rate than the classic island division method, thereby providing a novel solution to the fault-recovery problem of an imbalanced distribution network.

The integration of new high-permeability distributed energy into the power grid has increased the demand for flexibility in the distribution network owing to the randomness and volatility of its output. A method for improving the flexibility of distribution networks that considers the complementary characteristics of soft open point (SOP) and smart load (SL) is proposed. First, the flexibility characteristics and mathematical models of the SOP, SL, controllable DG, and flexibility evaluation indicators were established from two aspects: node flexibility adaptability and the load margin of lines containing the SOP. Second, we constructed an objective function that considers the operating costs, voltage quality, and flexibility indicators, striving to achieve optimal economic performance while minimizing flexibility gaps and voltages exceeding the limits. Simultaneously, a reconstruction whale optimization algorithm (Re-WOA) was proposed to solve the optimization model, and two improved search predator strategies were introduced in the algorithm. The SL load balancing degree during the search phase was balanced using the SL capacity margin index. Finally, the optimization effect of the model on the distribution network and the effectiveness of the proposed Re-WOA were verified using an improved IEEE33 node system.

To exploit the schedulable potential of electric vehicles (EVs) efficiently and relieve the energy supply pressure of microgrids with a high proportion of new energy sources, a multi-time-scale optimization scheduling model of the microgrid is proposed, considering the participation of EV resources combined with the multi-demand response technology. In the day-ahead scheduling stage, some EV resources are combined with the price-based demand response technology to optimize comprehensive user satisfaction. Based on the scheduling plan of the EV resources based on price, the microgrid is optimized focusing on minimizing economic cost, ensuring low-carbon expenditure, and maximizing flexibility satisfaction, and the scheduling arrangement of the adjustable resources on each side is determined. In the intra-day scheduling phase, another portion of the EV resources is combined with the incentive demand response technology. The microgrid energy management center, as the leader, aims to minimize the operating costs, and the incentive EV group, as the follower, aims to minimize the electricity costs. The intra-day master-slave game model of the microgrid was constructed for rolling optimization, and the two sides played the game based on a subsidized price and energy use strategy. Finally, a simulation verification was conducted based on a microgrid scenario, and the results show that the proposed model can reduce the electricity cost of users, reduce the peak-valley difference of the load curve, and realize the full absorption of new energy.

Battery-swap stations reduce the replenishment time of electric vehicles and have considerable regulation potential. An accurate load prediction model is the key to their participation in grid auxiliary services. To address the stochastic nature of the power exchange demand of users, a fuzzy clustering-Markov chain-based load prediction model for swap stations is established. First, the Poisson distribution is used to predict the number of EVs demanding power exchange at each moment and establish the demand constraint for power exchange. Second, the adaptive fuzzy C-mean clustering algorithm is used to adaptively partition the battery clusters in the swap stations based on the charge state to avoid the subjectivity of artificial partitioning. Finally, the Markov chain is used to establish the battery cluster model of the swap stations under multiple states of charging, discharging, and waiting. The demand prediction method and load prediction model are simulated, validated, and compared with the Monte Carlo simulation method. The results showed that Poisson distribution accurately predicted the demand quantity of electric vehicles, and the proposed load prediction model obtained the power of the swap station under charging and discharging states, while reducing the volatility of load prediction.

The rapid development of the charging station industry has further intensified market competition and precise service range division. Reasonable and effective pricing strategies are at the core of competition among charging station operators. The classical service range model tends to ignore the influence of competition among multiple subjects in a region. The service range calculated using a simple model is extremely regular and circular; this is not in line with the market distribution law. Therefore, this study proposes a charging station service range model based on the variable range field strength that portrays the relationship between the dynamic change in the charging station service range and the electricity price and optimizes the calculation to obtain a real-time pricing strategy. First, the competitiveness of charging stations is quantitatively evaluated. The charging station service range field strength model is established based on the meta-charge field strength formula and Wilson model to identify the market boundary of the charging stations based on the service range. Then, the demand relationship between the charging load and the tariff is described based on the market share of the charging stations, and a real-time pricing optimization model is established with the targets of both power deviation rate and operator profitability. The relationship between the real-time tariff strategy and the service scope of the charging stations is analyzed by considering a region in Changsha City as a simulation example. The results showed that the proposed method could effectively reduce the real-time power deviation while bringing more profit to charging station operators.