The wind power (WP) has strong random volatility and is not coordinated with the load in time and space, resulting in serious wind abandonment. Based on this, an orderly charging and discharging strategy for electric vehicles (EVs) considering WP consumption is proposed in this paper. The strategy uses the vehicle-to-grid (V2G) technology to establish the maximum consumption of WP in the region, minimizes the peak–valley difference of the power grid and maximizes the electricity sales efficiency of the power company in the mountainous city. The dynamic electricity prices are set according to the predicted values and the true values of WP output, and the improved adaptive particle swarm optimization (APSO) and CVX toolbox are used to solve the problems. When the user responsiveness is 30%, 60% and 100%, the WP consumption is 72.1%, 81.04% and 92.69%, respectively. Meanwhile, the peak shaving and valley filling of the power grid are realized, and the power sales benefit of the power company is guaranteed.

In order to improve the efficiency of the “last-mile” distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and load constraints (TW-LCs). Taking the optimal transportation path as the objective function and considering the time-window and vehicle load constraints, a charging station siting model was established. For the TW-LC problem, an improved genetic algorithm combining the farthest-insertion heuristic idea and local search operation was designed. Three different types of standardized arithmetic examples, C type, R type, and RC type, were used to test the proposed algorithm and compare it with the traditional genetic algorithm. The results indicate that, under the same conditions, compared to the traditional genetic algorithm, the improved genetic algorithm reduced the optimal path length by an average of 11.12%. It also decreased the number of charging stations selected, the number of vehicles in use, and the algorithm complexity by 22.97%, 13.71%, and 46.81%. Building on this, a case study was conducted on the TW-LC problem in a specific area of Chongqing, China. It resulted in a 50% reduction in the number of charging stations and a 25% reduction in the number of vehicles selected. In terms of economic indicators, the proposed algorithm improves unit electricity sales by 73.88% and reduces the total annualized cost of the logistics company by 18.81%, providing a theoretical basis for the subsequent promotion of ELVs.

Due to numerous distributed power sources connecting to the grid, which results in strong grid volatility and diminished power quality, the traditional energy storage configuration is limited in terms of flexibility and economy. Based on this, integrating electric vehicles (EVs) into the distribution network as energy storage devices has emerged as a promising development direction. This paper proposes a frequency-response optimization study considering the strong uncertainty model of EVs. First, from the perspective of temporal-spatial characteristics, energy storage resources, and users’ willingness to respond, the strong uncertainty model of EVs is constructed by fitting the trip chain and the access probability of their participation in energy storage. Second, the frequency optimization model is integrated and constructed according to the response capability of a single EV. Finally, examples and scenarios are analyzed to verify that the maximum and minimum frequency offsets are reduced by 69.41% and 66.69%, respectively, which significantly reduces frequency fluctuations and stabilizes the output of EV clusters.

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain.