Based on the comprehensive utilization of energy storage, photovoltaic power generation, and intelligent charging piles, photovoltaic (PV)-storage charging stations can provide green energy for electric vehicles (EVs), which can significantly improve the green level of the transportation industry. However, there are many challenges in the PV-storage charging station planning process, making it theoretically and practically significant to study approaches to planning. This paper promotes a bi-level optimization planning approach for PV-storage charging stations. First, taking PV-storage charging stations and EV users as the upper- and lower-level problems, respectively, during the planning process, a bi-level optimization model for PV-storage charging stations considering user utility is established for capacity allocation and user behavior-based electricity pricing. Second, the model is converted into a single-level mixed-integer linear programming model using the piecewise linear utility function, Karush–Kuhn–Tucker (KKT) conditions, and linearization methods. Finally, to verify the validity of the proposed model and the solution algorithm, a commercial solver is used to solve the optimization model and obtain the planning scheme. The results show that the proposed bi-level optimization model can provide a more economical and reasonable planning scheme than the single-level model, and can reduce the investment cost by 8.84%, operation and maintenance cost by 13.23%, and increase net revenue by 5.11%.

In this study, an interval fuzzy-stochastic chance-constrained programming based energy-water nexus (IFSCP-WEN) model is developed for planning electric power system (EPS). The IFSCP-WEN model can tackle uncertainties expressed as possibility and probability distributions, as well as interval values. Different credibility (i.e., γ) levels and probability (i.e., q_i) levels are set to reflect relationships among water supply, electricity generation, system cost, and constraint-violation risk. Results reveal that different γ and q_i levels can lead to a changed system cost, imported electricity, electricity generation, and water supply. Results also disclose that the study EPS would tend to the transition from coal-dominated into clean energy-dominated. Gas-fired would be the main electric utility to supply electricity at the end of the planning horizon, occupying [28.47, 30.34]% (where 28.47% and 30.34% present the lower bound and the upper bound of interval value, respectively) of the total electricity generation. Correspondingly, water allocated to gas-fired would reach the highest, occupying [33.92, 34.72]% of total water supply. Surface water would be the main water source, accounting for more than [40.96, 43.44]% of the total water supply. The ratio of recycled water to total water supply would increase by about [11.37, 14.85]%. Results of the IFSCP-WEN model present its potential for sustainable EPS planning by co-optimizing energy and water resources.

Under the influence of the reform of transmission and distribution price, the profit model and operation mode of power grid enterprises have undergone major changes and changes, and it is urgent to transform the operation concept and investment decision of enterprises. The reformed power grid investment demand and investment capacity coordination optimization model provides a direction that can be optimized for power grid investment decision-making. The first step is to analyze the investment demand of the power grid, and divide the power grid investment capacity into distributable profits, financing funds, and depreciation costs, and then build a power grid investment capacity calculation model based on the transmission and distribution price reform. Then, the coordination coefficient of power grid investment capacity and investment demand is calculated, considering multiple constraints, an optimization model of power grid investment demand and investment capacity is established, and particle swarm optimization algorithm is introduced for coordination optimization. Finally, the simulation analysis is carried out considering whether the investment demand and investment ability are coordinated, and the corresponding investment management strategy is proposed according to the simulation results. The verification of the example shows that the model can provide a more effective reference for power grid enterprises to formulate a reasonable investment plan.

This article considers the optimal bidding model of traditional thermal power generation companies’ electricity sales companies, taking into account transmission congestion, under the influence of new policies such as the renewable portfolio standard (RPS) and the renewable exchange certificate (REC). Firstly, the transaction cost of green certificates is included in the cost of electricity sales, and an optimization model is established with the goal of maximizing the profit of the electricity sales company; Secondly, the node electricity price method is used to manage transmission congestion, and an optimization model is established with the goal of minimizing the cost of transmission congestion; Finally, multi agent technology and dynamic game theory are used to solve the optimal bidding strategy of the electricity selling company.

Under the “double carbon” goal and background, new energy power generation will usher in great development. The investment and construction of new energy projects and the scale of grid connected transmission are increasing, which puts forward higher requirements for the investment decision-making management and income of county new energy subjects. Based on the power curve trading and non cooperative game theory and the day ahead real-time two-stage adjustment and correction mechanism of county subjects, this paper briefly analyzes the new energy consumption and income of county subjects based on internal non cooperative game under curve trading.

The power emerging market entities connected to the grid in a decentralized manner can increase the local new energy consumption rate. Under the background of accelerating the development of new energy, it is urgent to clarify the output characteristics of emerging market entities such as wind power, photovoltaics, energy storage, and electric vehicles to adapt to the development of distributed energy and the progress of power market reform. Major emerging entities contribute to mathematical analysis, promote the transaction design of emerging entities, and promote the rapid and healthy development of new energy.