An Optimization Approach Considering User Utility for the PV-Storage Charging Station Planning Process

Abstract

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%.

Application Research of Soft Computing Based on Machine Learning Production Scheduling

“…However, the lowest aggregate target value was obtained from the lower run increments. On the contrary, the average target value was close to 1.5% lower for the higher runner step [4]. It was clear that the process did not converge, as can be seen in the examples.…”

“…At the same time, due to increasing complexity and decreasing batch sizes in production, there is an everincreasing need for software for planning, automation, and optimization. To meet these increasing requirements, new methods and technologies have to be developed that enable optimized planning and control under different target values [4][5][6][7].…”

Special Issue on “Green Technologies for Production Processes”

“…The results show that: (1) a micro energy grid can make the most use of the complementary characters of different energy sources to meet different energy demands for electricity, heat, cold, and gas; (2) the risk aversion scheduling model can represent the influence of uncertainty variables in objective functions and constraints, and provide a basis for decision makers who have different attitudes; and (3) demand response (DR) can smooth the energy load curves. Liu et al [17] promotes a bilevel optimization planning approach for photovoltaic (PV)-storage charging stations. The results show that the proposed bilevel 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%.…”

Analysis and Design of a Standalone Electric Vehicle Charging Station Supplied by Photovoltaic Energy

“…These techniques represent large electrical loads on the utility grid and affect it adversely. Especially, when there is a high number of charging stations connected simultaneously to the utility grid, many problems would be generated such as excessive overload, voltage instability, and voltage variation [6][7][8]. One of the solutions to these problems is the upgrading of the power system but this will lead to high costs.…”