Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System

Abstract

In pursuit of widespread adoption of renewable energy and the realization of decarbonization objectives, this study investigates an innovative system known as a wind-solar-hydrogen multi-energy supply (WSH-MES) system. This system seamlessly integrates a wind farm, photovoltaic power station, solar thermal power station, and hydrogen energy network at the power grid level. Central to the study is the introduction of a bi-level collaborative optimization model—an innovative algorithmic framework specifically tailored for complex multi-energy systems. This model co-optimizes both the capacity planning of essential system components and their annual load distribution, adeptly navigating the complexities of optimizing capacity and annual load distribution under uncertain energy sources and load conditions. A layered methodology synergistically combines linear programming with an advanced version of non-dominated sorting genetic algorithm-II. When applied to a real-world case study in Zhangbei, China, this approach identifies an optimal system capacity, leading to annual green power generation of 201.56 GW and a substantial reduction of over 173,703 tons of CO2 emissions. An economic analysis further reveals that each 1% reduction in CO2 emissions corresponds to a modest 1.7% increase in the system’s levelized cost of energy. Moreover, a comprehensive exploration of the impacts of various capacity parameters on the WSH-MES system’s performance is conducted. These insights offer invaluable guidance for the large-scale advancement of efficient renewable energy utilization and the attainment of decarbonization targets.

Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

“…The research location is a region in Guangxi, China, where the number of electric vehicles is large [15], and most small EVs have a maximum charging power of less than 8kW [16]. To cope with the increasing peak load demand [17], based on the interaction of all EVs in the region with the regional grid in the state of charging [18], the two are called "vehicle network systems". In the system, the power grid is at the "Core level", and the energy obtained by EV from the grid is at the "Carrier level" [19].…”