The optimal design method proposed in this study avoids the nested optimization algorithms and provides a reliable and flexible design of IES capacity. Further, this design method is applicable to the independent design of energy-storage systems, redesign of constructed systems, and optimal operation of systems in action.
The capacity configuration of energy storage system has an important impact on the economy and security of PV system . Excessive capacity of energy storage system will lead to high investment, operation and maintenance costs, while too small capacity will not fully mitigate the impact of PV system on distribution network.
In the conventional capacity design method of IES, storage units are operated passively, which means that the energy generation of production units did not consider the operation of storage units, and the charging or discharging of storage units passively adapts to the difference between generation and demand.
Model solving At present, intelligent algorithms, such as genetic algorithm, whale optimization algorithm, simulated annealing algorithm and particle swarm optimization algorithm (PSO), are often used to solve energy storage optimal configuration problems.
Excessive capacity of energy storage system will lead to high investment, operation and maintenance costs, while too small capacity will not fully mitigate the impact of PV system on distribution network. Therefore, the configuration of energy storage capacity has become the focus of current research.
In Case 1, the component capacity of IES without storage units is optimized. In Case 2, the component capacity of IES is optimized through the design method with PAM. By contrast, in Case 3, the component capacity is optimized through the design method with ADM, where the preset time interval is 12 h.
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