Planning of distributed energy storage by a complex network approach

An energy storage system (ESS) has been considered one promising technology in dealing with challenges from the risk of power fluctuations and load mismatch in power girds. A distributed ESS (DESS) has better efficiency in reducing net losses and operating costs. The net-ability quantifies the power transmission ability across the grid where power is delivered from generators to loads under constraints. This paper proposes a new complex network-based metric: energy storage performance (ESP), for assessing the significance of the DESS inside a power grid. It aids the optimal location selections by improving grids' net-ability structurally. An auxiliary genetic algorithm (GA) sizing strategy is also deployed for deciding the optimal capacity of each DESS with the minimum daily operating and investment costs. The result shows that the DESS improves the rate of cost reduction within an equivalent 24-h daily operation. Moreover, this methodology finds quasi-optimal solutions with better feasibility and efficiency. The improvement of network performance by the DESS depends on its original structure. The result shows that with the assistance of siting plan by a complex network theory, the calculation efficiency improves and performs better in larger power grids. In the IEEE-30 test system, our solution is about 1/3 calculation time as the GA search. The quasi-optimal costs 1.8% more than the optimal searched by the GA. Meanwhile, the DESS can save more cost for networks with higher network-wide ESP value. In the IEEE-118 and IEEE-300 test systems, only the proposed hybrid-GA search can find a solution within a limited calculation time. Therefore, it could be promising in solving siting issues in the planning of smart grids.