Economic Allocation for Energy Storage System Considering Wind Power Distribution

TLDR

Energy storage systems improve power system cost and voltage profiles, but improper sizing and placement can increase costs and threaten voltage stability, especially with high renewable penetration. This study proposes a hybrid multi‑objective particle swarm optimization (HMOPSO) to minimize cost and enhance voltage by optimally sizing and locating storage units under wind power uncertainty, and introduces a probability‑cost analysis. HMOPSO integrates MOPSO with NSGA‑II and a probabilistic load‑flow technique, employs a five‑point estimation method to discretize wind power distribution, and is evaluated on the IEEE 30‑bus system. Simulation results demonstrate that optimal storage allocation is essential and that the proposed method effectively reduces cost and improves voltage performance.

Abstract

Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.