Optimal Sizing and Siting of Multi-purpose Utility-scale Shared Energy Storage Systems

TL;DR

This paper introduces a genetic algorithm-based method for optimally sizing and siting shared energy storage systems in distribution networks with high solar PV penetration, balancing multiple grid services and economic considerations.

Contribution

It presents a novel multi-objective optimization approach using NSGA-II for placement and sizing of utility-scale shared energy storage, considering diverse grid services and economic factors.

Findings

Generated Pareto-optimal solutions for storage placement and sizing.

Demonstrated effectiveness on IEEE 123-node test feeder.

Highlighted trade-offs between primary and secondary grid services.

Abstract

This paper proposes a nondominated sorting genetic algorithm II (NSGA-II) based approach to determine optimal or near-optimal sizing and siting of multi-purpose (e.g., voltage regulation and loss minimization), community-based, utility-scale shared energy storage in distribution systems with high penetration of solar photovoltaic energy systems. Small-scale behind-the-meter (BTM) batteries are expensive, not fully utilized, and their net value is difficult to generalize and to control for grid services. On the other hand, utility-scale shared energy storage (USSES) systems have the potential to provide primary (e.g., demand-side management, deferral of system upgrade, and demand charge reduction) as well as secondary (e.g., frequency regulation, resource adequacy, and energy arbitrage) grid services. Under the existing cost structure, storage deployed only for primary purpose cannot justify the economic benefit to owners. However, the delivery of storage for primary service utilizes only 1-50\% of total battery lifetime capacity. In the proposed approach, for each candidate set of locations and sizes, the contribution of USSES systems to grid voltage deviation and power loss are evaluated and diverse Pareto-optimal front is created. USSES systems are dispersed through a new chromosome representation approach. From the list of Pareto-optimal front, distribution system planners will have the opportunity to select appropriate locations based on desired objectives. The proposed approach is demonstrated on the IEEE 123-node distribution test feeder with utility-scale PV and USSES systems.