Impact Analysis of Battery Energy Storage System and Demand Response in Microgrid

Coordinated scheduling of distributed generation (DG), battery energy storage systems (BESS) and demand response (DR) is needed to reduce grid procurement costs without compromising customer comfort or network constraints. This paper proposes a two-stage framework for a grid-connected IEEE 33-bus microgrid. In the first stage, the Red Panda Optimization (RPO) algorithm jointly determines the locations and ratings of renewable DG units and BESS by minimizing power losses and voltage deviation. In the second stage, five customer agents with different cost–comfort preferences and three BESS agents learn their hourly schedules under real-time pricing using multi-agent Q-learning. Two incentive regimes are compared within the same decentralized framework: a price-only, customer-driven regime and an incentive-coordinated regime in which the grid operator broadcasts a shadow-price signal. Compared with the DG-only case, the two regimes reduced daily demand by 2.36% and 12.21%, energy loss by 4.53% and 27.79%, and procurement cost by 3.25% and 12.99%, respectively. Customer load curtailment provides all the demand and loss reduction and most of the cost reduction, while BESS arbitrage lowers the cost by a further 0.62 and 0.61% points at the expense of slightly higher losses. The incentive design, rather than the control architecture, largely determines how these benefits are shared between the operator and customers.