Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting

Abstract

Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue.