MPC-Based Optimal Operation for a PV Farm With Dual ESSs Using Spectral Density Analysis of Market Signals

Abstract

We propose a model predictive control-based optimal offer and operation strategy for a photovoltaic (PV) farm consisting of PV panels and dual energy storage systems (ESS)s to maximize profits in the energy and regulation markets. Although a PV farm owner can better respond to regulation signals with an ESS, it cannot continuously respond to unidirectional regulation signals since the ESS is limited in size. Furthermore, the lifespan of the ESS might be reduced by alternating between charging and discharging because of regulation signals that fluctuate often. To improve the response, we use dual ESSs with separate signals: one small, fast ESS to respond to fluctuations, and one large, slow ESS to respond to unidirectional signals. We decompose the regulation signal into two signals: one for fast and one for slow frequencies based on power spectral density (PSD) analysis. We determine the optimal operation for dual ESSs to maximize the profit through a closed-loop model predictive control (MPC). We can also increase the lifespan of each ESS by limiting their state of charge (SOC) levels through the optimization constraints obtained from the PSD analysis so that the dual ESSs can operate a larger number of cycles. We verify our approach by adjusting the day-ahead (DA) market schedule in the real-time (RT) using the most recently predicted signals and errors between the DA and RT market situations. We show that our strategy incurs lower penalties by tracking the actual regulation signal in the RT market better than conventional approaches based on open-loop controls.