Optimal Posturing in Day-Ahead Market Clearing for Uncertainties Considering Anticipated Real-Time Adjustment Costs

This paper proposes a market-clearing mechanism which explicitly takes into account the impact of uncertainties in wind power generation and load forecast. Since market clearing is a multisettlement process-day ahead and real time (RT), a strategy is proposed, which provides best-fit day-ahead (DA) schedule, which minimizes the twin (both DA and RT adjustment) costs/maximizes social welfare, under all possible scenarios in RT. This two-stage optimization strategy consists of a genetic algorithm (GA) based DA market clearing and a two-point estimate-based probabilistic RT optimal power flow (OPF). The former generates sample schedules, while the latter provides mean adjustment costs. Two commonly employed standard market practices to incorporate wind energy into wholesale electricity markets have been presented. The results for a sample system with GA and two-point estimate OPF, and GA and Monte Carlo simulation have been obtained to ascertain the effectiveness of the proposed method.