Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Abstract

AbstractThis paper addresses the risk‐based self‐scheduling problem using a hybrid technique between Lagrangian relaxation (LR) and particle swarm optimizer (PSO). The paper analyses a self‐scheduling model that accounts for profit and risk simultaneously. The effect of risk is explicitly modelled in the self‐scheduling problem taking into account the variance of the market‐clearing prices. The forecasted hourly probabilities that spinning and non‐spinning reserves are called and generated are also considered in the formulation to simulate the reserve uncertainty. Artificial neural network (ANN) is applied for forecasting the hourly reserve probability. The proposed approach is applied to a 36 unit test system. Copyright © 2008 John Wiley & Sons, Ltd.