A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty

This paper presents a new approach for GenCos Profit Based Unit Commitment (GPBUC) in day-ahead competitive electricity markets. Generation, spinning and non-spinning reserves are considered in the proposed formulation. The estimated probability that spinning and non-spinning reserves are called and generated is also considered in the formulation to simulate the reserve uncertainty. The artificial neural network (ANN) is applied for forecasting the reserve probability considering line limits, line and generator outages, market prices, bidding strategy, load and reserves patterns. Fuel and emission constraints are included in the model. A hybrid method between Lagrangian relaxation (LR) and evolutionary programming (EP) is applied to solve the proposed GPBUC problem. The proposed approach is applied to a 36 unit test system and the results are compared with those obtained from other approaches.