An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response

Demand response (DR) and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems. However, the uncertain wind power generation (WG) which has anti-peaking characteristic would be hard to exert its ability in carbon reduction. This paper introduces DR into traditional unit commitment (UC) strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems, since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods. Firstly, net load curve is obtained by forecasting load and wind power output. Then, considering the behavior of DR, a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission. Non-dominated sorting genetic algorithm-II (NSGA-II) and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained. Finally, a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.