A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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.
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.
Economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response
2015
Article (Journal)
Electronic Resource
Unknown
Low-carbon electricity , Unit commitment (UC) , Day-ahead scheduling , Multi-objective optimization , Demand response (DR) , Non-dominated sorting genetic algorithm-II (NSGA-II) algorithm , Production of electric energy or power. Powerplants. Central stations , TK1001-1841 , Renewable energy sources , TJ807-830
Metadata by DOAJ is licensed under CC BY-SA 1.0
Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors
| DOAJ | 2023
Economic evaluation of demand response in power systems with high wind power penetration
| American Institute of Physics | 2014