The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy stora...The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage systems.However,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation.To unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators.The uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs.Moreover,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon.This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs.Subsequently,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the system.This model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully preserved.The proposed strategy is proven to achieve the objective of low-carbon economic operation.展开更多
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 h...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.展开更多
基金partially supported by the National Natural Science Foundation of China(General Program)(No.52077107)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY220082).
文摘The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage systems.However,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation.To unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators.The uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs.Moreover,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon.This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs.Subsequently,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the system.This model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully preserved.The proposed strategy is proven to achieve the objective of low-carbon economic operation.
基金This work is supported by National Natural Science Foundation of China(No.51277015).
文摘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.
