A Joint Scheduling Optimization Model for Wind Power and Energy Storage Systems considering Carbon Emissions Trading and Demand Response

Abstract

To reduce the influence of wind power random on system operation, energy storage systems (ESSs) and demand response (DR) are introduced to the traditional scheduling model of wind power and thermal power with carbon emission trading (CET). Firstly, a joint optimization scheduling model for wind power, thermal power, and ESSs is constructed. Secondly, DR and CET are integrated into the joint scheduling model. Finally, 10 thermal power units, a wind farm with 2800 MW of installed capacity, and 3 × 80 MW ESSs are taken as the simulation system for verifying the proposed models. The results show backup service for integrating wind power into the grid is provided by ESSs based on their charge-discharge characteristics. However, system profit reduces due to ESSs' high cost. Demand responses smooth the load curve, increase profit from power generation, and expand the wind power integration space. After introducing CET, the generation cost of thermal power units and the generation of wind power are both increased; however, the positive effect of DR on the system profit is also weakened. The simulation results reach the optimum when both DR and CET are introduced.

Real-Time Pricing for Demand Response in Smart Grid Based on Alternating Direction Method of Multipliers

“…DSM mechanism based on price can effectively motivate electricity consumers to adjust their inherent mode of using 2 Mathematical Problems in Engineering electricity and to change their behaviors with the smart grid system through following varying price signals of electricity [2]. For example, an individual customer may change his/her air conditioner temperature or reprogram the charging process of his/her electric car or/and battery banks during the valley hours, while his/her watt hour bill of electricity is the lowest.…”

Optimal operation management of grid-connected microgrids under uncertainty

“…Reference [16] proposes an approach to integrate power to gas technology to the multi-source MG including wind power, electric boiler, micro turbine and gas boiler. In [17], the energy storage systems and DR are introduced to the traditional scheduling model of wind and thermal power with carbon emission trading for reducing the influence of random nature of wind power on system operation. From the above literature review, it can be observed that there is a pressing requirement for an optimal operation of MG considering the RESs, BES systems, EVs and DR [18][19][20].…”

Optimal Operation of Microgrid considering Renewable Energy Sources, Electric Vehicles and Demand Response

“…Reference [16] proposes an approach to integrate power to gas technology to the multi-source MG including wind power, electric boiler, micro turbine and gas boiler. In [17], the energy storage systems and DR are introduced to the traditional scheduling model of wind and thermal power with carbon emission trading for reducing the influence of random nature of wind power on system operation. From the above literature review, it can be observed that there is a pressing requirement for an optimal operation of MG considering the RESs, BES systems, EVs and DR.…”