Demand response(DR)is a flexible way to improve distributed energy resource scheduling.The innovative contribution of this paper is to include complex contracts in the model,which can accommodate the constraints accor...Demand response(DR)is a flexible way to improve distributed energy resource scheduling.The innovative contribution of this paper is to include complex contracts in the model,which can accommodate the constraints according to the special expectations of each player.Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers.Multi-period DR events are considered.In this way,consumers can specify the limits on the time,power,and remuneration regarding participation in DR events,which has not been considered in the literature.The state of the art treats these aspects separately or uses a statistical approach,without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment.The model has been validated for 218 consumers using several scenarios and different types of distributed generation,showing that it is possible to increase DR with respect to the preferences of consumers.展开更多
基金This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under project DOMINOES(No.771066)the FEDER Funds through COMPETE program,and the National Funds through FCT under the projects UIDB/00760/2020 and CEECIND/02887/2017 CIND.
文摘Demand response(DR)is a flexible way to improve distributed energy resource scheduling.The innovative contribution of this paper is to include complex contracts in the model,which can accommodate the constraints according to the special expectations of each player.Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers.Multi-period DR events are considered.In this way,consumers can specify the limits on the time,power,and remuneration regarding participation in DR events,which has not been considered in the literature.The state of the art treats these aspects separately or uses a statistical approach,without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment.The model has been validated for 218 consumers using several scenarios and different types of distributed generation,showing that it is possible to increase DR with respect to the preferences of consumers.
