This paper presents a tri-level model for the simultaneous management of energy and ancillary services markets between transmission and distribution networks integrated with renewable energy sources, smart homes based on the Internet of Things, and electric vehicles. In More
This paper presents a tri-level model for the simultaneous management of energy and ancillary services markets between transmission and distribution networks integrated with renewable energy sources, smart homes based on the Internet of Things, and electric vehicles. In the first level of the proposed model, smart homes plan their participation in the energy and regulation markets and send it to the distribution network operator. In the second level, the operators of the distribution networks plan their area according to the programs received from the smart homes and determine their strategy for participation in the energy, reservation and adjustment markets. In the third level, the strategy of distribution networks is sent to the operator of the transmission system so that the final planning of the energy, reservation and adjustment markets can be done according to them. The proposed model is formulated as a mixed integer linear programming problem and solved by GUROBI solver in GAMS. The implementation of the proposed model showed that this model was able to significantly use the potential of subscribers based on the Internet of Things, electric vehicles, storage systems and demand response programs to improve the technical aspects of transmission and distribution networks as well as to improve the economic aspects of energy markets and ancillary services.
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In this paper, a comprehensive energy management model is proposed in order to the operation of a ‎modified 33-radial bus distribution system, in the presence of smart homes. In the proposed model, ‎smart home customers are able to participate in a demand respon More
In this paper, a comprehensive energy management model is proposed in order to the operation of a ‎modified 33-radial bus distribution system, in the presence of smart homes. In the proposed model, ‎smart home customers are able to participate in a demand response (DR) program and their comfort ‎index is also considered as the main constraint. The model also considers uncertainties related to the ‎load demand, the generation of renewable energy resources and electricity price. The Monte Carlo ‎simulation method and the ScenRed tool are utilized to generate and reduce the scenarios, respectively. ‎In order to mimic the actual operating conditions, in the simulation, the seasonal variations of load ‎and generation are considered and the operation problem is solved for four seasons.‎‏ ‏A linear AC ‎power flow is also used in the model. Finally, the problem is modeled as a mixed-integer linear ‎programming (MILP) problem and solved by the CPLEX solver in GAMS software. The simulation ‎results demonstrate that the model proposed in this study is a comprehensive framework for the ‎operation of distribution systems in the presence of smart homes, which not only reduces operating ‎costs but also increases the consumers’ comfort index.‎
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In this paper, a three-layer optimization strategy for flexible energy management of the active distribution system under the high penetration of wind and solar renewable energy sources is introduced, in which microgrids are responsible for providing the flexibility ser More
In this paper, a three-layer optimization strategy for flexible energy management of the active distribution system under the high penetration of wind and solar renewable energy sources is introduced, in which microgrids are responsible for providing the flexibility services to the main distribution network. In the proposed strategy, microgrid operators provide flexibility services through distributed generation resources, electrical storage systems, smart homes, and electric vehicles. In the first layer of the proposed strategy, smart homes are planned considering the energy and flexibility markets and then announce their final plan to the microgrid operator. In the second layer, microgrids plan their service area according to the plans received from smart homes and then send their participation plan in the energy and flexibility markets to the main distribution network operator. Eventually, in the third layer, the main distribution network operator plans the energy and flexibility markets according to the plans received from the microgrids. The proposed three-stage strategy is modeled as a mixed integer linear programming problem and solved by CPLEX solver in GAMS. The proposed optimization strategy has been implemented on several case studies and the simulation results demonstrate that this strategy can effectively provide the flexibility capacities required for sustainable operation through cheap resources within microgrids, thereby significantly reducing the daily costs of microgrids and distribution network.
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