--The ever-increasing penetration level of renewable energy and electric vehicles may threaten power grid operation. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This research work proposes a two-stage stochastic model for large-scale energy resources scheduling for aggregators. The proposed model is designed for aggregators managing a smart grid. The idea is to address the challenge brought by the variability of demand, renewable energy, electric vehicles, and market price variations while pursuing cost minimization. Benders' decomposition approach is implemented to improve the tractability of the original model and its' computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness and efficiency of the proposed approach when compared with a deterministic formulation and suggest that demand response and storage systems can mitigate the uncertainty.
Index Terms--Benders
Number of EVs
The ever-increasing penetration level of renewable energy and electric vehicles may threaten power grid operation. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This research work proposes a two-stage stochastic model for large-scale energy resources scheduling for aggregators. The proposed model is designed for aggregators managing a smart grid. The idea is to address the challenge brought by the variability of demand, renewable energy, electric vehicles, and market price variations while pursuing cost minimization. Benders' decomposition approach is implemented to improve the tractability of the original model and its' computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness and efficiency of the proposed approach when compared with a deterministic formulation and suggest that demand response and storage systems can mitigate the uncertainty.
Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for endusers without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its' effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%-31.5% by using the proposed optimizationbased algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.
The dawn of smart grid is posing new challenges to grid operation. The introduction of Distributed Energy Resources (DER) requires tough planning and advanced tools to efficiently manage the system at reasonable costs. Virtual Power Players (VPP) are used as means of aggregating generation and demand, which enable smaller producers using different generation technologies to be more competitive. This paper discusses the problem of the centralized Energy Resource Management (ERM), including several types of resources, such as Demand Response (DR), Electric Vehicles (EV) and Energy Storage Systems (ESS) from the VPP's perspective to maximize profits. The complete formulation of this problem, which includes the network constraints, is represented with a complex large-scale mixed integer nonlinear problem. This paper focuses on deterministic and metaheuristics methods and proposes a new multi-dimensional signaling approach for population-based random search techniques. The new approach is tested with two networks with high penetration of DERs. The results show outstanding performance with the proposed multi-dimensional signaling and confirm that standard metaheuristics are prone to fail in solving these kind of problems.
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