Optimal Demand Response of Controllable Loads in Isolated Microgrids
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The electric power industry worldwide has undergone significant changes over the last decade. Environmental compliance and energy conservation issues have occupied the forefront of the new age power system which have opened the possibility of an increased integration of Distribution Energy Resources (DER). With the presence of DERs, reliable system operation and control has become increasingly difficult as the power flow no longer remains unidirectional. Microgrids with their decentralized system operations offer solutions to the challenges posed by this transformation. It has been generally regarded that the key to increased operational efficiency and economy of microgrids especially under its isolated mode of operation lies with improved customer participation in Demand Response (DR) programs. Developing a DR scheme with a novel customer interaction inside a microgrid setup will provide a key solution that would drive the system performance to its peak. This thesis proposes a mathematical model of DR integrated into the generation scheduling problem of an isolated microgrid. Controllable demand is modeled as a function of external parameters such as outside temperature, Time-of-Use (TOU) pricing and maximum limit on demand Pmax through supervised learning of neural networks. An optimal DR model is proposed to learn the load behavior and produce a control action on the controllable load profile of the end users. A novel Microgrid Energy Management System (MEMS) is proposed as the central unit of this DR model to determine the control signal and perform a least-cost operational schedule of the microgrid. Realistic data from an actual Energy Hub Management System (EHMS) is used to better replicate the real-world modeling scenario. Continuing with the DR model, the effect of customer response through energy payback model is also studied. The impact of this response on the customer load profile and an estimate of the expected peak reduction is also presented. The proposed model and the case studies are simulated on an CIGRE IEEE Medium Voltage (MV) benchmark system. The system under consideration is an appropriate approximation of the actual isolated microgrid system with their dispatchable diesel generators, Energy Storage System (ESS), photovoltaic (PV) panels and wind turbines. Finally, the results illustrating the effectiveness of the proposed DR scheme and the computational procedures are discussed. This work is concluded by exploring the possible research directions while addressing some pertinent problems for the same.