Optimizing microgrid distributed energy resources with varying building loads: Analysis and simulation
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As microgrids continue to evolve and become more prevalent, there arises a need to understand how best to design while addressing the fundamental objective of meeting energy loads. As a localized energy entity, a microgrid brings together distributed energy resources such as photovoltaics and energy storage systems with an array of building loads within a well-defined electrical boundary. Microgrids can vary considerably in scope, co-existing with the utility grid infrastructure, or being able to operate independently of it, or some combination in between of grid-tie and off-grid operation. Many challenges face the design and operation of a microgrid involving intelligent controllers and dispatchers, balancing generation resources, interacting with the utility grid, and doing all this in a cost-effective manner. This study examines the role of building load profiles in optimization of distributed energy resources, in particular, photovoltaics and storage system. The grid is assumed to be stable and contrasting rate structures are explored. Similarly, contrasting load profiles can shed light on a microgrid’s ability to meet demand versus energy loads. Modeling and simulation is done via an industry standard tool, HOMER GRID. Detailed hourly load profiles for various building mix profiles are generated via an expanded building energy modeling tool, Energy Performance Calculator (EPC), developed at the Georgia Institute of Technology. Demand response is also handled via EPC. Optimization is across the spectrum of net present cost, operating cost, return on investment, and a redefined levelized cost of electricity metric. A simple methodology is derived that can aid in the general design of balancing and optimizing distributed energy resources based on the findings of optimization across scenarios. Of vital importance to a microgrid stakeholder is risk mitigation in the deployment and usage of distributed energy resources, operating costs, and load fulfillment. This study paves the path of better understanding of integration of microgrids within an evolving smarter utility grid. Future studies will explore an even wider mix of buildings, the effect of electric vehicle (EV) charging stations via the building load profiles, and the evolution of microgrid rate structures from the perspective of Independent System Operators (ISO) and Regional Transmission Organizations (RTO). In addition, scope will be expanded to include microgrids that service villages and islands where grid stability cannot be assumed thus covering the gamut of microgrid presence worldwide.