Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage
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The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.