Multi-Objective Optimization (MOO) is a mathematical approach to solving problems with multiple conflicting objectives by finding optimal trade-offs. Instead of a single best solution, MOO identifies a set of Pareto-optimal solutions where improving one objective may compromise another. It is widely used in engineering, finance, energy management, and artificial intelligence to balance efficiency, cost, performance, and sustainability in complex decision-making scenarios.

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Optimal Sizing and Operation of Energy Storage for Demand Charge Management and PV Utilization

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This paper presents a method to determine optimal energy and power capacity of distributed Energy Storage Systems (ESS) in behind-the-meter applications to maximize local Photovoltaic (PV) utilization or minimize Demand Charge (DC) cost. The problem is solved as a multi-objective optimization model to obtain a set of Pareto optimal solutions for each scenario in each month. An approach is then presented to map the monthly Pareto fronts into a single yearly Pareto front. A cost benefit analysis has also been carried out to show the compromise between PV utilization, DC cost, and ESS cost.

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