Large-Scale Modeling and Optimization Strategy for Multi-Energy Management Systems

Abstract

A multi-energy system that controls the generation and storage of different types of energy offers great potential for improving the overall system efficiency and operating cost. The economic dispatch problem for multi-energy systems is to find the optimal system configuration that satisfies the demands under a host of constraints. The problem is often formulated as a mixed integer programming problem, which is very hard to solve since the number of variables involved could be in the order of hundreds. The difficulty of the problem is further compounded when model predictive control is implemented because the set points for controlling the multi-energy system at each time step have to be modeled, drastically increasing the number of variables in the optimization problem. In this paper, a large-scale hybrid optimization strategy is proposed to solve the economic dispatch in a multi-energy management system. Mixed-integer linear programming is used to solve a linearly approximated problem to determine the discrete set points followed by nonlinear programming for the continuous variables. We compare different optimization methods for the nonlinear programming problem with respect to the computational time and cost savings. The cost savings is also compared to a rule-based economic dispatch as the baseline. The simulation is performed using the data obtained from an office building in an R modeling and data management environment. We show that the proposed optimization strategy is able to solve the economic dispatch problem of a multi-energy management system with 720 mixed-integer variables within a short timeframe while still accounting for the nonlinearity of the optimization problem.