Iterative Linearization Approach for Optimal Scheduling of Multi-Regional Integrated Energy System

Abstract

It is challenging to deal with the optimal scheduling problem of the multi-regional integrated energy system (MIES) precisely and efficiently due to its multi-dimensional nonlinear characteristics. This article proposes an iterative linearization approach to solve the complicated and nonlinear MIES optimization problem with a well-balanced trade-off between accuracy and computation efficiency. In particular, the proposed approach is a combination of the modified piecewise linearization (PWL) tactic and the sequential linear programming (SLP) algorithm. The modified PWL method is developed to improve the speed-accuracy trade-off of the linearization, while the SLP algorithm is used to linearize the multi-dimensional nonlinear functions and narrow the approximation error iteratively. In this way, accurate but highly nonlinear formulations such as heat network models in the variable flow and variable temperature (VF-VT) mode can be considered in the optimization and solved efficiently. Finally, the effectiveness of the given approach is validated in a day-ahead optimal scheduling case of a four-region MIES.