Sequential Linear Programming and Particle Swarm Optimization for the optimization of energy districts

In this paper we deal with the optimization of energy resources management of industrial districts, with the aim of minimizing the customer energy expenses. In a district the number of possible energy system combinations is really large, and a manual design approach might lead to a suboptimal solution. For this reason we designed a software package that builds a model of the energetic district and optimizes its resources management. Here we focus on the solution of the arising nonlinear constrained optimization problem. Two different methods are considered for its solution: a Sequential Linear Programming (SLP) and a Particle Swarm Optimization (PSO) method. A theoretical support for the used SLP method is given and efficient implementations of both approaches are devised. The results of the tests performed on several energetic districts are reported.