Genetic optimization of multi-plant heat production in district heating networks

Abstract

Smart metering is providing spatially and temporally much more accurate and detailed customer level information about district heating (DH) consumption than before. Currently this information is mainly used for billing only, but it could be used to operate the system more efficiently. In this study we develop a new method for optimizing the heat production simultaneously at multiple heat plants at different locations of a DH network in order to minimize the combined production and distribution costs. Optimization determines the optimal supply temperatures at different heat plants and optimal load allocation between the plants. The method can be used to optimize the current heat production based on real-time customer measurements. The method can also be used for production planning based on more accurate and detailed customer level demand forecasts. Optimization is based on a static DH system model that can estimate the state of the entire DH network based on real-time measurements or demand forecasts. Because the objective function is a non-convex and non-smooth function of the decision variables, we use the genetic algorithm (GA) to solve the problem. The method can be applied to arbitrary DH networks with multiple heat plants. Optimization can result in savings in fuel and pumping costs. We illustrate the method with a sample district heating network with two parallel heat plants and real-life DH network segments. We also show extensive sensitivity analysis results for the two-plant case.