Supervisory control of a combined heat and power plant by economic optimization

Abstract

Using combined heat and power (CHP) units within district heating systems (DHS) has been an effective way of meeting residential energy demand in Germany. Generally speaking, electricity fed in to the grid by the CHP is usually sold at a fixed price in today’s electricity market. Assuming that the share of renewable energies will be higher in the near future, it can be anticipated that the electricity prices will highly fluctuate due to the uncertainties within the renewable energy sources, such as wind speed and solar irradiance. Therefore, control mechanisms for heat and power producing plants are expected to switch their operation strategy from heat-driven to power-driven operation. A power-driven operation makes sure that the CHPs are shut down when the electricity market is not competitive enough to produce electricity. In this master’s thesis, a power-driven operation is achieved through an economic optimization. The optimization problem, which is formulated as a discrete optimization problem, is to find out the best ON/OFF operation trajectory of the units involved in a DHS; namely a CHP, a boiler and a storage tank. A simplified model capturing the power-based dynamics of a physical DHS model is implemented at simulation and modeling tool Dymola (Dynamic Modeling Laboratory). Optimization tool GenOpt (Generic Optimization Program) with particle swarm optimization (PSO) algorithm is used to solve the discrete optimization problem. The implementation of the model is verified by several test cases. Finally, a future scenario of the year 2023 is approximated in order to compare the financial gains and grid interactivity of the power-driven and the heat-driven operation. In addition, the effect of varying the storage size on plant gains and grid interactivity is investigated and discussed.