AbstractA short-term operational planning tool for geothermal plants with heat and power production connected to large district heating systems is developed. The software tool contains, among other features, a heat demand forecasting model for district heating systems. Two options, such as linear regression and artificial neural networks, are compared. As the result shows, artificial neural networks with the Bayesian Regularization Backpropagation Algorithm have a high generalization capability and are suitable to forecast the heat demand of large district heating systems with high accuracy. Data from a district heating system with about 70 MWth load supplied by a geothermal plant in the south of Munich (Germany) are used for comparison and assessment of all methods. After developing a suitable heat forecast, the heat and power production site is modeled by using mixed integer linear programming. Mixed-integer linear programming has proven to be a suitable method to model the operation of geothermal plants with heat and power production as well as to solve the planning optimization problem. As the results show, the short-term operational planning tool can optimize the operation of single components as well as of the overall geothermal plant with regard to various objective functions. The tool maximizes the revenues from the sold heat and electricity minus the costs for the boiler fuel and the heat purchased from a connected adjacent geothermal plant. A retro perspective operation investigation has proven that the profitability of the considered geothermal plant could be significantly increased by using the developed software.
This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. With a lower network temperature, the consideration of nonlinear transport phenomena within the district heating network becomes increasingly important. Therefore, a new nonlinear optimization method is introduced, where graph preprocessing reduces the computational effort of the subsequent nonlinear optimization. A cost penalization method, using a smooth approximation of a Heaviside function is applied to pipe investment costs to account for discrete piping diameters. To guarantee fast convergence of the optimization algorithm, the Jacobian matrixes are calculated and the problem is solved with an interior point algorithm. As a proof of concept, the district heating system for a small fictional town with 42 consumers is optimized and analyzed. The whole nonlinear optimization is performed in 19.37 sec and in most cases discrete or near discrete diameters are achieved in a nonlinear continuous optimization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.