Development of an Operational Planning Tool for Geothermal Plants With Heat and Power Production

Abstract

<jats:title>Abstract</jats:title> <jats:p>A 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.</jats:p>

Structural Changes, Efficiency Improvements, and Energy Demand Forecasting Based on Empirical Analysis of Decomposition Models

“…In the existing research on the impact of new energy participation on the electricity spot market, it has been confirmed that the priority effect of new energy causes the price of electricity market clearing to drop, but the impact of new energy participation on the electricity spot market price still lacks a comprehensive method system. In addition, there is a lack of a quantitative model for the impact of new energy on the spot market price of electricity, and it is impossible to transfer the impact of new energy to the electricity price in the spot market [16][17].…”