Developing a model for consumer management of decentralized options - Cordelia Frings; Broghan Helgeson (Institute of Energy Economics at the University of Cologne (EWI), 2022)
The shift from centralized to decentralized energy provision has created an opportunity for a wide range of distributed energy resources. In deciding how to best serve their long-term energy needs, end consumers face a plethora of investment options together with complex regulatory instruments as well as growing uncertainty regarding, e.g. techno-economic and political developments. Optimization models using linear programming methods are one option to help shed light on possible technology combinations and the economic consequences for end consumers. Yet the existing literature indicates a clear lack of models capable of accounting for high technical, regulatory and economic detail while optimizing investments in multiple future years. Therefore, within this paper, the mixed-integer linear programming model COMODO (Consumer Management of Decentralized Options) is developed to determine the cost-minimal energy provision for end consumers. The model uses its extensive technology catalog to perform an investment and dispatch optimization for multiple years, minimizing total costs over a long-term time horizon while accounting for developments in techno-economic data, regulatory frameworks and energy market conditions. Furthermore, piecewise-linear functions are created to represent costs and subsidies for different systems sizes and for future years. In order to demonstrate the capabilities of the model developed, an exemplary application is presented to investigate the energy provision of four single-family homes in Germany for the years 2025 to 2045. Three scenarios are designed that build upon each other regarding the amount of information available to consumers and their decentralized energy technologies. The results show a clear preference for gas boilers as a base technology coupled with electric heaters to cover demand peaks. Households with higher demand levels invest in PV systems in 2025, while other households with lower demands either wait until 2040 or do not invest. A sensitivity analysis then examines the effects of higher carbon pricing in the German building sector on the consumer's energy provision. The subsequent increase in the retail gas price leads to households choosing to fully electrify their heat provision, i.e., installing a heat pump combined with thermal storage, PV and an electric heater. On average, these households experience an increase in total costs ranging from 3.5% to 5.4% over the complete time horizon and realize a long-term decrease in annual carbon emissions of up to 80% compared to the analysis with lower carbon pricing. Lastly, this work also presents a novel method of analyzing the marginal costs of electricity and heat provision, revealing a strong correlation between the implicit marginal costs of energy provision and the assumptions on retail energy prices.
Keywords: C26; C53; D11; D13; D15; H20; 330; Distributed energy resources; mixed-integer linear programming; consumer investment behavior; consumer modeling; heating; electricity; techno-economic optimization; energy system design