1. Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties.
- Author
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Chaudhry, Afraz Mehmood, Ghorbaniasl, Ghader, Hachez, Jonathan, Chicherin, Stanislav, and Bram, Svend
- Subjects
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NET present value , *RATE of return , *ECONOMIC indicators , *OPTIMIZATION algorithms , *ECONOMIC uncertainty , *HEATING from central stations , *DEMAND forecasting - Abstract
Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale. • A generalized thermodynamic and economic framework to assess the potential business case of 5GDHC networks • A framework to quantify the impact of uncertain energy market and demands on network KPIs • Polynomial chaos expansion for uncertainty quantification on 5GDHC networks • A robust machine learning-based modeling approach to improve the computational efficiency • Multi-objective robust design optimization to find the optimal design under the influence of uncertainties [ABSTRACT FROM AUTHOR]
- Published
- 2024
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