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.

中文翻译：

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在未来能源市场和需求不确定性下，通过稳健的设计和运营优化，提高第五代区域供热和制冷网络的潜力


网络投资和预计能源价格会极大地影响第五代区域供热和制冷 （5GDHC） 网络的财务可行性和环境影响。与上一代相比，由于参与的产消者的能源供需分散，5GDHC 网络的前期投资成本相对较高。向 5GDHC 的过渡也受到不确定的能源市场和产消者波动的能源需求的阻碍。它使投资者不愿承诺，即使是有前途的商业案例也是如此。在这项工作中，提出了一个框架，用于评估 5GDHC 商业案例的经济和环境绩效，基于多目标稳健优化算法 （MO-RDO），以确定环境和经济设计之间的最佳权衡。建议的方法包括四个步骤。在前两个步骤中，对一个特定的商业案例进行建模，并针对不确定的能源市场分析技术经济和环境绩效。在第三步中，使用技术经济和环境指标响应来开发数据驱动的机器学习模型。与高保真非线性模型相比，该模型提供了更好的计算效率，并通过全局敏感性分析评估了驱动经济和环境绩效的最有影响力的参数。这有助于通过在数据不确定性中精确定位关键设计变量和目标来完善多目标稳健设计优化 （MO-RDO） 框架。此外，制定的 MO-RDO 提供了环境和经济上最佳的网络设计和运营参数的组合，并强调了它们之间的权衡。 使用净现值 （NPV）、投资回报率和贴现投资回收期等财务指标评估设计和权衡。这些指标是在项目生命周期内计算的，考虑了初始投资和净节省。对于所考虑的案例，与经济上最优的解决方案相比，环境设计的投资成本高出 10%，但将总排放量 （TE） 减少了 13%，并提高了运营成本节省，这对于更好的财务指标至关重要。拟议的框架与欧盟转型计划一致，以纳入废弃能源和脱碳路径，估计 NPV 仅增加 3% 就会成为财务规模上环境最佳解决方案的风险。