Background The residential building sector is a major contributor to Germany’s greenhouse gas emissions. Over 60 % of the energy used for space and domestic hot water heating comes from fossil fuels sources and remains the predominant energy choice in this sector. In order to achieve greenhouse gas emission targets, it is imperative to develop new energy systems for buildings. Energy system modeling is an effective tool for evaluating different energy systems. The evaluation process should encompass an analysis of both costs and greenhouse gas emissions, with the aim of minimizing the two objectives in order to identify suitable energy systems. As these goals are anticipated to conflict with each other, a multi-objective optimization approach is employed. Results This study simulates a multifamily residential building constructed in Germany prior to 1918. The energy system comprises: - photovoltaic-thermal roof tiles; - a battery energy storage system; - an air source heat pump; - a warm water storage; - a natural gas boiler with the option of replacement by a hydrogen-fueled boiler; - an energy management software for the electric vehicle battery. In addition, optimization enables the selection of six distinct energy renovation measures. AGE-MOEA, an evolutionary algorithm, is used for multi-objective optimization with the objectives being a reduction in total annual system costs and carbon dioxide (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> </InlineEquation>) emissions. The resulting Pareto front provides an optimized range of solutions, each with a specific system design, ranging from 49 to 116 €/(m2a) for annuity costs and 74 to 4 kg/(m2a) for annual <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> </InlineEquation> emissions. Conclusions The outcomes of the proposed model demonstrate an appropriate representation of the expected behavior of a real-world energy system. The first step in the resulting energy system configurations can be classified as ’business as usual’. The second lowest cost option is characterized by self-sufficiency, offering a balanced trade-off between costs and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> </InlineEquation> emissions, as well as hydrogen heating to minimize <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> </InlineEquation> emissions. While increased levels of renovation were observed to contribute to a reduction in <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> </InlineEquation> emissions, the cost reduction does not offset the respective investment costs. Consequently, in the analysed example case, it can be inferred that improving energy efficiency through renovation only offsets associated costs if fuel prices are rising.

Modeling residential energy systems and building renovation using evolutionary algorithms with multi-objective optimization

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2026年4月28日

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Henrik Lukas Naß,
Jannis Bela Grunenberg,
Fares Aoun,
Marius Bartkowski,
Arjuna Nebel

摘要

Background

The residential building sector is a major contributor to Germany’s greenhouse gas emissions. Over 60 % of the energy used for space and domestic hot water heating comes from fossil fuels sources and remains the predominant energy choice in this sector. In order to achieve greenhouse gas emission targets, it is imperative to develop new energy systems for buildings. Energy system modeling is an effective tool for evaluating different energy systems. The evaluation process should encompass an analysis of both costs and greenhouse gas emissions, with the aim of minimizing the two objectives in order to identify suitable energy systems. As these goals are anticipated to conflict with each other, a multi-objective optimization approach is employed.

Results

This study simulates a multifamily residential building constructed in Germany prior to 1918. The energy system comprises: - photovoltaic-thermal roof tiles; - a battery energy storage system; - an air source heat pump; - a warm water storage; - a natural gas boiler with the option of replacement by a hydrogen-fueled boiler; - an energy management software for the electric vehicle battery. In addition, optimization enables the selection of six distinct energy renovation measures. AGE-MOEA, an evolutionary algorithm, is used for multi-objective optimization with the objectives being a reduction in total annual system costs and carbon dioxide ( \(\hbox {CO}_{2}\) ) emissions. The resulting Pareto front provides an optimized range of solutions, each with a specific system design, ranging from 49 to 116 €/(m²a) for annuity costs and 74 to 4 kg/(m²a) for annual \(\hbox {CO}_{2}\) emissions.

Conclusions

The outcomes of the proposed model demonstrate an appropriate representation of the expected behavior of a real-world energy system. The first step in the resulting energy system configurations can be classified as ’business as usual’. The second lowest cost option is characterized by self-sufficiency, offering a balanced trade-off between costs and \(\hbox {CO}_{2}\) emissions, as well as hydrogen heating to minimize \(\hbox {CO}_{2}\) emissions. While increased levels of renovation were observed to contribute to a reduction in \(\hbox {CO}_{2}\) emissions, the cost reduction does not offset the respective investment costs. Consequently, in the analysed example case, it can be inferred that improving energy efficiency through renovation only offsets associated costs if fuel prices are rising.