Evolutionary Algorithms Optimize Residential Energy Transition Pathways

Published in Energy, Sustainability and Society, the study utilizes evolutionary algorithms with multi-objective optimization to balance the conflicting goals of minimizing annual system costs and reducing carbon dioxide emissions. The model focuses on a multifamily residential building constructed in Germany prior to 1918, representing the characteristics of historic city centers where over 50 percent of the existing housing inventory was built before 1970. By integrating building renovation measures with advanced energy system components, the study provides a detailed framework for evaluating the trade-offs between economic feasibility and environmental impact in the residential sector.

The primary motivation for this research stems from the significant contribution of the residential building sector to Germany’s greenhouse gas emissions. Over 60 percent of the energy used for space and domestic hot water heating currently comes from fossil fuel sources, which remain the predominant energy choice in this sector. Although renewable energy reached 54.4 percent of the German electricity sector in 2024, its share of final energy usage for heating and cooling remains limited at 17.7 percent. The heating sector alone is responsible for more than 50 percent of Germany’s final energy usage, creating a substantial gap that must be addressed to achieve national climate goals. This study addresses the need for new energy systems that can effectively evaluate both costs and emissions to identify suitable transition strategies for older residential structures.

The energy system modeled in the study comprises a diverse array of components designed to represent a realistic decarbonization strategy. The system includes photovoltaic-thermal roof tiles, a battery energy storage system, an air-source heat pump, and a warm water storage unit. A natural gas boiler is included with the option of replacement by a hydrogen-fueled boiler, allowing the model to assess the potential benefits of a fuel switch. Additionally, the model incorporates an energy management software for the battery electric vehicle battery, reflecting the increasing integration of electric vehicles into residential energy systems. The optimization process enables the selection of six distinct energy renovation measures, ranging from no renovation to comprehensive deep energy renovation including ventilation systems.

Multi-objective optimization was executed using the AGE-MOEA evolutionary algorithm, which was selected through a systematic performance test comparing various algorithms. The algorithm sorts solutions by considering diversity and proximity to the Pareto front, ensuring a wide range of evenly distributed solutions. The optimization objectives are the reduction in total annual system costs, represented as annuities, and the reduction in carbon dioxide emissions. The resulting Pareto front provides an optimized range of solutions, each with a specific system design. The annuity costs for these solutions range from 49 to 116 euros per square meter per year, while annual carbon dioxide emissions range from 74 to 4 kilograms per square meter per year. This broad range illustrates the significant trade-offs between cost and environmental performance in residential energy systems.

The results reveal distinct segments within the Pareto front, each characterized by specific energy carriers and renovation cases. The most cost-effective solutions are classified as a ‘business as usual’ strategy, characterized by the use of a natural gas-fueled boiler without financial investments in enhancing the thermal system. As the system moves toward lower carbon dioxide emissions, the capacity of the air-source heat pump and thermal energy storage increases. The heat production from the natural gas boiler is progressively replaced by the heat pump, which provides heating with reduced emissions. This transition involves a self-sufficiency strategy where the photovoltaic-thermal plant generates power that is stored and utilized to mitigate emissions. The introduction of the energy management system for electric vehicles represents a discrete investment cost that is unrelated to the installed capacities of the thermal system.

Segment two of the Pareto front represents a more competitive zone where the self-sufficiency strategy continues, but with an increased utilization of hydrogen. The use of hydrogen is not associated with carbon dioxide emissions within the model, making it an attractive option for reducing emissions. This renovation reduces space heating demand, thereby lowering the possible carbon dioxide emissions and allowing it to compete with the unrenovated case. The segment displays a rough competition between renovation cases, the inclusion of the energy management system, and the type of energy carrier, with high capacities of the thermal system being a common feature.

Segment three reveals a similar pattern to the first segment but with the exception of the renovation case. The reduction in space heating demand resulting from renovation case four, which includes the renovation of the roof, windows, walls, and basement ceiling, has the effect of reducing the capacity of the heat pump and thermal energy storage to their respective lower boundaries. This reduction in demand allows the system to achieve lower emissions with smaller thermal system components. The switch in the energy management system decision variable is realized at lower capacities for the thermal energy system components. The second last solution in this segment exhibits a small battery energy storage capacity before being traded off for a substantial increase in thermal energy storage volume in the last solution.

Segment four is defined by a strategy focused on achieving zero emissions. For all solutions in this segment, there is a declining interest in the decision variables of the thermal system, with the bivalence point being as high as in no other segment. The increase in the bivalence point allows the model to minimize the operating time of the heat pump, thus reducing its thermal energy output. This approach facilitates the maximum utilization of hydrogen for the production of thermal energy. Since electricity use is associated with carbon dioxide emissions in this model, this method helps minimize emissions. The segment also demonstrates an increase in the capacity of the battery energy storage system, with the aim of achieving the ultimate reduction of carbon dioxide emissions by maximizing the exploitation of the electrical energy derived from the photovoltaic-thermal plant.

Sensitivity analyses were performed to investigate the effects of the prices of natural gas, hydrogen, and household electricity on the Pareto front. The analysis for the price of household electricity shows a moderate distortion of the front, with lower electricity prices compressing the range of annuity costs for solutions with natural gas as the energy carrier. The distortion in the front due to an increase in natural gas prices indicates that the segments dominated by solutions with natural gas diminish as the price increases. This could resemble a future scenario that includes a higher carbon dioxide taxation, which would serve its purpose to transition towards cleaner residential energy systems. The sensitivity analysis for the hydrogen price exhibits a fundamental distortion towards lower prices, with the front gradually including more solutions with hydrogen as the energy carrier. These solutions are decreasing in annuity costs and therefore evade other solutions with less hydrogen utilization.

The study acknowledges several limitations, including a simplified life-cycle assessment that exclusively accounts for carbon dioxide emissions associated with energy carriers such as natural gas and grid electricity. The investigation overlooks numerous carbon dioxide emissions attributed to the construction of the proposed energy systems, which would fundamentally alter the analysis if included. Additionally, simplified cost assessments, especially for the renovation cases, limit the significance of the study. The methodology used to estimate temperatures in the thermal energy storage does not adhere to the principles of energy conservation, and it is questionable whether a conventional photovoltaic plant outperforms the photovoltaic-thermal plant investigated. These limitations present opportunities for further exploration, such as employing multi-node models or plug flow models for temperature estimation and adopting a broader perspective through a more comprehensive life-cycle assessment.

The findings confirm that photovoltaic technologies constitute a highly effective measure for reducing carbon dioxide emissions in the building sector. The most cost-effective strategy for curtailing emissions appears to be the adoption of electricity generation from photovoltaic systems, potentially incorporating photovoltaic-thermal technology. Considerable further reductions in emissions can be achieved by integrating additional heat pump and thermal storage capacities, although this results in a moderate increase in annuity costs. The approach to minimizing emissions involves substituting the energy carrier for the gas boiler from natural gas to hydrogen. The inclusion of an energy management system alongside the electric vehicle appears to be promising, while battery energy storage is not competitive with a combination of heat pump and thermal storage under the premise of low building energy efficiency. The costs incurred for renovation cases are not offset by the corresponding reductions in the costs related to the components of the heating system.

This study presents a viable methodological approach to support the transition of residential energy systems. The results are specific to one location and should be subjected to change under other climate conditions. Comparing the overall findings with those of other research endeavors is challenging due to disparities in the components of the energy system, modeling approaches, geographic context, and parameterization. However, the study successfully simulates a complex energy system of a multifamily residential building with smart capabilities, including a wide range of innovative components such as electric vehicle charging with an energy management system and a hydrogen-capable boiler. The model demonstrates behavior that aligns with expectations for a real-world analogous residential energy system and is thus considered plausible.

The primary conclusion of the study is that the method of multi-objective optimization combined with evolutionary algorithms is a suitable tool for identifying ways for residential energy systems to transition in regard to greenhouse gas emissions and economic feasibility. This assertion is substantiated by the extensive configurations that were identified for the examined energy system. The proposed method appears to hold considerable promise in identifying economically feasible pathways for transitioning residential energy systems in terms of reducing greenhouse gas emissions. Subsequent research should take into account embodied carbon emissions and improve the precision of modeling thermal energy storage to provide a more comprehensive understanding of the decarbonization potential of residential buildings.

Subject of Research: Modeling residential energy systems and building renovation using evolutionary algorithms with multi-objective optimization

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

Article References: Naß, H. L., Grunenberg, J. B., Aoun, F., Bartkowski, M., & Nebel, A. (2026). Modeling residential energy systems and building renovation using evolutionary algorithms with multi-objective optimization. Energy, Sustainability and Society, 16(1), Article 23. https://doi.org/10.1186/s13705-026-00581-z

Image Credits: AI Generated

DOI: 10.1186/s13705-026-00581-z

Keywords: Modeling, residential, energy, systems, building, renovation, evolutionary, algorithms, multi-objective, optimization, scientific research