Climate change is often cited in renewable energy research by stating motives in environmental, technical, social, and economic categories, which can be linked to metrics that are called key performance indicators (KPIs). Furthermore, these indicators can be set as a target for an optimization model and become optimization objectives. This paper critically reviews the motives, optimization objectives, and KPIs selected in studies of solar photovoltaic (PV) systems with energy storage within the built environment in Nordic countries. A subset of 36 scientific articles, sorted as relevant from a selection of 349, was analyzed to make the review. The results reveal that even when environmental motives are expressed by 75% of the authors, only 8% focus on optimizing with environmental indicators. This imbalance suggests that the environmental problems intended to be addressed with the optimization model could be left unchanged or even exacerbated by the strategic choices made during the energy modeling. In addition to this, there is a lack of consistency in the way the different indicators are calculated, especially for environmental indicators where the impacts of materials and manufacturing were not included. Furthermore, in the economic and technical categories, the economic volatility and peak power motives do not have a matching indicator within the articles analyzed. Therefore, it is recommended to conduct comprehensive optimization studies that align with carefully considered motives.  

The optimization of solar photovoltaic (PV) systems has been proposed to reduce greenhouse gas emissions in building energy use. However, despite the environmental concerns shown, most of the studies optimize technical and/or economic objectives. Recent studies show that optimizing costs rather than carbon can worsen life cycle emissions, driven by battery operation. Whether this conflict remains in storage-free PV systems in buildings remains unexplored. Therefore, this study aims to quantify the life cycle global warming potential (GWP) effects of selecting different optimization objectives in a PV capacity optimization model. To achieve this, three multi-objective optimization models of a PV system are compared, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II): (1) maximizing self-sufficiency and self-consumption, (2) maximizing the internal rate of return and net present value (NPV), and (3) minimizing global warming potential and maximizing NPV. The results show that the solutions provided by models with only technical or economic objectives do not always lead to a reduction in GWP. Furthermore, optimizing technical or economic objectives can increase GWP by up to 10.3% or reduce it by up to 5.3%, depending on the embodied impact of the PV system. These results are obtained under Swedish conditions, where the electricity supplied by the grid has a low carbon intensity. Nevertheless, this study shows that optimizing PV generation capacity for technical or economic objectives does not necessarily reduce life-cycle GWP.

The fastest growing renewable energy technology in Europe is solar photovoltaic (PV). Despite no burning fossil fuels during its operation, the production of PV modules and other components cause the emission of greenhouse gases. The global warming potential (GWP) associated with the generation of PV energy can be higher than that from the grid in some locations. This study explores the fraction of the year, and its related energy generation, during which PV energy has a lower global warming potential than the local grid. This analysis is done in seven different locations within Europe, for three PV systems with different GWP. The results show that in most cases the PV systems have the capacity of reducing the GWP from the grid 100% of the time. However, there are a few exceptions in countries with a low-carbon grid such as France, Sweden and Norway with a GWP reduction related to only 46%, 16% and 17% of the energy generation respectively. © 2025, Scanditale AB. All rights reserved.

Solar photovoltaic (PV) is a key technology for decarbonization. However, these systems cause an environmental impact along their life cycle. Greenhouse gas (GHG) emissions and mitigation from PV have been studied on a world level, with a yearly resolution, concluding that there are significant differences in decarbonization depending on where PV systems are deployed. This study explores the life-cycle GHG mitigation potential in the European Union countries with hourly resolution. The lifecycleGHGmitigation potential from PV can vary from 0.6 tCO2ekWp−1 in Sweden to 18.6CO2ekWp−1 in Cyprus. Furthermore, a difference between calculating with an hourly and yearly resolution up to 49% is found. Despite this estimation being dependant on future decarbonization scenarios and other limitations, the variation between countries suggest that these results could be used as a decisionmaking tool to prioritize PV deployment in regions with higher mitigation potential.