Plug-in electric vehicles (PEVs) are a sustainable choice in response to electrification and decarbonization policies. Their widespread adoption poses challenges and opportunities, particularly for distribution systems (DSs). This study introduces a comprehensive probabilistic method to assist DS operators (DSOs) in infrastructure decision-making under uncoordinated PEV charging scenarios. Using real-world case studies and employing a Monte Carlo-based approach, it evaluates the impact of PEV charging on various aspects of DSs, including voltage magnitudes, imbalance, technical losses, and transformer loading. Mitigation strategies are explored through adjustments of substation transformer tap settings, modifications of no-load tap changers (NLTCs), and installation of low voltage regulators (LVRs). Results indicate that while transformer tap adjustments can improve voltage profiles, NLTC modifications may worsen overvoltage issues. In contrast, LVR implementation significantly reduces the number of customer units with voltage violations and lowers daily compensation costs. However, relying solely on LVRs may be insufficient when PEV penetration exceeds 35%. Moreover, their effectiveness in mitigating voltage imbalance diminishes due to independent LVR control and increased load. Still, economic analysis shows that LVRs can be financially viable even under these conditions. Sensitivity analyses highlight the critical influence of both PEV penetration levels and their spatial distribution within the DS in realistic PEV modeling simulations. In conclusion, this study proposes a probabilistic method to assist DSOs in the decision-making process of enhancing voltage regulation on DSs, comprehensively addressing losses, voltage imbalances, and loading in DSs impacted by PEV adoption.

PV technologies are regarded as one of the most promising renewable options for the transition towards Net Zero. Despite the rapid development of PV systems in recent years, achieving the necessary goals requires more than a threefold increase in annual capacity deployment by 2030. However, current PV systems often fall short of optimal performance due to improper installation angles. In high-latitude cold regions, the actual PV generation capacity is frequently overestimated due to the omission of snow conditions. This study introduces a novel model designed for high-latitude regions to predict local optimal PV installation angle that maximizes PV power generation, utilizing historical weather big data, including snowfall and melting effects. A case study is presented within a Swedish context to demonstrate the implementation of these methods. The results highlight the crucial role snow conditions play in determining PV performance, resulting in an average reduction of 14.7% in annual PV power generation. Optimal installation angle could yield approximately a 4.8% improvement compared to common installation angles. The study also explores the application of snow removal agents, which could potentially increase PV generation by 0.1–2.3%. Additionally, the new PV installation angle successfully captures the impact of the local weather changes on PV power generation, potentially serving as a bridge between climate change adaptation and future PV power generation endeavors.

The large-scale penetration of electric vehicles (EV) in road transport brings a challenging task to ensure the balance between supply and demand from urban districts. EVs, being shiftable loads can provide system flexibility. This work investigates the potential role of smart charging of EVs in mitigating the impact of the integration of a mix of residential and public EV charging infrastructure on power networks. Furthermore, the impact of integrating solar photo-voltaic (PV) and battery energy storage systems (BESS) has been explored where BESS improves PV self-consumption and helps in peak shaving during peak load hours. Annual losses, transformer congestion, and cost of electricity import assessment are detailed by considering the power network of Stockholm as a case study. Smart charging with loss-optimal and cost-optimal charging strategies are compared to uncoordinated charging. The cost-optimal charging strategy is more favorable as compared to the loss-optimal charging strategy as it provides more incentives to the DSOs. The loss-optimal charging strategy reduces 35.5 % of losses in the network can be reduced while the cost-optimal solution provides a 4.3 % reduction in the electricity cost. The combined implementation of smart charging, PV, and BESS considerably improves energy and economic performance and is more effective than EV smart charging alone.

The transition towards sustainability necessitates robust growth of renewable energy, especially solar power. However, increasing the shares of solar power may lead to mismatch of electricity supply and demand, thus worse solar power performance could be received due to lower self-consumption and self-sufficiency. This issue has attracted more attention recently as it also requires additional costs for controlling and battery integration. This study combines several load profiles from consumers having different behaviors with different proportions to find out the optimal shares with higher level of PV penetration and self-sufficiency. A visualizing matching layout is proposed to present the PV-load matching level of all the possibilities at different seasons based on a Swedish context. It is found an 8% improvement of self-sufficiency reaching 70% in summer without additional equipment and scarification of self-consumption. The results could assist in the design and planning of the future power sector. Regarding specific targets, various optimal solutions could be found based on the layout.

In response to the ongoing climate crisis, the European Union has introduced a legislative package aimed at promoting clean energy initiatives. In this context, energy communities (E C s) are defined as core actors in the energy transition plan, by promoting local electricity generation, consumption, and sharing. This study aims to investigate the feasibility of E C s in Sweden, by performing a case study on a real distribution system in a Stockholm district. Solar photovoltaic (P V) and energy storage (E S) technologies are modeled and implemented into the system, and different scenarios are tested to identify optimal techno-economic solutions. The results indicate that self-consumption can increase up to 95%, and annual electricity costs are reduced by up to 30% compared to a case without PV generation. The analysis of the net present value (NPV) shows that collective self-consumption through shared solar applications increases the profitability of investing in PV by more than 24%.

Solar power generation in Sweden is far from required capacity to help with transition towards 100% renewables in the power sector by 2040. Decentralized PV system attracts attentions given the conflicts of future increasing demands and land scarcity in the urban areas. However, it is not easy to implement it due to challenges on local conditions and lack of references. This paper aims to propose an overview of the potential of small-scale grid-connected PV systems in a Swedish context and offer an example for urban PV system planning in Sweden or high latitude areas. A model considering weather, space, infrastructures and economics is developed and implemented with a real case in the Swedish context. The findings verify the technical and economic feasibility of urban decentralized rooftop PV systems in the Swedish context. It is found that this kind of system does have considerable power potential in the Swedish context without land requirements. This kind of PV system could be a promising option for future power generation which satisfies part of demands and reduces pressure on external grids. The full potential could be only achieved with improved infrastructures, and the profitability of the system relies heavily on market and political conditions. This study can be a refence for other high latitude areas.

Sweden is at the forefront of the energy transition to reach carbon neutrality by 2045. Within this context, electric vehicles (EVs) are the most promising solution to lead the transport sector towards fossil freedom. However, while on the transport side, high integration of EVs is a positive step to meeting the energy transition goals, the expansion of electric mobility presents challenges for operators of electric power distribution systems (DS) due to the increased power demand that these vehicles will generate. This work focuses on analyzing the impact of increased shares of plug-in EVs (PEVs) at the low voltage (LV) level. For this, a case study is developed analyzing the effects of PEVs charging on the local 400V DS in Lidingö, Stockholm County. Steady-state power flow analysis for different loading scenarios was performed on a digital twin of the local DS and the different scenarios were built and ran based on stochastic variables involved in the PEVs charging behavior using local empirical dataset. The results obtained for the system studied show that PEV penetration levels (Pl) equal to or greater than 30% could become a concern for the existing systems due to the increase in power demand, specifically on the aggregated peak power. Furthermore, these results provided insights into how to identify possible limitations, bottlenecks, PEV hosting capacity and capacity shortages in the network and can be applied to different DS, which can help the local distribution system operator (DSO) planning for the energy infrastructure for the future.

Heat recovery from local resources is shown to be a promising solution to reduce the carbon footprint of district heating. Supermarkets equipped with a CO2 refrigeration system and a geothermal storage offer a larger heating capacity, compared to traditional solutions. While district heat could benefit from this higher heat recovery availability, supermarkets could generate an income from a capacity that would be otherwise unused. For the first time, this study applies a detailed modelling approach considering both sides of such a synergy. The objective is to assess the techno-economic and environmental impact of a coordinated control strategy. Since the heat recovery from the supermarket consumes additional electricity, power-to-heat is implemented as a solution to reduce the overall CO2 emissions. This is demonstrated by scenarios simulated for a district in Stockholm. Hourly electricity CO2 intensity and prices are implemented as signals to prioritize either the district heating central supply or heat recovery. By boosting the use of electricity when cleaner, a CO2 intensity-driven control show the potential of reducing the carbon footprint of the district (−9.4%). A control based on prices, instead, is more convenient economically both for the district (−1.4% heat cost) and for the supermarket (−32% operational cost).

The planning of energy infrastructures in new districts often follows the practice adopted for the rest of the city. In Stockholm, district heating is a common solution for multi-apartment neighborhoods. Recently, because of an average clean electricity mix, heat pumps have gained interest. However, European studies suggest to limit the reliance on electrification to avoid an extreme demand increase. Thus, an effort is required to improve the environmental impact of alternative options. This study proposes waste heat recovery in low temperature networks as a promising solution. By means of a techno-economic and environmental analysis, this option is compared to domestic heat pumps. A new approach is proposed to combine a district level perspective with simulation tools able to capture sector-coupling interactions. Scenarios, for a real neighborhood, assess waste heat recovery potential and electricity grid loading status. Results show that a waste heat recovery capacity equal to 10% of the peak load can reduce fossil fuel use of 40%. Local grid limitations are shown to be a bottleneck for the feasibility of domestic heat pumps. Their heat generation cost is 28% higher than for district heating. The carbon footprint is strongly dependent on the emission factor of the electricity mix (+11%/-24%).