Accurate reconstruction of probability density functions (PDFs) from data is essential in engineering applications. Classical global moment-based polynomial approximations often suffer from oscillations, instability in the tails, and sensitivity to the choice of support. This work proposes a quantile-based piecewise polynomial density reconstruction approach that combines equal-probability binning with local moment-matched polynomials within each bin. Two variants are considered: piecewise monomial and piecewise Lagrange polynomials with Chebyshev nodes. The numbers of bins and polynomial degrees are selected by a proposed grid search approach guided by the Kolmogorov-Smirnov (K-S) test statistic under non-negativity constraints. Across several benchmark distributions, the proposed methods reduce K-S errors by about 80-96% relative to standard monomial and Lagrange polynomial approaches, and by about 83-97% compared with spline density estimation. For real-world household electricity consumption and solar irradiance data, the piecewise approaches achieve K-S test statistic performance comparable to kernel density estimation while offering improved control over tail behavior and oscillations. Overall, the results demonstrate that quantile-based localization substantially enhances the robustness and fidelity of moment-based polynomial PDF reconstruction.

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 life-cycle GHG mitigation potential from PV can vary from 0.6 t CO2e kWp−1 in Sweden to 18.6 CO2e kWp−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 decision-making tool to prioritize PV deployment in regions with higher mitigation potential.

Electric vehicle (EV) smart charging can support the grid and improve owner economics. Simultaneously, European network tariffs are transitioning from energy-based network tariffs (EBTs) to power-based network tariffs (PBTs). Existing studies rarely distinguish between EBTs and newer tariff designs when evaluating EV smart charging strategies. To bridge this gap, this study compares the technical and economic consequences of shifting from EBTs to PBTs for Swedish prosumers with EVs and rooftop photovoltaics (PV) systems under 5 distinct PBTs introduced for 2025. The optimization framework includes three economic objectives, comprising: total electricity cost, EV battery degradation, and ancillary service revenues. In addition, one technical optimization is used to minimize net load variance. Average results show that switching from EBT to PBT increases electricity costs by 12.5% under uncontrolled EV charging. Nevertheless, using EV smart charging results in net cost reduction percentage (CR) of 2.5-16% for EBT and 7-34% for PBT without the ancillary services (AS) participation, and 6-32.5% for EBT and 10-41% for PBT with the AS participation. The mean CR is nearly double under PBT compared to EBT across all optimized cases, with average CR of 23.6% and 12.2%, respectively. The majority of this reduction is attributable to lower power-based charges by rescheduling EV charging demand. CR is greater under PBT, particularly for smaller fuse ratings, higher production-to-load ratios, and the monthly single power peak rather than the multi-peak averaging count method. Although economic performance differs between tariff structures, technical metrics remain similar. The sensitivity analysis indicates that costs increase more significantly under PBT when the PV system is not implemented or when the EV arrives in the evening. Thus, these findings underscore the critical role of active load management in mitigating net electricity costs under PBT.

Abstract 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.

Electric vehicles (EVs) are becoming an important part of the power system, offering flexibility for grid support through frequency regulation services. Frequency regulation markets rely on resources to balance supply and demand in real time, making EVs a promising asset for grid support services. This study examines the most profitable way to operate EV chargers in a parking lot of an electrified office building powered by a building-integrated photovoltaics (PV) system. The model schedules the charging and discharging of 22 EVs, assuming a single building operator participates in both the dayahead electricity market and the manual frequency restoration reserve (mFRR) balancing market. The model inputs are the EV mobility patterns, historical PV generation, building load profiles, and market price data for 2024. The results show that mFRR energy activation market (mFRR-EAM) prices are irregularly concentrated in a small number of high-value activation hours, whereas mFRR capacity market (mFRR-CM) prices follow a more stable and predictable seasonal pattern. Furthermore, smart charging reduces annual costs by up to $\mathbf{1. 4 0 ~ k}$ € compared to uncontrolled charging, and V2G operation improves local PV utilization and reduces network peak demand. Participation in the mFRR market yields additional savings of 0.17k€, with limited impact on battery degradation. The study provides practical insight into the economic value of smart EV charging at commercial workplaces with co-located PV generation.

In the Nordic countries, seasonal variations limit the use of solar energy due to a mismatch between energy supply and demand. As the expansion of solar technology installation, especially photovoltaics (PV), evaluating multiple solar energy production systems in combination with storage systems, including both heat and electricity demand, is of particular interest for large industry and business parks. This study aims to assess the matching of solar energy supply with the local community’s power and heat demands of a business park consisting of warehouses. Various installation coverage of rooftop PV panels and solar Thermal (ST) systems, combined with thermal energy storage (TES) of different sizes, have been evaluated. The input data includes roof area, solar irradiation, PV power production from an existing system, electricity (annual amount 1.41 GWh), and heat (annual amount 6.58 GWh) demand profiles, as well as district heating distribution and ambient temperatures. Key technical parameters analyzed are the self-Consumption (SC) and self-Sufficiency (SS) ratios, evaluated for electricity, heat, as well as the overall energy system (i.e., the combined electricity and heat). Additionally, excess and imported electricity, energy balance, and waste heat are estimated. The results show that a significant portion of the produced heat is wasted, while surplus PV electricity can be exported to the grid, generating some economic value. The currently assumed TES capacity (70 MWh) is insufficient to store the total excess heat, leading to poor SC and SS ratios. Overall, the TES required to reach high self-sufficiency should be large in size, which in fact is infeasible, while smaller storage does not make a noticeable impact. Moreover, achieving a zero-waste heat and a complete heat SC scenario with a 70 MWh TES capacity would require allocating 7% installation coverage of the roof area to ST (equivalent to 1.2 MW). Concurrently, the PV system will cover 48% installation coverage of the roof area due to the power point of connection limit (equivalent to 1.4 MW). Therefore, leaving unutilized area could be an advantageous solution for the future, when TES or the power point of connection capacity is expanded or excess thermal energy is utilized. Future research could focus on a techno-economic assessment to determine the optimal system size by balancing economic and technical metrics, potentially including other energy storage or boiler technologies.

Electrification is expanding across several end-use sectors, including construction, where industrial battery electric vehicles are emerging as an alternative to conventional diesel-powered machinery. While construction-site electrification can reduce local emissions, it also introduces new challenges for the power grid, particularly in terms of increased electricity demand, peak loads, and interaction with intermittent renewable generation. At the same time, the large battery capacities of industrial electric vehicles create opportunities for smart charging, vehicle-to-grid (V2G) operation, energy arbitrage, and participation in ancillary-service markets. This paper presents a techno-economic assessment of a medium-sized electrified construction site using industrial battery electric vehicles. A city in central Sweden is used as the case study. The study evaluates the synergy between construction-site electrification, net-zero energy solar PV integration, smart charging, and flexibility-market participation. Four charging schemes are investigated: (1) opportunistic charging, (2) technically oriented smart charging for net-load variability minimization, (3) economically oriented smart charging for cost minimization, and (4) V2G multimarket operation including energy arbitrage and frequency regulation services. The simulation results indicate that electrifying the construction site in the case study requires an average daily electricity demand of 3.16 MWh and results in a peak demand of 600 kW. Net-zero energy solar PV integration reduces the average monthly peak demand by approximately 15%, achieves around 50% self-sufficiency even without smart charging, and lowers the average monthly operational cost by 33.6% under the studied assumptions. This implies a strong synergy potential between electrified construction sites and solar PV generation. Unidirectional smart charging can further improve both technical and economic performance. The technically oriented strategy achieves the highest self-sufficiency improvement, 11.6 percentage points, and the highest peak-load reduction, 54%, while the economically oriented strategy provides the highest V1G cost saving, 38.6%. V2G multimarket operation provides the highest economic benefit among the evaluated scenarios, reducing the average monthly cost by 65.8% compared with opportunistic charging. Lastly, battery degradation results show that cycling ageing is less dominant than calendar ageing, mainly due to the relatively low C-rates of large industrial vehicle batteries. This finding highlights the importance of state-of-charge management, as prolonged exposure to high state-of-charge levels can accelerate calendar ageing. Overall, the results indicate that electrified construction sites have strong potential to provide technical and economic benefits when coordinated with solar PV, smart charging, and V2G operation.

There is a potential dual usage of Autonomous Electric Vehicles (AEVs) for ride-hailing and electricity market participation in a vehicle-to-market (V2M) setting. In this study a model for early adoption of AEVs is developed based on sequential optimization at each time-step for dual market participation choosing between ride-hailing and energy arbitrage market participation via Vehicle-to-Grid (V2G). Simulations with the model shows that there are, depending on ride-hailing rate and electricity price cost, yearly revenue estimations of upwards of $45k in extreme cases, with considerable revenue from in particular AEV ride-hailing payoff. Simulations also show that the activation fraction of AEV ride-hailing is dependent on both ride-hailing rate and electricity price. This dual market participation model thus reveals a potential demand-side management for V2G interaction, and ride-hailing, via for example time-based energy price tariffs.

Accurate separation of global tilted irradiance (GTI) becomes important when the measured irradiance is used for quality control or PV simulation purposes, for which the latter often requires global horizontal irradiance (GHI), or diffuse and beam irradiance fractions. This study presents an evaluation of irradiance reverse transposition and separation models for the application with GTI in high latitudes. The evaluation is made based on measured and quality controlled six-second irradiance from latitude 59.53 degrees N, containing GTI at 30 degrees, 40 degrees, and 90 degrees tilt angles, as well as GHI and diffuse horizontal irradiance (DHI). Based on a literature review, two specialized GTI reverse transposition and separation models-GTI-DIRINT and PEREz-DRIESSE-and four GHI separation models were chosen for evaluation. The latter were tested in an optimization loop developed for this study that utilizes existing GHI separation models combined with transposition models for reverse transposition and separation of GTI. Specifically, the separation models ERaS, S << ARtVEIt1, ENGERER2, and YANG4 were tested with HAY & DAVIES and PEREz1990 transposition. The models were investigated using both statistical evaluation metrics and Diebold-Mariano test to compare measured and predicted GHI and DHI. An evaluation with measured data showed that for GTI reverse transposition and separation at high latitudes, the use of the proposed optimization model with ENGERER2 in combination with HAY & DAVIES transposition, or the PEREz-DRIESSE model is recommended. This is based on overall good ranking and low bias of GHI prediction with-2.0 W/m2 and-2.3 W/m2, respectively.

A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.

This study investigates probabilistic and scenario-based forecasting of solar irradiance with Markov-chain mixture (MCM) distribution modeling, Persistence Ensemble (PeEn) and Climatology. Forecasts from MCM models with uniform and empirical emission distribution settings, respectively, are compared with PeEn and Climatology in terms of probabilistic forecasting performance. The MCM model is also extended with scenario generation capabilities and compared to scenario generation of the Climatology by means of Monte Carlo sampling. Forecasts were made on minute resolution normalized solar irradiance, i.e. the clear-sky index, from National Renewable Energy Laboratory and Swedish Meteorological and Hydrological Institute for two climatic regions: Oahu, Hawaii, USA and Norrköping, Sweden, respectively. Results show that the MCM models are neither necessarily the most reliable, nor the sharpest in terms of Prediction Normalized Average Width (PINAW), but they are the most accurate in terms of Continuous Ranked Probability Score (CRPS). MCM models with uniform and empirical emission distribution settings perform similar in the tested probabilistic forecasting metrics. In terms of scenario forecasting, MCM models with N=30 perform similar in probability distribution goodness-of-fit and autocorrelation Mean Absolute Error (MAE) and superior to N=2 and N=10 number of states. Mathematically, forecasts from the MCM model with empirical distribution setting are shown to correspond to PeEn and Climatology forecasts given special settings of the MCM model. Based on the conclusions, the suggestion is to use the MCM scenario forecast generator with uniform emission distribution setting as benchmark for scenario forecasts of very short-term solar irradiance.

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

Renewable energy and electric vehicles (EVs) are crucial technologies for achieving sustainable cities. However, intermittent power generation from renewable energy sources and increased peak load due to EV charging can pose technical challenges for the power systems. Improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). A city on the west coast of Sweden is used as a case study. The smart charging and V2G schemes aim to minimize the mismatch between generation and load, and are formulated as quadratic programming problems. The simulation results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance in the optimal net-zero energy city is increased from 68% with opportunistic charging to 73% with smart charging and further to 84% with V2G. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings indicate that EVs have a high potential to provide flexibility to urban energy systems.

This study introduces a recursive model framework for augmenting separate temporal probabilistic forecasts of multiple correlated time-series with a copula correlation model. The model is applied to the Markov-chain mixture distribution (MCM) model to spatiotemporally forecast minute resolution normalized solar irradiance, c.f. the clear-sky index, derived from radiometer array measurements of Global Horizontal Irradiance (GHI) for 18 geographically adjacent stations at Oahu, Hawaii, USA. The results are evaluated by univariate and multivariate probabilistic forecast metrics in comparison with forecasts from purely temporal MCM, Climatology and the spatiotemporal Multivariate Persistence Ensemble (MuPEn) benchmark. Results show that the Climatology and the MuPEn forecasts are most reliable, while superiority in sharpness, based on Prediction Interval Normalized Average Width (PINAW), depends on forecast horizon. In terms of accuracy, measured with the univariate measure Continuous Ranked Probability Score (CRPS), the MCM model (with and without copula) forecasts are most accurate for the first two steps ahead forecasts, while the Climatology and MuPEn both have superior score for longer horizons. In terms of multivariate scores, the accuracy estimate Energy Score results for the forecasts are similar to the CRPS, while the Variogram Score, which takes into consideration the correlational structure of the multivariate time-series, is significantly improved by the copula-augmented MCM model compared to the univariate MCM model. The MuPEn model generated the lowest Variogram Score among all models. Tests with fewer stations and swapped training and test data gave similar model-to-model relative dynamics with variations in magnitude.

The increasing trend of small-scale residential photovoltaic (PV) system installation in low-voltage (LV) distribution networks poses challenges for power grids. To quantify these impacts, hosting capacity has become a popular framework for analysis. However, previous studies have mostly focused on small-scale or test feeders and overlooked uncertainties related to rooftop azimuth and tilt. This paper presents a comprehensive evaluation of city-level PV hosting capacity using data from over 300 real LV systems in Varberg, Sweden. A previously developed rooftop azimuth and tilt model is also applied and evaluated. The findings indicate that the distribution systems of the city, with a definition of PV penetration as the percentage of houses with 12 kW installed PV systems, can accommodate up to 90% PV penetration with less than 1% risk of overvoltage, and line loading is not a limiting factor. The roof facet orientation modeling proves to be suitable for city-level applications due to its simplicity and effectiveness. Sensitivity studies reveal that PV system size assumptions significantly influence hosting capacity analysis. The study provides valuable insights for planning strategies to increase PV penetration in residential buildings and offers technical input for regulators and grid operators to facilitate and manage residential PV systems.

To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26%. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43% reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7%. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance.