Search Results (149)

The selection of georeferenced meteorological databases is a critical technical and financial factor in photovoltaic (PV) system sizing. This study evaluates how database choice affects PV generation estimates and economic feasibility by combining bibliometric screening, technical validation, financial indicators, and multicriteria analysis. A bibliometric review of 5658 documents indexed in Scopus and Web of Science supports the selection of seven databases, compared across ten Brazilian locations. PV generation is estimated using both a simplified sizing approach and a higher-temporal-resolution model based on hourly irradiance, ambient temperature, and module thermal coefficients, enabling comparison with measured generation in a three-year case study in João Pessoa. Performance is assessed using MAE, MAPE, RMSE, $R^2$, NPV, payback and the Analytic Hierarchy Process. Under the adopted PV model and AHP weighting structure, NASA POWER shows the strongest overall multicriteria performance in the analyzed Brazilian sample, ranking first in seven locations. Database choice causes variations of up to 4.70 years in payback and BRL 105,709.36 in NPV, equivalent to USD 21,082.84 at the 22 May 2026 exchange rate. The simplified CRESESB-based method overestimates annual generation by 6.79–12.64%, whereas NASA POWER reaches a best annual deviation of $-1.66\%$ in 2024. Full article

The DC/AC ratio is a critical design variable in utility-scale photovoltaic (PV) systems because it governs inverter loading, clipping behavior, energy yield, and long-term economic performance. However, conventional sizing approaches often rely on heuristic rules or deterministic annual yield optimization without explicitly accounting for the thermodynamic, optical, and stochastic mechanisms that reshape the DC power envelope. This study develops a physics-informed and bankability-oriented PVsyst-based framework for optimal DC/AC sizing by integrating irradiance transposition, incidence-angle modifier losses, temperature-dependent semiconductor behavior, inverter clipping dynamics, degradation, and discounted lifetime levelized cost of electricity (LCOE). A 10 MWp fixed-tilt PV plant located in Western Türkiye under Mediterranean climatic conditions is analyzed. The base-case simulation yields 15.20 GWh/year with a specific yield of 1519 kWh/kWp/year and a performance ratio of 87.5%, while temperature losses are identified as the dominant loss mechanism, accounting for 6.21% of the annual energy reduction. A regression-based thermal sensitivity analysis shows that monthly PR decreases by approximately $4.9\times {10}^{-3}$ per °C increase in ambient temperature. The DC/AC sweep identifies an optimum range of 1.35–1.40, where improved inverter utilization balances nonlinear clipping growth. A temporal clipping analysis confirms that clipping is concentrated during summer midday periods and is sensitive to sub-hourly irradiance variability. Correlated Monte Carlo simulations and LCOE cost-sensitivity analyses demonstrate that the optimum remains structurally robust under uncertainty, degradation, and inverter cost assumptions. The results show that DC/AC sizing should be treated as a coupled thermodynamic–optical–electrical–economic optimization problem rather than a simple capacity-matching decision. Full article

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV and CSP interact at the grid level and (ii) an EH-integrated configuration in which an electric heater (EH) uses PV electricity to heat molten salt in a topping cycle. The main contribution of this study lies in the two-stage optimization workflow, in which leading candidates are selectively re-simulated at higher temporal resolution. This workflow is applied to a common design framework that compares EH-integrated and co-located concepts while considering multiple PV technologies and a broad set of interdependent sizing variables. A surrogate-assisted genetic algorithm evaluates more than 200,000 candidate designs across PV technology, inverter size, TES capacity, EH capacity, and battery energy storage system (BESS) size. The optimization minimizes the levelized cost of energy (LCOE) subject to a $200 {\mathrm{MW}}_{\mathrm{el}}$ export limit, a CAPEX ceiling, and a nighttime-delivery constraint of ${\mathrm{CF}}_{\mathrm{night}}\ge 39\%$. Candidate designs are screened at 600 s and selectively re-simulated at 120 s, showing that temporal refinement affects not only KPI values but also candidate feasibility, final ranking, and preferred component sizing. The lowest-LCOE solution is the EH-integrated bifacial configuration, achieving 64.5% overall capacity factor, ${\mathrm{CF}}_{\mathrm{night}}=39.1\%$, less than 0.1% curtailment, a specific CAPEX of $4698/kW, and an LCOE of 7.29 ¢/kWh. Pareto-front and parameter-trend analyses further show that stricter nighttime-delivery targets shift the dominant sizing levers and define a neighborhood of near-optimal solutions rather than a single fixed design. Full article

Increasing load demand and localized constraints are driving the need for cost-effective alternatives to traditional network reinforcement. However, existing Non-Wires Alternative (NWA) planning approaches often rely on simplified assumptions or computationally intensive full-year optimization, limiting their practical applicability. This study proposes a planning-oriented method integrating 8760-h Direct Load Flow (DLF)-based assessment, worst-case screening, and Mixed-Integer Linear Programming (MILP)-based resource sizing for the coordinated deployment of Energy Storage Systems (ESSs), Demand Response (DR), and Photovoltaic (PV) resources, along with building-scale microgrid candidates. The proposed microgrid candidates are modeled as grid-connected, building-scale configurations in which PV, ESSs, and DR are co-located at a single node, representing integrated resource units within the distribution system. The results show that voltage constraints are the dominant limiting factor and that NWAs primarily function as an investment deferral strategy rather than a full replacement for traditional reinforcement, delaying constraint violations by approximately 2 to 14 years. An ESS provides the most direct contribution to constraint mitigation, while DR and PV offer complementary support. The results also highlight the importance of locational deployment. In particular, a co-located microgrid configuration (MG_111) is selected as the optimal portfolio under moderate load growth conditions (Case B, 2%), demonstrating the practical feasibility of integrated DER deployment at a single node. Economic feasibility is found to be highly sensitive to incentive design, with profitability achieved only under favorable compensation conditions. These results demonstrate that coordinated DER portfolios can effectively extend deferral periods and provide practical insights into cost-effective NWA planning under realistic operating conditions. Full article

This study assessed photovoltaic (PV) soiling losses at Alamogordo, New Mexico, USA, located within the Chihuahuan Desert and near the White Sands gypsum dune field, a region with frequent dust events. Soiling material collected from PV module surfaces showed seasonal variations in mineral composition, with quartz being the main component during the fall season and calcite predominating during the spring. All samples collected during the following spring season contained large amounts of gypsum, indicating transport from White Sands, supported by HYSPLIT back-trajectories and surface wind data. Soiling materials collected from PV module surfaces generally had a mineral composition similar to that of the surrounding local soils. The mean particle size of collected soiling material samples ranged from 8 to 21 µm, with ~90% of particles being dust (<50 µm) and ~10% of the soiling particles being sand (>50 µm). Despite Alamogordo experiencing 22 dust events during this study, soiling-related power losses were relatively low, about 2% to 3%, much lower than reported for Global Dust Belt locations. The prevailing south-to-southwest winds and their gusts acted as a passive cleaning mechanism, as they were aligned with the front of the PV modules and likely resuspended particles off panel surfaces. Additionally, relatively low rainfall (about 2.2 mm per hour) was effective in restoring PV performance. These findings suggest that, due to the relatively low soiling losses observed, frequent cleaning may not be necessary at this location, resulting in potential savings in maintenance costs over the long-term operation of the PV system. Full article

This paper addresses the optimal management of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in modern electrical distribution networks. A mixed-integer nonlinear programming (MINLP) model is formulated which co-optimizes device placement, sizing, and multi-period dispatch to minimize the total annualized system costs while satisfying AC power flow and operational constraints. To solve this challenging problem, a decomposition methodology is proposed, wherein the binary location decisions for the PVs and D-STATCOMs are treated as predefined inputs, upon the basis of site selections commonly reported in the literature. With the integer variables fixed, the problem is reduced to a continuous nonlinear programming (NLP) subproblem for optimal capacity sizing and operational scheduling, which is solved using the interior point optimizer (IPOPT) via the Julia/JuMP environment. The core contribution of this work lies in its comprehensive demonstration of the economic superiority of variable reactive power injection over conventional fixed compensation schemes. Through numerical validation on standard 33- and 69-bus test systems, it is shown that a variable D-STATCOM operation yields substantial and consistent economic gains. Compared to optimized fixed-injection solutions, variable injection provides additional annual savings averaging USD 120,516 (33-bus feeder) and USD 125,620 (69-bus grid), corresponding to a further 3.4% reduction in total costs. These benefits prove robust across different device location sets identified by various metaheuristic algorithms, and they scale effectively to larger network topologies. The results demonstrate that transitioning to variable power injection is not merely an incremental improvement but a fundamental advancement for achieving techno-economic optimality in distribution system planning. The proposed methodology provides utilities with a computationally efficient framework for determining near-optimal PV and D-STATCOM management strategies by first fixing deployment locations based on established planning insights and then rigorously optimizing sizing and dispatch, in order to maximize economic returns while ensuring reliable network operation. Full article

The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by considering the main current and forward-looking risk factors that may arise during the life of the loan, including environmental, social and governance (ESG) risks, the intrinsic characteristics of the property and expectations regarding the economic cycle. This paper proposes a quantitative model for the determination of PV, applied to a practical case involving a residential property located in a medium-sized city in Italy’s Veneto region. The model adopts a deterministic and a probabilistic approach, the latter implemented through Monte Carlo simulation, which is indeed a generalization of the deterministic one. The model links the assessment of PV to the possible evolution of the property’s key parameters and the real estate cycle over the duration of the loan. It was tested under the assumption of a twenty-year mortgage originated in 2025 for the purchase of a residential property in Italy, considering two alternative locations: a suburban area and a city-centre area. The analysis conducted showed a substantially higher MV haircut for the suburban property compared with the central location. This difference reflects the fact that PV is less sensitive to real estate cycle fluctuations in more premium, central locations. Furthermore, the use of Monte Carlo simulation in the probabilistic approach enabled the calibration of the haircut according to a predefined confidence level, confirming the pattern observed in the deterministic framework. The combined evidence strengthens the empirical robustness of the model and highlights the importance of locational and cyclical dynamics in collateral valuation. Full article

Urban energy infrastructure in dense metropolitan regions must decarbonize essential public services while avoiding additional land take and excessive costs. This study presents a techno-economic and sustainability assessment of a grid-connected hybrid wind–solar street-lighting microgrid, created by retrofitting an existing nine-node off-grid installation at Odaiba Beach, Tokyo Bay, into an embedded distributed generation asset. The design departs from complementary sizing by independently scaling PV and wind so that each source can satisfy worst-case winter lighting demand, enabling both a reduction in battery autonomy from five to two days and the deliberate use of seasonal surpluses for grid export. Steady-state load-flow analysis in ETAP indicates annual generation of 1803.73 kWh from PV and 1192.54 kWh from wind, corresponding to approximately 1.84 MWh/year of net clean energy export after supplying 1009.15 kWh/year of lighting demand and incurring 149.09 kWh/year of distribution losses, with voltages compliant with industry standards. Sensitivity analysis under conservative solar and wind scenarios shows that the system remains export-positive in all cases, thereby supporting sustainable urban development by decarbonizing street-lighting, improving land-use efficiency through infrastructure co-location, and providing a replicable framework for similar coastal cities. Full article

Global data center electricity demand is projected to double to 945 TWh by 2030, yet no optimization framework jointly sizes renewable generation, battery storage, hydrogen export infrastructure, and flexible computing loads within a single industrial hub. This paper develops a two-layer techno-economic workflow for an integrated renewable–hydrogen–data center hub in Yanbu Industrial City, Saudi Arabia. HOMER Pro provides baseline capacity sizing and dispatch across four scenarios; a Pyomo-based mixed-integer linear program, calibrated to within 2% of the baseline, then extends the system to include a 60 MW data center (30 MW critical, 30 MW flexible), multi-sink hydrogen allocation (domestic, ammonia, methanol), and low-grade waste heat recovery. Battery storage emerges as the dominant cost–carbon lever: its removal raises the levelized cost of electricity (LCOE) from 0.052 to 0.181 USD/kWh (+250%) and increases CO₂ emissions from 1.83 to 2763 kt/yr, a factor of 1510. The Integrated Hub reduces annualized costs by 8.2% (36.9 M USD/yr) and emissions by 28% relative to a separate-build counterfactual, driven by shared PV–battery infrastructure and hydrogen export revenues of 58.5 M USD/yr. Export demand raises the electrolyzer capacity factor from 8.65% to 24.3%, cutting the levelized cost of hydrogen from 10.5 to 6.8 USD/kg. Waste heat recovery reduces the levelized cost of heat by 17%, and co-location lowers the levelized cost of compute by 23% (from 0.055 to 0.042 USD/GPU/hr). These results provide quantitative design principles for industrial hub planners considering data center co-location in high-solar regions with hydrogen export ambitions. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex mixed-integer non-linear programming (MINLP) problem. This paper addresses this gap by proposing a novel hybrid evaluator–optimizer framework for the optimal daily placement and sizing of PV-STATCOM devices. The framework synergistically integrates the metaheuristic crow search algorithm (CSA) for global exploration of discrete device locations with a high-fidelity, multi-period optimal power flow (OPF) model—implemented efficiently in Julia with the Ipopt solver—for continuous operational evaluation and constraint validation. The methodology incorporates realistic 24 h load and solar irradiance profiles. Extensive validation on standard IEEE 33- and 69-bus test systems demonstrates the efficacy of the proposed approach. The results indicate substantial reductions in daily energy losses—by up to 70.4% and 72.9% for the 33- and 69-bus systems, respectively—and corresponding operational costs, outperforming recent state-of-the-art metaheuristic and convex optimization methods reported in the literature. The CSA also exhibits robust convergence and repeatability across multiple independent runs. This work contributes a computationally efficient, open-source planning tool that leverages modern optimization solvers, providing a scalable and effective strategy for enhancing the power quality and economic performance of PV-rich distribution networks. Full article

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

This study examines whether green hydrogen production using combined wind and solar energy on Marmara Island can meet the island’s electricity demand and fuel the fuel needs of a hydrogen-powered ferry. A hybrid system consisting of a 10 MW wind farm, a 3 MW solar PV system, and a PEM electrolyzer sized to meet the island’s hydrogen demand was modeled for the island, located in the southwestern Sea of Marmara. The hydrogen production potential, energy flows, and techno-economic performance were evaluated using HOMER-Pro 3.18.4 version. According to the simulation results, the hybrid system generates approximately 62.6 GWh of electricity annually, achieving an 82.8% renewable energy share. A significant portion of the produced energy is transferred to the electrolyzer, producing approximately 729 tons of green hydrogen annually. The economic analysis demonstrates that the system is financially viable, with a net present cost of USD 61.53 million and a levelized energy cost of USD 0.175/kWh. Additionally, the design has the potential to reduce approximately 2637 tons of CO₂ emissions over a 25-year period. The results demonstrate that integrating renewable energy sources with hydrogen production can provide a cost-effective and low-carbon solution for isolated communities such as islands, strengthening energy independence and supporting sustainable transportation options. It has been demonstrated that hydrogen produced by PEM electrolyzers powered by excess energy from the hybrid system could provide a reliable fuel source for hydrogen-fueled ferries operating between Marmara Island and the mainland. Overall, the findings indicate that pairing renewable energy generation with hydrogen production offers a realistic pathway for islands seeking cleaner transportation options and greater energy independence. Full article

This study investigates the optimal operation of distribution systems incorporating Photovoltaic (PV) units, Electric Vehicle Charging Stations (EVCSs), and DSTATCOM devices using the Starfish Optimization Algorithm (SFOA). The main goal of the SFOA is to minimize a combined function that encompasses three key objectives: reducing system losses, increasing PV capacity, and enhancing EVCS power. By applying the SFOA within a multi-objective optimization framework, the optimal locations and sizes of PV units, EVCSs, and DSTATCOMs are identified to meet these objectives. This study analyzes and compares several case studies with different numbers of EVCSs, focusing on the operation of a modified 51-bus distribution system over 24 h. Results show that PV hosting energy increases to 21.73, 23.83, and 29.22 MWh for cases with 1, 2, and 3 EVCSs, respectively. EVCS energy also rises to 12.41, 19.50, and 37.23 MWh for the same cases. The corresponding optimized DSTATCOM reactive powers are 11.02, 12.02, and 13.74 MVarh. Throughout all cases, system constraints—such as voltage limits, utility current, and power flow equations—remain within acceptable ranges. The findings demonstrate the SFOA’s effectiveness in optimizing distribution systems with various devices, ensuring efficient operation and meeting all key objectives while adhering to system constraints. Full article