Search Results (1,999)

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

This article presents a technical and economic analysis of a photovoltaic system operating in conjunction with a heat pump in a single-family home. The aim of this study was to compare the cost-effectiveness of two prosumer billing systems currently in use in Poland, net metering and net billing, implemented in accordance with the provisions of the Renewable Energy Sources (RES) Act and the Energy Law and to assess the effectiveness of a proprietary algorithm for managing surplus electricity produced by the photovoltaic system. The energy performance of the facility was determined using ArCADia Termo 11.1 software, while energy and economic calculations were performed using Microsoft Excel 365 and a developed heat pump control algorithm. The algorithm is based on an analysis of the building’s energy balance with a 15 min resolution and utilizes data on outdoor temperature, energy production from the PV system, building heat loss, heat pump operating parameters, and energy self-consumption. Its goal was to maximize the use of energy produced for the building’s own needs by appropriately controlling the heat pump and storing surplus energy as heat stored in domestic hot-water tanks. The annual electricity consumption of the analyzed building was 6902.18 kWh, of which 3724.13 kWh was for heating and domestic hot water provided by the heat pump. The algorithm reduced grid energy consumption by approximately 900 kWh per year and achieved a self-consumption level of 12.73 (%). Full article

Currently, heating supply in rural areas of China still predominantly relies on conventional coal-fired heating, which suffers from poor thermal insulation performance and severe environmental pollution. To address the issues of energy waste and environmental pollution associated with traditional heating methods in rural China, this study selects a detached rural residential building in Jilin City as the research object. A building thermal load calculation model incorporating phase-change material (PCM) walls and dynamic simulation models for five clean heating coupling systems are developed using TRNSYS software, so as to analyze the influence of PCM placement at different positions within the wall assembly on the building’s thermal load, as well as the technical and economic performance of the five heating systems. The results show that, when PCM is placed on the inner side of the building envelope, the peak heating load is reduced from 15,234.2 W to 11,266.5 W, and the cumulative heating load drops from 33,744.3 kWh to 25,688.9 kWh. Compared with the conventional PV (photovoltaic) system, the PVT (photovoltaic–thermal) system achieves an 11% improvement in power generation efficiency. Among the five clean heating systems, the PVT–ground-source heat pump system exhibits the lowest energy consumption, while the PVT–biomass boiler system records the highest energy consumption. Based on life-cycle cost analysis, the PVT–biomass boiler system delivers the optimal economic performance, with a levelized annual cost of 9285.48 CNY. Full article

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article

Türkiye’s strategic geographical location offers an exceptional opportunity for solar energy harvesting, yet optimizing large-scale investments requires rigorous pre-assessment methodologies. This study presents a comprehensive multi-criteria techno-economic analysis and real-world validation of a 13.18 MWp solar power plant (SPP) located in Kirsehir, a region characterized by high solar irradiance (1750 kWh/m²). Utilizing PVsyst software, four distinct configurations—monofacial and bifacial modules at 21° and 25° tilt angles—were systematically simulated and evaluated across varying equity-to-loan ratios using key financial indicators (NPV, IRR, PI, and Payback Period). The simulation results identified the 21° bifacial configuration, enhanced by the innovative integration of high-albedo industrial calcite (CaCO₃) waste as ground cover, as the optimal engineering solution. Crucially, the accuracy of this optimization was evaluated against 12 months of field data. While the raw measured annual production was recorded as 22,793,323 kWh, the validation was strictly based on the production adjusted for grid outages (23,499,604 kWh). Comparing this adjusted value with the simulated annual generation (22,816,114 kWh) yielded a total annual discrepancy of only 3% and a volumetrically weighted average error of 5.07%. Furthermore, to isolate model fidelity from inter-annual meteorological variability, the validation was assessed using the Performance Ratio (PR). The adjusted volumetrically weighted PR (87.43%) demonstrated a remarkably close alignment with the simulated PR (87.48%), exhibiting a marginal deviation of merely 0.05%. These performance metrics indicate a general consistency between the simulation model and operational field records across the evaluated period. Environmentally, the maximized energy yield of the 21° bifacial system facilitates the avoidance of approximately 6507.58 tonnes of CO₂ emissions annually. This research not only establishes the viability of scalable, low-cost calcite ground covers but also provides a highly robust, de-risked decision-support framework for utility-scale PV investments in similar geographic latitudes. Full article

Escalating energy costs and peak power demand charges pose significant challenges to the manufacturing sector. In response, industries are increasingly adopting on-site renewable energy sources and Battery Energy Storage Systems (BESSs). However, maximizing their economic benefit requires sophisticated control strategies that integrate energy management with production operations. This paper proposes and evaluates an integrated and adaptive rule-based coordination framework for BESS and machine-level switch-off policies in a production environment. Using discrete-event simulation, we model a four-machine manufacturing flow line powered by the grid and an on-site solar PV plant. We compare six distinct control policies, ranging from a benchmark case without storage to progressively more integrated context-aware strategies that incorporate price-aware BESS charging, dynamic peak-shaving, and adaptive machine switch-offs. The results demonstrate that integrated policies yield substantial economic benefits. The most advanced policy dynamically coordinates BESS dispatch with machine-level switch-off decisions based on electricity prices, production conditions, and energy availability, achieving the largest reduction in total energy costs and peak grid demand among the evaluated policies. This study quantifies the synergistic effects of combining supply-side (BESS) and demand-side (switch-off) strategies, providing a framework for developing resilient and cost-effective energy management systems in modern manufacturing. Full article

Grid unreliability can compromise electricity supply in developing regions, yet comparative evidence on residential-district hybrid systems under consistent outage assumptions remains limited. This study compares four grid-connected architectures for a residential district in Egypt: wind/battery/grid, PV/wind/battery/grid, PV/battery/grid, and PV/wind/grid, using a simulation–optimization framework with net present cost (NPC) as the sole optimization objective. Supply adequacy, renewable fraction, grid interaction, and grid-related operational CO₂ emissions were then assessed. Under baseline conditions, PV/wind/battery/grid was the minimum-NPC hybrid configuration, with an NPC of USD 481,851, LCOE of USD 0.0711/kWh, renewable fraction of 73.8%, unserved energy of 0.306 MWh/yr, and operational CO₂ emissions of 139,533 kg/yr. Relative to grid-only supply, it reduced unserved energy by 98.5% and emissions by 65.0%, although grid-only had the lower NPC of USD 369,414. To distinguish fixed-design deterioration from adaptive redesign, baseline-optimal capacities were evaluated unchanged under a common severe grid-availability profile and compared with re-optimized counterparts. For the three battery-containing architectures with complete adaptive results, re-optimization reduced unserved energy by 73.2–97.2%, while resizing differed markedly. Among these architectures, wind/battery/grid had the lowest severe-condition NPC, only 0.63% below PV/wind/battery/grid. Deterministic ±10% sensitivity analysis retained PV/wind/battery/grid as the minimum-NPC architecture. Overall, baseline cost optimality, fixed-design transferability, and adaptation requirements are distinct, architecture-dependent planning considerations. Full article

Dynamic energy tariffs increasingly mirror renewable generation and market conditions, yet most techno-economic assessments of residential retrofit and flexibility measures still rely on simplified, static tariffs, risking-biassed cost estimates. This study systematically maps energy tariff structures across three classification tables, showing that dynamic and weather-responsive tariffs are underrepresented in ex-ante building energy modelling. Building on this gap, four ex-ante tariff-modelling methods are developed and validated, namely a fixed tariff, a time-of-use (ToU) tariff, a weather-dependent real-time-pricing (RTP) mechanism grounded in a residual-load proxy, and a capacity-based network tariff, illustrated for the Flemish context. The RTP mechanism is calibrated against two years of Belgian day-ahead prices (ENTSO-E) and Elia demand, as well as PV- and wind-generation data, reaching a monthly correlation of ρ = 0.78 (hourly ρ = 0.43, excluding 2022) and ρ = 0.71 for the final quantile-mapped tariffs, using only weather data (.epw) and three calibratable parameters. An illustrative building-level application shows that, for an identical unoptimised demand profile, a weather-driven RTP tariff changes the projected annual electricity cost by roughly 33–44%, against only 1–3% for a static ToU tariff, confirming that tariff structure must be an explicit ex-ante modelling choice. Full article

The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied. Full article

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

This paper proposes a stochastic dispatch framework for multi-terminal high-voltage direct current (MT-HVDC) systems that explicitly accounts for uncertainty in photovoltaic (PV) generation and electrical demand while preserving computational tractability. The economic–environmental dispatch problem is formulated as a mixed-integer second-order cone programming (MI-SOCP) model, where the SOCP relaxation provides a convex representation of the network constraints, and the mixed-integer component captures the discrete charging/discharging states of battery energy storage systems (BESS). This formulation ensures that, for any fixed set of binary decisions, the remaining problem reduces to a standard convex SOCP, enabling efficient solution via branch-and-bound methods with tight continuous relaxations. Uncertainty is incorporated through a chance-constrained optimization (CCP) approach, where forecast errors are modeled using bounded truncated distributions and reformulated into deterministic convex constraints via quantile-based approximations, yielding a risk-aware dispatch strategy that avoids optimistic bias. Numerical studies on an 11-bus MT-HVDC test system demonstrate that accounting for uncertainty increases total operating costs by up to 31.87% and CO₂ emissions by up to 37.41% when both PV and demand uncertainties are considered simultaneously at a confidence level of 0.9, compared to the deterministic solution. Power demand uncertainty has a substantially greater impact than PV generation uncertainty, leading to cost increases of 28.09% versus 3.85% at the highest confidence level. Validation on a modified IEEE 24-bus MT-HVDC system confirms the scalability and computational efficiency of the proposed approach, achieving global optimality in a pure solver time of 1.34 s with a maximum relative relaxation gap of $5.81\times {10}^{-9}$, demonstrating numerical exactness and suitability for day-ahead scheduling. The results also highlight the critical role of BESSs in providing operational flexibility, with storage strategies differing significantly under uncertainty during early hours while converging to deterministic behavior later. The findings reveal a clear trade-off between economic performance and operational reliability as the confidence level increases, confirming the effectiveness of the proposed approach for integrating renewables and storage in modern HVDC grids, while also identifying important limitations regarding independence assumptions and scalability to larger systems. Full article

Saudi Arabia’s transition towards electric mobility requires scalable charging infrastructure that can reduce dependence on grid-supplied energy and support national sustainability objectives. This study presents a preliminary planning assessment of petrol-station rooftops in Riyadh and Jeddah for photovoltaic (PV)-supported electric vehicle charging. Gross rooftop areas were measured for an exploratory, non-probability sample of 30 petrol stations—20 in Riyadh and 10 in Jeddah—using Google Earth Pro. A retained aggregate PVsyst output was used as a historical technical reference, while HelioScope was used only for preliminary layout visualisation. The retained 5.968 GWh/year PVsyst result cannot be independently reproduced from the materials currently available to the authors because the complete station-level project files and meteorological inputs were not preserved. A low–central–high scenario framework accounts for usable rooftop fractions of 70–90%, irradiance multipliers and combined operational-retention assumptions. The assumption-based scenarios produce 3.572–5.640 GWh/year, with a central value of 4.536 GWh/year. The 25, 40, and 60 kWh charging cases are defined as battery-side energy delivered per session and, after applying a 95% charging efficiency, correspond to approximately 71,800–172,400 energy-equivalent sessions annually under the central scenario. The PV-only economic screening gives an LCOE of USD 0.053–0.113/kWh and a simple payback period of 6.8–19.0 years under a full-self-consumption assumption; it excludes chargers, battery storage, structural reinforcement, and grid upgrades. The findings support further field investigation but do not establish complete EV-charging-infrastructure feasibility, national potential, or grid-independent operation. Full article

This study assesses a hybrid photovoltaic/wind/diesel/battery-based renewable energy system in an off-grid area in Zambia. System performance was evaluated through simulation using different photovoltaic panel ratings, battery types, and dispatch strategies. Using the predictive dispatch strategy, the lowest-cost option was a combination of 445 W photovoltaic panels and Li-ion batteries, which had an estimated net present cost of approximately USD 3.857 million over a project lifetime of 25 years, yielding a unit energy cost of USD 0.151/kWh. This system was 73% less expensive than one using a diesel generator, with a 96.4% decrease in emissions and a 95% renewable fraction. Sensitivity analysis revealed that changes in battery price have the greatest impact on operating costs at approximately 12%. The combined methodology using HOMER Pro^®Version 3.18 for optimization and MATLAB/Simulink Version R2024b for partial verification of the PV sub-system provides a reproducible framework that can be tailored to other regions facing similar energy challenges. Beyond techno-economic optimization, this study informs sustainable rural electrification planning by evaluating reliability, cost, affordability, lifecycle emissions, and diesel reduction while supporting energy access and climate-resilient off-grid policies in Sub-Saharan Africa. Full article

Building-applied photovoltaics (BAPV) offer a practical retrofit pathway for the prefabricated social housing estates of Central and Eastern Europe. This study assesses the solar and photovoltaic potential of post-WW II housing districts in Poznań, Poland, through a multi-scale workflow spanning the city, district, and building levels. Measured municipal rooftop data for 336 residential buildings in four districts were combined with tree-inclusive parametric solar simulations, calibrated against the rooftop solar cadastre (normalized mean bias error of +0.4% after calibration), to select South Winogrady and two representative buildings. Three PV design scenarios were then evaluated in roof, facade, and combined configurations using a techno-economic model that incorporates manufacturer-warranted degradation, maintenance, inverter replacement, and a ±25% electricity price and installation cost sensitivity envelope. Roof configurations pay back in 6.6–7.2 years (30-year return on investment of 286–321%), facade systems are economically defensible only on the best-exposed surfaces (8.9–13.8 years), and all configurations remain profitable even under the pessimistic bounding case. ENVI-met simulations of the same scenarios show localized pedestrian-level reductions in the Universal Thermal Climate Index of up to 2.41 °C near the PV-equipped buildings, persisting under 2050 climate projections. The results provide a transferable evidence chain for prioritizing BAPV retrofits in standardized post-war housing stock. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article