Search Results (141)

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs. Full article

With the large-scale integration of photovoltaic power plants—comprising power electronic devices—into power systems, electromagnetic transient simulation has become a key tool for ensuring power system security and stability. The accuracy of photovoltaic unit controller parameters is crucial for the reliability of such simulations. However, as the issue of sub/super-synchronous oscillations becomes increasingly prominent, existing parameter identification methods are primarily based on high/low voltage ride-through characteristics. This limits the applicability of the identification results to specific scenarios and lacks targeted simulation and parameter identification research for sub/super-synchronous oscillations. To address this gap, this study proposes a mathematical model tailored for sub/super-synchronous oscillations and performs sensitivity analysis of converter control parameters to identify dominant parameters across different frequency bands. A frequency-segmented parameter identification method is introduced, capable of fast convergence without relying on a specific optimization algorithm. Finally, the proposed method’s identification results are compared with actual values, voltage ride-through-based identification, particle swarm optimization results, and results under uncertain conditions. It was found that, compared with traditional identification methods, the proposed method reduced the maximum identification error from 7.67% to 4.3% and the identification time from 2 h to 1 h. The maximum identification error of other intelligent algorithms was 5%, with a difference of less than 1% compared to the proposed method. The identified parameters were applied under conditions of strong irradiation (1000 W/m²), weak irradiation (300 W/m²), rapidly varying oscillation frequency, and constant oscillation frequency, and the output characteristics were all close to those of the original parameters. The effectiveness and superiority of the proposed method have been validated, along with its broad applicability to different intelligent algorithms and its robustness under uncertain conditions such as environmental variations and grid frequency fluctuations. Full article

This article is devoted to the study of a 20 MW large-scale photovoltaic power plant (LS-PVPP), connected to the grid and located in Naàma, Algeria. The power plant is included in the National Program for the Development of Renewable Energies 2015–2030. Among the parameters analyzed in detail in this work, the performance ratio recorded an average value of 67.55%, the capacity factor had an average of 17.10%, the total losses had an average of 2.10 kWh/kWp/day, the system efficiency had an average of 4.10 kWh/kWp/day and an annual average of 9.84% of the efficiency. A linear regression equation with a coefficient of determination R² of 0.91 confirms the importance of irradiation impact in the region; less significant linearity for the effect of temperature with a coefficient of determination R² = 0.28 is recorded for production. A comparative study conducted with the Adrar plant (Algeria) with an extremely hot desert climate and the Saida plant (Algeria) with a semi-arid climate demonstrated that the efficiency of the Naàma station is equal to 91.22% of the efficiency of Adrar and 73.47% of the efficiency of Saida. Naàma is known for its semi-arid climate; it is very cold in winter and hot in summer, with sandstorms becoming more frequent due to climate change. PVsyst software (Version 7.4.8) is used to validate the results. Full article

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints. Full article

The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened the volatility and unpredictability of the power system. Consequently, this presents formidable challenges to ensuring the reliable operation of the grid. This study introduces a novel stacked model for photovoltaic power prediction, integrating multiple conventional data processing methods as base learners, including Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), and Emotional Neural Network (ENN). A Backpropagation Neural Network (BPNN) serves as the meta-learner, utilizing the outputs of the base learners as input features to enhance overall prediction accuracy by mitigating individual model errors. To assess the model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates and McCabe Index (LM), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), ensuring long-term stability in photovoltaic power output forecasting. Additionally, the model’s effectiveness and accuracy are validated using operational data from photovoltaic power plants in a particular province of China. The results indicate that the stacked model, after training, testing, and validation on multiple performance metrics, surpasses baseline single models in performance. Full article

In Europe, renewable energy sources such as photovoltaic panels and wind power plants are developing dynamically. The growth of renewable energy is driven by rising energy prices, greenhouse gas emission restrictions, the European Union’s Green Deal policy, and decarbonization efforts. Photovoltaic farms generate energy intermittently, depending on weather conditions. Given the increasing number of new installations, ensuring the power balance and transmission capacity of the electrical grid has become a major challenge. To address this issue, the authors propose a technical solution that allows the energy generated by photovoltaic systems to be stored in the form of heat. Thermal energy from solar power and wind energy offers significant potential for energy storage. It can be accumulated during summer in specially designed sand-based heat storage systems and then used for heating purposes in winter. This approach not only reduces heating costs but also decreases greenhouse gas emissions and helps balance the power grid during sunny periods. Post-industrial areas, often located near city centers, are suitable locations for large-scale heat storage facilities supplying, among others, public utility buildings. Therefore, this article presents a concept for utilizing high-temperature sand-based heat storage systems built in decommissioned underground mining excavations. Full article

Shared energy storage (SES) and some photovoltaic prosumers (PVPs) are difficult to aggregate by the virtual power plant (VPP) in the short term. In order to realize the optimal operation of the VPP in the incomplete resource aggregation environment and to promote the mutual benefit of multiple market entities, the energy trading strategy based on the hybrid game of SES–VPP–PVP is proposed. Firstly, the whole system configuration with incomplete resource aggregation is proposed, as well as the preconfigured market rules and the general problem for the optimal energy trading strategy of VPP. Secondly, the novel hybrid game theory-based optimization for the energy trading strategy of VPP is proposed based on the multi-level game theory model. And, the corresponding solving process using Karush–Kuhn–Tucker (KKT), dichotomy, and alternating direction method of multipliers (ADMM) algorithms are also constructed to solve nonconvex nonlinear models. The effectiveness of the proposed strategy is verified through the comparison of a large number of simulation results. The results show that our proposed energy trading strategy can be used for optimal low-carbon operation of VPPs with large-scale renewable energy and some unaggregated electricity consumers and distributed photovoltaic stations, while SES participates as an independent market entity. Full article

The European Union (EU) has introduced a range of policies to promote energy efficiency, including setting specific targets for energy-efficient renovations across the EU building stock. This study provides a comprehensive environmental and economic assessment of energy-efficient renovation scenarios in a large-scale multifamily building project that is district-heated, considering both the building and the broader urban energy system. A systematic framework was developed for this assessment and applied to a real case in Sweden, where emission factors from energy production are significantly lower than the EU average: 114 g CO₂e/kWh for district heating and 37 g CO₂e/kWh for electricity. The project involved the renovation of four similar district-heated multifamily buildings with comparable energy efficiency measures. The primary distinction between the measures lies in the type of HVAC system installed: (1) exhaust ventilation with air pressure control, (2) mechanical ventilation with heat recovery, (3) exhaust ventilation with an exhaust air heat pump, and (4) exhaust ventilation with an exhaust air heat pump combined with photovoltaic (PV) panels. The study’s findings show that the building with an exhaust air heat pump which operates intermittently with PV panels achieves the best environmental performance from both perspectives. A key challenge identified for future research is balancing the reduced electricity production from Combined Heat and Power (CHP) plants within the energy system. Full article

In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluctuation of the outputs of wind turbines and distributed photovoltaic power plants by changing their robustness indicators, generates economic operating cost data for microgrids in multiple scenarios, and uses an improved grey correlation theory algorithm to analyze the correlation between new energy and various scheduling costs. Subsequently, a weighted analysis is performed on each correlation degree to obtain the correlation degree between new energy and total scheduling operating costs. The experimental results show that the improved grey correlation theory optimization scheduling algorithm for new energy microgrids proposed obtains weighted correlation degrees of 0.730 and 0.798 for photovoltaic power stations and wind turbines, respectively, which are 3.1% and 4.6% higher than traditional grey correlation theory. In addition, the equipment maintenance costs of this method are 0.413 and 0.527, respectively, which are 25.1% and 5.4% lower compared to the traditional method, respectively, indicating that the method effectively improves the accuracy of quantitative analysis. Full article

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals. Full article

Solar energy is a quick-producing source of energy in Saudi Arabia. Solar photovoltaic (PV) energy accounts for 0.5% of electricity output, with a total installed capacity of 9.425 GW and 9353 solar power plants of various types globally. Many solar power stations will be established on different sites in the coming years. The capacity of these stations reaches hundreds of megawatts. The primary aim of this study is to facilitate the strategic and systematic assessment of the solar energy resource potential that impacts both large and small-scale solar power projects in Saudi Arabia. This study describes in detail the analysis, simulation, and sizing of a 400 MW grid-connected solar project for the Riyadh, Saudi Arabia site using the PVSyst 8 software program. The software-generated trajectories primarily represent the performance of a PV system at a certain location. It provides data for the geographical position used by maps for component sizing, projecting the installation under extremely realistic conditions. The report further examines the system’s behavior with various tilt and orientation settings of the PV panel, which yields superior simulation results at equivalent latitudes for any practical sizing. Three types of PV modules with different sizes are used to design the solar plant. The main project was designed using 580 WP and was compared with 330 WP and 255 WP power modules. This study confirmed that high-power PV modules are more efficient than small modules. Full article

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality. Full article