Search Results (613)

Jordan Vision prioritizes the utilization of domestic resources, particularly renewable energy. The transportation sector, responsible for 49% of national energy consumption, remains central to this transition and accounts for around 28% of total greenhouse gas emissions. Electric vehicles (EVs) offer a promising solution to reduce waste and pollution, but they also pose challenges for grid stability and charging infrastructure development. This study addresses a critical gap in the planning of renewable-powered EV charging stations along Jordanian highways, where EV infrastructure is still limited and underdeveloped, by optimizing the design of a hybrid energy charging station using HOMER Grid (v1.9.2) Software. Region-specific constraints and multiple operational scenarios, including rooftop PV integration, are assessed to balance cost, performance, and reliability. This study also investigates suitable locations for charging stations along the Sahrawi Highway in Jordan. The proposed station, powered by a hybrid system of 53% wind and 29% solar energy, is projected to generate 1.466 million kWh annually at USD 0.0375/kWh, reducing CO₂ emissions by approximately 446 tonnes annually. The findings highlight the potential of hybrid systems to increase renewable energy penetration, support national sustainability targets, and offer viable investment opportunities for policymakers and the private sector in Jordan. Full article

As states within the United States respond to future grid development goals, there is a growing demand for reliable and resilient nighttime generation that can be addressed by low-cost, long-duration energy storage solutions. This report studies the potential of including concentrating solar power (CSP) in the technology mix to support California’s goals as defined in Senate Bill 100. A joint agency report study that determined potential pathways to achieve the renewable portfolio standard set by the bill did not include CSP, and our work provides information that could be used as a follow-up. This study uses a capacity expansion model configured to have nodal spatial fidelity in California and balancing-area fidelity in the Western Interconnection outside of California. The authors discovered that by applying current technology cost projections CSP fulfills nearly 15% of the annual load while representing just 6% of total installed capacity in 2045, replacing approximately 30 GWe of wind, solar PV, and standalone batteries compared to a scenario without CSP included. The deployment of CSP in the results is sensitive to the technology’s cost, which highlights the importance of meeting cost targets in 2030 and beyond to enable the technology’s potential contribution to California’s carbon reduction goals. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

To effectively address the issues of curtailed wind and photovoltaic (PV) power caused by the high proportion of renewable energy integration and to promote the clean and low-carbon transformation of the energy system, this paper proposes a “chemical–mechanical” dual-pathway synergistic mechanism for the reversible solid oxide cell (RSOC) and flywheel energy storage system (FESS) electricity–hydrogen hybrid system. This mechanism aims to address both short-term and long-term energy storage fluctuations, thereby minimizing economic costs and curtailed wind and PV power. This synergistic mechanism is applied to regulate system operations under varying wind and PV power output and electricity–hydrogen load fluctuations across different seasons, thereby enhancing the power generation system’s ability to integrate wind and PV energy. An economic operation model is then established with the objective of minimizing the economic costs of the electricity–hydrogen hybrid system incorporating RSOC and FESS. Finally, taking a large-scale new energy industrial park in the northwest region as an example, case studies of different schemes were conducted on the MATLAB platform. Simulation results demonstrate that the reversible solid oxide cell (RSOC) system—integrated with a FESS and operating under the dual-path coordination mechanism—achieves a 14.32% reduction in wind and solar curtailment costs and a 1.16% decrease in total system costs. Furthermore, this hybrid system exhibits excellent adaptability to the dynamic fluctuations in electricity–hydrogen energy demand, which is accompanied by a 5.41% reduction in the output of gas turbine units. Notably, it also maintains strong adaptability under extreme weather conditions, with particular effectiveness in scenarios characterized by PV power shortage. Full article

The shift to renewable energy generation increases risks of frequency and voltage instability. This transition can cause significant voltage and frequency fluctuations during load changes, generation interruptions, and grid faults. One potential solution is the deployment of synchronous condensers to mitigate these issues; however, this approach may also increase operational and maintenance costs. To address this limitation, this paper proposes a method called the virtual synchronous condenser (VSCon) that enables renewable energy systems such as PV-solar energy systems or wind farms to emulate the behavior of synchronous condensers. Unlike traditional VSGs with simplified models, VSCon uses the mathematical equivalent circuit of a real synchronous condenser. This enables sub-transient and inertial behavior. Voltage support improves by adjusting sub-transient reactance, and frequency support enhances by tuning inertia and damping coefficients, thereby enhancing the local voltage and frequency stability. The proposed approach has been validated through case studies, demonstrating both its effectiveness and practicality. Full article

This paper focuses on the hydro–solar–wind complementary system, targeting two typical scenarios (both include PV output fluctuations driven by solar radiation intensity: wind power not participating in frequency regulation and wind power participating in frequency regulation) to conduct research on system frequency characteristic analysis and Proportional-Integral-Derivative (PID) controller parameter (K_P, K_I) optimization. By constructing a frequency response model that accounts for wind power penetration and output fluctuations, the dynamic regulation characteristics of the system under different scenarios are quantitatively analyzed. Given the limitations of single-objective optimization algorithms in balancing multiple performance indicators, the Decomposition-based Evolutionary Algorithm Guided by Growing Neural Gas (DEAGNG) multi-objective algorithm is introduced, with the Integral Time Absolute Error (ITAE) and the Integral Time Squared Error (ITSE) as the objective functions for parameter collaborative optimization. The results show that the optimization method based on DEAGNG can effectively improve the frequency stability of the system, reduce the mean value and maximum deviation of frequency fluctuations, and exhibit good adaptability in both scenarios. This study provides a multi-scenario-adapted PID parameter optimization scheme for hydro–solar–wind complementary systems, offering theoretical and technical support for achieving high-precision frequency control and enhancing the operational reliability of the system. Full article

Access to renewable energy is vital for rural development and climate change mitigation. The intermittency of renewable sources necessitates efficient energy storage, especially in off-grid applications. This study evaluates the technical, economic, and environmental performance of an off-grid hybrid system for the rural settlement of Soma, Turkey. Using HOMER Pro 3.14.2 software, a system consisting of solar, wind, battery, and hydrogen components was modeled under four scenarios with Cyclic Charging (CC) and Load Following (LF) control strategies for optimization. Life cycle assessment (LCA) and hydrogen leakage impacts were calculated separately through MATLAB R2019b analysis in accordance with ISO 14040 and ISO 14044 standards. Scenario 1 (PV + wind + battery + H₂) offered the most balanced solution with a net present cost (NPC) of USD 297,419, with a cost of electricity (COE) of USD 0.340/kWh. Scenario 2 without batteries increased hydrogen consumption despite a similar COE. Scenario 3 with wind only achieved the lowest hydrogen consumption and the highest efficiency. In Scenario 4, hydrogen consumption decreased with battery reintegration, but COE increased. Specific CO₂ emissions ranged between 36–45 gCO₂-eq/kWh across scenarios. Results indicate that the control strategy and component selection strongly influence performance and that hydrogen-based hybrid systems offer a sustainable solution in rural areas. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

Distributed energy systems have gained increasing popularity due to their plethora of benefits. However, their evaluation in terms of reliability mostly concerns the time frequency domain, and, thus, merits associated with the spatial scale are often overlooked. A recent study highlighted the benefits of distributed production over centralized one by establishing a spatial reliability framework and stress-testing it for decentralized solar photovoltaic (PV) generation. This work extends and verifies this approach to wind energy systems while also highlighting additional challenges for implementation. These are due to the complexities of the non-linear nature of wind-to-power conversion, as well as to wind turbine siting, and turbine model and hub height selection issues, with the last ones strongly depending on local conditions. Leveraging probabilistic modeling techniques, such as Monte Carlo, this study quantifies the aggregated reliability of distributed wind power systems, facilitated through the capacity factor, using Greece as an example. The results underscore the influence of spatial complementarity and technical configuration on generation adequacy, offering a more robust basis for planning and optimizing future wind energy deployments, which is especially relevant in the context of increasing global deployment. Full article

This study evaluates the technical, economic, and environmental feasibility of grid-connected and off-grid hybrid energy systems designed to meet the energy demands of a greenhouse facility. Various system configurations were developed based on combinations of solar, wind, diesel, and battery storage technologies. The analysis considers a daily electricity consumption of 369.52 kWh and a peak load of 52.59 kW for the greenhouse complex. Among the grid-connected systems, the grid/PV configuration was identified as the most optimal, offering the lowest Net Present Cost (NPC) of USD 282,492, the lowest Levelized Cost of Energy (LCOE) at USD 0.0401/kWh, and a reasonable emissions reduction of 54.94%. For off-grid scenarios, the generator/PV/battery configuration was the most cost-effective option, with a total cost of USD 1.19 million and an LCOE of USD 0.342/kWh. Environmentally, this system showed a strong performance, achieving a 64.58% reduction in CO₂ emissions; in contrast, fully renewable systems such as PV/wind/battery and wind/battery configurations succeeded in reaching zero-emission targets but were economically unfeasible due to their very high investment costs and limited practical applicability. Sensitivity analyses revealed that economic factors such as inflation and energy prices have a critical effect on the payback time and the Internal Rate of Return (IRR). Full article

Hybrid Renewable Energy Systems (HRESs) can be an effective and sustainable way to provide electricity for remote and rural villages in Morocco; however, the design and optimization of such systems can be a challenging and difficult task. In this context, the objective of this research is to design and optimize different (HRESs) that incorporate various renewable energy technologies, namely Photovoltaics (PVs), wind turbines, and Concentrating Solar Power (CSP), whereas biomass generators and batteries are used as a storage medium. Overall, 15 scenarios based on different HRES configurations were designed, simulated, and optimized by the HOMER software for the site of Ain Beni Mathar, located in eastern Morocco. Furthermore, the potential CO₂ emissions reduction from the different scenarios was estimated as well. The results show that the scenario including PVs and batteries is most cost-effective due to favorable climatic conditions and low costs. In fact, the most optimal HRES from a technical and economic standpoint is composed of a 48.8 kW PV plant, 213 batteries, a converter capacity of 43.8 kW, and an annual production of 117.5 MWh with only 8.8% excess energy, leading to an LCOE of 0.184 USD/kWh with a CO₂ emissions reduction of 81.7 tons per year, whereas scenarios with wind turbines, CSP, and biomass exhibit a higher LCOE in the range of 0.472–1.15 USD/kWh. This study’s findings confirm the technical and economic viability of HRESs to supply 100% of the electricity demand for rural Moroccan communities, through a proper HRES design. Full article

Renewable energy systems like solar and wind power are the main source of sustainable energy production; however, their intermittent nature produces challenges for grid integration, so they require realistic forecast models. This study developed a Long Short-Term Memory (LSTM) neural network model to predict solar irradiance and wind power over a 24 h horizon using a 240 h (10-day) dataset. The dataset, being hourly measurements of solar irradiance (W/m²) and wind speed (m/s), was divided and normalized into 193 sequences of 24 h each, with 80% for training and 20% for validation. Two LSTM models, each consisting of 100 hidden units, were trained using the Adam optimizer to predict the next 24 h for each of the variables using forget, input, and output gates to capture temporal dependencies. The results have shown that the model accurately forecasted solar irradiance with a clear day–night cycle, while forecasts of wind speed revealed higher variability, although the PV system was better than the wind system due to low wind speeds. The results reveal that the LSTM model can effectively predict renewable energy output by predicting the wind speed and Solar Irradiance, which are the main parameters that control the output power of wind turbines and PV power, respectively. Full article

The performance of lettuce, pepper, and chili pepper, and the biological soil quality, in a ground-mounted PV system under cultivation conditions typical of the Mediterranean environment of the Puglia region were evaluated. Microclimatic parameters, plant growth and yield response, soil quality assessed using the QBS-ar index, and land equivalent ratio (LER) were determined in three different cultivation areas: a cultivation area outside the photovoltaic plant but immediately adjacent to it (‘Control’); the inter-row area closest to the row of panels exposed to sunlight (‘Area close PV structure’); the inter-row area distant from the row of panels (‘Area distant PV structure’). Cumulated solar radiation, in particular during the summer growing cycles, was only slightly affected in the Area distant PV structure (1616 and 2130 MJ m⁻² for pepper and chili pepper, respectively, in the control area, in comparison to 1630 and 2044 MJ m⁻², in the Area distant PV structure), while it was strongly reduced in the Area close PV structure (883 and 1091 MJ m⁻² for pepper and chili pepper, respectively). In general, a reduction in air temperature and wind speed, as well as an increase in relative air humidity, was observed under PV conditions. On average, the evapotranspirative demand was reduced in the PV growing conditions compared to open field, with a more relevant effect in the sub-zone close to the photovoltaic structures, where cumulative ET0 was 28% and 34% lower than the Control in the pepper and chili pepper growing cycle, respectively. Lettuce growth was impaired by PV cultivation conditions, with an average reduction of 15% in plant height and 37% in marketable yield per plant, with no significant differences between the two sub-zones in the PV system. For pepper, the best growing conditions were observed in open field control compared to PV, but with differences related to the PV sub-zone. The plants grown in the Area distant PV structure were more negatively affected by the modified growing conditions, showing the lowest shoot and fruit fresh weight, the latter reduced by 51% compared to the Control; intermediate values were observed for these parameters in the Area close PV structure, with a less severe tendency to yield reduction. For chili pepper, both shoot and fruit fresh weight were lower in PV conditions, regardless of the sub-zone, with a reduction of 82% in yield per plant compared to the Control. However, despite the yield reductions, the LER was improved (1.60 and 1.40 in case of a lettuce + pepper or lettuce + chili pepper annual cropping program, respectively), highlighting a more efficient use of land, without negative or even ameliorative impacts on biological soil quality and biodiversity in terms of QBS-ar and microarthropods taxa abundance. Knowledge of the response of different crops under cultivation conditions typical of specific environments is necessary to define optimal cropping programs aimed at maximizing resource-use efficiency and land use. Full article

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy. Full article