Search Results (162)

Although the combination of solar power and electric vehicles is widely considered beneficial, practical applications reveal substantial variance. To determine the proportion of solar energy used for charging and to identify the main drivers of a high solar share, a dataset containing measured 5 min energy time series of 725 households with PV and EVs was analyzed. In the existing literature, this represents a novelty, as most studies in this field are simulation-based, rely on synthetic profiles, use lower time resolutions, or are based on questionnaires. The share of solar energy used for EV charging is highly dispersed and varies by about ±40% around a median of 60%. The analysis shows that clustering by preferred charging times has strong explanatory potential: at the median, EVs charged predominantly during the daytime achieve a solar share that is more than 40% higher than those charged in the evening. In the latter case, home battery storage increases the solar share by an average of 20 percentage points. A similar magnitude of a 25-percentage-point increase could be reached with solar surplus charging compared to uncontrolled charging. On average, households with PV, battery, and EVs cover more than 56% of their total demand with self-generated solar energy; with solar-adapted charging, median values exceed 77%. If a heat pump is used on site, the self-sufficiency decreases but can still reach median values above 45% and up to 61% for optimized households. Full article

Sustained demand growth and the increasing share of renewable energy sources pose challenges for the operation of modern electrical systems. The variability in wind and solar photovoltaic generation causes temporary imbalances between supply and demand, requiring the incorporation of energy management and storage strategies to guarantee supply. In this context, the need arises to develop optimization models that allow for efficient energy dispatch, minimizing costs and promoting the appropriate use of both conventional and renewable resources. This study formulated a time block dispatch optimization model implemented in the IEEE 24-node system, integrating thermal, hydroelectric, photovoltaic, wind, and energy storage systems. The methodology was based on DC power flows and was developed in MATLAB R2024b, incorporating nodal balance constraints, transmission and generation capacity limits, as well as the operating conditions of the storage systems. The model allowed for the evaluation of both energy and economic performance, validating its behavior under conditions of peak demand and renewable variability. The results demonstrate that the inclusion of energy storage systems allows for a reduction in high-cost thermal generation, optimizing demand coverage with a greater share of renewable energy. An average storage efficiency of 85.5% was achieved, and total system costs were reduced by USD 40,392.39 per day, equivalent to annual savings of USD 14.75 million. Furthermore, power flows remained below 85% of transmission capacity, confirming the proper operation of the grid. In this sense, the model fulfills the proposed objectives and proves to be a tool for energy planning and the technical-economic integration of storage in electrical networks. Full article

Desert–Gobi–wasteland regions possess abundant wind resources and are strategic areas for future renewable energy development and meteorological monitoring. However, existing studies have limited capability in addressing the highly complex and dynamic environmental characteristics of these regions. In particular, few modeling approaches can jointly represent terrain variability, solar radiation distribution, and wind-field characteristics within a unified framework. Moreover, conventional deep reinforcement learning methods often suffer from learning instability and coordination difficulties when applied to multi-agent layout optimization tasks. To address these challenges, this study constructs a multidimensional environmental simulation model that integrates terrain, solar radiation, and wind speed, enabling a quantitative and controllable representation of the meteorological monitoring network layout problem. Based on this environment, an Environment-Aware Proximal Policy Optimization (EA-PPO) algorithm is proposed. EA-PPO adopts a compact environment-related state representation and a utility-guided reward mechanism to improve learning stability under decentralized decision-making. Furthermore, a Global Layout Optimization Algorithm based on EA-PPO (GLOAE) is developed to enable coordinated optimization among multiple monitoring nodes through shared utility feedback. Simulation results demonstrate that the proposed methods achieve superior layout quality and convergence performance compared with conventional approaches, while exhibiting enhanced robustness under dynamic environmental conditions. These results indicate that the proposed framework provides a practical and effective solution for intelligent layout optimization of meteorological monitoring networks in desert–Gobi–wasteland regions. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article

Many nations have been investing in hydrogen energy in the most recent wave of development and numerous projects have been proposed, yet a substantial share of these projects remain at the conceptual or feasibility stage and have not progressed to final investment decision or operation. There is a need to identify initial potential sites for green hydrogen production from renewable energy on an objective basis with minimal upfront cost to the investor. This study develops a decision support system (DSS) for identifying optimal locations for green hydrogen production using solar and wind resources that integrate economic, environmental, technical, social, and risk and safety factors through advanced Multi-Criteria Decision Making (MCDM) techniques. The study evaluates alternative weighting scenarios using (a) occurrence-based, (b) PageRank-based, and (c) equal weighting approaches to minimize human bias and enhance decision transparency. In the occurrence-based approach (a), renewable resource potential receives the highest weighting (≈34% total weighting). By comparison, approach (b) redistributes importance toward infrastructure and social indicators, yielding a more balanced representation of technical and economic priorities and highlighting the practical value of capturing interdependencies among indicators for resource-efficient site selection. The research also contrasts the empirical and operational efficiencies of various weighting methods and processing stages, highlighting strengths and weaknesses in supporting sustainable and economically viable site selection. Ultimately, this research contributes significantly to both academic and practical implementations in the green hydrogen sector, providing a strategic, data-driven approach to support sustainable energy transitions. Full article

This study presents a cradle-to-grave Life Cycle Assessment (LCA) and cost analysis of next-generation low-temperature district heating networks based on water-condensed electric booster heat pumps. The research, conducted within the ENEA Portici Research Center framework, evaluates multiple case studies to assess environmental and economic sustainability. The system boundaries include heat generators (geothermal heat pump, solar thermal, and photovoltaic–thermal hybrid), network configurations (tree vs. ring), supply temperatures (25 °C vs. 45 °C), and renewable electricity shares (0–100%). Environmental impacts were quantified using the Environmental Footprint 3.1 method, focusing on key indicators such as climate change, resource use, and human toxicity. The results show that supply temperature is a critical factor: 45 °C scenarios lead to notably higher impacts, while network configuration has only marginal effects. Among generation technologies, the photovoltaic–thermal system proved the most sustainable, despite higher investment costs, whereas the solar thermal system displayed the largest environmental burden but lower costs. Geothermal systems showed intermediate performance, with notable impacts from mineral resource use. Renewable electricity integration consistently improved environmental outcomes, reducing climate change impacts by up to 10%. Storage system comparison revealed lithium iron phosphate (LFP) batteries as the most advantageous solution for electrical storage, and phase-change materials (PCM), particularly Rubitherm15, as the most environmentally favorable option for thermal storage, although traditional water tanks remain more cost-effective. Overall, the study highlights the crucial role of renewable integration and temperature optimization in enhancing the eco-efficiency of low-temperature district heating networks, providing guidelines for future sustainable design and deployment. Full article

Enhancing system strength to ensure voltage security has become a critical challenge for power systems with high penetration of renewable energy (RE). As China accelerates its clean-energy transition, the conventional grid dominated by synchronous generators is evolving into a dual-high system characterized by both high shares of wind–solar generation and extensive power-electronic interfaces. This shift fundamentally alters the mechanisms of voltage support, rendering traditional short circuit ratio (SCR) index inadequate for describing grid strength. To address this gap, this study proposes a multi-renewable-station short circuit ratio (MRSCR) index that quantitatively evaluates the voltage support strength of RE-dominated systems, and further analyzes the mechanism by which multiple agents on the generation and grid sides affect MRSCR, enhancing the generality and applicability of the proposed index. The MRSCR is further formulated as a voltage security constraint and integrated into an energy storage planning model considering multi-agent collaborative optimization. The proposed model jointly optimizes the siting and capacity configuration of grid-forming energy storage under voltage security constraints. Case studies on the IEEE 14-bus system and a real provincial grid show that incorporating the MRSCR indicator effectively enhances the system’s voltage support performance and operational resilience, achieving these improvements with only a 5.45% increase in daily operating cost compared with baseline planning results. The framework provides a practical offline tool for energy storage planning, enabling both enhanced renewable integration and improved voltage security. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

Large-scale renewable energy bases in desert regions face challenges of unstable output and inefficient utilization due to the fluctuating nature of wind and solar power. To address these issues, this study proposes an optimization model for shared hybrid electricity–hydrogen energy storage across multiple micro-energy systems. The model minimizes the total investment and operation cost under electricity–hydrogen coupling and system balance constraints, and an improved Shapley value method is introduced to ensure fair cost allocation among participants. A case study based on a desert renewable base shows that the proposed shared configuration reduces the total annualized cost by 10.36% and increases renewable energy utilization by 12.19% compared with independent electrical storage systems. These results demonstrate that shared hybrid storage can effectively enhance energy utilization and cost efficiency in large-scale renewable energy bases, providing a feasible approach for integrated power–hydrogen energy management. Full article

Against the backdrop of the full market integration of renewable energy, determining a reasonable proportion between medium- and long-term (MLT) contracts and spot trading has become a core issue in power market reform. Current Chinese policy requires that the share of MLT contracts should not be less than 90%, which helps ensure system security but may suppress the price discovery function of the spot market and limit renewable energy integration. This paper constructs a three-layer model: the first layer describes spot market clearing through Direct Current Optimal Power Flow (DC-OPF), yielding system energy prices and nodal prices; the second layer models bilateral contract decisions between generators and users based on Nash bargaining, incorporating risk preferences via a mean–variance framework; and the third layer introduces two evaluation indicators—contract penetration rate and economic proportion—and applies outer-layer optimization to search for the optimal contract ratio. Parameters are calibrated using coal prices, wind speed, solar irradiance, and load data, with numerical solutions obtained through Monte Carlo simulation and convex optimization. Results show that increasing the share of spot trading enhances overall system efficiency, primarily because renewable energy has low marginal costs and high supply potential, thereby reducing average market prices and mitigating volatility. Simulations indicate that the optimal contract coverage rate may exceed the current policy lower bound, which would expand spot market space and promote renewable energy integration. Sensitivity analysis further reveals that fuel price fluctuations, renewable output, load structure, and risk preferences all affect the optimal proportion, though the overall conclusions remain robust. Policy implications suggest moderately relaxing the constraints on MLT contract proportions, improving contract design, and combining this with transmission expansion and demand response, in order to establish a more efficient and flexible market structure. Full article

The current evolution of the energy context and progress in sustainable energy technologies are enabling the development of new energy supply systems for the residential sector. However, the techno-economic assessment of such energy systems is not straightforward and depends, among others, on the building type, its thermal insulation rate, and user patterns, as well as on the climatic conditions or energy and technology prices. This study therefore aims to develop an investment model for a typical UK household energy system that is applied to a diversity of scenarios to highlight the sensibility of the output results over stochastic input data such as electricity and heat demands, ambient temperature, and global solar irradiation. This dwelling diversity dataset is generated using a thermal–electrical demand model that uses stochastic techniques to model uncertainty. This contribution concludes with a discussion on how end-users can effectively take part in the energy transition while minimizing their energy bill and potentially generate long-term revenues. The main results show stable economic performance, with capital expenditure (CAPEX) ranging from GBP 15,400 to GBP 17,000 and NPV from GBP 21,000 to GBP 26,000 over 2000 individual scenarios. This study also confirms the leveraging effect of policy instruments, such as subsidies, in shifting the optimal system design towards higher shares of renewable and storage technologies, further reducing the reliance on fossil fuels and the impact on distribution systems. Full article

The rapid adoption of hydrogen fuel cell vehicles (HFCVs) in the Beijing–Tianjin–Hebei (BTH) hub accentuates the mismatch between renewable-based hydrogen supply in Hebei and concentrated demand in Beijing and Tianjin. We develop a mixed-integer linear model that co-configures a hydrogen pipeline network and optimizes hourly flow schedules to minimize annualized cost and CO₂ emissions simultaneously. For 15,000 HFCVs expected in 2025 (137 t d⁻¹ demand), the Pareto-optimal design consists of 13 production plants, 43 pipelines and 38 refueling stations, delivering 50 767 t yr⁻¹ at 68% pipeline utilization. Hebei provides 88% of the hydrogen, 70% of which is consumed in the two megacities. Hourly profiles reveal that 65% of electrolytic output coincides with local wind–solar peaks, whereas refueling surges arise during morning and evening rush hours; the proposed schedule offsets the 4–6 h mismatch without additional storage. Transport distances are 40% < 50 km, 35% 50–200 km, and 25% > 200 km. Raising the green hydrogen share from 10% to 70% increases total system cost from USD 1.56 bn to USD 2.73 bn but cuts annual CO₂ emissions from 142 kt to 51 kt, demonstrating the trade-off between cost and decarbonization. The model quantifies the value of sub-day pipeline scheduling in resolving spatial–temporal imbalances for large-scale low-carbon hydrogen supply. Full article

Multi-story residential buildings present distinct challenges for demand-side management due to shared infrastructure, diverse occupant behaviors, and complex load profiles. Although demand-side management strategies are well established in industrial sectors, their application in high-density residential communities remains limited. This study proposes a cost-optimized energy management framework for urban multi-story apartment buildings, integrating rooftop solar photovoltaic (PV) generation, shared battery energy storage, and flexible electric vehicle (EV) charging. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. EVs are modeled as flexible common loads under strata ownership, alongside shared facilities such as hot water systems and pool pumps. The optimization framework ensures equitable access to battery storage and prioritizes energy allocation from the most cost-effective source solar, battery, or grid on an hourly basis. Two seasonal scenarios, representing summer (February) and spring (September), are evaluated using location-specific irradiance data from Joondalup, Western Australia. The results demonstrate that flexible EV charging enhances solar utilization, mitigates peak grid demand, and supports fairness in shared energy usage. In the high-solar summer scenario, the total building energy cost was reduced to AUD 29.95/day, while in the spring scenario with lower solar availability, the cost remained moderate at AUD 31.92/day. At the apartment level, energy bills were reduced by approximately 34–38% compared to a grid-only baseline. Additionally, the system achieved solar export revenues of up to AUD 4.19/day. These findings underscore the techno-economic effectiveness of the proposed optimization framework in enabling cost-efficient, low-carbon, and grid-friendly energy management in multi-residential urban settings. Full article

A microgrid (MG) topology combines various kinds of resources like solar photovoltaic (PV) systems, wind turbines (WTs), energy storage systems, and the conventional utility grid. These different resources need to be coordinated in an optimal way to keep the power balanced, reduce the operational cost, and make the system resilient to any kind of failures. Therefore, an efficient energy management system (EMS) is essential in an MG system to provide suitable and reliable operation under different weather and demand load conditions. In this paper, a new EMS-based multi-objective Nizar Optimization Algorithm (NOA) is proposed. The suggested EMS aims to improve the power quality problem caused by the unpredictable nature of renewable energy sources and then minimize the grid power and battery degradation costs. By leveraging the adaptability of the NOA, the applied EMS method simply optimizes the allocation and energy sharing of the resources in a grid-connected MG. The proposed EMS was verified in simulation using MATLAB software. The performance of the proposed EMS was tested under different weather conditions, and the obtained results have been compared with those obtained in the existing methods. The obtained results indicate that the proposed EMS based on the NOA is capable of adjusting the multi-source energy allocation with minimal grid costs and the battery degradation issue. The proposed NOA indicates robust performance with total cost savings varying from USD 17 to USD 34 compared to other optimizers, as well as a great reduction in degradation cost, up to 27% improvement over the conventional methods. Finally, the proposed EMS offers several advantages over the conventional methods, including the improved dynamic system, faster convergence, lower operational costs, and higher energy efficiency. Full article

In DC microgrids, the combination of pulsed loads and renewable energy sources significantly impairs system stability, especially in highly dynamic operating environments. The resilience and reaction time of conventional proportional–integral (PI) controllers are often inadequate when managing the nonlinear dynamics of hybrid energy storage systems. This research suggests a frequency-decomposed fractional-order control strategy for stabilizing DC microgrids with solar, batteries, and supercapacitors. The control architecture divides system disturbances into low- and high-frequency components, assigning high-frequency compensation to the ultracapacitor (UC) and low-frequency regulation to the battery, while a fractional-order controller (FOC) enhances dynamic responsiveness and stability margins. The proposed approach is implemented and assessed in MATLAB/Simulink (version R2023a) using comparison simulations against a conventional PI-based control scheme under scenarios like pulsed load disturbances and fluctuations in renewable generation. Grey Wolf Optimizer (GWO), a metaheuristic optimization procedure, has been used to tune the parameters of the FOPI controller. The obtained results using the same conditions were compared using an optimal fractional-order PI controller (FOPI) and a conventional PI controller. The microgrid with the best FOPI controller was found to perform better than the one with the PI controller. Consequently, the objective function is reduced by 80% with the proposed optimal FOPI controller. The findings demonstrate that the proposed method significantly enhances DC bus voltage management, reduces overshoot and settling time, and lessens battery stress by effectively coordinating power sharing with the supercapacitor. Also, the robustness of the proposed controller against parameters variations has been proven. Full article