Search Results (423)

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

Renewable Energy Communities (RECs) are voluntary coalitions of citizens, small and medium-sized enterprises and local authorities, which cooperate to share locally produced renewable energy, providing environmental, economic, and social benefits rather than profits. Despite a favorable European and Italian regulatory framework, their development is still limited in the Member States. To this end, this paper proposes a methodology to identify optimal spatial configurations of RECs, based on proximity criteria and maximization of energy self-sufficiency. This result is achieved through the mapping of the demand, expressive of the energy consumption of residential buildings; the suitable areas for installing photovoltaic panels on the roofs of existing buildings; the supply; the supply–demand balance, from which it is possible to identify Positive Energy Districts (PEDs) and Negative Energy Districts (NEDs). Through an iterative process, the optimal configuration is then sought, aggregating only PEDs and NEDs that meet the chosen criteria. This method is applied to the case study of the Avellino Province in the Campania Region (Italy). The maps obtained allow local authorities to inform citizens about the areas where it is convenient to aggregate with their neighbors in a REC to have benefits in terms of energy self-sufficiency, savings on bills or incentives at the local level, including those deriving from urban plans. The latter can encourage private initiative in order to speed up the RECs’ deployment. The presented model is being implemented in the framework of an ongoing research and development project, titled Renewable Energy Communities Monitoring, Optimization, and Planning (RECMOP). Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property. Full article

As an important unconventional oil and gas resource, tight oil exploration and development is of great significance to ensure energy supply under the background of continuous growth of global energy demand. Low-porosity and low-permeability reservoirs are characterized by tight rock properties, poor physical properties, and complex pore structure, and as a result the fine calculation of logging reservoir parameters faces great challenges. In addition, the crude oil in this area has high viscosity, the formation water salinity is low, and the oil reservoir resistivity shows significant spatial variability in the horizontal direction, which further increases the difficulty of oil and water reservoir identification and affects the accuracy of oil saturation calculation. Targeting the above problems, the Nutcracker Optimization Algorithm (NOA) was used to optimize the hyperparameters of the random forest classification model, and then the optimal hyperparameters were input into the random forest model, and the conventional logging curve and oil test data were combined to identify and classify the reservoir fluids, with the final accuracy reaching 94.92%. Compared with the traditional Hingle map intersection method, the accuracy of this method is improved by 14.92%, which verifies the reliability of the model for fluid identification of low-porosity and low-permeability reservoirs in the research block and provides reference significance for the next oil test and production test layer in this block. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

The use of a rock-bed accumulator for a short-term heat storage and air exchange in a building facility is an economical and energy-efficient technological solution to balance and optimize the energy supplied to the facility. Existing scientific studies have not addressed, as yet, the environmental impacts of using a rock bed for heat storage. The purpose of the research is the environmental life cycle assessment (LCA) of a heat storage system in a rock-bed accumulator supported by a photovoltaic installation. The boundaries of the analyzed system include manufacturing the components of the storage device, land preparation for the construction of the accumulator, the entire construction process, including transportation of materials, and its operation in cooperation with a horticultural facility (foil tunnel) during one growing season, as well as the photovoltaic installation. The functional unit in the analysis is 1 square meter of rock-bed accumulator surface area. SimaPro 8.1 software and Ecoinvent database were used to perform the LCA, applying the ReCiPe model to analyze environmental impact. The analysis showed the largest negative environmental impact occurs during raw materials extraction and component manufacturing (32.38 Pt). The heat stored during one season (April to October) at a greenhouse facility reduces this negative impact by approx. 7%, mainly due to the reduction in the use of fossil fuels to heat the facility. A 3 °C increase in average air temperature results in an average reduction of 0.7% per year in the negative environmental impact of the rock-bed thermal energy storage system. Full article

Aeration used in wastewater treatment is energy-intensive, subsequently increasing the cost of treatment. Aeration is used to supply oxygen that is required for bacterial metabolism that degrades organic compounds in wastewater. In this review, we will focus on the effect of aeration rates on the performance of biofilm-based technologies for wastewater treatment and the evaluation of the oxygen transfer rate (OTR) of these technologies. The performance of biofilm reactors in terms of removal efficiency increases with air flow rate, as increased flow helps to increase the contact area between wastewater and the biofilm on the carrier. The same is true for the OTR due to the greater availability of oxygen at higher airflow rates. Excessive aeration can negatively affect wastewater treatment through biofilm shearing and detachment from the carrier. Through a critical review of these technologies, the optimal air flow rate and aeration methods can be determined in biofilm reactors to improve the quality of the treated water, increase the efficiency of the aeration system, and attain energy savings. Full article

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

To explore the feasibility of renewable hybrid energy systems for expressway infrastructure, this study proposes a scenario-based design methodology integrating solar, wind, and hydropower resources within the expressway corridor. A case study was conducted on a highway service area located in southern China, where a solar/wind/hydro hybrid energy system was developed based on the proposed approach. Using the HOMER Pro 3.14 software platform, the system was simulated and optimized under off-grid conditions, and a sensitivity analysis was conducted to evaluate performance variability. The results demonstrate that the strategic integration of corridor-based natural resources—solar irradiance, wind energy, and hydrodynamic potential—enables the construction of a technically and economically viable hybrid energy system. The system includes 382 kW of PV, 210 kW of wind, 80 kW of hydrokinetic power, a 500 kW diesel generator, and 180 kWh of battery storage, forming a hybrid configuration for a stable and reliable energy supply. The optimized configuration can supply up to 1,095,920 kWh of electricity annually at a minimum levelized cost of energy of USD 0.22/kWh. This system reduces CO₂ emissions by 23.2 tons/year and NO_x emissions by 23 kg/year. demonstrating strong environmental performance and long-term sustainability potential. Full article

This study evaluates the feasibility of repurposing produced water—an abundant byproduct of hydrocarbon extraction—for green hydrogen production in Wyoming, USA. Analysis of geospatial distribution and production volumes reveals that there are over 1 billion barrels of produced water annually from key basins, with a general total of dissolved solids (TDS) ranging from 35,000 to 150,000 ppm, though Wyoming’s sources are often at the lower end of this spectrum. Optimal locations for hydrogen production hubs have been identified, particularly in high-yield areas like the Powder River Basin, where the top 2% of fields contribute over 80% of the state’s produced water. Detailed water-quality analysis indicates that virtually all of the examined sources exceed direct electrolyzer feed requirements (e.g., <2000 ppm TDS, <0.1 ppm Fe/Mn for target PEM systems), necessitating pre-treatment. A review of advanced treatment technologies highlights viable solutions, with estimated desalination and purification costs ranging from USD 0.11 to USD 1.01 per barrel, potentially constituting 2–6% of the levelized cost of hydrogen (LCOH). Furthermore, Wyoming’s substantial renewable-energy potential (3000–4000 GWh/year from wind and solar) could sustainably power electrolysis, theoretically yielding approximately 0.055–0.073 million metric tons (MMT) of green hydrogen annually (assuming 55 kWh/kg H₂), a volume constrained more by energy availability than water supply. A preliminary economic analysis underscores that, while water treatment (2–6% LCOH) and transportation (potentially > 10% LCOH) are notable, electricity pricing (50–70% LCOH) and electrolyzer CAPEX (20–40% LCOH) are dominant cost factors. While leveraging produced water could reduce freshwater consumption and enhance hydrogen production sustainability, further research is required to optimize treatment processes and assess economic viability under real-world conditions. This study emphasizes the need for integrated approaches combining water treatment, renewable energy, and policy incentives to advance a circular economy model for hydrogen production. Full article

The buffalo species (Bubalus bubalis) is crucial for the global economy, supplying high-nutritional-value animal proteins vital for children’s growth. These animals efficiently convert fiber into energy and thrive in various harsh environments, from frigid climates to hot, humid areas, including wetlands. They produce milk and meat while supporting the sustainability of ecosystems that other ruminants cannot inhabit. Buffalo offers a unique opportunity to supply resources for both rural communities and larger farms located in specific regions, such as marshlands and humid savannahs. They also thrive on extensive pastures and family farms, thus preserving biodiversity, habitats, and cultural practices. Intensive farming brings distinct challenges and is often criticized for its negative effects on climate change. To counter these impacts, multiple strategies have been researched and implemented. These include enhancing livestock genetics, adopting sustainable agricultural practices, optimizing local feed resources (including by-products), managing manure (with an emphasis on renewable energy), and improving animal health and welfare. This review explores various buffalo farming system applications in different global contexts. It is based on the hypothesis that the adaptable traits of buffalo, as well as the environmental and economic challenges that must be addressed for sustainability, are the key factors in determining the viability of such enterprises. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article