Renewable Energy Focus最新文献

Renewable energy is not only a viable economic choice in Palestine, but it is also an imperative requirement to end the country's current energy crisis, which is particularly acute in the West Bank and Gaza Strip. The main focus of this study, which makes it the most thorough in its sector, is showcasing Palestine's distinct renewable energy potentials (thermal solar, PV, wind, biomass, and hydropower). The System Advisor Model software (SAM) was used to predict the power potentials for a year. The results indicate that Palestine has a significant potential for PV power generation within 1,700 kWh/kWp. Wind energy can see a considerable difference in capacity, with a mean power density in the high mountains of WB of 600 W/m², a mean power density for all of WB of 300 W/m², and a relatively low power density for GS of less than 100 W/m². Options for investments in the high seas and with the nearby Arabic nations were also offered. About 1,717 GWh of energy equivalent comes from biomass resources. It is determined that the best designed system can produce 82 % of the total while only 18 % is purchased from the grid using HOMER to retrieve the optimum on-grid hybrid energy system. Furthermore, only 70.7 % of the energy produced is consumed, with the remainder being sold back to the grid. Therefore, using renewable energy sources in addition to the grid is advised to cut costs and potentially generate income. Reduce reliance on fossil fuels and combat global warming, as well.

Battery energy storage systems can address energy security and stability challenges during peak loads. This study examines the integration of such systems for peak shaving in industries, whether or not they have photovoltaic capacity. The battery-sizing problem has been analyzed extensively. However, most analyses assume a specific battery operation strategy and ignore the impact of battery-charging schemes on system behavior. In this paper, the authors compare three different operation strategies for charging batteries in an industrial peak-shaving application based on historical demand data from a large electricity consumer in El Salvador. The three strategies are fast charging, time-based charging, and low-power threshold charging. The study analyzes the possible integration of a photovoltaic system with two different sizes for a range of battery sizes (from 250 to 1,500 kWh capacity), examining optimal peak shaving levels, economic savings, and battery degradation. Results indicate that fast-charging reduces monthly billing but degrades batteries faster. The estimated lithium-ion battery lifespan is 10-10.5 years, with a maximum difference of five months. These differences could affect the life cycle economics due to the high costs of battery replacement. The economic savings achieved by the peak shaving operation of the storage system are not enough to compensate the battery investment in this study. However, other case studies with different load profiles or other locations with more expensive electricity tariffs could make the adoption of these systems economically viable.

Alkaline Water Electrolyzer (AWE) technology shows promising potential for shifting towards green hydrogen production. With the growing global interest in green hydrogen, understanding the dynamics of AWE systems becomes crucial to improving their performance. Therefore, this paper aims to provide a novel sensitivity analysis aspect to investigate the correlation within parameter variables associated with AWE's electrode separation media. These parameters include electrode-diaphragm gap, temperature, diaphragm thickness, and porosity, aiming to evaluate their impact on AWE current density. The methodology involves the development of a Computational Fluid Dynamics (CFD) model, conducting a parametric study, performing Analysis of Variance (ANOVA), and sensitivity testing within specified parameter ranges. The findings show that diaphragm porosity has a considerable effect, especially between 15 % and 60 % porosity, where the trend levels off at higher values. The electrode-diaphragm gap trend reveals a sensitive, nonlinear increase in cell current density as the gap decreases from its average, with a 75 % decrease yielding over 100 % higher current density, while adjustments beyond 10 mm have minimal impact on current density despite significant variations in other parameters. A 50 % temperature rise increases current density by 40 %, while a 50 % diaphragm width reduction modestly boosts current density by around 10 %. Understanding these sensitivities is vital for optimizing AWE's performance.

Combined heat and power economic dispatch (CHPED) based optimal power flow (OPF) problem has been studied in this article using a new, practical approach based on chaotic driving training optimization (DTBO) (CDTBO). In the proposed technique (CDTBO), the chaotic based learning is integrated with DTBO to overcome the local optimal problem and inferior convergence speed of the existing algorithms. OPF is an important concern to retain the power system running effectively. In order to meet the demand for reasonably priced power generation with optimal power flow in transmission lines, the authors combined CHPED and OPF. Since fuel is changing daily in the current environment, using renewable energy sources to generate electricity economically is crucial. The renewable energy source like wind energy is integrated with thermal units for economic power generation with reducing the thermal fuel consumption of CHPED-based OPF system. The proposed technique implemented on CHPED based IEEE-30 bus system for renewable and without renewable energy sources with considering different cases. The suggested problem considering with valve point loading of thermal units, transmission losses and uncertainties of wind speed to address the non-linearity of the renewable-based CHPED-OPF system. Cost minimization, voltage deviation control, transmission losses minimization and stability index are the single objectives of the prospective system. Furthermore tested on multi-objective functions for simultaneously minimization of cost with emission and simultaneously minimization of active power loss with voltage profile. It is observed that the proposed CDTBO technique helps to reduce the cost by 2% and 12.8% for renewable based system as compared to non-renewable system for multi-objective function. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.0035%. A comparison has been made with recent well known optimization techniques to address the superiority of the suggested CDTBO algorithm.

The world is endeavoring to transition to clean, renewable resource-based power generation systems such as wind and solar due to decreasing fossil fuel availability, a severe increase in power demand, environmental pollution, and global warming caused by unwanted gas emissions from current fossil fuel-based conventional power generation systems. An emerging technology, Microgrid (MG), has the capability to interface power generated from multiple distributed generation energy resources (DGER) such as wind, solar, fuel cells, biomass, geothermal, diesel engine generators, electric vehicles (EVs), and energy storage systems like batteries, compressed air storage, supercapacitors, and flywheels. This complexity in control and protection renders traditional power grids outdated. MGs are designed to operate in two modes: grid-connected mode (GCM) and islanded mode (IM), addressing power demand increases, transmission issues, and electricity storage challenges. MGs offer numerous advantages for power systems, yet their integration into distribution networks presents various challenges in protection systems, including fault current (FC) under different operating conditions, bidirectional fault current flow, unexpected relay tripping, disconnections due to sudden accidents, and cybersecurity issues. This paper provides a comprehensive review of various protection issues associated with MG and its possible solutions as suggested by various researchers.

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This study addresses the challenge of mitigating global warming by focusing on DC microgrids integrating renewable energy sources. The research specifically explores the modeling and nonlinear control design of DC microgrids featuring a novel renewable source called hybrid photoelectrochemical and voltaic cells (HPEV), alongside fuel cells and an energy storage system. The HPEV and fuel cells serve as primary sources, while the energy storage system includes a battery bank and ultracapacitor as secondary power sources. The primary objective is to derive a mathematical model for the considered DC microgrid, ensuring each power source maximizes output despite disturbances and varying climatic conditions. To optimize power extraction from HPEV, an artificial neural network is implemented. Subsequently, a nonlinear sliding mode control is applied to manage and stabilize the DC bus voltage, with global asymptotic stability confirmed through Lyapunov stability criteria. Additionally, the study introduces an energy management algorithm for effective power management within the microgrid. The system’s efficiency is validated through MATLAB Simulink simulations under variable load demands, comparing the results with those obtained from a Lyapunov redesign controller. The study concludes with real-time hardware-in-loop experiments, further validating the system’s performance and comparing experimental results with simulated outcomes.

Demand Side Management (DSM) is an effective strategy for balancing the supply and demand of electricity and improving the reliability of the smart grid by addressing current grid constraints. In this study, a novel methodology that leverages artificial intelligence and computer science techniques is proposed to solve the problem of cooperative energy demand planning. Specifically, Constraint programming (CP) is used to minimize the Peak-to-Average ratio (PAR), optimize cost savings, and ensure secure energy trading within a community of heterogeneous smart homes. To guarantee the integrity of information exchanges during energy trading, a blockchain structure is employed. The efficiency and computing performance of the CP method are compared with Mixed integer programming (MIP) solutions for a range of load profiles. Simulations demonstrate that both techniques effectively handle the proposed scheduling of collective smart buildings in a community of up to 100 smart homes. In particular, both approaches can effectively reduce the cost of electricity by 10% and 7% respectively, and lower PAR by 25%. However, the CP algorithm outperforms the MIP-based solutions in terms of efficiency and speed in dealing with large-scale optimization issues.

With an increasing share of renewable energies, flexibility is becoming an important factor to ensure the German electricity grid stability. Therefore, decision-makers have to deal with the implementation of the necessary transformations and adaptions. To provide an indication, a cost–benefit analysis and comparison of grid-stabilizing energy flexibility options and their applications for a current and an outlook scenario is conducted. In the results, due to its low costs, the flexibilization of existing CHP (combined heat and power generation) plants stands out as a particularly attractive option. Similarly, the flexibilization of existing wind turbines and photovoltaic systems, as well as the flexibilization of small heat pumps, appear to be highly attractive in the outlook (integration is enabled by pooling). Furthermore, in the future, large-scale batteries and PEM (Polymer electrolyte membrane) electrolysis as well as controlled charging (pooling) will become increasingly beneficial for the energy system. The overall worst performing flexibility option is power-to-methane, which requires additional effort from the system in both scenarios. In general, the research showed that even for similar flexibility options, the resulting systemic benefit highly depends on the specific flexibility application. Nonetheless, the results give an indication towards important sources of flexibility and further research priorities.

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by discussing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies and above-average electric utility rates.

Indonesia possesses the greatest potential in the world for bio-compressed natural gas (bio-CNG). This bio-CNG is purified from biogas that is generated from decomposing organic liquid waste at palm oil mills (POMs). Unfortunately, this great potential has not been utilized much due to several obstacles such as the remote location of POMs, transportation mode, and utilization purposes. These factors have a significant impact on the feasibility of bio-CNG selling prices. However, a case study conducted in the Riau province of Indonesia showed that by clustering POMs, selecting appropriate transportation modes, and generating additional income from carbon trading, the feasibility for bio-CNG selling price can be achieved. The study resulted in obtaining two clusters of seven POMs that were spread over a 30km radius from Pekanbaru City. Bio-CNG has a potential to substitute 97 % of gasoil for bus fuel, and trucking transportation was found to result in the lowest bio-CNG selling price, which was 10.7 USD/MMBTU. With additional income from carbon trading, the minimum price can reach 4.84 USD/MMBTU. The utilization of bio-CNG contributes to reducing greenhouse gas emissions, which, in turn, can increase the economic value of bio-CNG. The pattern of usage and the selection of the factors mentioned above should be considered when utilizing bio-CNG resulting from biogas-POME purification. It is still necessary to provide supportive policies from the government.