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As a deep connection between agriculture and energy, the rural integrated energy system (RIES) is a micro-scale supply-distribution- storage-demand network, which provides an important means to realize the utilization of rural clean energy. This paper proposes a day-ahead scheduling model of the RIES to improve its economical effectiveness, where three energy carriers, namely, biogas, electric power, and heat, are integrated. To address the source and load uncertainties composed of photovoltaic power, power load, and heat load, this paper develops a constrained distributionally robust optimization (CDRO), which optimizes the cost expectation related to the extreme distribution to enhance the robustness, while limiting the loss of cost expectation in the historical distribution to ensure economical effectiveness. In addition, an ambiguous set of the source and load uncertainties incorporating 1-norm and infinity-norm constraints is established, which realizes a flexible adjustment for the conservativeness of CDRO. The distributionally robust dispatch is formulated as a deterministic programming in a two-stage solving framework, where the subproblem uploads its extreme probability distribution to the master problem, and these two problems are iteratively optimized until the convergence. Finally, the numerical simulations in a modern farm park prove the performance of the constructed dispatch model and the flexibility of CDRO in balancing the economical effectiveness and robustness of the dispatch. [ABSTRACT FROM AUTHOR]

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach--the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model's characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals. [ABSTRACT FROM AUTHOR]

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid's security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections. [ABSTRACT FROM AUTHOR]

Returning monarch butterflies (Danaus plexippus) to sustainable levels of abundance will require an array of contributors to protect and restore habitat over broad areas. Due to the diversity and scale of land managed by electric power companies across the monarch range, plus an additional 32 million hectares needed for new solar arrays by 2050 to meet renewable energy goals, the industry may have potential to contribute to monarch conservation. However, it is challenging to clearly understand an individual company's potential for monarch conservation because of the scale and distribution of their specific land assets (ranging from 4,800 to 240,000 hectares in this study alone), the complexity of monarch science, and the lack of a science-based approach for evaluating large land assets for monarch habitat. With monarchs potentially being protected under the United States Endangered Species Act in the future and thereby limiting land management approaches, there is interest from electric power companies to understand how their lands relate to monarchs. In collaboration with companies, we developed a GIS-based model to identify company landholdings that contain high-quality monarch habitat and applied the model to specific landholdings of eight power companies in the United States. We then facilitated discussions with company teams to balance conservation goals, corporate risk, and social opinion. This paper describes non-confidential results for developing a national GIS-based monarch habitat model and applying it to electric power companies who are considering monarch conservation while simultaneously transitioning to a new clean energy future. The model and applied experience may be useful for other organizations working across large landscapes to manage monarchs. [ABSTRACT FROM AUTHOR]

In this paper, a two-layer optimization approach is proposed to facilitate the multi-energy complementarity and coupling and optimize the system configuration in an electric-hydrogen-integrated energy system (EH-IES). Firstly, an EH-IES with virtual energy storage is proposed to reduce the cost of physical energy storage equipment. Secondly, a two-layer optimal allocation method is proposed under a multi-timescale strategy to examine the comprehensive evaluation index of environmental protection and economy. The upper layer utilizes the NSGA-II multi-objective optimization method for system capacity allocation, while the lower layer performs economic dispatch at the lowest cost. Ultimately, the output includes the results of the equipment capacity allocation of the EH-IES that satisfies the reliability constraint interval and the daily scheduling results of the equipment. The results demonstrate that the electric-hydrogen-integrated energy system with the coupling of multiple energy equipment not only enhances the utilization of RENEWABLE energy sources but also reduces the usage of fossil energy and improves the system's reliability. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) has been widely used to improve the system inertia and damping in the renewable energy generation system. However, the in-depth understanding of VSG's stability under disturbances on different control parameters is lacked. In order to solve the problem, the small-signal model of single-VSG is established at first. The influences of key control parameters on the stability of system are analyzed by using the eigenvalue analysis method in detail. On this basis, a novel optimization strategy for control parameters is proposed based on the Particle Swarm Optimization (PSO) algorithm. The control parameters are optimized to realize excellent damping and stability of VSG system. Finally, the simulation and experimental results verify the effectiveness of stability analysis and parameter optimization strategy. [ABSTRACT FROM AUTHOR]

The surge in global energy demand mandates a significant expansion of electric power substations. Nevertheless, the ecological consequences of electric power substation operation, particularly concerning the electromagnetic field, on soil microbial communities and nitrogen enrichment remain unexplored. In this study, we collected soil samples from six distinct sites at varying distances from an electric power substation in Xintang village, southeastern China, and investigated the impacts of electromagnetic field on the microbial diversity and community structures employing metagenomic sequencing technique. Our results showed discernible dissimilarities in the fungal community across the six distinct sites, each characterized by unique magnetic and electric intensities, whereas comparable variations were not evident within bacterial communities. Correlation analysis revealed a diminished nitrogen fixation capacity at the site nearest to the substation, characterized by low moisture content, elevated pH, and robust magnetic induction intensity and electric field intensity. Conversely, heightened nitrification processes were observed at this location compared to others. These findings were substantiated by the relative abundance of key genes associated with ammonium nitrogen and nitrate nitrogen production. This study provides insights into the relationships between soil microbial communities and the enduring operation of electric power substations, thereby contributing fundamental information essential for the rigorous environmental impact assessments of these facilities. [ABSTRACT FROM AUTHOR]

The transition to renewable energy sources is critical for sustainable development, yet integrating these sources into existing power systems poses significant challenges. Energy Storage Systems (ESS) are essential in enhancing the reliability and efficiency of renewable energy systems. Despite growing research, a comprehensive scientometric analysis mapping development and trends in this field is lacking. This study addresses this gap by conducting a detailed scientometric analysis of power systems and new energy research from 2014 to 2023. The novelty of this study lies in its systematic use of advanced bibliometric tools to provide a thorough analysis of the research landscape. Utilizing 425 research articles from the Web of Science database, the study employs CiteSpace to visualize academic networks, identify research hotspots, and outline current trends. Specific methodologies include burst detection to identify significant shifts in research focus, centrality measurement to determine the influence of key studies, and heterogeneous network analysis to map the interconnectedness of various research themes. The analysis reveals extensive international collaborations, with China leading in publication volume (344 articles) and centrality (0.69), followed by the United States (29 articles, centrality 0.53). Significant contributions come from institutions like North China Electric Power University, China Electric Power Research Institute, and Tsinghua University. The findings underscore the importance of international cooperation and the need for broader geographical representation in this research field. This study provides valuable insights into the evolution and current state of power systems and renewable energy research, offering essential guidance for future research and development. The results highlight the critical role of ESS in the transition to renewable energy and suggest directions for future investigations, particularly focusing on enhancing ESS efficiency and reliability and expanding international research collaborations. [ABSTRACT FROM AUTHOR]

The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms. [ABSTRACT FROM AUTHOR]

The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency. [ABSTRACT FROM AUTHOR]

Power generation from fossil fuels is the biggest challenge in the next half of the century. Alternative power generation techniques such as solar photovoltaic (PV) show potential to act as a future fuel with a challenge to efficiently convert the harvested solar energy into electrical power. This investigation conclusively focused on setting a 2.160-kW solar PV system capable of working at a higher efficiency by developing a mechanical structure that optimizes power production and minimizes energy losses. In addition to that, solar PV system efficiencies at various tracking positions, performance coefficients during rainy and sunny days, and system degradation rates have also been investigated. The PVsyst v6.8 simulation tool was used to obtain the simulated results, which were compared with the actual experimental results. The parameters considered for the investigations include ambient temperature, irradiance, solar PV module surface temperature, solar PV voltage and current, wind velocity, and atmospheric turbidity. The solar PV system was evaluated based on two modes, namely, M1 (no tracking/fixed type) and M2 (manual tracking by changing the position of the solar PV system every hour). The predictive results obtained using PVsyst v6.8 concluded that total energy production from the installed system was 3,242 kWh/yr and 3,984 kWh/yr for M1 and M2, respectively. The performance ratio (PR), obtained from simulation, was 72% and 78% for M1 and M2, respectively, which was consistent with the experimental results, i.e., 70% and 72% for M1 and M2, respectively. Similarly, the power conversion efficiencies under standard temperature and conditions for both modes, simulated and experimental, were found to be 16.50% and 12.75%, respectively. The estimated degradation rate was observed in the range of -0.6% to -5.0%. [ABSTRACT FROM AUTHOR]

In order to achieve the Paris climate targets, the German government has developed phased plans and anchored these in an amendment to the Renewable Energy Sources Act (EEG). This not only defines expansion targets for renewable energy producers, but also the additional amounts of energy that must be available in the electricity grid in future if it is to supply the mobility and building heating sectors, which are currently still powered by fossil fuels. While the lack of grid transport capacities is recognized and discussed in the public debate, another known and critical hurdle is rarely presented here. This is the temporal distribution of power from renewable energies and its effects on the processes involved in controlling the grid balance. By analyzing a target scenario specified in the EEG for 2030 and another for 2040+, which a German economic research institute recommends to policymakers, the aim is to investigate whether their targets can be achieved under the conditions of the natural fluctuations occurring in Germany and the quantities of renewable energy that can be harvested with a reasonable use of resources. The focus is on simulating future generation curves using historical data in such a way that they show the typical weather-driven behaviour to be expected in Germany. Unavoidable Surplus generation is used as efficiently as possible with electricity and hydrogen storage systems. Both scenarios fall considerably short of their targets. CO2-free coverage of the demand specified for the long-term future is only possible with a quantity of generators that is at the limit of what is feasible. The reason for this is that the current plans are obviously based on an unrealistic picture of the temporal distribution of renewable energy output. Achieving the transformation goals in Germany could be much more complex than politicians currently believe and may require a change in strategy sooner or later. [ABSTRACT FROM AUTHOR]

A significant portion of wind power conversion systems worldwide comprise wind farms (WFs) that use Squirrel Cage Induction Generator (SCIG) and are directly linked to the power grid. In facilities that generate electrical energy at a moderate level, WFs are connected by means of distribution systems that use medium voltage (MV). It is not uncommon for such a system to produce a scenario in which the amount of electricity generated corresponds to the grid's transit volume. When a wind farm's wind power generation system is connected to a weak grid, the lack of potential control of the Point of Common Coupling (PCC) is a primary issue. This strategy is called a "Wind Farm with Weak Grid Connection." Therefore, the amalgamation of weak grids, fluctuating electricity from wind, and variations in load on the system cause disruptions in the PCC voltage, further degrading the Power Quality (PQ) and the WF stability. Either the control method at the production level or the compensating strategies at the PCC level can improve this situation. If wind farms are built on SCIG and are directly linked to the grid, it is essential to utilise the last substitute. The technology known as Custom Power Devices (CUPS), proved extremely helpful for this type of application. This study presents a compensation technique based on a specific CUPS device, known as the Unified Power Quality Compensator (UPQC), as a possible solution. The potential terminals of WF needed to be regulated, and the voltage fluctuations on the grid side required to be reduced, so a custom- made control strategy for the UPQC device was designed internally. The control of power, such as active and reactive in the UPQC's series and shunt converters, as well as the transmission of power via the UPQC DC-Link between converters, are the foundation of the internal control strategy that has been developed. Compared to other bespoke tactics that use reactive power, this strategy increases the UPQC's capability to provide compensation. The suggested study calculates THD using a FUZZY controller. The results are compared to PI controller results. Simulation findings show how the suggested compensating strategy can minimise THD values and improve wind farm power and stability. The simulations suggest that the proposed compensating strategy enhances WF power and stability. [ABSTRACT FROM AUTHOR]

The significant increase in the proportion of renewable energy access has led to a burgeoning and urgent demand for flexible resources within the power system, and the prominence of flexible resources on the demand side has been underscored. The existing research on demand-side response primarily focuses on addressing peak load challenges, while the optimization of demand response for accommodating renewable energy remains inadequate. Considering this issue, the present study initially examines the flexibility mechanism offered by demand-side resources and proposes a calculation approach for determining the adjustable capacity of demand-side flexible resources. Furthermore, considering the limitations of the load that can be shifted, transferred, and curtailed, we develop a collaborative optimization operational model for accommodating renewable energy with multiple types of demand response. Finally, based on the provincial power grid example, the simulation study verifies the efficacy of a multi-type demand response. The results demonstrate that shiftable and transferable loads can effectively facilitate the accommodation of new energy, and their optimal allocation capacity ratios are 5.8% and 2.3%, respectively. [ABSTRACT FROM AUTHOR]