Search Results (10,490)

The rapid growth of renewable energy and increasing electricity demand pose challenges to the reliability and flexibility of traditional distribution networks. To address these issues, the construction of AC/DC hybrid distribution networks (AC/DC-HDNs) based on existing AC grids has become a promising solution. However, planning the expansion of such networks faces challenges like complex device and line topologies, dynamic fluctuations in distributed generation (DG) and load, and high power electronics costs. This paper proposes a time- and space-integrated expansion planning method for AC/DC-HDNs. The approach builds a distribution grid model based on graph theory, integrating the spatial layouts of AC distribution lines, DGs, main grids, and loads, while capturing dynamic load and renewable energy generation characteristics through time-series analysis. A modified graph attention network (MGAT)-based deep reinforcement learning (DRL) algorithm is used for optimization, balancing economic and reliability objectives. The simulation results show that the modified algorithm outperforms traditional algorithm in terms of both training efficiency and stability, with a faster convergence and lower fluctuation in cumulative rewards. Additionally, the proposed algorithm consistently achieves higher cumulative rewards, demonstrating its effectiveness in optimizing the expansion planning of AC/DC-HDNs. Full article

In this study, we conducted experimental and numerical investigations of a Darrieus rotor with asymmetrical blades, which has two structural configurations—with and without horizontal parallel plates. Experimental tests were conducted in a wind tunnel at various air flow velocities (ranging from 3 m/s to 15 m/s), measuring rotor rotation frequency, torque, and thrust force. The computational simulation used the ANSYS 2022 R2 Fluent software package, where CFD simulations of air flow around both rotor configurations were performed. The calculations employed the Realizable k-ε turbulence model, while an unstructured mesh with local refinement in the blade–flow interaction zones was used for grid generation. The study results showed that the rotor with horizontal parallel plates exhibits higher aerodynamic efficiency at low wind velocities compared to the no-plates rotor. The experimental findings indicated that at wind speeds of 3–6 m/s, the rotor with plates demonstrates 18–22% higher torque, which facilitates the self-start process and stabilizes turbine operation. The numerical simulations confirmed that horizontal plates contribute to stabilizing the air flow by reducing the intensity of vortex structures behind the blades, thereby decreasing aerodynamic drag and minimizing energy losses. It was also found that the presence of plates creates a directed flow effect, increasing the lift force on the blades and improving the power coefficient (Cp). In the case of the rotor without plates, the CFD simulations identified significant low-pressure zones and high turbulence regions behind the blades, leading to increased aerodynamic losses and reduced efficiency. Thus, the experimental and numerical modeling results confirm that the Darrieus rotor with horizontal parallel plates is a more efficient solution for operation under low and variable wind conditions. The optimized design with plates ensures more stable flow, reduces energy losses, and increases the turbine’s power coefficient. These findings may be useful for designing small-scale wind energy systems intended for areas with low wind speeds. Full article

The generation output of distributed power sources and the load possess periodic changes, which cause stability problems in the operation of the power grid. To ensure stability, energy storage devices are generally installed in the power grid, but their effectiveness is also limited by their fixed installation location and capacity. In this paper, to overcome the drawback of stationary energy storage devices, mobile energy storage devices are introduced to reduce power losses and enhance voltage stability. The installation location and capacity of these mobile energy storage devices can be changed with the generation output and load demands. Firstly, the influence of the mobile energy storage devices on power losses and voltage stability is analyzed. Next, a multi-objective optimization model is established to select the optimal installation location and the ideal capacity, which has optimization objectives including the improvement of voltage stability, the minimization of power losses, and the enhancement of utilization of energy storage devices. Finally, the particle swarm optimization algorithm is applied to solve the multi-objective optimization problem. The simulation results prove that the novel method of this paper enhances the dynamics of the power grid, i.e., the voltage vulnerability and power losses are reduced by 29% and 36%, respectively. In addition, it also increases the utilization rate of energy storage devices by 33.5% in different operating scenarios. Full article

Endogeneity is an important issue that needs to be addressed in research. By integrating infrastructure into the input–output system based on a profit function framework, this paper investigates the impact of electricity infrastructure on economic development and energy consumption. Using city-level data from Anhui Province spanning 2012 to 2022 and applying seemingly unrelated regression techniques for parameter estimation, this study finds that an increase in grid density leads to a reduction in energy consumption. While the short-term effect of increased grid density may cause a decline in output, a positive long-term effect on output is observed. This study concludes that the advantages of robust power infrastructure in lowering energy intensity manifest only over an extended time horizon. Based on our findings, we provide relevant recommendations that can be applied to other regions as well. Full article

This study investigated the sustainability aspects of implementing a small-scale biogas digester project at the EARTH Centre, a horse-riding facility for the disabled, in South Africa. Firstly, an energy audit of the facility was conducted. From this exercise, energy-saving opportunities through anaerobic digestion of horse manure were identified. Biomethane potential tests (BMPs) were then performed using the Automatic Methane potential test system II (AMPTS II) of BioProcess Control (Lund, Sweden). The horse manure BMP result was 106 L/kg.VS with the biogas averaging a methane content of 40%. This BMP was lower than that of common substrates such as cow manure which can range from 150–210 L/kg.VS. The gas production rate was almost constant in the first 13 days indicating a long hydrolysis period for horse manure. The microbial species in the digester did not change much during the incubation period although small changes were visible in the proportions of each species as the reaction progressed from start to finish. The energy audit showed that 47% of the EARTH Centre’s energy requirements, which equated to 14,372 kWh/year, could be secured from biogas or solar instead of obtaining it from the national grid which is powered mainly by unsustainable coal-fired systems. As a starting point, a 10 cubic meter biogas digester was installed to produce 5512 kWh of energy per year in the form of biogas. To boost biogas production and continue running the system smoothly, it was evident that the horse manure-fed digester would require regular spiking with cow manure as a bioaugmentation strategy. The digester also produced bio-fertiliser and several sustainable development goals were fulfilled by this project. Current efforts are focused on process optimization of this technology at the Earth Centre to further improve the sustainability of the whole business. Full article

The increasing integration of renewable energy sources (RESs) introduces significant uncertainties in both generation and demand, presenting critical challenges to the convergence, feasibility, and real-time performance of optimal power flow (OPF). To address these challenges, a multi-agent deep reinforcement learning (DRL) model is proposed to solve the OPF while ensuring constraints are satisfied rapidly. A heterogeneous multi-agent proximal policy optimization (H-MAPPO) DRL algorithm is introduced for multi-area power systems. Each agent is responsible for regulating the output of generation units in a specific area, and together, the agents work to achieve the global OPF objective, which reduces the complexity of the DRL model’s training process. Additionally, a graph neural network (GNN) is integrated into the DRL framework to capture spatiotemporal features such as RES fluctuations and power grid topological structures, enhancing input representation and improving the learning efficiency of the DRL model. The proposed DRL model is validated using the RTS-GMLC test system, and its performance is compared to MATPOWER with the interior-point iterative solver. The RTS-GMLC test system is a power system with high spatial–temporal resolution and near-real load profiles and generation curves. Test results demonstrate that the proposed DRL model achieves a 100% convergence and feasibility rate, with an optimal generation cost similar to that provided by MATPOWER. Furthermore, the proposed DRL model significantly accelerates computation, achieving up to 85 times faster processing than MATPOWER. Full article

The aim of this paper is to develop a self-adaptive control methodology capable of optimizing in real-time the operation of PV-powered microgrids by dynamically managing both the output powers of battery energy storage systems (BESSs) and power exchanges with the utility grid. Control actions are evaluated by solving a constrained multi-objective optimization problem that integrates the optimal state-of-charge (SoC) management of BESSs within a broader economic dispatch framework. In this way, the SoC is continuously optimized alongside other economic objectives, such as minimizing operating costs and maximizing revenues from energy sales to the grid, while maintaining the microgrid’s energy balance. This ensures that BESSs operate efficiently within their optimal ranges, preventing premature depletion or overload and thereby safeguarding overall microgrid performance. To enable real-time adaptability, the methodology employs a Lyapunov-based optimization algorithm combined with a sensitivity analysis, ensuring rapid convergence to optimal solutions, even under rapidly changing conditions. Computer simulations performed on a low-voltage PV-BESS-based microgrid under different operating conditions confirm the effectiveness of the proposed methodology in enhancing real-time economic performance, operational efficiency, and microgrid reliability. Full article

In light of the soaring growth of pumped hydro energy storage (PHES) plants in China in recent years, there is an urgent need for a comprehensive understanding of their developmental trajectory and the identification of their multidimensional impacts. This paper reviews the development of PHES in China and highlights its various impacts. Despite the relatively late start of PHES development in China, the country has recently ranked first worldwide with an aggregated installed capacity of 50.94 GW in operation in 2023. These plants are primarily distributed in North China, East China, and South China, contributing to the safe and stable operation of regional power grids. Furthermore, over 300 plants are under construction or in the planning stage across the whole country, aiming to support large-scale renewable energy development and facilitate a sustainable energy transition. However, it is important to recognize that such extensive PHES development requires significant land resources, which can lead to disturbances in local ecosystems and affect nearby residents. Additionally, environmental emissions may arise from a life-cycle perspective. Finally, several countermeasures are proposed to enhance sustainable PHES development in China. They include strengthening the rational planning of new plants to optimize their spatial distribution, refining the engineering design of new plants, and exploring avenues for sharing the benefits of PHES development with a broad spectrum of local residents. Full article

The integration of Distributed Energy Resources (DERs) into power grids presents significant challenges to grid performance, requiring innovative solutions for effective operation. Dynamic Operating Envelopes (DOEs) offer a promising approach by optimizing the use of existing infrastructure while ensuring compliance with network constraints. This paper reviews various DOE calculation methodologies, focusing on Optimal Power Flow (OPF)-based methods. Key findings include the role of DOEs in optimizing import and export limits, with critical factors such as forecast accuracy, network modelling, and the effects of mutual phase coupling in unbalanced networks identified as influencing DOE performance. The paper also explores the integration of DOEs into smart grid frameworks, examining both centralized and decentralized control strategies, as well as their potential for providing ancillary services. Challenges in scaling DOEs are also discussed, particularly regarding the need for accurate forecasts, computational resources, communication infrastructure, and balancing efficiency and fairness in resource allocation. Lastly, future research directions are proposed, focusing on the practical application of DOEs to improve grid performance and support network operations, as well as the development of more robust DOE calculation methodologies. This review provides a comprehensive overview of current DOE research and identifies avenues for further exploration and advancement. Full article

Due to the volatility and uncertainty of wind power generation, energy storage can help mitigate the fluctuations in wind power grid integration. During its use, the health of the energy storage system, defined as the ratio of the current available capacity to the initial capacity, deteriorates, leading to a reduction in the available margin for power fluctuation smoothing. Therefore, it is necessary to predict the state of health (SOH) and adjust its charge/discharge control strategy based on the predicted SOH results. This study first adopts a Genetic Algorithm-Optimized Support Vector Regression (GA-SVR) model to predict the SOH of the energy storage system. Secondly, based on the health prediction results, a control strategy based on the model predictive control (MPC) algorithm is proposed to manage the energy storage system’s charge/discharge process, ensuring that the power meets grid integration requirements while minimizing energy storage lifespan loss. Further, since the lifespan loss caused by smoothing the same fluctuation differs at different health levels, a fuzzy adaptive control strategy is used to adjust the parameters of the MPC algorithm’s objective function under varying health conditions, thereby optimizing energy storage power and achieving the smooth control of the wind farm grid integration power at different energy storage health levels. Finally, a simulation is conducted in MATLAB for a 50 MW wind farm grid integration system, with experimental parameters adjusted accordingly. The experimental results show that the GA-SVR algorithm can accurately predict the health of the energy storage system, and the MPC-based control strategy derived from health predictions can improve grid power stability while adaptively adjusting energy storage output according to different health levels. Full article

Neutral transmission line models are essential for analyzing stability and periodicity in systems influenced by nonlinear and delayed dynamics, particularly in modern smart grids. This study utilizes Krasnoselskii’s fixed-point theorem to establish sufficient conditions for the existence and asymptotic stability of periodic solutions, eliminating the need for differentiability in delay terms and coefficients. The results extend existing findings and are validated through a single test example, demonstrating the theoretical applicability of the proposed approach. These findings provide a mathematical framework for understanding the behavior of power distribution systems under nonlinear and delayed influences. Full article

With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind–solar generation with hybrid electric–hydrogen energy storage systems to simultaneously enhance economic efficiency and system stability. The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale Optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. By integrating electric–hydrogen hybrid storage technology, the inner layer effectively mitigates power fluctuations. Furthermore, this study designs a modal decomposition-based charging and discharging scheduling strategy to ensures the system’s continuous and stable operation. Simulations performed on MATLAB 2018b and CPLEX 12.8 platforms indicate that the proposed dual-layer model decreases annual total expenses by 27.5% compared to a single-layer model while keeping grid-connected power variations within 10% of the installed capacity. This research provides innovative perspectives on microgrid optimization design and offers substantial technical support for ensuring stability and economic efficiency in intricate operational settings. Full article

This paper focuses on the development and experimental validation of a single-phase modular multilevel converter (MMC) based on a battery energy storage system (BESS) for residential uninterruptible power supply (UPS) with two-level active SoC balancing control. The configuration and mathematical modeling of the single-phase MMC-BESS are first presented, followed by the details of the control strategies, including dual-loop output voltage and current control in islanded mode, grid-connected control, circulating current control, and two-level active state-of-charge (SoC) balancing control. The design and optimization of the quasi-proportional-resonant (QPR) controllers were investigated by using particle swarm optimization (PSO). Simulation models were built to explore the operating characteristics of the UPS under islanded mode with an RL load and grid-connected mode and assess the control performance. A 500 W experimental prototype was developed and is herein presented, including results under different operating conditions of the MMC-BESS. The experimental results show that for both RL load and grid-connected tests, balancing was achieved. The response time to track the reference value was two grid periods (0.04 s). In the islanded mode test, the THD was 1.37% and 4.59% for the voltage and current, respectively, while in the grid-connected mode test, these values were 1.72% and 4.24% for voltage and current, respectively. Full article

Smart inverters are pivotal in modern renewable energy systems, enabling efficient grid integration, stability, and advanced control of distributed energy resources. While existing literature addresses their technical functionalities, significant research gaps persist in areas such as interoperability, cybersecurity, standardization, and the integration of artificial intelligence for adaptive control. This article provides a comprehensive review of smart inverter technologies, emphasizing their role in renewable energy applications, advanced control strategies, and unresolved challenges. By systematically analyzing recent advancements and case studies, the paper identifies critical limitations in current practices, including economic barriers, regulatory misalignments, and fault tolerance under dynamic grid conditions. The review contributes to the field by synthesizing dispersed knowledge, highlighting under-researched areas, and proposing actionable pathways for future innovation. The main findings reveal the transformative potential of AI-driven grid-forming inverters for enhancing grid stability and resilience. However, their widespread adoption is hindered by the absence of harmonized standards and misaligned policy frameworks. Consequently, this review underscores the urgent need for policymakers to develop and implement supportive regulatory structures that facilitate the deployment of AI-enabled smart inverters and establish unified standards to ensure interoperability and cybersecurity. This work serves as a foundational reference for researchers and policymakers aiming to address technical and systemic bottlenecks in smart inverter deployment. Full article

In recent years, PV power plants have been widely used on the roofs of commercial buildings with grid connections, primarily to enhance self-consumption in distributed energy systems. In addition, installing PV plants on commercial buildings’ roofs is becoming increasingly important, especially in crowded cities where land is limited. Since the Sun is an intermittent energy source, PV power plants cause frequency and voltage fluctuations in the grid. The way to avoid this problem is to install PV plants together with battery storage systems. Battery storage systems prevent frequency and voltage fluctuations in the grid and provide economic benefits. This article presents the sizing and techno-economic analysis of a factory building’s rooftop PV system with a battery. The amount of energy produced by the PV plant, PV temperature, and irradiation were recorded in a data logger obtained by various sensors. These real-time measurements were continuously collected and analyzed to evaluate system performance and assess seasonal variations.Load demand data were collected through an automatic meter reading system. The installed capacity of the PV power plant is 645 kW. The optimum battery capacity determined for this factory is 130 kW for 5 h. Techno-economic analysis was carried out using metrics such as the payback period, net present value, and levelized cost of energy. As a result of the analysis using various input variables, LCOE, NPV, and PBP were determined as 0.1467 $/kWh, 4918.3 $, and 7.03 years, respectively. Full article