Search Results (54)

Integrated energy systems (IESs) serve as pivotal platforms for realizing the reform of energy structures. The rational planning of their equipment can significantly enhance operational economic efficiency, environmental friendliness, and system stability. Moreover, the inherent randomness and intermittency of renewable energy generation, coupled with the peak and valley characteristics of load demand, lead to fluctuations in the output of multi-energy coupling devices within the IES, posing a serious threat to its operational stability. To address these challenges, this paper focuses on the economic and stable operation of the IES, aiming to minimize the configuration costs of hybrid energy storage systems, system voltage deviations, and net load fluctuations. A multi-objective optimization planning model for an electric–hydrogen hybrid energy storage system is established. This model, applied to the IEEE-33 standard test system, utilizes the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to optimize the capacity and location of the electric–hydrogen hybrid energy storage system. The Multi-Objective Artificial Hummingbird Algorithm (MOAHA) is adopted due to its faster convergence and superior ability to maintain solution diversity compared to classical algorithms such as NSGA-II and MOEA/D, making it well-suited for solving complex non-convex planning problems. The simulation results demonstrate that the proposed optimization planning method effectively improves the voltage distribution and net load level of the IES distribution network, while the complementary characteristics of the electric–hydrogen hybrid energy storage system enhance the operational flexibility of the IES. Full article

Although the integration of large-scale energy storage with renewable energy can significantly reduce electricity costs for steel enterprises, existing energy storage technologies face challenges such as deployment constraints and high costs, limiting their widespread adoption. This study proposes a gravity energy storage system and its capacity configuration scheme, which utilizes idle steel blocks from industry overcapacity as the energy storage medium to enhance renewable energy integration and lower corporate electricity costs. First, a stackable steel-based gravity energy storage (SGES) structure utilizing idle blocks is designed to reduce investment costs. Second, a gravity energy storage capacity planning model is developed, incorporating economic and structural collaborative optimization to maximize profitability and minimize construction costs. Finally, a Rime and particle swarm optimization (RI-PSO) fusion algorithm is proposed to efficiently solve the optimization problem. The results demonstrate that under equivalent power and capacity conditions, the SGES structure achieves 90.11% lower costs than compressed air energy storage and 59.7% lower costs than electrochemical storage. The proposed algorithm improves convergence accuracy by 21.19% compared to Rime and 4.21% compared to PSO and increases convergence speed by 72.34% compared to Rime. This study provides an effective solution for steel enterprises to reduce costs. Full article

With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments. Full article

In the contemporary landscape of power systems, the escalating integration of renewable energy resources and electric vehicle infrastructures into distribution networks has intensified the imperative to ensure power quality, operational optimization, and system reliability. Distribution network reconfiguration emerges as a pivotal strategy to mitigate power losses, facilitate the seamless assimilation of renewable generation, and regulate the charging and discharging dynamics of EVs, thereby constituting a critical endeavor in modern electrical engineering. While the Particle Swarm Optimization algorithm is renowned for its rapid convergence and effective exploitation of solution spaces, its capacity to thoroughly explore complex search domains remains limited, particularly in multifaceted optimization challenges. Conversely, the Grey Wolf Optimization algorithm excels in global exploration, offering robust mechanisms to circumvent local optima traps. Leveraging the complementary strengths of these approaches, this study proposes a hybrid PSO-GWO framework to address the distribution network reconfiguration problem, explicitly accounting for the integration of renewable energy sources and EV systems. Empirical validation, conducted on the IEEE 33-bus test system across diverse operational scenarios, underscores the efficacy of the proposed methodology, revealing exceptional precision and dependability. Notably, the approach achieves substantial reductions in power losses during peak demand periods with distributed generation incorporation while maintaining voltage profiles within the stringent operational bounds of 0.94 to 1.0 per unit, thus ensuring stability amidst variable load conditions. Comparative analyses further demonstrate that the hybrid method surpasses conventional optimization techniques, as evidenced by enhanced convergence rates and superior objective function outcomes. These findings affirm the proposed strategy as a potent tool for advancing the resilience and efficiency of next-generation distribution networks. Full article

The ongoing energy transformation and growing share of renewable energy sources (RES) in electricity production force the search for large-scale energy storage facilities as a possibility for storing electricity from RES because its production is not correlated with the current demand in the power grid. This article discusses the use of salt caverns as large-scale energy storage facilities, proposing a combination of the possibilities of storing energy in natural gas and energy stored in compressed air. Based on the selected potential area where such a storage facility could operate in Poland, the optimal operating parameters of storage caverns were estimated. Several possible cavern exploitation scenarios were analyzed to estimate the impact of the convergence phenomenon in salt caverns on the active storage volume over the long term of exploitation. The obtained results showed that even a high frequency of cavern exploitation cycles does not significantly affect the loss of its capacity due to the convergence phenomenon. The results confirmed the possibility of the effective use of this type of installation for storing surpluses from RES. Full article

To handle and manage battery degradation in electric vehicles (EVs), various capacity estimation methods have been proposed and can mainly be divided into traditional modeling methods and data-driven methods. For realistic conditions, data-driven methods take the advantage of simple application. However, state-of-the-art machine learning (ML) algorithms are still kinds of black-box models; thus, the algorithms do not have a strong ability to describe the inner reactions or degradation information of batteries. Due to a lack of interpretability, machine learning may not learn the degradation principle correctly and may need to depend on big data quality. In this paper, we propose a physics-informed recurrent neural network (PIRNN) with a fractional-order gradient for fast battery degradation estimation in running EVs to provide a physics-informed neural network that can make algorithms learn battery degradation mechanisms. Incremental capacity analysis (ICA) was conducted to extract aging characteristics, which could be selected as the inputs of the algorithm. The fractional-order gradient descent (FOGD) method was also applied to improve the training convergence and embedding of battery information during backpropagation; then, the recurrent neural network was selected as the main body of the algorithm. A battery dataset with fast degradation from ten EVs with a total of 5697 charging snippets were constructed to validate the performance of the proposed algorithm. Experimental results show that the proposed PIRNN with ICA and the FOGD method could control the relative error within $5\%$ for most snippets of the ten EVs. The algorithm could even achieve a stable estimation accuracy (relative error < 3%) during three-quarters of a battery’s lifetime, while for a battery with dramatic degradation, it was difficult to maintain such high accuracy during the whole battery lifetime. Full article

In the context of dual-carbon goals, it is essential to coordinate low-carbon policies and technologies. As a promising approach for clean energy integration, the combined heat–power–hydrogen virtual power plant (CHP-H VPP) effectively consolidates electricity, heat, and hydrogen to meet increasing energy demands and reduce carbon emissions. To this end, this paper proposes an optimal scheduling method for CHP-H VPPs based on a stepped carbon trading mechanism. First, at the low-carbon technology level, a CHP-H VPP architecture is constructed, incorporating thermal power units, hydrogen-doped gas turbines, hydrogen-doped gas boilers, and two-stage power-to-gas (P2G) systems. Second, at the policy level, a stepped carbon trading model is established to constrain system carbon emissions and an optimization model is formulated to minimize operating costs and emissions. Finally, a particle swarm optimization (PSO) algorithm with linearly decreasing constraints is employed to refine solution accuracy and accelerate convergence by progressively narrowing the search space and guiding the algorithm toward optimal solutions. Simulation results demonstrate that the proposed model enhances both the economic performance and carbon-reduction capabilities of the system; the simulation results also show that the proposed model effectively improves economic returns by reducing operating costs and enhancing carbon-reduction capacity, with a 7% reduction in run time. Full article

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios. Full article

Energy sources, one of the key elements of economic growth and development, have recently come to the forefront in terms of sustainability, security of supply, low cost, and environmental impact. Therefore, the diversification of energy sources is becoming more important; in this regard many countries are investing especially in renewable energy sources. This trend plays an important role in the decarbonization of the energy sector. The aim of this study is to analyze the convergence of electricity capacity in G20 countries, which account for two-thirds of the world population and have a dominant position in the world economy. Accordingly, the analysis was carried out for total electricity capacity and its sources (nuclear, fossil fuels, and renewables). Unlike other studies in the literature, this study utilizes nonlinear unit root tests with Fourier function, which models nonlinearity and structural break, the two main problems in unit root tests, within the framework of recent developments in time series analysis. According to the findings of the analysis, it was concluded that the converging countries are in line with the G20 policies in terms of electricity capacity and its sources and that there is no need for policy changes in these countries. Full article

The rise of new energy technologies has accelerated progress towards sustainable development, and many companies are beginning to invest in renewable resource-related facilities. Electric bicycles have always been an important mode of green transportation; however, they also have problems such as slow charging, difficult charging, and that burning and short circuiting may occur during charging. Electric bicycle battery exchange cabinets effectively solve these problems by exchanging low batteries with full batteries instead of charging. However, current battery exchange cabinets face the problems of insufficient construction and unreasonable site selection. Therefore, this paper proposes a location selection model for electric bicycle battery exchange cabinets based on point demand theory, aiming to maximize rider satisfaction and the service capacity of exchange cabinets. The immune algorithm is introduced to solve the location model; however, the traditional immune algorithm has some problems such as poor stability and slow convergence. In this paper, the mutation process of the traditional immune algorithm is improved by introducing multi-point mutation, guided mutation, and local search. Finally, based on the data of electric bicycle riders in Shanghai, we verify that the location model based on point demand theory performs well on two objective functions of rider satisfaction and battery exchange cabinet service capability. We also expand the application of point demand theory to location models. Then, by conducting experiments with different parameter groups, through sensitivity analysis and convergence analysis, we verified that the improved immune algorithm performs better than the traditional immune algorithm in its accuracy, search accuracy, stability, and convergence. Full article

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes. Full article

This study investigated the economic feasibility of introducing a new energy system, the greenhouse–fuel cell convergence system (GFCS), to a greenhouse that consumes a lot of energy. The GFCS is a concept that uses the heat generated during the power generation process to cool and heat the greenhouse, uses the emitted CO₂ as fertilizer inside the greenhouse, and sells the generated electricity. For economic evaluation, the annual energy consumption of the greenhouse was first calculated through simulation, and then the appropriate fuel cell capacity was determined. Next, a farmer-led business model and a utility-led business model were presented, and the economic feasibility of these models was evaluated for tomatoes and mangoes. The economic evaluation of the GFCS confirmed the economic feasibility by comparing it with a greenhouse equipped with a geothermal heat pump. The results of the economic evaluation revealed that the farmer-led model had a benefit–cost ratio (B/C) ranging from 0.62 to 0.65 even with government support for heat utilization facilities, which was lower than that of a typical greenhouse (1.03 to 1.06). On the other hand, the utility-led model showed high B/C ranging from 1.19 to 1.86. If the initial investment cost of the fuel cells is reduced and a government policy is appropriately supported, the GFCS can be economically applied to greenhouses. Full article

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners. Full article

The early detection and diagnosis of cancer presents significant challenges in today’s healthcare. So, this research, suggests an original experimental biosensor for cell cancer detection using a corona-shaped metamaterial resonator. This resonator is designed to detect cancer markers with high sensitivity, selectivity, and linearity properties. By exploiting the unique properties of the corona metamaterial structure in the GHz regime, the resonator provides enhanced interaction of electromagnetic waves and improved detection skills. Through careful experimental, simulation, and optimization studies, we accurately demonstrate the resonator’s ability to detect cancer. The proposed detection system is capable of real-time non-invasive cancer detection, allowing for rapid intervention and better patient outcomes. The sensitivity value was confirmed through simulation, estimated at 0.1825 GHz/RIU. The results of two different simulation methods are used: the simulation software CST Studio Suite (version 2017) based on the finite element method (FEM), and the simulation software ADS (version 2019) based on the equivalent circuit method, thereby increasing confidence in the convergence of simulation and measurement results. This work opens new avenues for developing advanced detection technologies in the field of oncology, and paves the way for more effective cancer diagnosis. The experimental study verified that this realized sensor has very small frequency shifts, significantly small electrical dimension and miniaturization, high sensitivity, and good linearity. The suggested configurations showed a capacity for sensing cancer cells in the GHz regime. Full article

Modern electrical systems are required to provide increasing standards of power quality, so converters in microgrids need to cooperate to accomplish the requirements efficiently in terms of costs and energy. Currently, power quality compensators (PQCs) are deployed individually, with no capacity to support distant nodes. Motivated by this, this paper proposes a consensus-based scheme, augmented by the conservative power theory (CPT), for controlling clusters of PQCs aiming to improve the imbalance, harmonics and the power factor at multiple nodes of a grid-tied AC microgrid. The CPT calculates the current components that need to be compensated at the point of common coupling (PCC) and local nodes; then, compensations are implemented by using each grid-following converter’s remaining volt-ampere capacity, converting them in PQCs and improving the system’s efficiency. The proposal yields the non-active power balancing among PQCs compounding a cluster. Constraints of cumulative non-active contribution and maximum disposable power are included in each controller. Also, grid-support components are calculated locally based on shared information from the PCC. Extensive simulations show a seamless compensation (even with time delays) of unbalanced and harmonics current (below 20% each) at selected buses, with control convergences of 0.5–1.5 [s] within clusters and 1.0–3.0 [s] for multi-cluster cooperation. Full article