Search Results (38)

This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article

Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs. Full article

In ultra-wideband (UWB) radio fuze architectures, the receiver serves as the core component for receiving target-reflected signals, with its performance directly determining system detection accuracy. Manufacturing tolerances and operational environments induce inherent stochastic perturbations in circuit components, causing deviations of actual parameters from nominal values. This consequently degrades the signal-to-noise ratio (SNR) of receiver outputs and compromises ranging precision. To overcome these limitations and identify critical sensitive components in the receiver, this study proposes the following: (1) A dual-channel time-domain correlated UWB fuze detection model; and (2) the integration of an asymmetric tolerance mathematical model for dual-channel correlated receivers with a Morris-LHS-Sobol collaborative strategy to quantify independent effects and coupling interactions across multidimensional parameter spaces. Simulation results demonstrate that integrating capacitors and resistors constitute the dominant sensitivity sources, exhibiting significantly positive synergistic effects. Physical simulation correlation and hardware circuit verification confirms that the proposed model and sensitivity analysis method outperform conventional approaches in tolerance resolution and allocation optimization, thereby advancing the theoretical characterization of nonlinear coupling effects between parameters. Full article

The electrical power system is composed of three essential sectors, generation, transmission, and distribution, with the latter being crucial for the overall efficiency of the system. Enhancing the capabilities of active distribution networks involves integrating various advanced technologies such as distributed generation units, energy storage systems, banks of capacitors, and electric vehicle chargers. This paper provides an in-depth review of the primary strategies for incorporating these technologies into the distribution network to improve its reliability, stability, and efficiency. It also explores the principal metaheuristic techniques employed for the optimal allocation of distributed generation units, banks of capacitors, energy storage systems, electric vehicle chargers, and network reconfiguration. These techniques are essential for effectively integrating these technologies and optimizing the active distribution network by enhancing power quality and voltage level, reducing losses, and ensuring operational indices are maintained at optimal levels. Full article

This article proposes a high-efficiency isolated three-port resonant converter for DC microgrids, combining a dual active bridge (DAB)–LLC topology with hybrid Pulse Width Modulat-Pulse Frequency Modulation (PWM-PFM) phase shift control. Specifically, the integration of a dual active bridge and LLC resonant structure with interleaved buck/boost stages eliminates cascaded conversion losses. Energy flows bidirectionally between ports via zero-voltage switching, achieving a 97.2% efficiency across 150–300 V input ranges, which is a 15% improvement over conventional cascaded designs. Also, an improved PWM-PFM shift control scheme dynamically allocates power between ports without altering switching frequency. By decoupling power regulation and leveraging resonant tank optimization, this strategy reduces control complexity while maintaining a ±2.5% voltage ripple under 20% load transients. Additionally, a switch-controlled capacitor network and frequency tuning enable resonant parameter adjustment, achieving a 1:2 voltage gain range without auxiliary circuits. It reduces cost penalties compared to dual-transformer solutions, making the topology viable for heterogeneous DC microgrids. Based on a detailed theoretical analysis, simulation and experimental results verify the effectiveness of the proposed concept. Full article

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure. Full article

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority. Full article

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management. Full article

In this paper, an optimization approach based on an arithmetic optimization algorithm (AOA) is proposed for specifying the optimal allocation of distribution generations/generators (DGs) and capacitor banks (CBs) in radial distribution systems. The AOA is a new population-based meta-heuristic algorithm that is essentially based on using basic arithmetic operators in mathematics. The proposed approach is employed to specify the optimum placement, capacity, and power factor of DGs and CBs to decrease the distribution systems’ total power loss and voltage deviation. To state the performance of the proposed approach, DGs and CBs are placed in IEEE 33-bus and 69-bus systems separately or together. When only DGs are used and the parameters of location, capacity, and power factor of DGs are determined simultaneously, the total active power loss reductions in the IEEE 33-bus and 69-bus systems are achieved at 94.42% and 98.03%, respectively. When the results of other optimization algorithms are examined, it is seen that better results are obtained with AOA. Full article

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm${}^2$. The modulator occupies 1.41 mm${}^2$ and the decimator occupies 0.55 mm${}^2$. Full article

Power distribution systems (PDS) comprise essential electrical components and infrastructure that facilitate the delivery of electrical energy from a power transmission system to end users. Typically, the topology of distribution systems is radial, so that power goes from the substations to end users through main lines or feeders. However, the expansion of new feeders to accommodate new users and ever-growing energy demand have led to higher energy losses and deterioration of the voltage profile. To address these challenges, several solutions have been proposed, including the selection of optimal conductors, allocation of voltage regulators, utilization of capacitor banks, implementation of distributed generation, and optimal reconfiguration. Although reconfiguring the network is the most cost-effective approach, this solution might not be sufficient to completely minimize technical losses and improve system performance. This paper presents a novel approach that combines optimal distribution network reconfiguration (ODNR) with optimal conductor selection (OCS) to minimize power losses and enhance the voltage profiles of PDS. The key contribution lies in the integration of the ODNR and OCS into a single MILP problem, ensuring the attainment of globally optimal solutions. The proposed model was tested with benchmark 33-, 69-, and 85-bus test systems. The results allowed us to conclude that the combined effect of ODNR and OCS presents better results than when any of these approaches are applied either separately or sequentially. Full article

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA’s simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA. Full article

This paper presents the generation and transmission expansion planning (GTEP) considering the switched capacitive banks (SCBs) allocation in the power system, including the demand response program (DRP). This scheme is based on the system flexibility. The objective function of the scheme minimizes the expected planning cost that is equaled to the summation of the total construction costs of the SCBs, the generation units (GUs) and the transmission lines (TLs), and the operating cost of the GUs. It is concerned with the AC power flow constraints, the planning-operation model of the mentioned elements, the DRP operation formulation, and the operating and flexibility limits of the network. In the following, the scenario-based stochastic programming is used to model the uncertainty parameters, such as the load and renewable power of wind farms. Then, the hybrid evolutionary algorithm, based on the combination of the crow search algorithm and the grey wolf optimizer, is used to determine the optimal point with the approximate unique solution. Finally, the scheme is applied on the transmission networks, the numerical results confirm the capabilities of the proposed scheme in simultaneously improving the flexibility, operation, and economic situation of the transmission network, so that the hybrid algorithm achieves the optimal solution in a shorter computation time, compared with the non-hybrid algorithms. This algorithm has a low standard deviation of about 92% in the final response. The proposed scheme with the optimal planning of the lines, sources, and capacitor banks, together with the optimal operation of the DRP succeeded in improving the energy loss and the voltage deviation by about 30–36% and 25–30%, compared with those of the power flow studies. Full article

In the past few decades, there has been increasing recognition of the importance of optimum power flow (OPF) studies in the context of economic analyses of power systems. There is a need for power system development to maximize efficiency by emphasizing cost and power losses for smart grids to operate effectively in the current situation. This study aims to develop an optimal capacitor bank allocation schedule that minimizes power losses in the distribution networks under equality constraints. This will be achieved by integrating the loss factor and voltage stability into a new approach to determine where the capacitor banks should be located. It aims to reduce the operating costs of power systems and maximize efficiency by applying an optimization model for economic dispatch, which considers distributed power generation and demand response. The NSGA-II optimization algorithm was used in this study to determine the optimal size and location of the capacitor bank. A NSGA-II solves this problem by minimizing cost and power losses while determining the best operating strategy. We used an IEEE 26-bus distribution system to test the proposed method with every possible generation change. Comparing the power flow analysis with/without capacitor optimization showed that the operation optimization model of OPF with NSGA-II can reduce operation costs and improve the power system. Full article

The usage of Electric Vehicles (EVs) for transportation is expected to continue growing, which opens up new possibilities for creating new smart grids. It offers a large-scale penetration of Fast Charging Stations (FCE) in a local utility network. A severe voltage fluctuation and increased active power loss might result from the inappropriate placement of the FCE as it penetrates the Distribution System (DST). This paper proposes a multi-objective optimisation for the simultaneous optimal allocation of FCEs, Distributed Generators (DGs), and Shunted Capacitors (SCs). The proposed Pareto dominance-based hybrid methodology incorporates the advantages of the Grey Wolf Optimiser and Particle Swarm Optimisation algorithm to minimise the objectives on 118 bus radial distribution systems. The proposed method outperforms some other existing algorithms in terms of minimising (a) active power loss costs of the distribution system, (b) voltage deviations, (c) FCE development costs, (d) EV energy consumption costs, and (e) DG costs, as well as satisfying the number of FCEs and EVs in all zones based on transportation and the electrical network. The simulation results demonstrate that the simultaneous deployment technique yields better outcomes, such as the active power loss costs of the distribution system being reduced to 53.21%, voltage deviations being reduced to 68.99%, FCE development costs being reduced to 22.56%, EV energy consumption costs being reduced to 19.8%, and DG costs being reduced to 5.1%. Full article