A mathematical method for quantifying uncertainty in electrical networks using renewable energy, plug-in electric vehicles, and hydrogen energy storage based on Marine Predator Optimization algorithm to solve unit commitment problem is proposed in this research, and the outcomes are compared with Grey Wolf Optimizer (GWO) and Biogeography-Based Optimization (BBO) and Sine Cosine Algorithm (SCA).ANOVA test findings demonstrate the resilience and efficacy of the MPA method in unit commitment. The article illustrates the applicability and validity of the proposed method based on the IEEE-57 bus network. Thermal investigation is the initial step throughout this research, followed by adding the use of renewable energy sources to reduce expenses as well as emissions, and, lastly, hydrogen storage units were added to accommodate rising needs. A total of 100 iterations are counted, and we get the best value after 30 trials in this investigation. Optimizing the one and multiple goals of operations, such as total cost minimization with the valve point effect and emission minimization, as well as simultaneous minimization of cost and emission, is the main aim. The analysis found that between the 13th and 21st hours, when the load is at its maximum, all generators are operating efficiently to meet demand. Important data like the average, median, and surrounding variability are displayed using a Table. This effort concurrently minimized 3.95% (cost) and 8.3% (emission) while achieving a 1.89% cost reduction and a 7.4% emission reduction compared to early research. Goals decreased by 0.58% and 0.72% with the combination of RES and HES, indicating better multifaceted performance.

The photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) based hybrid system design and optimal placement using chaotic quasi-oppositional crayfish optimization algorithm (CQOCOA) in a radial distribution network (RDN) under load uncertainty is the main objective of this study. Here, crayfish optimization algorithm (COA) is modified and improved by adding quasi-oppositional behavior to it. Then chaos theory is added to speed up the convergence pace and avoid the local optimality. For optimal placement of hybrid PV/WT/BES system, simultaneous active power loss and annual operation costs minimization is taken as the objective to enhance the efficacy of the RDN. The uncertainty modeling of PV and WT distributed generation (DG) is considered for power generation as solar irradiance and wind speed can change. This algorithm is validated on 69-bus and 94-bus to establish the potency of the suggested CQOCOA algorithm. The active power loss cost is also evaluated after the installation of hybrid PV/WT/BES system. Adjusting the growing load demand, 25% increased load and 10% decreased load is considered for load uncertainty modeling. In both (69-bus and 94-bus) systems, the placement of hybrid PV/WT/BES system using the CQOCOA method reduces the active power loss by 58.93%, 60.53%, 53.53%, and 62.19%, 65%, 62.99% for normal, 25% increased, and 10% decreased loading conditions, respectively. In yearly running cost of hybrid system design by CQOCOA method for 69-bus at normal and 10% decreased load gives yearly savings of 25 364$, 31 951$, 88 951$ and 16 511$, 1527$, 25 608$ than COA, DAOA, and AOA methods. The comparative study of results revealed that the CQOCOA algorithm is better than several optimization algorithms.

This study aims to determine the most effective operational strategy for economic load dispatch (ELD). The practical ELD problem can be difficult to solve because of its non-smooth cost function and nonlinear constraints; specifically, electric power generation’s nonlinear multi-objective economic emission dispatch (EED) problem with valve point loading is solved using the prairie dog optimization algorithm-based optimization (PDO). The problem considers nonlinear generator aspects such as valve point effect, ramp rate limits, and restricted operating zones. The PDO technique identifies the optimal solution without requiring any prior knowledge of the gradient of the objective function. The application of the PDO algorithm to solve nonlinear ELD problems appears to be an effective and reliable optimization technique. Three test cases are considered. For example, 40 units with losses (thermal units and renewable energy), 140 units (renewable energy), and 160 units (thermal units and renewable energy) are used for executing and evaluating the suggested algorithm. The outcomes demonstrate the suggested algorithm’s potential and efficacy in comparison to other alternative techniques, and the suggested approach outperforms other established algorithms in terms of proficiency and robustness, as demonstrated by numerical data.

The inclusion of electric vehicle (EV) charging station (EVCS) into radial distribution network (RDN) along with renewable distributed generation (RDG) and distributed static compensators (DSTATCOM) allocation by a novel method, namely, quasi oppositional arithmetic optimization algorithm (QOAOA) is the main focus of this study. The inclusion of EVCS increases the load demand and the power loss. Also, the cost of energy loss of the system is increased. For the compensation of the power loss and energy loss cost, RDG, and DSTATCOM are allocated. The RDG is based on two sources, the photovoltaic (PV) module and the wind turbine (WT) which provide environmental benefits. To validate the usefulness of the presented QOAOA algorithm, it is examined on 33-bus, 69-bus, and Portuguese 94-bus systems with four separate cases namely, (i) EVCS placement, (ii) EVCS and DSTATCOM placement, (iii) EVCS and RDG (PV, WT based DG) placement, and (iv) EVCS, RDG (PV, WT based DG), and DSTATCOM placement. To upgrade the overall performance of the RDN, real power loss minimization and annual economic loss reduction are taken as the two main objective functions for this study. The percentage improvement in active power loss for case 2, case 3, and case 4 are 30.94

This paper develops a contingency-based multi-period optimal power flow (MP-OPF) problem for a thermal-wind-battery energy storage system (BESS) under peak plug-in hybrid electric vehicle (PHEV) charging load. Different case studies are considered to assess the effect of peak PHEV charging, wind uncertainty, energy storage, contingency issues, and FACTS support on the power system. A BESS model is presented that implements state-of-charge continuity, charging-discharging dynamics, and time-coupled operational constraints to ensure reliable energy management across the scheduling horizon. Furthermore, a STATCOM is strategically placed and configured with optimal control parameters to enhance voltage and reactive power support under stressed operating conditions. A quasi-oppositional birds-of-prey-based optimization (QOBPBO) algorithm is used to solve the highly nonconvex and complicated MP-OPF problem. Quasi-oppositional learning enhances QOBPBO's exploration, leading to better solution quality and a more robust convergence profile. The proposed methodology is examined on the IEEE 57-bus benchmark system under normal and contingency operation. The simulation results indicate that the combined operation of wind and BESS substantially reduces peak-hour system stress due to PHEV charging load, decreases thermal generation dependence, and improves operational security during line outages. Furthermore, incorporating STATCOM lowers generation costs by improving voltage regulation and reducing transmission losses. The simulation results confirm that the suggested algorithm delivers higher-quality, more robust solutions that are also computationally more efficient than the sophisticated methods employed.

The integration of renewable energy resources into a standalone microgrid (MG) requires effective sizing methodologies that balance cost, reliability, and environmental performance. To optimize the configuration of a hybrid microgrid that combines photovoltaic (PV), wind turbine (WT), biomass generator (BMG), battery energy storage systems (BESS), and diesel generator (DG) systems, a new synergistic swarm-evolution optimization (SSEO) algorithm is proposed in this work. To achieve quick and steady convergence, the SSEO algorithm combines the local refinement strength of differential evolution (DE) with the exploration power of particle swarm optimization (PSO). Net present cost (NPC), cost of energy (COE), loss of power supply probability (LPSP), renewable fraction (RF), and carbon emissions (CE) are used as performance criteria in the formulation of a multi-objective optimization approach. The proposed approach performs better than PSO, genetic algorithm (GA), grey wolf optimization (GWO) technique, and DE. SSEO achieves an NPC of USD 8.21 million, COE of USD $0.106 / \text{kWh}$, CE of $421,000 ~\text{kg} /$ year, and a renewable portion of 95.1 % with an LPSP of just 1.2 % for the ideal PV/WT/BMG/BESS/DG configuration. Convergence curves show that SSEO provides computational advantages over alternative approaches by producing high-quality solutions in fewer iterations comparatively. SSEO is a dependable and effective optimization technique for microgrid planning, providing excellent environmental and techno-economic performance.

The need to transition to renewable energy sources (RESs) including wind, solar photovoltaic (PV), and hydro is highlighted by the accelerated depletion of fossil fuel reserves and the increasing need for sustainable power generation. In this regard, the present study explores the use of a probabilistic optimal power flow (POPF) framework that addresses the inherent uncertainties related to the power outputs of various RES technologies. The artificial hummingbird algorithm (AHA), inspired by hummingbirds adaptive foraging behavior, is used to solve the ensuing highly nonlinear, multi-modal problem. Several IEEE 57-bus test systems are used to thoroughly validate the efficacy of the suggested AHA-based POPF model. Comparative studies indicate that the AHA consistently provides notable cost and emission reductions, outperforming advanced metaheuristic approaches in all cases. When compared to coral reef optimization and gray wolf optimizer, the AHA reduces emissions by 1.74%–3.08% and costs by 0.27%–0.33% in the traditional IEEE 57-bus system. Additional gains are shown with cost reductions of up to 0.148% and emission reductions of up to 2.40% when RES units are integrated. AHA achieves the greatest results in the most complicated Test System 3, with an ideal generating cost of 4949 $ / h and emissions of 1.0299 t/h. The development of a comprehensive POPF system that concurrently incorporates wind, solar, and hydro power with precise uncertainty modeling and related cost penalties is what makes the study new. The superiority of the suggested solution over sophisticated modern approaches is further shown by extensive statistical assessment of 24-hour dynamic load tests across both IEEE 57-bus system as well as IEEE 118-bus system.

A modified differential search algorithm with gradient-based repair is developed to obtain the solution to the optimal power flow problem with renewable energy resources, such as wind energy, photovoltaic energy, combined wind–tidal systems, and plug-in electric vehicles. Implemented modifications of the proposed algorithm include a random- as well as Lévy step-based hybrid population initialization technique for enhanced exploration capability and improved convergence speed, a self-adaptive affinity index-based global-best-guided mechanism to achieve a balanced exploration–exploitation trade-off, and a β -hill climbing technique to strengthen the exploitation phase. An efficient constraint-handling method, referred to as gradient-based repair, is integrated with the proposed evolutionary optimization algorithm to address explicit system constraints. Exhaustive test studies are carried out on two test systems considering both single- and multi-objective functions, and the obtained results are compared with those of seven sophisticated algorithms. The results indicate that the proposed algorithm outperforms the compared optimization techniques in terms of solution quality and convergence speed. Furthermore, the findings confirm that the proposed approach yields superior performance in terms of solution quality, accuracy, and convergence speed for both small-scale and large-scale test systems when compared with the seven sophisticated algorithms. The high dimensionality, strong nonlinearities, and probabilistic uncertainty modeling associated with the large-scale test system impose significant computational demands, thereby demonstrating that the proposed approach is well suited for high-performance computing platforms and parallel or distributed implementations.

ABSTRACT The increasing penetration of renewable energy resources, energy storage technologies, and flexible AC transmission system (FACTS) devices has significantly increased the complexity of day‐ahead dynamic optimal power flow (DOPF) problems. The simultaneous consideration of wind power uncertainty, multi‐mode compressed air energy storage (CAES), STATCOM control, and valve‐point loading effects results in a highly nonlinear, nonconvex, and time‐coupled optimization problem. To address this challenge, this paper proposes an oppositional supercell thunderstorm algorithm (OSTA), in which opposition‐based learning is incorporated into the supercell thunderstorm algorithm (STA) to enhance population diversity, improve exploration capability, and mitigate premature convergence. A stochastic multi‐period DOPF framework is developed by integrating wind power uncertainty modeled through the Weibull probability distribution, detailed CAES operational characteristics including charging, discharging, and gas turbine modes, and STATCOM control within a unified optimization model. The objective is to minimize the total operating cost while satisfying power balance, network security, and operational constraints over a 24‐h scheduling horizon. The effectiveness of the proposed methodology is validated through multiple case studies on the IEEE 30‐bus system, while its scalability is further assessed on the IEEE 118‐bus system. Comparative evaluations against STA, marine predators algorithm (MPA), particle swarm optimization (PSO), and grey wolf optimizer (GWO) demonstrate that OSTA consistently provides superior solution quality, improved convergence characteristics, enhanced voltage profile performance, and better voltage stability margins. The obtained results confirm the effectiveness, robustness, and scalability of the proposed OSTA for solving large‐scale stochastic DOPF problems in renewable‐rich power systems.

The use of the oppositional crayfish optimization algorithm (OCOA) for the solution of electric vehicle charging station (EVCS) incorporation and network reconfiguration (NR) with distributed generation (DG) and capacitors placement problems in a radial distribution network (RDN), where active power loss and annual energy loss cost minimization are the main objectives of the study. An improved version of the crayfish optimization algorithm (COA) is created by adding oppositional behaviour into the primary COA algorithm for the generation of an opposite primary population to find the optimal solution. Two test networks (33-bus and 69-bus) are used to examine the effectiveness of the proposed OCOA algorithm with three different scenarios and they are (i) EVCS with unity power factor (UPF) DG and capacitor placement, (ii) EVCS with optimal power factor (OPF) DG and capacitor placement, and (iii) EVCS inclusion and network reconfiguration with optimal power factor (OPF) DG and capacitor placement. The OCOA method allows improvements of 81.45

The main objective of this research is to optimize power generation by reducing generation costs and ensuring voltage stability across transmission lines. To accomplish this, hydro-thermal scheduling (HTS) is integrated with diverse renewable energy sources, including solar, wind, tidal energy, and electric vehicles (EVs), enabling efficient power production that aligns with load demands and reduces energy losses. With the global transition towards EV adoption and cleaner energy systems, maintaining grid stability becomes increasingly complex due to the intermittent nature of renewables like solar and wind. However, combining these sources offers complementary advantages; i.e. solar energy is available during daylight hours, whereas wind energy often continues throughout the night, providing a more balanced power supply. Tidal energy and energy storage systems (ESS) further contribute to grid reliability, despite their inherent operational challenges. Numerous studies have used traditional and meta-heuristic optimization strategies to solve hydro-thermal scheduling and hybrid renewable integration that integrates solar, wind, energy storage, and electric cars. Nevertheless, the majority of current methods take into account restricted combinations of renewable resources, simplify uncertainty modeling, or concentrate mainly on cost optimization without sufficiently addressing grid resilience and voltage stability.The novelty of this work lies in the coordinated integration of various renewable energy sources such as solar, wind, and tidal energy as well as electric vehicles (EVs) and energy storage systems (ESS) into HTS to solve issues with grid dependability and renewable intermittency. A mixed computational methodology is adopted, using the chaotic-oppositional moth flame optimization (COMFO) technique, which improves solution accuracy and convergence speed over traditional MFO variations. In comparison to OMFO and normal MFO, numerical findings show a reduction in overall generating cost of 0.46% and 0.60%, respectively, while preserving adequate voltage stability and lower transmission losses. This study supports robust grid operation in the face of growing EV and renewable energy penetration by offering a sustainable and economical scheduling technique for contemporary power networks. Specifically, there is still a lack of research on the simultaneous integration of electric vehicles, energy storage, and tidal energy in a network-constrained hydro-thermal scheduling paradigm. Furthermore, there is currently a dearth of thorough uncertainty-aware modeling and systematic performance evaluation of sophisticated optimization techniques for resilient grid operation in the literature.The resulting chaotic-oppositional moth flame optimization (COMFO) approach is benchmarked through extensive statistical and comparative analysis, demonstrating superior performance over conventional optimization methods. A comprehensive strategy for integrating renewable energy into modern power systems has been introduced to facilitate cleaner, more cost-effective, and more reliable grid operations.

In the near future, fossil fuel reserves are expected to become progressively depleted. In response, contemporary research efforts worldwide are intensifying the exploration of renewable energy integration into electrical power systems, driven by both environmental imperatives and economic rationale. The principal contribution of the proposed research lies in the development of a scheduling framework for thermal units in coordination with hydro and wind energy sources (HTWS), aimed at minimizing fuel consumption and enhancing economic power generation. A secondary contribution involves the integration of battery energy storage systems (BES) into the HTWS configuration - resulting in the hybrid HTWBS system - to improve voltage stability and optimize economic power delivery under dynamically varying load conditions. Finally, the optimal power flow (OPF) analysis of the hydro-thermal-wind-battery scheduling (HTWBS) within the IEEE-39 bus system is conducted to ensure the most efficient operational outcomes of the integrated power network while reliably meeting load demand. The system's complexity is significantly heightened by non-linear factors such as valve-point loading in thermal units, transmission losses, water availability constraints in hydro units, wind power uncertainties, and the dynamic charging-discharging behavior of batteries. These non-linearities introduce challenges like local optima and slow convergence in scheduling processes, which can be effectively addressed using a relatively recent optimization approach known as the chaotic-opposition-based sine cosine algorithm (COSCA). Through statistical analysis using the ANOVA test and Box plot across three systems, the proposed approach demonstrated minimal variance in mean values and achieved optimal cost outcomes within a tolerance of less than 0.025%, thereby validating its robustness. By effectively reducing generation costs and enhancing the voltage profile, COSCA surpasses alternative optimization strategies, with comparative analysis confirming its superior performance across both test systems.

This paper presents the Chaotic Oppositional Bald Eagle Search (COBES) algorithm, a refined version of the Bald Eagle Search (BES) method. The proposed technique optimizes the design errors in fractional-order low-pass Chebyshev filters (FOLPCF). The proposed method incorporates oppositional learning for improved initialization of population and logistic chaotic mapping to enhance the balance between exploration and exploitation phases. COBES is used to determine optimal transfer function coefficients for third- and fifth-order FOLPCFs and stability is assured using Jury-Marden constraints. COBES surpasses PSO, FA, and RGA in terms of error minimization (MSE <-80 dB), faster convergence (39 iterations), and streamlined computation time (23 seconds). Statistical analysis across 30 independent runs validates the consistency of the proposed approach, with standard deviations under 10−4. The results prove COBES as an efficient optimizer for the design of digital fractional-order filter.

Renewable energy has always been very helpful in reducing emissions and minimizing generation in order to solve environmental problems. Renewable energy sources, however, are irregular, volatile, and unpredictable. Therefore, the safe and reliable usage of energy is at danger due to its huge proportionate integration. However, fossil fuels are one of the primary causes of severe pollution. Hybrid energy i.e. hydrothermal has been merged with solar and wind power systems. By meeting load demand and transmission losses while adhering to all restrictions, the goal is to lowering the generation costs. In order to reduce costs, the advanced Improved Sailfish Optimizer (ISFO) has been applied to solar-wind integrated hydro-thermal (SWHT) systems.

To place renewable distributed generators (DG) optimally in reconfiguration-based radial distribution networks (RDN), a quasi-oppositional arithmetic optimization algorithm (QOAOA) is introduced which uses solar and wind energy resources as DG, with wind speed variation and solar irradiation considered for the generation of electrical energy. In this study, by adding quasi-oppositional behavior to the existing arithmetic optimization algorithm (AOA), an improved version of the original AOA is developed. The effectiveness of QOAOA is demonstrated on the 33-bus, 69-bus, and 118-bus radial distribution networks. Three separate single-objective functions, namely, active power loss, pollutant gas emissions, and annual operation cost reduction, along with a multi-objective function based on annual operation cost and pollutant gas emissions, are executed. In this study, two separate cases are investigated, solar DG and wind turbine (WT) based DG placement (case 1), and simultaneous reconfiguration with solar DG and WT DG placement (case 2). For Case 2 studies, the percentage improvements in active power loss, emission, and annual operating cost are 84.41%, 95.60%, and 20.23%, respectively, for the 33-bus system, and 95.00%, 99.87%, and 20.40% for the 69-bus system. Additionally, the improvement in active power loss for the 118-bus network is 47.08%.

As a means of tackling the problems of depleting fossil fuels, rising energy consumption, and man-made global warming, renewable energy sources like photovoltaic and wind turbines are becoming widespread. Because of its intermittent nature, integrating renewable energy resources (RERs) into the electrical grid is an extremely difficult task. With benefits like rapid response times and continuous power delivery, battery energy storage systems are often regarded as one of the possible ways to address these variability. Now a days, energy storage devices are crucial for interconnected power systems. They may be applied for peak shaving in addition to Blackuction variations brought on by dispersed power sources. In this paper, the impact of renewable energy with battery energy storage system (BESS) on the power system is examined, using the optimal power flow (OPF) model. The suggested model aims to reduce the overall generation cost, emission, and the frequency deviation. This study resolves an optimal scheduling issue with the hybrid generation system taken into account. Conventional thermal generator, wind, and solar photovoltaic (PV) modules with batteries are the main elements of this hybrid system. The suggested model establishes the ideal output power for every interval and the timing of battery charging and discharging. The usefulness and validity of the suggested model are demonstrated by the numerical example based on the IEEE-57 system. This study examines three different situations: optimal power flow in the absence of RES (wind, solar) & BESS, optimal power flow in the presence of RES (wind, solar) & BESS, and the combination of RES (wind, solar) & BESS with fractional order proportional integral derivative (FOPID) controller. Outcome of the test reveals the better result in resolving the OPF problem, by incorporating RES & BES with FOPID controller. In power systems, fractional order controllers are used to regulate frequency and voltage. Controlled wind turbine dynamics in particular helps to improve grid operations' resilience to uncertainties and disruptions. After integrating RES & BESS with FOPID controller, the total fuel cost & emission are reduced by 11.43%, 11.32% during 24 h and frequency deviation reduces OS-88.34%, US-25.88%, ST-38.59% for area 1 and OS-19.79%, US-54.07%, ST-35.76% for area 2 and OS-65.43%, US-73.46% and ST-21.03% for area 3. Quasi opposition driving training based optimization (QODTBO) with FOPID controller has been employed to obtain optimal solution. The statistical methods, such as one-way ANOVA (analysis of variance), has been used to validate the superior outcomes of the proposed algorithm.

This paper aims to develop a novel variant of the moth swarm algorithm (MSA) for addressing nonlinearity coupled and complex flexible AC transmission systems integrated optimal power flow problems for two standard IEEE test systems. The MSA is a swarm intelligence-based technique that imitates the moth’s navigational strategy towards moonlight. Despite its simple algorithmic structure and ability to explore a large search space for better solutions, the MSA has some drawbacks, including a sluggish convergence rate, insufficient intensification, and high chances of getting entrapped in the sub-optimal solution point. To address the limitations of the orthodox MSA, this paper devises a chaos-incorporated partial opposition-based MSA with rank-based mutation (CPOMSA-RM). A chaotic mapping technique is used in the proposed algorithm to initialize the moth’s population to enhance the global search ability. The proposed CPOMSA-RM is integrated with a chaotic local search scheme to fortify the local search skill. Furthermore, the number of prospector moths in the proposed algorithm is chaotically determined to achieve a better balance between diversification and intensification. The proposed algorithm is integrated with a rank oriented Levy flights based mutation technique to improve the intensification and the convergence speed. In addition, partial opposition-based learning is combined in the proposed algorithm to prevent sub-optimal solution entrapment. Here, diversification is the process of searching for new areas of the search space, while intensification is the process of refining the current solution. To confirm the proposed algorithm’s efficacy, it is compared with different algorithms using statistical measures and non-parametric statistical tests are performed on evolutionary computation based 2017 benchmark functions. Moreover, the proposed algorithm is applied to solve the optimal power flow problem while considering the optimal placement and configuration of a single flexible AC transmission system device, i.e. static synchronous series compensator, and the results are compared with existing sophisticated algorithms. The simulation results confirm that the proposed algorithm outperforms its competitors in terms of solution quality, accuracy, convergence speed, and statistical metrics for the optimal power flow problem and benchmark functions.

The current study’s objective is to reveal the best possible solution for an optimal power flow (OPF) problem. The driving training-based optimization (DTBO) technique has been applied in this work to achieve the goal where quasi-oppositional based learning (QOBL) has been integrated with DTBO and referred to as quasi-oppositional driving training-based optimization (QODTBO). The experiments have been carried out on IEEE 57 & 118 bus systems. Four different test scenarios have been considered here. The first one is the traditional IEEE 57 bus network; the IEEE 57 bus with renewable energy sources (RESs) (i.e., solar and wind units) is chosen in the second one, and the third one considers the IEEE 57 bus with RESs and unified power flow controller (UPFC) and finally the IEEE 118 bus network with RESs and UPFC. In each test scenario, there are four objective functions, among which one is single objective and three of them are multi-objective. Obtaining minimum total cost comes under the single-objective function. Simultaneous reduction in the overall cost and emission, concurrent reduction in overall cost and voltage deviation (VD), and simultaneous reduction in overall cost and voltage stability index come under multi-objective cases. The acquired test outcomes by QODTBO have been contrasted with the outcomes found by the use of DTBO, backtracking search optimization algorithm (BSA), and sine cosine algorithm (SCA). The effect of inherent uncertainties within RESs is gauged in the current study by the choice of appropriate probability density functions (PDF). Based on the experimental outcomes using different optimization techniques over thirty trials, a statistical report has been prepared that ascertains that QODTBO is the most robust optimization scheme among the optimization tools taken into consideration in this study. To represent the statistical analysis, pictorially box plots and error-bar plots are provided. One-way analysis of variance (ANOVA) tests have also been conducted on test outcomes to enhance the degree of reliability of the inferences made based on statistical results. From this work, it is also explored that integrating RESs and UPFC with the traditional IEEE-57 bus system can improve the overall execution of the test system. If the performances of the conventional system, RES-based system, and RES- and UPFC-based system are observed, it can be noticed that for cost reduction, the RES-based system gives a better result by 1.364790635% and the RES- and UPFC-based system gives a better result by 2.175247484% better result as compared to the conventional system.

In order to address the environmental concerns, renewable energy has always been quite beneficial in lowering emissions and cutting generation costs. However, renewable sources are unpredictable, erratic and volatile. So, its large proportional integration poses risks to the secure and dependable use of electricity. However, one of the main sources of extreme pollution is fossil fuels. To overcome all drawbacks, EV is considered and virtual power plant (VPP) concept of EV have been integrated with SHT (Solar, Hydro, Thermal) System. The objective is to reduce generation cost by fulfilling transmission losses and load demand while satisfying all constraints. In this research, advanced moth flame optimization technique (MFO) has been applied to Electric Vehicle (EV) integrated SHT Systems for cost minimization.

The optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well-known test systems (33-bus and 69-bus). The uncertainty of power generation from PV- and WT-based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33-bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69-bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS- based 33-bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS-based 69-bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.