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This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (PLoss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes PLoss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

Purpose The increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance. Design/methodology/approach To achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software. Findings The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives. Originality/value The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.

Aged and insufficient domestic power plants and insecure, unreliable and expensive power imports pose significant challenges for the power sector of Afghanistan. On the other hand, due to the absence of a suitable transmission grid, the internal renewable energy resources are not adequately developed, despite their abundant resource potential throughout the country. This paper proposes a voltage source converter (VSC)-based high-voltage direct current (HVDC) transmission link to connect the Herat province in the west, which has huge solar and wind energy potential, to Kabul, the capital of the country and the main load center. A techno-economic analysis of this HVDC against high-voltage alternating current (HVAC) technology was performed to determine the suitability and effectiveness of the proposed transmission system. The active, reactive and corona losses were calculated as the technical parameters and the discounted cash flow (DCF) method was deployed to economically compare both technologies. The outcomes of the paper disclose that the implementation of this transmission project is techno-economically feasible, and can result in the energy security and economic stability of the country.

Power loss reduction can be considered as one of the main purposes for distribution system operators. Reconfiguration is an operation process used for this optimisation by means of changing the status of switches in a distribution network. Recently, all system operators tried their best in order to obtain well-balanced distribution systems to decrease the operation cost, improve reliability and reduce power loss. This study presents an efficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conventional distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements, because it only considers loss and voltage deviation as objective functions. The proposed approach considers reliability, operation cost and loss simultaneously. By adding the reliability objective to the DFR problem, this problem becomes more complicated than before and it needs to be solved with an accurate algorithm. Therefore this study utilises an Enhanced Gravitational Search Algorithm called EGSA which profits from a special mutation strategy in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The proposed approach has been applied to two distribution test systems including IEEE 33 and 70-node test systems.

This paper proposes an improved equilibrium optimizer (IEO) for selecting the most suitable location and the most effective size of photovoltaic systems (PVSs) in radial distribution power networks (RDPNs). The objective of the study is to reduce the total active power loss on all distribution lines of RDPNs while constraints regarding node voltage limits, branch current limits and active and reactive power balance must be satisfied exactly. IEO is first developed in the paper by modifying the newly generated solution mechanism of the conventional equilibrium optimizer (EO). In addition to the proposed IEO method and EO, two previously published methods including modified equilibrium optimizer (MEO) and adaptive equilibrium optimizer (AEO) are also implemented for three study cases with one, two and three PVSs placed in the IEEE 33-node and 85-node RDPNs. Compared to the base systems, the proposed IEO can reach the loss reduction with 65.5% for the IEEE 33-node RDPN and 52.96% for the IEEE 85-node RDPN. Total loss, loss reduction and computation speed comparisons indicate that the proposed IEO outperforms EO, MEO, AEO and other previously published methods shown in the literature for approximately all study cases. As a result, it concludes that the proposed IEO is a favorable optimization algorithm applied to the problem of PVSs placement in RDPNs.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

The photovoltaic (PV) system center inverter architecture comprises various conventional array topologies such as simple-series (S-S), parallel (P), series-parallel (S-P), total-cross-tied (T-C-T), bridge-linked (B-L), and honey-comb (H-C). The conventional PV array topologies under non-uniform operating conditions (NUOCs) produce a higher amount of mismatching power loss and represent multiple maximum-power-points (M-P-Ps) in the output characteristics. The performance of T-C-T topology is found superior among the conventional topologies under NUOCs. However, T-C-T topology’s main limitations are higher redundancy, more number of electrical connections, higher cabling loss, poor performance during row-wise shading patterns, and more number of switches and sensors for the re-configuration of PV modules. This paper proposes the various optimal hybrid PV array topologies to overcome the limitations of conventional T-C-T array topology. The proposed hybrid topologies are such as series-parallel-cross-tied (S-P-C-T), bridge-link-cross-tied (B-L-C-T), honey-comb-cross-tied (H-C-C-T), series-parallel-total-cross-tied (S-P-T-C-T), bridge-link-total-cross-tied (B-L-T-C-T), honey-comb-total-cross-tied (H-C-T-C-T), and bridge-link-honey-comb (B-L-H-C). The proposed hybrid topologies performance is evaluated and compared with the conventional topologies under various NUOCs. The parameters used for the comparative study are open-circuit voltage, short-circuit current, global-maximum-power-point (GMPP), local-maximum-power-point (LMPP), number of LMPPs, and fill factor (FF). Furthermore, the mismatched power loss and the conversion efficiency of conventional and hybrid array topologies are also determined. Based on the results, it is found that the hybrid array topologies maximize the power output by mitigating the effect of NUOCs and reducing the number of LMPPs.