Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

The recent increase in plug in electric vehicles (PEVs) load has posed severe accommodation issues for the power grids that were not primarily designed to serve PEVs’ charging load. Therefore, the unplanned PEVs’ integration into the distribution networks will deteriorate the system performance by increasing the power loss and voltage deviation at nodes. In this context, this study proposes a methodology to enhance the distribution networks’ PEVs hosting capacity, without degrading the system performance, by optimally allocating the distributed generation (DG). Initially, aiming to minimize the power losses in the distribution network, the optimal siting, sizing and number of the DG units were determined using the particle swarm optimization (PSO) algorithm. Later, the best network bus for PEV accommodation was selected on the criteria of maximum hosting capacity and loss reduction in the distribution network. Then, considering the accessible power output from the grid and the mounted DGs, the PEVs accommodation in the network is evaluated based on different battery capacities (BCs) and state-of-charge (SoC) as decisive factors. The test results on the IEEE 33-bus demonstrate that in the presence of DGs, the distribution network carries a PEV load of 4.418 MW with 48.82% reduction is losses (compared to case without optimal DG allocation) and voltage improvement of 6.98%. It was also examined that the PEVs accommodation in the distribution network varied from 2492 units to 61,446 units, depending on the size of battery pack and its SoC. The proposed research will enable the distribution companies to handle the large number of PEVs in the long-term planning paradigm.KeywordsPlug in electric vehicles accommodationDistributed generationParticle swarm optimizationPower lossesState-of-chargeBattery capacities

The next decade is likely to witness considerable growth in the application of plug-in electric vehicles (PEVs). However, uncoordinated PEV charging in low-voltage distribution networks may cause grid issues, such as transformer overload and voltage unbalance (VU). Therefore, it is essential to consider a more suitable methodology of voltage regulation for the consumers placed at the end of the feeder when the network is predominantly resistive. With this background, this article investigates the capability of dynamic PEV load transfer within the three phases of a distribution network to propose a new hybrid PEV coordination approach. In addition to the online centralized battery charging and var discharging, this approach performs local voltage profiles improvement by switching PEV at selected single-phase residential houses such that a PEV can be switched from a heavily loaded phase to a (relatively) lightly loaded phase. This results in better voltage regulations and VU reductions. The impacts of uncoordinated and proposed genetic algorithm (GA) coordinated PEV charging on VU and node voltage profiles and losses are simulated for a real unbalanced Western Australian distribution network in the Perth solar city over 24 h.

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

Plug-in electric vehicles (PEVs) appear to offer a promising option for mitigating greenhouse emission. However, uncoordinated PEV charging can weaken the reliability of power systems. The proper accommodation of PEVs in a power grid imposes many challenges on system planning and operations. This work aims to investigate optimal PEV coordination strategies with cost-benefit analysis. In Part I, we first present a new method to calculate the charging load of PEVs with a modified Latin hypercube sampling (LHS) method for handling the stochastic property of PEVs. We then propose a new two-stage optimization model to discover the optimal charging states of PEVs in a given day. Using this model, the peak load with charging load of PEVs is minimized in the first stage and the load fluctuation is minimized in the second-stage with peak load being fixed as the value obtained in the first stage. An algorithm based on linear mixed-integer programming is provided as a suitable solution method with fast computation. Finally, we present a new method to calculate the benefit and cost for a PEV charging and discharging coordination strategy from a social welfare approach. These methods are useful for developing PEV coordination strategies in power system planning and supporting PEV-related policy making.

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

Distributed generations (DGs) are widely used to meet electric energy demand in distribution networks. DGs are advantageous compared to the centralized generating units in many ways including reducing power loss, improving voltage profile, enhancing network stability, decreasing pollution, releasing transmission line capacity, and reducing congestion of distribution lines. The size and site of DGs logically influence power loss in distribution networks. The optimal place and capacity of DGs are determined aiming at minimizing power loss in distribution networks. In this article, firefly algorithm (FA) is used for optimum placement of various DGs in distribution networks. Optimum power factor of DGs along with active and reactive powers are obtained in this work. Various DGs of generating/consuming types are considered in the suggested method. Performance analysis of the proposed method has been done on standard IEEE 33 bus system in MATLAB.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Turatbek Omorov, Beishenaly Takyrbashev, Taalaibek Koibagarov, Tilebaldy Zhanybaev; Identification and monitoring of power losses in distribution networks as a composition of AMR. AIP Conf. Proc. 5 January 2023; 2552 (1): 020002. https://doi.org/10.1063/5.0114527 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper presents a study that considers reconfiguring a given substation’s distribution network at Dilla, Ethiopia, to minimize losses with the help of the Grasshopper Optimization Algorithm and Particle Swarm Optimization. The main aim is to achieve the best switch combination to minimize power loss, subject to system-imposed constraints. A mathematical model was carried out of the best network configuration at the Dilla feeder, inspired by grasshopper behavior. The proposed method was tested over a 76-bus radial distribution system at the Dilla substation. Compared with PSO and other existing methods, the performance of GOA is far better in reducing active and reactive power losses. The reduction of power loss with the help of GOA comes to be 70.3%, while with PSO, it comes out to be 68.8%. The voltage profile improved reasonably, stabilizing around one pu for all the buses. The results illustrated that GOA could be an effective and practical strategy for optimizing the distribution networks with increased reliability and decreased power losses. The paper concludes with a comparative analysis of the proposed GOA and existing methods for distribution network reconfiguration (DNR), showing that the GOA is more effective in reducing voltage deviation (VD) and power losses in the system.

The distribution system is the largest segment of the electrical power system and the final stage in delivering electricity to consumers. It experiences power losses which could be technical or non-technical due to its radial configuration and connected components. Technical losses occur as energy dissipation in the form of heat in the system components, which is unavoidable. It affects the efficiency of the system and increases operational costs. Therefore, the reduction is essential for adequate power supply and improvement in utility revenue generation. This study focuses on reducing power losses on 11 kV Taiwo distribution feeder in Ilorin using the Network reconfiguration approach. The feeder was modelled, and its power losses were evaluated based on the Backward Forward Sweep method suitable for the radial network. The reconfigured network was optimised using the Particle Swarm Optimisation technique. The study shows that the values of feeder real power losses before and after reconfiguration are 176.883 kW and 121.972 kW, respectively.Further improvement was, however, achieved when PSO was used for the reconfiguration, as the power loss value stands at 98.465 kW. This is 44.33 % reduction compared to the initial power loss value and 19.273 % after reconfiguration. Furthermore, the values of the reactive power losses before and after reconfiguration are 9.474 kVar and 6.527 kVar, respectively, amounting to a 31.11 % reduction. However, PSO reduces the value to 3.101 kVar, which is 67.27 % of the initial value. Thus, radial distribution network reconfiguration using PSO has proven to be a robust method for reducing power loss in the distribution network.

This paper utilize two novel optimization techniques namely Particle Swarm Optimization (PSO) and Craziness based Particle Swarm Optimization (CRPSO) to find the optimum location and optimum size of single and two Distributed generator (DG) in distribution system. Minimization of active power loss in distribution network is carried out through DG placement. The method is tested with two standard IEEE test cases namely 33-node radial distribution network and 69-node radial distribution network. The node with minimum system power loss is considered as the optimum location for DG placement which in turn enhances the voltage profile of overall system. The results of two optimization techniques for optimum size and optimum location of DG placement are found competitive.