Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm

Dharavat, Nagaraju; Sudabattula, Suresh Kumar; Suresh, Velamuri; Mishra, Sachin; Sharma, Naveen Kumar; Bajaj, Mohit; Elgamli, Elmazeg; Shouran, Mokhtar; Kamel, Salah

doi:10.3390/en15186698

Cited by 44 publications

(16 citation statements)

References 26 publications

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“…The uncertain parameters and methods used in the surveyed articles focused on a single CS are shown in Table 1 brief. √ SOC using lognormal distribution and arrival time using normal distribution [40] √ Probabilistic method (Scenario-based modelling) [41] √ Probabilistic method (normal distribution)…”

Section: Modelling the Uncertainty Of Parameters In A Single-cs Systemmentioning

confidence: 99%

“…Figure 5 shows each objectives' category of the research works referred to in this article. For example articles [29][30][31] and [35][36][37][38][39][40][41][42][43][44]46] considered minimum cost while [32][33][34][35], focused on maximum profit of the charging station as objectives. References [31][32][33][34]36,46] presented methodologies for optimal penalty/reward of EV charging/discharging.…”

Section: Coordinated Charging Management In a Single Csmentioning

confidence: 99%

“…Moreover, mathematical approaches and heuristic methods are used mostly as solvers in optimization problems. MILP and QP/Robust optimal scheduling algorithm using Gurobi solver in MATLAB [29] Approximate dynamic programming, big-bang-big-crunch algorithm [42] Heuristic algorithm [26] Fuzzy Integer LP/Heuristic Fuzzy [30] Grey wolf Optimization, Chance-constraints model predictive control [31] Chance constraint LP/LP software in MATLAB (i.e., linprog) [36] NLP/WORHP solver in MATLAB [35] Convex optimization/solvers in MATLAB toolbox [43] MIP and QP/Augment Epsilon-constrained technique [33] PSO Algorithm [37] Mixed real and binary vector/PSO algorithm [44] GA algorithm [39] LP/CPLEX in Visual Studio [40] Greedy algorithm and max flow algorithm (Fold-Fulkerson) [41] Political optimization algorithm [34] Centralized & decentralized Reinforcement learning [47] Decentralized MILP/Big-M method and CPLEX, BARON [16] MILP/Sequence operation theory Chance-constrained/CPLEX [49] A multi-agent-based cooperative algorithm-Graph theory [50] Non-cooperative game theory/KKT, distributed consensus algorithms [38] Hybrid Agent-based deep reinforcement learning and fuzzy logic…”

Section: Coordinated Charging Management In a Single Csmentioning

confidence: 99%

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A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

2023

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The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed.

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Section: Modelling the Uncertainty Of Parameters In A Single-cs Systemmentioning

confidence: 99%

Section: Coordinated Charging Management In a Single Csmentioning

confidence: 99%

Section: Coordinated Charging Management In a Single Csmentioning

confidence: 99%

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A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

2023

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“…Furthermore, there is a substantial body of literature on the simultaneous installation of EVCS with DGs and DSTAT-COMs/capacitors. Table-1 [2], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] provides a complete review of current research on EVCS planning using Distribution Generator (DG), Capacitor, Network Reconfiguration (NR), Battery Energy Storage System (BESS), and DSTATCOM for various types of distribution systems, optimization methodologies, and objective functions.…”

Section: Related Workmentioning

confidence: 99%

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

et al. 2023

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Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system's performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system's net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS. INDEX TERMS Electrical vehicle (EV), bald eagle search algorithm (BESA), bat algorithm (BA), distribution STATicCOMpensator (DSTATCOM), voltage stability index (VSI), distribution system (DS), electric vehicle charging station (EVCS). I. INTRODUCTIONLong-term development and poverty eradication depend heavily on energy. In India, 97.2% of the population has access to electricity. Natural gas and coal are the most frequent fuels utilized in the power sector. India has a total capacity for electricity generation of 388.134GW, with offgrid renewable energy (RE) accounting for 21.26% of that capacity. The lack of coal and natural gas might threaten the production of power. To address the nation's expandingThe associate editor coordinating the review of this manuscript and approving it for publication was Junho Hong .

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“…The UPQC plays a crucial role in preserving the source voltage quality when faults occur in renewable energy systems, such as photovoltaic (PV) and wind (Millnitz dos Santos et al, 2014). The vehicle-to-grid (V2G) mode of operation is a focal point in distributed systems (Karanki et al, 2013;Mohamed et al, 2021;Dharavat et al, 2022). The charging and discharging of an energy storage device (ESD) is utilized in a UPQC to stabilize the direct current voltage for the series and parallel APFs (Hegazy et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

Prasada Rao,

Pandian,

Reddy

et al. 2024

Front. Energy Res.

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This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

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Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm

Cited by 44 publications

References 26 publications

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

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