Dynamic Economic Dispatch Incorporating Commercial Electric Vehicles

Abinaya, K.; Suresh, Velamuri; Sudabattula, Suresh Kumar; Kaveripriya, S.

doi:10.1007/978-981-15-7241-8_5

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…This algorithms are used in several problems such as extraction of photovoltaic parameters using gradient based optimizer [12] and Turbulent flow of water optimization [13]. In addition, solving ELD problem is performed with several al-gorithms such as non dominated sorting genetinc algorithm [14], modified jrill algorithm [15], Whale Optimization Algorithm [16], Henry gas solubility optimization [17], gravitational search algorithm [18], improved Firework Algorithm [19], grasshopper optimization algorithm [20], multi gradient practical swarm optimization [21], Salp Swarm Algorithm [22], Differential Evolution [23], Equilibrium Optimizer [24], reinforcement learning [25], virus colony search algorithm [26], Harmony Search Algorithm [27], improved grey wolf optimization [28], bat algorithm [29], improved class topper optimization algorithm [30], improved bat algorithm [31], Ant Colony Optimization [32], improved Jaya algorithm [33] and artificial cooperative search algorithm [34].…”

Section: Introductionmentioning

confidence: 99%

“…In [21], authors have solved real power limitations in the dynamic ELD of large-scale thermal power units under the effects of valvepoint loading and ramp time constrains by multi-gradient PSO. In [22], the authors have formulated the dynamic ELD problem incorporating commercial EVs and used SSA to solve the problem. In [23], the authors have solved the multiarea ELD problem by DE.…”

Section: Introductionmentioning

confidence: 99%

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Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

et al. 2021

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The economic load dispatch (ELD) problems considering nonlinear characteristics where an optimal combination of power generating units is selected in order to minimize the total cost by economic allocation of power produced and the emission cost. As a consequence, optimal allocation is performed by considering both fuel cost and emission leading to Combined Economic and Emission Dispatch (CEED). This study presents a new Meta-heuristic algorithms (MHs) called the Turbulent Flow of Water Optimization (TFWO), which is based on the behaviour of whirlpools created in turbulent water flow, for solving different variants of ELD and CEED. To verify the robustness of the TFWO, various test network of CEED with effect of valve, and ELD with losses of transmission are incorporated. In comparison with seven well-known MHs such as Cuckoo Search Algorithm (CSA), Grey Wolf Algorithm (GW), Sine Cosine Algorithm (SCA), Earth Worm Optimization Algorithm (EWA), Tunicate Swarm Algorithm (TSA), Moth Search Algorithm (MSA) and Teaching Learning Based Optimization (TLBO), the TFWO provides the minimum fuel cost and significantly robust solutions of ELD problem over all tested networks. The results confirm the potential and effectiveness of the GWO to be a promising technique to solve various ELD problems.INDEX TERMS Turbulent Flow of Water Optimization (TFWO)); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristic Optimization Algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

et al. 2021

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“…Reference [9] discussed about various AI techniques to solve DED problems such as Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Evolutionary Programming (EP) and differential evolution (DE). Salp-Swarm algorithm (SSA) is used to solve DED problem incorporating electric vehicles to minimize fuel cost and network power loss [10] Then improved Salp-Swarm algorithm is proposed in which a mutation process is added to SSA to improve the exploitation of search space in order to have better population for getting optimum solution [11]. Mojtaba Ghasemi et.al proposed a novel version of PSO with time varying acceleration coefficients to solve DED problems [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

Jain

Mani

Siddiqui

2021

mjee

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Grey Wolf optimizer is a well-known metaheuristic technique but it faces the problem of premature convergence, trapping in local optima and poor balance between exploration and exploitation. Hence, various modifications have been proposed over past few years. One such modification is modelling the prey position as dynamic in nature as shown in GWOLF. However, still it has been observed that to get better solution, GWOLF needs to be modified too, hence an improved version of GWO (IGWO) is proposed. IGWO has the advantage of both GWO and GWOLF. In this paper, IGWO is used to solve Dynamic Economic Dispatch (DED) problem. IGWO is having a better balance between exploitation and exploration for the complex problem such as Dynamic Economic Dispatch (DED) taking into account of valve-point effect, transmission losses and ramp-rate limits with and without Electric Vehicles (EVs). The efficiency of the algorithm is demonstrated on solving different DED problems for 5 generator and 15 generator test systems with and without losses along with different charging profile distribution of electric vehicles. The results showcased by IGWO is compared with the other algorithm. The results obtained by IGWO algorithm adopted in solving dynamic economic dispatch problem is giving competitive results as compared to the results given by other algorithm present in literature.

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Dynamic Economic Dispatch Incorporating Commercial Electric Vehicles

Cited by 2 publications

References 20 publications

Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

Performance of Turbulent Flow of Water Optimization on Economic Load Dispatch Problem

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

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