Stochastic renewable energy resources integrated  multi-objective optimal power flow

Pandya, Sundaram B.; Jariwala, Hitesh R.

doi:10.12928/telkomnika.v18i3.13466

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Table 8 provides the best, worst, average, and standard deviation results of the AEO, CSA, TLBO, ABC, and EO and the proposed IEO method after 50 independent runs. It can be noted that from Table 8, the total fuel cost minimization of methods SHADE-SF [41], JS [42], GWO [43], FPA [44], GWO [45], and MFO [46] give better results than the IEO method in terms of the best result. However, the voltage at the load buses is violated voltage limit.…”

Section: Case1 Generation Cost Minimizationmentioning

confidence: 98%

“…], while the voltage limit at the load buses of the proposed IEO method is [0.95 p.u.-1.05 p.u.]. Furthermore, to obtain the best value, these methods are used the number of solutions lager than the number of solutions of the proposed IEO method from 1,667 to 16,667 times, equivalent to 15000 solutions for GWO [45] and 150000 solutions for SHADE-SF [41] and FPA [44]. e ve remaining methods, which are listed in Table 8, have the same system data.…”

Section: Case1 Generation Cost Minimizationmentioning

confidence: 99%

“…A selfadaptive evolutionary programming (SAEP) [38] is applied to solve OPF with combined wind energy. A number of other algorithms are proposed to solve OPF problems with integrated renewable energy sources as introduced in [39][40][41][42][43][44][45][46]. In general, these methods are applied in the OPF problem with RES.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

Nguyen

et al. 2022

International Transactions on Electrical Energy Systems

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The paper is proposed an improved equilibrium optimizer (IEO) algorithm to solve the optimal power flow (OPF) problem with the participation of a renewable energy source (RES). In the proposed IEO method, the “exponential term” is replaced by a function that does not dependent on the number of iterations. This modification of the IEO algorithm increases exploration ability compared to EO algorithm. In addition, the exploration of the proposed IEO algorithm will not decrease according to the number of iterations which avoids to get stuck at the local optimal solution. The IEO algorithm is tested on two IEEE 30-bus and IEEE 118-bus systems with three different objective functions. The performance of the proposed IEO method is compared with equilibrium optimizer (EO), artificial ecosystem optimization (AEO), cuckoo search algorithm (CSA), teaching-learning-based optimization (TLBO), artificial bee colony (ABC), and many other existing methods. Besides, a simple probabilistic formula for calculating RES output power based on the Monte-Carlo simulation model is proposed in this paper to reduce the computation time for the OPF problem with RES. The simulation results obtained show that the proposed IEO algorithm has better quality of the solution as well as stability level compared to the original EO algorithm and other algorithm. Thus, the proposed IEO algorithm is also one of effective and reliable algorithms for handling OPF problem with RES.

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Section: Case1 Generation Cost Minimizationmentioning

confidence: 98%

Section: Case1 Generation Cost Minimizationmentioning

confidence: 99%

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An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

Nguyen

et al. 2022

International Transactions on Electrical Energy Systems

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“…Weibull and lognormal probability density function (PDF) is used to forecasting power from WT and solar farms. (Pandya and Jariwala, 2020) proposed moth flame optimization approach to solve MO-OPF incorporating wind and solar power plants. (Gonal and Sheshadri, 2019) solves OPF problem using hybrid batdragonfly algorithm incorporating wind-solar power.…”

Section: Introductionmentioning

confidence: 99%

Solution approach for optimal power flow considering wind turbine and environmental emissions

Maheshwari

Sood

2021

Wind Engineering

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The power grid is changing expeditiously with the increasing penetration of renewable energy sources (RES). Optimal utilization of RES reduces the burden on the primary grid and makes the grid more resilient. Traditional optimal power flow (OPF) is a complex problem in power management systems, and the complexity further increases with the integration of RES due to their intermittency. This paper presents the complete formulation of the OPF model incorporating wind turbines (WT) and environmental emissions for proper scheduling, planning, and efficient operation of thermal generating units (TGU) using the Ant Lion Optimization (ALO) algorithm. The formulation of the OPF problem comprises forecasted active power generation of WT, depending on the real-time measurement and probabilistic wind speed models. The results are analyzed from the perspective of operating cost, voltage profile, and transmission power losses in the system. The OPF approach and the solution methodology are tested on the IEEE 30 and IEEE 57-bus systems. The effectiveness of the proposed ALO algorithm is evaluated against well-established algorithms like Particle Swarm Optimization and Teaching-learning-based optimization. The comparison emphasizes the effectiveness of the ALO approach for solving various OPF problems with complex and non-smooth objective functions.

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“…Literature is also available for optimal scheduling of thermal generators along with hydro units along with wind production considering their probabilistic behavior [34]. Reference [35] utilized Moth Flame Optimization to find optimum power flow.…”

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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Stochastic renewable energy resources integrated multi-objective optimal power flow

Cited by 5 publications

References 20 publications

An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

Solution approach for optimal power flow considering wind turbine and environmental emissions

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

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