Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems

Chen, Gonggui; Yi, Xingting; Zhang, Zhizhong; Wang, Huiming

doi:10.1016/j.asoc.2018.04.006

Cited by 69 publications

(47 citation statements)

References 53 publications

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“…The software was developed in MATLAB 2012a platform and ran in a Microsoft Windows 7 PC with Intel(R) Core(TM) i5-2430M CPU @ 2.40 GHz with 4 GB Random Access Memory (RAM). Before choosing objective combinations, it is necessary to figure out the conflicting relationship among the four objective functions described in Section 2.1, as illustrated in the literature [25]. Thus, the details of the six cases are listed in Table 3.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The equality constraints are consisted of Equations (6) and (7). The specific meaning of each element can be taken from the literature [25].…”

Section: Equality Constraintsmentioning

confidence: 99%

“…The inequality constraints on dependent variables can be represented by Equations (13)- (17). The specific meaning of each element can be found in the literature [25].…”

Section: Inequality Constraintsmentioning

confidence: 99%

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Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Chen

Zhang

et al. 2018

Energies

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Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.

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Section: Simulation Results and Analysismentioning

confidence: 99%

“…The equality constraints are consisted of Equations (6) and (7). The specific meaning of each element can be taken from the literature [25].…”

Section: Equality Constraintsmentioning

confidence: 99%

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Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Chen

Zhang

et al. 2018

Energies

Self Cite

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“…The IEEE 30-bus system is used to validate the QOB modified Jaya algorithm. A modified firefly algorithm is proposed in [30] for the solution of MOOPF problem non-dominating sorting, and for well distributed PF crowding distance approach is utilized along with integration of fuzzy affiliation for picking BCS. IEEE 30 and 57-bus test models, are used to validate the results by considering nine different cases.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equality constraints shown in equations 17-20 are discussed in detail in [55]. The convergence process of running load flow will eventually minimized the equality constraints in the allowable boundaries.…”

Section: B Equality Constraintsmentioning

confidence: 99%

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

et al. 2020

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In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives. INDEX TERMS fuzzy membership function; multi-objective optimal power ïňĆow problem; renewable energy sources.

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Congestion management of integrated transmission and distribution network with RES and ESS under stressed condition

Prajapati

Mahajan

Padhy

2021

Int Trans Electr Energ Syst

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Summary In this article, the multi‐objective optimization‐based congestion management approach for integrated transmission and distribution system under stressed condition is presented. The congestion is managed by optimized charging and discharging of energy storage systems (ESSs). The coordination among transmission system operator (TSO) and distribution system operator (DSO) takes place for the economic and optimum exchange of power. The main intent of this study is to minimize the amount of load shedding (MW) and its cost ($/MWh) by minimizing the generation cost ($/MWh). These are conflicting objectives as for less load shedding, the system has to have more generation and hence higher cost. The negotiable solution among conflicting objectives is obtained by using the fuzzy min‐max approach. The ESS charging and discharging are optimized for their maximum contribution. This increases the available power (MW), thereby reducing the uncertainty of renewable energy sources and hence lesser load shedding. The analysis is conducted for constant load modeling (CLM) and voltage‐dependent load modeling. The results show that CLM has less load shedding. The proposed approach is implemented on a modified IEEE‐30‐bus test system integrated with the nine‐bus distribution network. Simulations and validations are done using General Algebraic Modeling Solver.

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Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems

Cited by 69 publications

References 53 publications

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

Congestion management of integrated transmission and distribution network with RES and ESS under stressed condition

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