A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch

Zhang, Yong; Gong, Dunwei; Ding, Zegang

doi:10.1016/j.ins.2011.06.004

Cited by 260 publications

(119 citation statements)

References 37 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…2.1 and 2.2, respectively; Step7: Update the particle positions. In order to reduce the influence of control coefficients, such as inertia weight and acceleration coefficients, on the performance of the proposed algorithm, a Gaussian sampling based on the global best position g i = (g i,1 , g i,2 , · · · , g i,n ) and the personal best position p i = (p i,1 , p i,2 , · · · , p i,n ), proposed in [12], is used to generate new particles, as follows:…”

Section: Steps Of the Proposed Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

Zhang

Gong

et al. 2016

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYConsidering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multiobjective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

show abstract

Section: Steps Of the Proposed Algorithmmentioning

confidence: 99%

“…Step8: Run the uniform mutation [12] to improve the global search capability of swarm, and go to Step3.…”

Section: Steps Of the Proposed Algorithmmentioning

confidence: 99%

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

Zhang

Gong

et al. 2016

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many research efforts were made for the COP [5][6][7][8][9][10][11]. Niknam et al [5] proposed an innovative tribe-modified differential evolution (Tribe-MDE) for the COP.…”

Section: Introductionmentioning

confidence: 99%

“…Rao and Vaisakh [6] provided a multi objective optimization approach based on adaptive clonal selection algorithm (ACSA) to solve the complex COP of thermal generators in power system. Zhang et al [7] presented a multi-objective optimization algorithm, called the bare-bones multi-objective particle swarm optimization (BB-MOPSO) for solving the COP. Niknam and Mojarrad [8] developed a modified adaptive Θ-particle swarm optimization (MAΘ-PSO) algorithm to investigate the COP.…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Power Economic Emission Dispatch Using an Integrated Algorithm

Chiang¹

2017

dteees

View full text Add to dashboard Cite

Abstract. This paper presents an integrated algorithm of power economic dispatch problem aiming to manage emissions for generating units of power systems. Differential evolution (DE) can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty to solution searching. To handle the constrained optimal problem (COP), the ε-constraint technique is employed. The proposed approach integrates the ε-constraint technique, the DE, and the MU. The proposed method (DE-MU) has the merit of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population. Numerical results indicate that the proposed approach is superior to previous methods in solution quality.

show abstract

“…However, the structure of distribution networks currently is becoming more complex and cannot be modeled as a passive node because of the demand response and distributed generators (DGs) [12]. Furthermore, distribution networks transport electricity from DGs instead of transmission systems to end customers, which not only reduce distribution network usage cost but also potentially avoid congestion in transmission systems [13][14][15][16][17][18][19][20][21][22]. In [15], the optimal objectives are maximizing the power usage from DGs and the power usage costs of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization of smart distribution networks considering real-time pricing

Zhang

Zhao

et al. 2014

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

With the development of smart meters, a realtime pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

show abstract

A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch

Cited by 260 publications

References 37 publications

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

A Case Study of Power Economic Emission Dispatch Using an Integrated Algorithm

Economic optimization of smart distribution networks considering real-time pricing

Contact Info

Product

Resources

About