A Fully Decentralized Approach for Solving the Economic Dispatch Problem

El-Sayed, Wael T.; El-Saadany, Ehab F.

doi:10.1109/tpwrs.2014.2360369

Cited by 142 publications

(70 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 3 shows the comparison of the statistical results of different algorithms. In the table, the results obtained by BLPSO are compared with CLPSO, SLPSO, NPSO-LRS [54], MTS [55], TS [55], SA [55], GAAPI [56], HCRO-DE [42], DE [57], MABC [31], CBA [58], RDPSO [26], IRDPSO [26], and ST-IRDPSO [26]. It can be seen that the minimum and mean fuel costs obtained by BLPSO are similar to SLPSO and less than all the other methods with the exceptions of 10 Complexity HCRO-DE [42].…”

Section: Resultsmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

View full text Add to dashboard Cite

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeographybased learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeographybased learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

show abstract

Section: Resultsmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

View full text Add to dashboard Cite

show abstract

“…Equations (23) and (22) mean that C r is 0 for the best wolf and 0.2 for the worst wolf in the pack of the current generation or iteration. C r ¼ 0 ensures elitism.…”

Section: Crossovermentioning

confidence: 99%

“…Gradient free, 'hard computing' methods e distinct from metaheuristic, or soft computing methods e have also been proposed [22]. contains a fully decentralized approach [23], a game-theoretic formulation [24], a distribution auction-based algorithm, and [25] a distribution consensus-based algorithm, for solving the ED problem.…”

Section: Introductionmentioning

confidence: 99%

Economic dispatch using hybrid grey wolf optimizer

Jayabarathi

Raghunathan

Adarsh

et al. 2016

Energy

312

View full text Add to dashboard Cite

“…Among them, consensus algorithm is considered to be an important solution for Smart Grid in a multi-agent system. For the economic dispatch problem, the traditional centralized economic dispatch problem was solved in a distributed way using consensus algorithm [24][25][26]. A strict analysis of convergence and optimality for the consensus algorithm under different topologies is addressed in [24].…”

Section: Introductionmentioning

confidence: 99%

“…A strict analysis of convergence and optimality for the consensus algorithm under different topologies is addressed in [24]. The economic dispatch problem is solved in distributed way which consist of two stages in [25]. A flooding-based consensus algorithm is proposed in the first stage, and in the second stage, a nondeterministic method is used for solving the economic dispatch problem in parallel.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

et al. 2018

View full text Add to dashboard Cite

Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat pump (HP) is introduced. Supporting by the PVT and HP, the prosumer has four role attributes with either heat or electricity producer/consumer. A social welfare maximization model is built for the CEI, including PVT-HP prosumers, CHP system, and utility grid. Considering there are multiply participants in the local market of CEI, the social welfare maximization problem is decoupled by using Lagrange multiplier method. Moreover, a consensus-based fully distributed algorithm is designed to solve the problem. Finally, six residential buildings are selected as the case study to validate the effectiveness of the proposed method.

show abstract

A Fully Decentralized Approach for Solving the Economic Dispatch Problem

Cited by 142 publications

References 28 publications

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

Economic dispatch using hybrid grey wolf optimizer

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

Contact Info

Product

Resources

About