Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Jayabarathi, T.; Yazdani, Afshin; Ramesh, V.; Raghunathan, Trivellore E.

doi:10.1007/s11708-013-0276-4

Cited by 45 publications

(27 citation statements)

References 21 publications

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“…From Table 3, it can be observed that the CPU time of TSCO algorithm is much less than all other compared algorithms. In addition, the TSCO algorithm obtains less fuel cost than the algorithm reported in the existing literature, 5,10,12,16,20,28,29,33,[35][36][37] and almost equal fuel cost with the CSO 35 algorithm. As shown in Table 2, the power production and the heat production are 600.6834 MW and 150 WMth, respectively.…”

Section: Test Casementioning

confidence: 88%

Social cognitive optimization with tent map for combined heat and power economic dispatch

Sun

2018

Int Trans Electr Energ Syst

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Summary Combined heat and power economic dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel social cognitive optimization algorithm with tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, adaptive constraints relaxing rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by conducting 2 typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state‐of‐the‐art algorithms.

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Section: Test Casementioning

confidence: 88%

Social cognitive optimization with tent map for combined heat and power economic dispatch

Sun

2018

Int Trans Electr Energ Syst

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“…For this case, MRFOA is employed on 300 MW power demand and 150 MWth heat demand, where the optimal solution is compared with respect to those illustrated by various optimization techniques as in Table 1. It is apparently seen that the MRFOA finds a minimum operational costs of 13673.13 $ that is lower than HS [8], firefly algorithm (FA) [42], TVAC-PSO [15], WVO [14], WVO-PSO [14], GSO [37], and IWO [21] where they acquired TFC of 13724. On the other side, it can be observed that EDHS [9] and CPSO [12] attained less TFC values compared to MRFOA, but their operating points were not feasible solutions as described in Figures 3 and 4.…”

Section: ) Casementioning

confidence: 98%

“…In [20], a quasidynamic pipeline model for EPHD with start-stop schedule of heat exchange stations is developed. Also, an invasive weed optimizer (IWO) [21] based on the process of weed colonization/distribution has been applied optimal operation of the CES for small scale EPHD problems. In addition, an integrated technique, which combined the civilized swarm optimization (CSO) and the Powell's pattern search (PPS) method [22], has been carried out to solve the EPHD problem in CES.…”

Section: Introductionmentioning

confidence: 99%

Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer

et al. 2020

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“…In the years since these pioneering works, the imaginativeness of researchers has continued to flourish. New metaheuristic methods have been applied to the CHP dispatch problem such as cuckoo search [23][24][25][26], invasive weed optimization [27], the artificial immune system algorithm [28], the firefly algorithm [29], krill herd optimization [30], the harmony search algorithm [31][32][33][34], grey wolf optimization [35,36], whale optimization [37], and the hybridizing bat algorithm with artificial bee colony [38]. This is by no means an exhaustive list of metaheuristic approaches investigated.…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

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Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.

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Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Cited by 45 publications

References 21 publications

Social cognitive optimization with tent map for combined heat and power economic dispatch

Social cognitive optimization with tent map for combined heat and power economic dispatch

Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

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