Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation

Haghrah, A.; Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam

doi:10.1016/j.applthermaleng.2015.12.136

Cited by 138 publications

(69 citation statements)

References 32 publications

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“…This is reasonable, as the NN-RCGA used the RCGA, which is robust and effective at reducing the bias and variation of the models [167]. Other published studies also confirmed the good capability of the RCGA in optimizing the parameters of the ML models [168,169]. A comparison of the results with previously published was works also conducted, and is shown in Figure 11 and Table 9 (including the applications of all data).…”

Section: Validation and Comparison Of The Hybrid Modelssupporting

confidence: 70%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Section: Validation and Comparison Of The Hybrid Modelssupporting

confidence: 70%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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“…In addition, the results of this work suggest significant practical implications for determining the best compromise solutions from all Pareto-optimal solutions, which is especially helpful to meet the diverse needs under changing operating conditions of a CHP system. 37 Our future work will focus on extending this study to extensive potential applications in the optimal operation and control for a smart integrated energy system. In addition, more realistic modeling techniques such as load and renewable generation uncertainties [43], and energy storage units [44] will be incorporated to improve the practicality of the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…Figs. 11 and 12 shows the extreme solutions and the corresponding power transmission loss obtained by θ-DEA, together with the existing results in literature using various algorithms including real coded genetic algorithm (RCGA) [37], particle swarm optimization (PSO) [38], evolutionary programming (EP) [39], artificial-immune system optimization (AIS) [40], differential evolution (DE) [41], bee colony optimization (BCO) [35] and MOPSO. As can be seen in Fig.…”

Section: Casementioning

confidence: 99%

A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making

Wang

Zhao

et al. 2018

Energy

192

108

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To address the problem of combined heat and power economic emission dispatch (CHPEED), a two-stage approach is proposed by combining multi-objective optimization (MOO) with integrated decision making (IDM). First, a practical CHPEED model is built by taking into account power transmission losses and the valve-point loading effects. To solve this model, a two-stage methodology is thereafter proposed.The first stage of this approach relies on the use of a powerful multi-objective evolutionary algorithm, called θ-dominance based evolutionary algorithm (θ-DEA), to find multiple Pareto-optimal solutions of the model. Through fuzzy c-means (FCM) clustering, the second stage separates the obtained Pareto-optimal solutions into different clusters and thereupon identifies the best compromise solutions (BCSs) by assessing the relative projections of the solutions belonging to the same cluster using grey relation projection (GRP). The novelty of this work is in the incorporation of an IDM technique FCM-GRP into CHPEED to automatically determine the BCSs that represent decision makers' different, even conflicting, preferences. The simulation results on three test cases with varied complexity levels verify the effectiveness and superiority of the proposed approach. (Yang Li). 2 multi-objective optimization; integrated decision making; valve-point loading effects; θ-dominance based evolutionary algorithm; grey relational projection; integrated energy system. NOMENCLATURE Acronyms CHP combined heat and power CHPED CHP economic dispatch CHPEED CHP economic emission dispatch MOEAs multi-objective evolutionary algorithms MOPSO multi-objective particle swarm optimization EFA enhanced firefly algorithm MOO multi-objective optimization

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“…Among a wide variety of ED approaches, heuristic algorithms and mathematical optimization approaches have been regarded as the most effective solutions for achieving the optimal ED strategy. For example, researchers in [25,26] employed the differential evolution technique and the real-coded genetic algorithm to study the optimal ED problem for a CHP system, respectively. However, two main disadvantages of the heuristic algorithms cannot be simply neglected [27,28].…”

Section: Introductionmentioning

confidence: 99%

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Liu

Chen

et al. 2020

Mathematical Problems in Engineering

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Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

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Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation

Cited by 138 publications

References 32 publications

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

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