Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch

Chen, J.J.; Qi, B.X.; Peng, Ke; Li, Y.; Zhao, Yili

doi:10.1016/j.apenergy.2019.114337

Cited by 28 publications

(18 citation statements)

References 33 publications

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“…With increasing carbon trading price P CDM , the satisfying solutions with the largest relative distance under different weights ω 2 , combined with the corresponding fuel costs and carbon emissions are summarized in Table 5. To clarify, ω 1 and ω 2 , as defined in Equation (19), are the normalizations of P CDM , and specifically, ω 2 is positively correlated with P CDM , thus the impact analysis of P CDM is roughly equivalent to the impact analysis of ω 2 .…”

Section: Impact Analysis Of Carbon Trading Pricementioning

confidence: 99%

“…To provide a more flexible and effective tool for carbon emission control compared with the traditional generation-side penalty scheme, Shao et al proposed a consumption-side carbon emission penalty scheme, where consumers were penalized according to their individual carbon emission responsibilities and penalty rates [18]. For hedging random fuzzy wind power in response to the demand for integrated multi-period economic emission dispatch, Chen et al presented a conditional value-at-credibility model, which proved to be feasible and effective in solving multi-period EED, considering wind power uncertainty [19].…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, since carbon trading is implemented by carbon trading price setting, it is necessary to set a rational P CDM according to the specific requirements of carbon emission reduction and power generation efficiency improvement. Table 6 mainly illustrates the comparison between ω 2 and P CDM based on Equations (19) and (20), and Table A1, besides the relationships with P CDM , shows the decrement rate of carbon emissions and the proportions shared by units 1 and 6. The P CDM for ω 2 = 0.5, with a moderate view on environmental protection, is assumed to be 41.39 (¥/ton), which is an average price from the Beijing carbon trading market in 2019 [13], so that different P CDM corresponding to various ω 2 can be obtained after deriving the compensation coefficient ρ in Equation (19).…”

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confidence: 99%

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Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism

et al. 2021

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Nowadays, the power system is faced with some new changes from low-carbon approaches, though these approaches have proved to be effective in developing low-carbon electricity. Specifically, wind power integration and carbon trading influence the traditional economic emission dispatch (EED) mode, allowing for the disturbance of wind power uncertainties and the fluctuation of carbon trading price. Aiming at the above problems, this study firstly builds a stochastic EED model in the form of chance-constrained programming associated with wind power reliability. Next, wind power features are deduced from the statistic characteristics of wind speed, and thus the established model is converted to a deterministic form. After that, an auxiliary decision-making method based on the technique for order preference by similarity to an ideal solution (TOPSIS) is designed to draw the optimal solution based upon the specific requirements of carbon emission control. The simulation results eventually indicate that the minimization of fuel costs and carbon emissions comes at the expense of wind power reliability. Meanwhile, carbon emission reduction can be effectively realized by carbon trading rather than a substantial increase in fuel costs, and carbon trading may help to improve power generation efficiency. Furthermore, carbon trading prices could be determined by the demands of carbon emission reduction and power generation efficiency improvement.

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Section: Impact Analysis Of Carbon Trading Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism

et al. 2021

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“…For optimal dispatch and operating electric energy, various multi-objective evolutionary algorithms were also suggested in the reference [39]. In reference [40], authors focused on the benefits of using neural-fuzzy methods to find an acceptable way to address multi-objective generation and energy management issue. The impacts of electric vehicles, smart homes, and smart buildings, including smart hospitals and smart schools, have been examined, optimized, and managed in references [41,42].…”

Section: Introductionmentioning

confidence: 99%

Optimal Multi-Operation Energy Management in Smart Microgrids in the Presence of RESs Based on Multi-Objective Improved DE Algorithm: Cost-Emission Based Optimization

et al. 2021

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Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.

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“…And this view has been verified by Wattoo et al 32 But in the references above, the study of FLR did not consider the willingness of the users to participate in the economic dispatch and the compensation for the carbon emission of the user‐side. Chen et al 33 set the carbon emission penalty factor in the objective function of economic dispatch, and Hajibandeh et al 34 set the carbon emission compensation for users in his new model for integration of wind power and DR. Although they got the most economical operation scheme, the uncertainty from the load side was ignored in both papers.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective economic dispatch of power system with wind farm considering flexible load response

2021

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The uncertainty of wind farm power and load both have a certain impact on the economic dispatch of the power system. First, this study deals with the fuzzy processing of power prediction error and load forecasting error of wind farms based on fuzzy random theory. On this basis, a multi-objective fuzzy stochastic dispatch model of a power system with flexible load is established under the carbon trading mechanism. And, the nonlinear cost of flexible load response and the cost of carbon emission compensation is introduced to the multi-objective function of the model. In addition, the fuzzy chance constraint of the spinning reserve is added to the constraint conditions. By introducing the variable, the fuzzy random dispatch model is transformed into a clear equivalent model. Finally, the discrete bacterial colony chemotaxis algorithm is used to process the model, and the optimal solution to the multi-objective function is obtained by a compromise strategy based on a small degree of satisfaction. In the simulation, a classic IEEE10 system and a wind farm are taken as examples. The results show that compared with the other three traditional dispatch models, the total power generation cost

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Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch

Cited by 28 publications

References 33 publications

Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism

Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism

Optimal Multi-Operation Energy Management in Smart Microgrids in the Presence of RESs Based on Multi-Objective Improved DE Algorithm: Cost-Emission Based Optimization

Multi‐objective economic dispatch of power system with wind farm considering flexible load response

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