A bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response

dc.contributor.authorPang, Xinfu
dc.contributor.authorWang, Yibao
dc.contributor.authorYang, Shengxiang
dc.contributor.authorCai, Lei
dc.contributor.authorYu, Yang
dc.date.acceptance2023-12-07
dc.date.accessioned2023-12-15T13:29:15Z
dc.date.available2023-12-15T13:29:15Z
dc.date.issued2023-12-14
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.description.abstractCarbon capture and storage (CCS), energy storage (ES), and demand response (DR) mechanisms are introduced into a cogeneration system to enhance their ability to absorb wind energy, reduce carbon emissions, and improve operational efficiency. First, a bi-objective low-carbon economic scheduling model of a cogeneration system considering CCS, ES, and DR was developed. In this model, the ES and CCS remove the coupling between power generation and heating. The DR mechanism, which is based on the time-of-use electricity price and heating comfort, further enhanced the flexibility of the system. In addition, an improved bare-bones multi-objective particle swarm optimisation (IBBMOPSO) was designed to directly obtain the Pareto front of the low-carbon economy scheduling model. The particle position update mode was improved to balance global and local search capabilities in various search stages. The Taguchi method was used to calibrate the algorithm parameters. The inverse generational distance (IGD), hypervolume (HV), and maximum spread (MS) were used to evaluate the distribution and convergence performance of the algorithm. The improved technique for order preference by similarity to an ideal solution (TOPSIS) method was utilised to obtain the optimal compromise solution. Finally, the proposed method was tested on a cogeneration system in Northeast China. According to the comparison results, the average economic cost of the cogeneration system considering CCS, ES, and DR was reduced by approximately 1.13%, and carbon emissions were reduced by 6.79%. The IBBMOPSO is more competitive than the NSGA-II, MOWDO, MOMA, MOPSO, and BBMOPSO in low-carbon economic scheduling for the cogeneration system.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.identifier.citationPang, X., Wang, Y., Yang, S. Cai, L., Yu, Y. (2023) A bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response. Expert Systems with Applications, 243, 122875
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2023.122875
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/2086/23393
dc.language.isoen
dc.peerreviewedYes
dc.projectid61773269, 62073226
dc.publisherElsevier
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/uk/
dc.subjectCogeneration system
dc.subjectCarbon capture
dc.subjectDemand response
dc.subjectImproved bare-bones multi-objective particle swarm optimisation
dc.subjectLow-carbon economic scheduling
dc.titleA bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response
dc.typeArticle

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