 Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind powerMenglin Zhang, Qiuwei Wu, Jinyu Wen, Bo Pan and Shiqiang Qi Applied Energy, 2020, vol. 275, issue C, No S0306261920308692 Abstract: Using the improved flexibility from the district heating system with multiple flexible devices is an effective solution to ensure the cost-effective and secure operation of the integrated energy system with high penetration of renewables. This paper exploits the improved flexibility of the integrated electricity and heat systems by using reserves from multiple flexible devices to accommodate more wind power and reduce operational costs. A two-stage stochastic optimal dispatching scheme is proposed for the integrated electricity and heat system considering both power networks and heat pipelines, and reserves from the condensing combined heat and power units, heat pumps, electric boilers, and heat storage tanks. The proposed scheme balances the power and heat sectors both in the day-ahead and real-time stages with the synergy of different flexible devices and linkage for each device in the two stages. A scenario generation method considering spatial-temporal correlation is proposed to provide reasonable wind power profiles for the two-stage dispatch scheme. The Gaussian mixture model and exponential function are used to construct the spatial and temporal correlation, respectively, and the Gibbs sampling is utilized to reduce the sampling complexity. The case studies were conducted on a 6-bus integrated electricity and heat system and a practical integrated energy system in Northern China. The results show that utilizing the scenario set with spatial-temporal correlation and improved flexibility can effectively reduce the operational cost and wind power curtailment. Keywords: Day-ahead stochastic scheduling of IEHS; Unit commitment; Reserve; Gaussian Mixture model; Gibbs sampling; Spatial-temporal correlation of wind power (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:275:y:2020:i:c:s0306261920308692 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2020.115357 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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