Probabilistic Pricing for Collaborative Demand-Side Management With Coordinated Operation of Energy Storage Systems for Optimal Peak Load Control in Smart Grids

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohsen Masoumi-Anaraki, Rahmat-Allah Hooshmand, Yahya Kabiri-Renani
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Abstract

Peak load management is a pivotal aspect of power generation and distribution, representing one of the primary challenges for power companies. A key feature of smart grids is their capability to manage available resources effectively to mitigate peak load while accounting for the inherent uncertainties in load demand and the generation of all renewable energy sources. Thereby, this paper proposes a two-stage coordination approach that integrates price-based demand response (PBDR) and energy storage systems, encompassing Battery Energy Storage Systems (BESS) and Compressed Air Energy Storage (CAES). This approach integrates CAES with BESSs to optimise the charging and discharging processes while minimising degradation costs. Specifically, it aims to address the substantial degradation expenses of BESSs by strategically utilising CAES as a complementary storage solution. The objective is to minimise operational costs while controlling peak demand load in smart microgrids. Moreover, to simultaneously address the inherent uncertainties associated with the demanded load and the generating power of renewable energy sources, a method incorporating scenario generation and reduction is introduced to improve scheduling accuracy and enhance the reliability of energy management. To tackle this multifaceted challenge, a novel scenario-based Developed Two-Stage Interval Optimisation (DTSIO) model has been proposed to effectively address uncertainty. By employing the scenario generation method in conjunction with the k-means technique to reduce scenarios with low probabilities of occurrence, the analysis process is optimised for better problem-solving efficiency. The proposed model's efficacy is validated through its implementation on a 33 and 69 bus microgrid, showcasing its ability to enhance profitability, manage peak load, reduce reliance on the upstream grid, and lower carbon dioxide emissions.

Abstract Image

智能电网需求侧协同管理与储能系统协同运行最优峰值负荷控制的概率定价
高峰负荷管理是发电和配电的关键环节,是电力公司面临的主要挑战之一。智能电网的一个关键特征是它们能够有效地管理可用资源以减轻峰值负荷,同时考虑到负荷需求和所有可再生能源发电的固有不确定性。因此,本文提出了一种整合基于价格的需求响应(PBDR)和储能系统的两阶段协调方法,包括电池储能系统(BESS)和压缩空气储能(CAES)。这种方法将CAES与bess集成在一起,以优化充电和放电过程,同时最大限度地降低降解成本。具体来说,它旨在通过战略性地利用CAES作为补充存储解决方案来解决bess的大量降解费用。目标是在控制智能微电网峰值需求负荷的同时最大限度地降低运营成本。此外,为了同时解决需求负荷和可再生能源发电的固有不确定性,提出了一种情景生成与情景缩减相结合的方法,以提高调度精度,增强能源管理的可靠性。为了应对这一多方面的挑战,研究人员提出了一种新的基于场景的已开发两阶段间隔优化(DTSIO)模型,以有效地解决不确定性。通过将场景生成方法与k-means技术结合使用,以减少低概率发生的场景,优化分析过程以提高解决问题的效率。通过在33和69总线微电网上的实施,验证了所提出模型的有效性,展示了其提高盈利能力、管理峰值负荷、减少对上游电网的依赖以及降低二氧化碳排放的能力。
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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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