Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market
Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market
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DOI：
10.1109/jproc.2022.3161053
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发表时间：
2023-07
影响因子：
20.6
通讯作者：
Federica Bellizio;Wangkun Xu;D. Qiu;Yujian Ye;D. Papadaskalopoulos;J. Cremer;Fei Teng;G. Strbac
中科院分区：
文献类型：
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作者：
Federica Bellizio;Wangkun Xu;D. Qiu;Yujian Ye;D. Papadaskalopoulos;J. Cremer;Fei Teng;G. Strbac
数字化是能源系统转型的关键驱动因素之一。通信技术和测量设备的进步提供了大量的业务数据，并使这种数据的存储和处理能够集中进行。更多地获取数据为更有效和更分散地管理能源系统提供了新的机会。在能源系统的分配层面，当地电力市场(LEM)通过在当地交易和平衡能源并向更广泛的输电和配电系统运营商提供辅助服务，提供了新的灵活性。要最大限度地利用这种灵活性对电网的影响，需要新颖的数据分析和人工智能技术来增强系统的安全性，并降低当地消费者的能源成本。然而，与此同时，依赖基于数据的方法增加了网络攻击的风险，因此，需要采取强有力的对策，作为数字化努力的一个组成部分。本文讨论了集中式数据分析在充分利用LEM在增强系统安全性和降低能源成本方面的优势方面所起的关键作用。研究了数据驱动的范例，这些范例允许从分散的市场中获得灵活性，降低物理安全风险，并设计防御战略，使系统免受网络威胁。
Digitalization is one of the key drivers for energy system transformation. The advances in communication technologies and measurement devices render available a large amount of operational data and enable the centralization of such data storage and processing. The greater access to data opens up new opportunities for a more efficient and decentralized management of the energy system. At the distribution level of the energy system, local electricity markets (LEMs) provide new degrees of flexibility by trading and balancing the energy locally and offering ancillary services to the wider transmission and distribution system operators. Maximizing the grid impact from this flexibility calls for novel data analytics and artificial intelligence techniques to enhance the system’s security and reduce the energy costs of local prosumers. At the same time, however, relying on data-based approaches increases the risk of cyberattacks, and robust countermeasures are, therefore, needed as an integral aspect of digitalization efforts. This article discusses the key role of centralized data analytics to fully benefit from the advantages of LEMs in terms of system’s security enhancement and energy costs’ reduction. Data-driven paradigms are investigated that allow for flexibility from decentralized markets, mitigate the physical security risks, and devise defensive strategies shielding the system from cyber threats.