Data-Driven Real-Time Pricing Strategy and Coordinated Optimization of Economic Load Dispatch in Electricity Market

Wang, Shunjiang; Zang, Yuxiu; Ge, Weichun; Wang, Aihua; Li, Dianyang; Tang, Jingyi

doi:10.3389/fenrg.2021.714951

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The above objects can be divided into guided load and direct control load according to the dispatching means. Guided load refers to the flexible resources that participate in regulation through electricity price [23], electricity market [24] and incentives [25]. The response time scale varies from minute level to hour level.…”

Section: Dispatching Object Of Nupgmentioning

confidence: 99%

New Urban Power Grid Dispatching Architecture and Key Technologies

Zhang

2023

Preprint

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Under the dual-carbon goal, the operation environment and form of the urban power grid have new characteristics, and the urban power grid will be transformed into a new urban power grid (NUPG), which puts forward higher requirements for flexibility. It is urgent to establish a flexible dispatching system to meet the operation of the NUPG. This paper first introduces the eight major new operation characteristics and challenges of the NUPG. Secondly, a flexible dispatching architecture with the load as the main body is constructed, and the types of dispatching objects, dispatching means and media, and internal and external dispatching tasks are introduced. On this basis, four basic dispatching key technologies of the NUPG are pointed out: load response capacity assessment and distribution technology, load reserve configuration and power plan formulation technology, load participation in power grid frequency regulation/peak shaving/voltage regulation technology, and internal and external fault handling technology based on load resources. Finally, the effectiveness of the NUPG dispatching architecture and key technologies is verified by simulation analysis of the IEEE39-bus system and the actual power grid in East China.

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Section: Dispatching Object Of Nupgmentioning

confidence: 99%

New Urban Power Grid Dispatching Architecture and Key Technologies

Zhang

2023

Preprint

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“…The adjustable load can be divided into guided and direct control loads according to the dispatching means. 1) Guided load refers to the flexible resources that participate in regulation through electricity price (Cai Q. et al, 2022), electricity market (Wang et al, 2021) and incentives (Wang et al, 2022). The response time scale varies from minute level to hour level.…”

Section: Scheduling Object Of Nupgmentioning

confidence: 99%

New urban power grid flexible load dispatching architecture and key technologies

Zhang

2023

Front. Energy Res.

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Towards the dual-carbon goals, the new urban power grid (NUPG) dominated by new energy has a low proportion of adjustable power generation units, resulting in insufficient scheduling flexibility. As a result, it becomes challenging to cope with the complex operating conditions of the NUPG. In this paper, after analyzing the new operational characteristics and challenges of the NUPG, a flexible load dispatching architecture and key technologies for the NUPG are proposed. These enhance the scheduling flexibility of the NUPG from the load side, which further provides support for the operation of the large power grid. Then, the types of scheduling objects, scheduling methods, scheduling tasks, and four essential dispatching technologies are introduced to provide a reference for building the NUPG flexible load dispatching system. Finally, the effectiveness of the NUPG flexible dispatching architecture and technologies is verified by simulation analysis of the IEEE39-bus system and the actual power grid in East China.

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“…For example, advanced data analysis technologies such as machine learning and deep learning are being applied to stock selection, risk assessment, market prediction and other fields. At the same time, unstructured data, such as text, voice and images, also provide a new analytical dimension for quantitative investment [4][5]. In view of the above background, this paper aims to explore how to optimize the quantitative investment strategy in the financial big data environment.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Quantitative Investment Strategies in the Financial Big Data Environment

Wang

2023

FBEM

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With the rapid progress of technology, the financial field is rapidly shifting towards a data-driven environment, with "financial big data" becoming its core component. This transformation has brought about profound changes in the financial market, especially with a significant impact on algorithmic quantitative investment strategies. This article delves into the optimization of quantitative investment strategies in the financial big data environment, analyzes how the characteristics of big data affect quantitative investment, and how to utilize these characteristics to optimize investment strategies. The article first outlines the definition, characteristics, and sources of financial big data, with a focus on describing its core characteristics such as capacity, speed, diversity, and authenticity. This article highlights the application of machine learning and deep learning in financial analysis, as well as the new perspective provided by unstructured data for quantitative strategies. Finally, the conclusion section of the article summarizes the opportunities and challenges brought by the integration of financial big data and quantitative strategies, calling on financial institutions and investors to have a deeper understanding and utilization of these two major trends.

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Data-Driven Real-Time Pricing Strategy and Coordinated Optimization of Economic Load Dispatch in Electricity Market

Cited by 6 publications

References 10 publications

New Urban Power Grid Dispatching Architecture and Key Technologies

New Urban Power Grid Dispatching Architecture and Key Technologies

New urban power grid flexible load dispatching architecture and key technologies

Optimization of Quantitative Investment Strategies in the Financial Big Data Environment

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