Research on sensitivity analysis of wind power consumption capability of integrated energy system based on unified optimal power flow model

Mu, Yongqiang; Wang, Chunsheng; Kang, Guangyou; Wang, Zheng; Jiang, Tao; Li, Jianfeng; Dou, Wenlei

doi:10.1049/joe.2019.0772

Cited by 7 publications

(5 citation statements)

References 19 publications

(17 reference statements)

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“…Once the browser receives the response, it can process it according to its needs and display the appropriate content. Of course, the user can choose to get the required information through other services on the Internet such as FTP, email, address book, etc [3][4][5].…”

Section: Basic Concepts Of Web Networkmentioning

confidence: 99%

Research on Provincial Power Grid Geographic Information System based on Web Network

Yuan,

Wang,

Zhai

et al. 2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Power grid geographic information system is a geographic information system based on computer network. Based on geospatial data, it can process, manage and analyze the power system in an all-round and multi-level way, so as to provide a series of auxiliary decisions for power production and planning. In this paper, the basic concepts of Web network and GIS system are discussed, and then the provincial power grid GIS system is designed on the basis of Web network, hoping to provide reference help.

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Section: Basic Concepts Of Web Networkmentioning

confidence: 99%

Research on Provincial Power Grid Geographic Information System based on Web Network

Yuan,

Wang,

Zhai

et al. 2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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“…The approach of [11], on the other hand, only determines the sensitivity factors for nodes at which gas-fired generation units are placed and only determine the impact on the GS. [12] uses sensitivity factors in an optimal power flow optimization of a gas-heating-electricity IES. The sensitivity factors, however, only determine the effect of a wind power change on the operation of coupling units, aiming to adapt the operation of the coupling units to the wind power generation.…”

Section: Introductionmentioning

confidence: 99%

Power-Transfer-Distribution-Factor-Based Sensitivity Factors for Integrated Energy Systems

Dancker,

Wolter

2024

IEEE Trans. Sustain. Energy

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The stronger the electric power, district heating and gas system are linked in an integrated energy system (IES) the larger their flexibility potential but also their complexity. To ensure a secure system operation, the effect of a coupling unit's power change on the overall system state must be analyzed before the power change is carried out. Otherwise, coupling units can unintentionally cause limit violations in one energy system. Currently, for such analyses a power flow calculation must be performed, which is computational expensive and timeconsuming. This paper presents sensitivity factors extending Power Transfer Distribution Factors from electric power system analysis to IES. The sensitivity factors include the dynamic behavior of district heating and gas systems as we derive the sensitivity factors from a joined quasi-steady-state power flow calculation. In two case studies we profoundly test the applicability of the IES sensitivity factors, their accuracy, and limitations. We show that the sensitivity factors are on average ten times faster in estimating a new system state after a unit's power change compared to a power flow calculation. Also, the sensitivity factors can provide good estimates and have a higher accuracy than sensitivity factors which are derived from a steadystate power flow calculation.

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“…The power system is a crucial infrastructure for social operation and economic development [1], and its stable and efficient operation is of great importance and value for assuring national economic security and stable development [2][3][4][5]. The accelerated growth of the economy has resulted in the expansion of the power system and distribution network.…”

Section: Introductionmentioning

confidence: 99%

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

Xiao,

Jiang,

Liang

et al. 2023

The Journal of Engineering

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During the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.

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Research on sensitivity analysis of wind power consumption capability of integrated energy system based on unified optimal power flow model

Cited by 7 publications

References 19 publications

Research on Provincial Power Grid Geographic Information System based on Web Network

Research on Provincial Power Grid Geographic Information System based on Web Network

Power-Transfer-Distribution-Factor-Based Sensitivity Factors for Integrated Energy Systems

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

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