Effect of Short-Term Risk-Aversive Dispatch on a Complex System Model for Power Systems

Fitzmaurice, Ronan; Keane, Andrew; O’Malley, Mark

doi:10.1109/tpwrs.2010.2050079

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…7,8 OPA and its extensions have been investigated by several research groups. [9][10][11][17][18][19][20][21][22][23] The OPA model calculates the long time behavior of cascading transmission line outages of a power transmission system under the dynamics of an increasing power demand, and the engineering responses to failure. In this model, the power demand increases at a constant rate and random fluctuations modulate the daily loads.…”

Section: The Opa Modelmentioning

confidence: 99%

Does size matter?

Carreras

Newman

Dobson

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Failures of the complex infrastructures society depends on having enormous human and economic cost that poses the question: Are there ways to optimize these systems to reduce the risks of failure? A dynamic model of one such system, the power transmission grid, is used to investigate the risk from failure as a function of the system size. It is found that there appears to be optimal sizes for such networks where the risk of failure is balanced by the benefit given by the size.

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Section: The Opa Modelmentioning

confidence: 99%

Does size matter?

Carreras

Newman

Dobson

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Scheduling strategies for a power producer in an electricity market are provided by considering uncertainities in wind availability, market prices and profit variability in [12] and [13]. Risk based approaches have also been employed for transmission expansion planning and dispatch problems [14]- [15].…”

Section: Means Of Rpo Compliancementioning

confidence: 99%

Risk based multi-objective optimal fulfillment of Renewable Purchase Obligation

Veena

Abhyankar

2012

2012 IEEE Power and Energy Society General Meeting

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It is known that renewable energy generation is associated with risk of uncertainty and market trading is associated with risk of price volatility. The mandatory Renewable Purchase Obligation (RPO) in India, exposes the Load Serving Entities (LSE) to these risks, in addition to the expenditure associated with it. This paper formulates the optimal fulfillment of RPO as a multi-objective problem. The optimal decision of meeting RPO is obtained by minimizing the two objectives -cost and risk. The two risks considered are the variability of wind power generation and volatility in the Renewable Energy Certificate (REC) price. These are modeled through fuzzy membership functions. Further, a best compromise solution is obtained through the fuzzy minmax approach.Index Terms-Fuzzy membership function, fuzzy min-max approach, Load Serving Entity (LSE), Renewable Energy Certificate (REC), Renewable Purchase Obligation (RPO), risk, variability, uncertainty.

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“…With the construction and development of the new-type power system, the transmission power of lines continues to increase. When power lines experience overload or voltage violations, the operational state of the power system may deviate or become unstable, often accompanied by an imbalance in power within sections, which threatens the safety and stability of the power system Fitzmaurice et al (2010). Hence, it is crucial to implement precise measures to mitigate overloads and excessive currents in power lines, aiming to avert the initiation of cascading failures.…”

Section: Introductionmentioning

confidence: 99%

Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

Li,

Liu,

Hong

et al. 2024

Front. Energy Res.

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With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the “black-box” nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control.

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Effect of Short-Term Risk-Aversive Dispatch on a Complex System Model for Power Systems

Cited by 8 publications

References 20 publications

Does size matter?

Does size matter?

Risk based multi-objective optimal fulfillment of Renewable Purchase Obligation

Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

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