A New Analytical Approach for Interval Availability Analysis of Markov Repairable Systems

Du, Sichun; Zio, Enrico; Kang, Rui

doi:10.1109/tr.2017.2765352

Cited by 13 publications

(7 citation statements)

References 42 publications

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“…Accordingly, evaluating the reliability and risk management of these grids is very important [32,33]. Analytic methods and methods based on probabilistic simulations have been presented to evaluate reliability and risk management in smart grids [34][35][36][37]. Methods based on probabilistic simulations such as the Monte Carlo (MC) method cover various system uncertainties, and new conditions with new stochastic nature can be easily added [38][39][40].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In some articles (for example, [12][13][14][15][16][17][18]), the authors presented a framework for developing sell/purchase strategies for a distribution company in the energy and retail markets considering demand uncertainty, but none of them examined the effect of elasticity on consumption demand. In some studies, no pricing scheme was used, such that the customers of the distribution network were ignored (for example, [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]). In some articles, load restoration resources in the distribution system and network reliability were considered, but no plan was considered for pricing and participation levels of consumers (for instance, [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]).…”

Section: Literature Reviewmentioning

confidence: 99%

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Impact of Demand Response on Reliability Enhancement in Distribution Networks

2021

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This paper presents an innovative instantaneous pricing scheme for optimal operation and improved reliability for distribution systems (DS). The purpose of the proposed program is to maximize the operator’s expected profit under various risk-taking conditions, such that the customers pay the minimum cost to supply energy. Using the previous information of the energy consumption for each customer, a customer baseline load (CBL) is defined; the energy price for consumption costs higher and lower than this level would be different. The proposed scheme calculates the difference between the baseline load and the consumption curve with the electricity market price instead of calculating the total consumption of the customers with the unstable price of the electricity market, which is uncertain. In the proposed tariff, the developed cost and load models are included in the distribution system operation problem, and the objective function is modeled as a mixed integer linear programming (MILP) problem. Also, the effect of demand response (DR) and elasticity on the load curve, the final profit of the distribution system operator, and payment risk and operation costs are examined. Since there are various uncertainties in the smart distribution grid, the calculations being time-consuming and volumetric is important in the evaluation of reliability indices. Thus, when computation volume can be decreased and computation speed can be increased, analytical reliability analysis methods can be used, as they were in the present work. Finally, the changes in the reliability indices were calculated for the ratio of the customers’ sensitivity to the price and the customers’ participation in the proposed tariff using an analytical method based on Monte Carlo simulation (MCS). The results showed the efficiency of the proposed method in increasing the operator profit, reducing the operation costs, and enhancing the reliability indices.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Impact of Demand Response on Reliability Enhancement in Distribution Networks

2021

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“…The state sojourn time is defined as the amount of time spent in a state before leaving that state. Many authors have considered modeling system availability assessment using a Markov process, examples include [11]- [13]. However, using a Markov process has several drawbacks.…”

Section: B Stochastic Processes Reviewmentioning

confidence: 99%

“…Following reduction of all cycles c ∈ C into reduced cycles (f, c) as presented in the previous paragraph, equations (5) and (6) can be reformulated into (12) and (13), where R M and R R represent those reduced cycles respectively requiring minimal and perfect maintenance:…”

Section: Mathematical Frameworkmentioning

confidence: 99%

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Stochastic Process for the Availability Assessment of Single-Feeder Industrial Energy System Sections

Acker

Hertem

2018

IEEE Trans. Rel.

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Given the high interruption costs of industrial processes, dependent on the cause, duration and time, accurate availability assessment of the industrial energy systems supplying those processes is crucial in order to avoid unnecessary investments or indicate necessary improvements. A method is presented to sectionalize an industrial energy system based on its radial topology and recoverability options. Additionally, this paper introduces a novel non-renewal stochastic process able to compute the (un)availability of single-feeder sections with respect to cause, duration and time, taking into account competing risks, general transition rates and both minimal and perfect corrective maintenance. An accompanying solution strategy is developed which allows decoupling of the very diverse time constants. Finally, a numerical illustration is provided validating the proposed approach against the Markov and semi-Markov processes under their respective assumptions and illustrating the possible errors when modeling the problem using the aforementioned stochastic processes.

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Multiagent reinforcement learning for strictly constrained tasks based on Reward Recorder

Ding

Yan

Liu

2022

Int J of Intelligent Sys

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Multiagent reinforcement learning (MARL) has been widely applied in engineering problems. However, many strictly constrained problems such as distributed optimization in engineering applications are still a great challenge to MARL. Especially for strict global constraints of agents' actions, it is very easy to lead to sparse rewards. Besides, existing studies cannot solve the instability caused by partial observability while making the algorithm fully distributed. Algorithms with centralized training may encounter significant obstacles in realworld deployment. For the first time, we provide theoretical analysis for MARL to determine the adverse effects of partial observability on convergence, and a fully distributed and convergent MARL algorithm based on Reward Recorder is proposed. Each agent runs an independent reinforcement learning algorithm and uses the average-consensus protocol to estimate the global state-action value locally to achieve global optimization. To verify the performance of the algorithm, we propose a novel generalized constrained optimization model, which includes local inequality constraints and strict global constraints. The proposed distributed reinforcement learning algorithm is supported by several simulation examples. The results reveal that the proposed algorithm has high stability and excellent decision-making ability.

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A New Analytical Approach for Interval Availability Analysis of Markov Repairable Systems

Cited by 13 publications

References 42 publications

Impact of Demand Response on Reliability Enhancement in Distribution Networks

Impact of Demand Response on Reliability Enhancement in Distribution Networks

Stochastic Process for the Availability Assessment of Single-Feeder Industrial Energy System Sections

Multiagent reinforcement learning for strictly constrained tasks based on Reward Recorder

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