Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers

Barth, Dominique; Cohen-Boulakia, Benjamin; Ehounou, Wilfried

doi:10.3390/en15041440

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…Equivalence of the instances We end this proof by demonstrating that the minimum load reserve of J is exactly 2 3 if and only if I is positive. Otherwise it is strictly greater.…”

Section: + Bmentioning

confidence: 90%

“…This algorithm is polynomial in the size of the instance and in 1/ε. 2 An FPTAS with absolute ratio for a minimization problem (respectively maximization problem) is an algorithm that, given a value ε > 0, returns a feasible solution for which the objective value is equal to the optimal value shifted by at most ε (respectively −ε). This algorithm is polynomial in the size of the instance and in 1/ε.…”

Section: Approximability Of Max-m and Min-rmentioning

confidence: 99%

“…This dynamic configuration of the distribution network is essential for the current development of smartgrids or microgrids [20] implementing collective self-consumption grids interconnecting on a local territory a set of prosumers (i.e. consumers able to produce, store and therefore share energy) [2,7,10].…”

Section: Introductionmentioning

confidence: 99%

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Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Barth,

Mautor,

Watel

et al. 2023

J Glob Optim

Self Cite

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We address the problem of configuring a power distribution network with reliability and resilience objectives by satisfying the demands of the consumers and saturating each production source as little as possible. We consider power distribution networks containing source nodes producing electricity, nodes representing electricity consumers and switches between them. Configuring this network consists in deciding the orientation of the links between the nodes of the network. The electric flow is a direct consequence of the chosen configuration and can be computed in polynomial time. It is valid if it satisfies the demand of each consumer and capacity constraints on the network. In such a case, we study the problem of determining a feasible solution that balances the loads of the sources, that is their production rates. We use three metrics to measure the quality of a solution: minimizing the maximum load, maximizing the minimum load and minimizing the difference of the maximum and the minimum loads. This defines optimization problems called respectively min-M, max-m and min-R.In the case where the graph of the network is a tree, it is known that the problem of building a valid configuration is polynomial. We show the three optimization variants have distinct properties regarding the theoretical complexity and the approximability. Particularly, we show that min-M is polynomial, that max-m is NP-Hard but belongs to the class FPTAS and that min-R is NP-Hard, cannot be approximated to within any exponential relative ratio but, for any ε > 0, there exists an algorithm for which the value of the returned solution equals the value of an optimal solution shifted by at most ε.

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“…Equivalence of the instances We end this proof by demonstrating that the minimum load reserve of J is exactly 2 3 if and only if I is positive. Otherwise it is strictly greater.…”

Section: + Bmentioning

confidence: 90%

Section: Approximability Of Max-m and Min-rmentioning

confidence: 99%

See 1 more Smart Citation

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Barth,

Mautor,

Watel

et al. 2023

J Glob Optim

Self Cite

View full text Add to dashboard Cite

show abstract

“…The references have been categorized in terms of the application class, objective function, and building type that were described in the immediately preceding section. Cost & Comfort [103], [104] Other Academic [105] Comfort Mixed/NA [106] Other [107], [98] P2P Trading Cost [108], [109] Residential [110] EV, ES, and RG [111], [112] Mixed/NA [113] Other Residential [114], [115] Other/Mixed Cost & Comfort Commercial [116] Academic [117], [96], [118], [119], [120], [121] Residential [122] Other [123], [124] Cost [125] Mixed/NA [126] Cost & Comfort [127], [128] Cost & Load Balance [129] Other [130] P2P Trading Cost Distributed RL [131], [132] Other [138] Cost & Comfort Commercial Model Based RL [139] HVAC, Fans, WH Cost Residential Other (CARLA) [140] Cost & Comfort Commercial Other (Context. RL)…”

Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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“…References that used either a combination of two or more approaches, or any other approach not commonly used in RL literature, are shown in Table 5. Cost and Comfort [114,115] Other Academic [116] Comfort Mixed/NA [117] Other [109,118] P2P Trading Cost [119,120] Residential [121] EV, ES, and RG [122,123] Mixed/NA [124] Other Residential [125,126] Other/Mixed Cost and Comfort Commercial [127] Academic [107,[128][129][130][131][132] Residential [133] Other [134,135] Cost [136] Mixed/NA [137] Cost and Comfort [138,139] Cost and Load Balance [140] Other [141] P2P…”

Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

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Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers

Cited by 7 publications

References 37 publications

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

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