A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks

Scarabaggio, Paolo; Carli, Raffaele; Dotoli, Mariagrazia

doi:10.1109/case48305.2020.9216800

Cited by 13 publications

(16 citation statements)

References 15 publications

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“… 22 Also, the game-theoretic approach was used toward targeting the optimal energy storage. 23 All of the aforementioned examples capitalized on a big amount of data that required a lot of time and efforts to reach a sufficient accuracy. This paper—to the authors’ knowledge—is the first to utilize the game-theoretic approach to automatically improve the drilling-data quality, which sets the stage for a new application in this domain.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Game-Theoretic Approach to Improve Energy-Related Data

et al. 2022

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With the increase in the energy demand, the magnitude of energy production operation increased in scale and complexity and went too far in remote areas. To manage such a big fleet, sensors were installed to send real-time data to operation centers, where subject matter experts monitor the operations and provide live support. With the expansion of installed sensors and the number of monitored operations, the operation centers were flooded with a massive amount of data beyond human capability to handle. As a result, it became essential to capitalize on the artificial intelligence (AI) capability. Unfortunately, due to the nature of operations, the data quality is an issue limiting the impact of AI in such operations. Multiple approaches were proposed, but they require lot of time and cannot be upscaled to support active real-time data streaming. This paper presents a method to improve the quality of energy-related (drilling) real-time data, such as hook load (HL), rate of penetration (ROP), revolution per minute (RPM), and others. The method is based on a game-theoretic approach, and when applied on the HL—one of the most challenging drilling parameters—it achieved a root mean square error (RMSE) of 3.3 accuracy level compared to the drilling data quality improvement subject matter expert’s (SME) level. This method took few minutes to improve the drilling data quality compared to weeks in the traditional manual/semiautomated methods. This paper addresses the energy data quality issue, which is one of the biggest bottlenecks toward upscaling AI technology into active operations. To the authors’ knowledge, this paper is the first attempt to employ the game-theoretic approach in the drilling data improvement process, which facilitates greater integration between AI models and the energy live data streaming, also setting the stage for more research in this challenging AI-data domain.

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Section: The Proposed Methodsmentioning

confidence: 99%

A Game-Theoretic Approach to Improve Energy-Related Data

et al. 2022

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“…2) We extensively improve our previous work [17], where we preliminarily introduce a noncooperative framework of energy storage providers participating in the grid optimization under power flow constraints. In particular, in this work we significantly enhance the grid model by integrating flexible loads and dispatchable generators, while additionally including power quality constraints.…”

Section: B Paper Contributionmentioning

confidence: 99%

Noncooperative Equilibrium-Seeking in Distributed Energy Systems Under AC Power Flow Nonlinear Constraints

Scarabaggio

Carli

Dotoli

2022

IEEE Trans. Control Netw. Syst.

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Power distribution grids are commonly controlled through centralized approaches, such as the optimal power flow. However, the current pervasive deployment of distributed renewable energy sources and the increasing growth of active players, providing ancillary services to the grid, have made these centralized frameworks no longer appropriate. In this context, we propose a novel noncooperative control mechanism for optimally regulating the operation of power distribution networks equipped with traditional loads, distributed generation, and active users. The latter, also known as prosumers, contribute to the grid optimization process by leveraging their flexible demand, dispatchable generation capability, and/or energy storage potential. Active users participate in a noncooperative liberalized market designed to increase the penetration of renewable generation and improve the predictability of power injection from the high voltage grid. The novelty of our gametheoretical approach consists in incorporating economic factors as well as physical constraints and grid stability aspects. Lastly, by integrating the proposed framework into a rolling-horizon approach, we show its effectiveness and resiliency through numerical experiments.

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“…One of the critical issues in DC microgrids -inherited from the operation of power systems -is the well-known optimal power flow (OPF) problem, which determines the best operating point regarding power losses or energy production costs. The OPF relies on the power flow model, which in DC microgrids corresponds to a set of nonlinear nonconvex algebraic equations that cannot be solved analytically [44]. When the OPF is addressed by a centralized approach, a central unit must have access to all system parameters.…”

Section: Application: Noncooperative Optimal Power Flow In DC Microgridsmentioning

confidence: 99%

Clarke's Local Generalized Nash Equilibria with Nonconvex Coupling Constraints

Scarabaggio¹,

Carli²,

Grammatico³

et al. 2022

Preprint

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We consider a class of Nash games with nonconvex coupling constraints where we leverage the theory of tangent cones to define a novel notion of local equilibrium: Clarke's local generalized Nash equilibrium (CL-GNE). Our first technical contribution is to show the stability of these equilibria on a specific local subset of the original feasible set. As a second contribution, we show that the proposed notion of local equilibrium can be equivalently formulated as the solution of a quasi-variational inequality, remarkably, with equal Lagrange multipliers. Next, we define conditions for the existence and uniqueness of the CL-GNE. To compute such an equilibrium, we propose two discrete-time distributed dynamics, or fixed-point iterations. Our third technical contribution is to prove convergence under (strongly) monotone assumptions on the pseudo-gradient mapping of the game. Finally, we apply our theoretical results to a competitive version of the optimal power flow control problem. Paper submitted for publication in IEEE Transactions on Automatic Control -- <a href="http://ieeecss.org/publication/transactions-automatic-control" target="_blank">http://ieeecss.org/publication/transactions-automatic-control</a>

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A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks

Cited by 13 publications

References 15 publications

A Game-Theoretic Approach to Improve Energy-Related Data

A Game-Theoretic Approach to Improve Energy-Related Data

Noncooperative Equilibrium-Seeking in Distributed Energy Systems Under AC Power Flow Nonlinear Constraints

Clarke's Local Generalized Nash Equilibria with Nonconvex Coupling Constraints

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