Reinforcement Learning for Reactive Power Control

Vlachogiannis, John G.; Hatziargyriou, Nikos D.

doi:10.1109/tpwrs.2004.831259

Cited by 117 publications

(54 citation statements)

References 15 publications

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“…In fact, in order to highlight the importance of secure and stable operation in power grid, it is also necessary to consider the reactive power optimization with the voltage inequality constraints [12,20]. Here, a new voltage performance index V measuring the voltage deviation of the system is introduced into the OPF objective function as follow:…”

Section: Opf Mathematical Modelmentioning

confidence: 99%

“…In recent years, Reinforcement Learning techniques have been proposed for reactive power control [12], economic dispatch [13], power market [14], etc. With the rapid development of multi-agent system (MAS) technology [15,16], Distributed Reinforcement Learning (DRL) [17], as an extension of Reinforcement Learning, plays an important role as the core enabling technology to achieve the MAS's goal.…”

Section: Introductionmentioning

confidence: 99%

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Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids

Yu¹,

Liu²,

Chan³

et al. 2012

International Journal of Electrical Power & Energy Systems

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Section: Opf Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids

Yu¹,

Liu²,

Chan³

et al. 2012

International Journal of Electrical Power & Energy Systems

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“…In turn, the customers in DECENT carry out distributed negotiations through their agents, and neither look-ahead nor auctioning are needed . Grid stability is addressed in Ernst et al, 2002, Ernst et al, 2004, Hadidi et al, 2009, Pipattanasomporn et al, 2009and Vlachogiannis et al, 2004 in terms of various learning strategies where a central agency would be enabled to ensure (optimal) stability. This theme has been an open issue in practice so far, and the proposed solutions are certainly not scalable.…”

Section: Previous and Relatedmentioning

confidence: 99%

Intelligent Agents under Collaborative Control in Emerging Power Systems

Wedde¹,

Lehnhoff²,

Rehtanz³

et al. 2010

Int. J. Eng. Sci. Tech

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In the world of liberalized power markets traditional power management concepts have come to their limits. Optimal pricing can no longer be achieved, e.g. for very short-time needs across grids. Power line overload and grid stability, increasingly resulting in regional or even global black-outs, are at stake. With the highly desirable expansion of renewable energy production these challenges are experienced in quite an amplified way: We argue that for this emergent technology the traditional top-down and long-term power management is obsolete, due to the wide dispersion and high unpredictability of wind and solar-based power facilities. In the DECENT0 F 1 R&D initiative we developed a multi-level, bottom-up solution where autonomous collaborative software agents negotiate available energy quantities and needs on behalf of consumer and producer groups (the DEZENT algorithm). We operate within very short time intervals of assumedly constant demand and supply, in our case periods of 0.5sec (switching delay for a light bulb). The solution has proven to be secure against a relevant variety of malicious attacks. Within this time interval we are also able to manage the coordinated power distribution, and achieve grid stability. In this paper the main contribution is to make the negotiation strategies themselves adaptive across periods: We derive the dynamic distributed learning algorithm DECOLEARN from Reinforcement Learning principles for providing the agents with collaborative intelligence and at the same time proving substantially superior to conventional (static) procedures. We report briefly on our extensive comparative simulation experiments.

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“…It only needs a reward function to evaluate the quality of a solution instead of complicated mathematical operations. Finally, it has the ability to escape local minima because it performs stochastic optimization [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Distributed reinforcement learning to coordinate current sharing and voltage restoration for islanded DC microgrid

Liu

Luo

Zhuo

et al. 2017

J. Mod. Power Syst. Clean Energy

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A novel distributed reinforcement learning (DRL) strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid. Firstly, a reward function considering both equal proportional current sharing and cooperative voltage restoration is defined for each local agent. The global reward of the whole DC microgrid which is the sum of the local rewards is regarged as the optimization objective for DRL. Secondly, by using the distributed consensus method, the predefined pinning consensus value that will maximize the global reward is obtained. An adaptive updating method is proposed to ensure stability of the above pinning consensus method under uncertain communication. Finally, the proposed DRL is implemented along with the synchronization seeking process of the pinning reward, to maximize the global reward and achieve an optimal solution for a DC microgrid. Simulation studies with a typical DC microgrid demonstrate that the proposed DRL is computationally efficient and able to provide an optimal solution even when the communication topology changes.

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Reinforcement Learning for Reactive Power Control

Cited by 117 publications

References 15 publications

Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids

Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids

Intelligent Agents under Collaborative Control in Emerging Power Systems

Distributed reinforcement learning to coordinate current sharing and voltage restoration for islanded DC microgrid

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