A reinforcement learning approach to parameter selection for distributed optimal power flow

Zeng, Sihan; Kody, Alyssa; Kim, Youngdae; Kim, Kibaek; Molzahn, Daniel K.

doi:10.1016/j.epsr.2022.108546

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…As the ADMM algorithm requires the optimization model to be decomposable, the auxiliary variables are used to facilitate the model decoupling (i.e., P la st = P la bt , P ev svt = P ev bvt , where P ev vt = P evc vt -P evd vt ). By incorporating these auxiliary variables and taking the negative form of ( 29), the augmented Lagrangian function of subproblem 1 can be formulated, as in (34).…”

Section: Admm Algorithm In Distributed Mannermentioning

confidence: 99%

“…The linearization technique has been extensively explored in the existing literature [32], [33] and will not be repeated here. In addition to using the linearization technique, various machine learning techniques have been applied in the existing literature to enhance the traditional ADMM (e. g., Q-learning in [34] and deep neural networks in [35]). The incorporation of either linearization or machine learning techniques into AD-MM will not affect the effectiveness of the proposed source− load−storage low-carbon cooperative scheduling strategy.…”

Section: Admm Algorithm In Distributed Mannermentioning

confidence: 99%

See 1 more Smart Citation

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

Xu,

Qiu,

Zhang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source−load−storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

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Section: Admm Algorithm In Distributed Mannermentioning

confidence: 99%

Section: Admm Algorithm In Distributed Mannermentioning

confidence: 99%

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

Xu,

Qiu,

Zhang

et al. 2024

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…Specifically, HMC will analyze the actions produced by a reinforcement learning agent and human, respectively, to determine which action the power system should take. Zeng et al [69] employed RL to devise an adaptive policy for selecting penalty parameters in the AC OPF problem, which is solved using the alternating direction method of multipliers. The objective of their approach is to minimize the number of iterations required for convergence.…”

Section: Learning Control Policy For Opfmentioning

confidence: 99%

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang,

Wang,

et al. 2024

Energies

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Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

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Taking the human out of decomposition-based optimization via artificial intelligence, Part II: Learning to initialize

Mitrai,

Daoutidis

2024

Computers & Chemical Engineering

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A reinforcement learning approach to parameter selection for distributed optimal power flow

Cited by 9 publications

References 39 publications

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Taking the human out of decomposition-based optimization via artificial intelligence, Part II: Learning to initialize

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