Autonomous Energy Grids: Controlling the Future Grid With Large Amounts of Distributed Energy Resources

Kroposki, Benjamin; Bernstein, Andrey; King, Jennifer; Vaidhynathan, Deepthi; Zhou, Xinyang; Chang, Chin-Yao; Dall’Anese, Emiliano

doi:10.1109/mpe.2020.3014540

Cited by 61 publications

(18 citation statements)

References 6 publications

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“…As millions of DESs are deployed at customer locations—and new methods of control are needed to successfully manage such a large number of distributed assets—a concept called autonomous energy grids (AEGs) could be used to enable resilient, reliable, and economic optimization. 19 The security and resilience of AEGs lies in their scalable, reconfigurable, and self-organizing control infrastructure, providing an ability to self-optimize and operate either in isolation or as part of a larger, interconnected grid.…”

Section: Sector Examples Of Integrated Energy Systemsmentioning

confidence: 99%

Integration of energy systems

et al. 2021

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This article in MRS Bulletin and the framework set out in the introductory article articulate a scenario of renewable electrons and electrification of end use appliances and industrial processes as a plausible paradigm to realize a carbon-free energy economy. The subsequent articles cover specific sectoral or chemical applications of those renewable electrons (e.g., for hydrogen, transportation, building use, electrochemical storage, and within the chemical industry). This article addresses the intersections among and across those sectors. We describe the importance of considering integrated systems and systems of systems as we consider pathways to a decarbonized energy economy. Further, we review and summarize key insights into the innovation challenges that reside at the particular integration interfaces among sectors, and highlight the opportunity for advances in materials and processes that will be critical to successful achievement of economy-wide, low-carbon energy systems. Graphical abstract

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Section: Sector Examples Of Integrated Energy Systemsmentioning

confidence: 99%

Integration of energy systems

et al. 2021

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“…We present the ACOPF problem formulation below in (1). This problem seeks the least costly operating points of the generators within their lower and upper limits while obeying physical laws.…”

Section: A Acopf Formulationmentioning

confidence: 99%

“…However, centralized methods may be unable to computationally manage the increase in problem size and complexity resulting from adding millions of DERs. Furthermore, centralized computing raises other issues such as data privacy [1], [2] and is also vulnerable via a single point of failure or attack. Consequently, there has been much interest from the power systems community in distributed computation methods, where large problems are partitioned into smaller problems that can be solved in parallel [3].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, there has been much interest from the power systems community in distributed computation methods, where large problems are partitioned into smaller problems that can be solved in parallel [3]. Distributed optimization can be used to either (1) physically spread computation across an electric network such that de-vices locally solve a small optimization problem and exchange solutions directly with neighboring devices until converging to the overall solution [4], or (2) partition large problems in the context of high-performance computing (HPC) [5].…”

Section: Introductionmentioning

confidence: 99%

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A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow

Zeng¹,

Kody²,

Kim³

et al. 2021

Preprint

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With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to their potential for superior scalability, privacy, and robustness to a single point-of-failure. The Alternating Direction Method of Multipliers (ADMM) is a popular distributed optimization algorithm; however, its convergence performance is highly dependent on the selection of penalty parameters, which are usually chosen heuristically. In this work, we use reinforcement learning (RL) to develop an adaptive penalty parameter selection policy for the AC optimal power flow (ACOPF) problem solved via ADMM with the goal of minimizing the number of iterations until convergence. We train our RL policy using deep Q-learning, and show that this policy can result in significantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvature-informed penalty parameter selection methods). Furthermore, we show that our RL policy demonstrates promise for generalizability, performing well under unseen loading schemes as well as under unseen losses of lines and generators (up to a 50% reduction in iterations). This work thus provides a proof-of-concept for using RL for parameter selection in ADMM for power systems applications.

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“…The continuing integration of distributed energy resources (DERs) in distribution networks, enhanced by the deployment of smart technologies at the end-user level, complicates balancing economic efficiency and system stability in distribution networks [1]. Such autonomous and intelligent DERs introduce both opportunities and challenges to the electricity market and electric power system operations.…”

Section: Introductionmentioning

confidence: 99%

Incorporate Day-ahead Robustness and Real-time Incentives for Electricity Market Design

Guo¹,

Han²,

Zhou³

et al. 2022

Preprint

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In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social-welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA market. We evaluate the conservativeness of bidding strategies distinguished by different risk aversion settings. In the RT market, a bi-level time-varying optimization problem is proposed to design the online incentive signals to tradeoff the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. This enables tracking their optimal dispatch to provide fast balancing services, in the presence of time-varying network states while satisfying the voltage regulation requirement. Simulation results on both DA wholesale market and RT balancing market demonstrate the necessity of this two-stage design, and its robustness to uncertainties, the performance of convergence, the tracking ability and the feasibility of the resulting network operations.

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Autonomous Energy Grids: Controlling the Future Grid With Large Amounts of Distributed Energy Resources

Cited by 61 publications

References 6 publications

Integration of energy systems

Integration of energy systems

A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow

Incorporate Day-ahead Robustness and Real-time Incentives for Electricity Market Design

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