Incremental-Oriented ADMM for Distributed Optimal Power Flow With Discrete Variables in Distribution Networks

Lu, Wentian; Liu, Mingbo; Lin, Shunfu; Li, Licheng

doi:10.1109/tsg.2019.2902255

Cited by 22 publications

(7 citation statements)

References 25 publications

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“…are the total line flow factor, total system losses factor, and voltage deviation factor, respectively. All three factors are calculated with respect to DER power injection, P DER i , from the constraint equations ( 28), (29), and (30). DLMP works as a price coordinator to ensure that any power imbalance in the system can be fully offset and objectives of all participating entities can be optimized simultaneously.…”

Section: Real-time Balancing Electricity Marketmentioning

confidence: 99%

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Distributed Optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

Al-Saffar

Musilek

2021

IEEE Access

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This article develops a special decomposition methodology for the traditional optimal power flow which facilitates optimal integration of stochastic distributed energy resources in power distribution systems. The resulting distributed optimal power flow algorithm reduces the computational complexity of the conventional linear programming approach while avoiding the challenges associated with the stochastic nature of the energy resources and loads. It does so using machine learning algorithms employed for two crucial tasks. First, two proposed algorithms, Dynamic Distributed Multi-Microgrid and Monte Carlo Tree Search based Reinforcement Learning, constitute dynamic microgrids of network nodes to confirm the electric power transaction optimality. Second, the optimal distributed energy resources are obtained by the proposed deep reinforcement learning method named Multi Leader-Follower Actors under Centralized Critic. It accelerates conventional linear programming approach by considering a reduced set of resources and their constraints. The proposed method is demonstrated through a real-time balancing electricity market constructed over the IEEE 123-bus system and enhanced using price signals based on distribution locational marginal prices. This application clearly shows the ability of the new approach to effectively coordinate multiple distribution system entities while maintaining system security constraints.INDEX TERMS Distributed architecture, distributed optimization, Monte Carlo tree search, multi-agent deep reinforcement learning, optimal power flow.

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Section: Real-time Balancing Electricity Marketmentioning

confidence: 99%

“…In the last few years, many studies on distributed OPF algorithms have started to use the ADMM for distribution networks as well. Similar to the transmission networks, these studies assume that distribution networks are static and not affected by changing grid configurations [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

Al-Saffar

Musilek

2021

IEEE Access

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“…In more related research, Lu et al 17 explored the decentralization features of the alternating direction method of multipliers algorithm (ADMM). Their proposed heuristic approach utilizes an adjusted version of ADMM to perform an optimal power flow algorithm, in a distributed fashion, while considering equipment discrete control variables.…”

Section: Introductionmentioning

confidence: 99%

An alternating direction method of multipliers ‐based approach to solve mixed‐integer nonlinear volt/var optimization problems in distribution systems

Alburidy

Fan

2021

Int Trans Electr Energ Syst

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Summary This paper presents a tailored version of the alternating direction method of multipliers to achieve a centralized voltage and VAR optimization (VVO) in the electric distribution system. The adopted VVO model is a mixed‐integer nonlinear optimization problem derived from the generic optimal power flow problem. A novel generalized and decomposable version of the VVO model is developed first to establish the proposed approach. The approach accounts for load tap‐changers and switchable capacitor banks for illustration. However, it can be expanded to a broad range of controllable VVO equipment with discrete/integer settings. The approach performance is validated on different distribution networks. Results are compared with current leading heuristic approaches used to solve nonconvex mixed‐integer nonlinear problems such as the Branch‐and‐Bound method. Furthermore, a comparison vs the branch flow model‐based mixed integer second‐order conic programming model is conducted to illustrate the proposed model's advantages. Moreover, to benchmark the proposed approach optimality, a direct search scheme is developed to capture guaranteed globally optimal results. Experiments show the superiority of the proposed approach with noteworthy convergence rates and promising optimal solution allocation.

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“…The improved ADMM is found to be more scalable than ALADIN for large-scale distributed systems. Reference [28] proposes a new incremental-oriented ADMM algorithm by fusing the extended interior-point method and ADMM so that the improved ADMM can solve mixed-integer non-linear programming. Reference [29] proposes an OCD based three-stage distributed OPF algorithm that can handle discrete variables.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm

Cheng

Chow

2021

Journal of Modern Power Systems and Clean Energy

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We propose a dual decomposition based algorithm that solves the AC optimal power flow (ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the second-order cone program (SOCP) relaxed branch flow ACOPF model. In the proposed algorithm, bus-level agents collaboratively solve the global ACOPF problem by iteratively sharing partial variables with its 1-hop neighbors as well as carrying out local scalar computations that are derived using augmented Lagrangian and primal-dual subgradient methods. We also propose two distributed computing platforms, i. e., high-performance computing (HPC) based platform and hardware-in-theloop (HIL) testbed, to validate and evaluate the proposed algorithm. The computation and communication performances of the proposed algorithm are quantified and analyzed on typical IEEE test systems. Experimental results indicate that the proposed algorithm can be executed on a fully distributed computing structure and yields accurate ACOPF solution. Besides, the proposed algorithm has a low communication overhead.

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Incremental-Oriented ADMM for Distributed Optimal Power Flow With Discrete Variables in Distribution Networks

Cited by 22 publications

References 25 publications

Distributed Optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

Distributed Optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

An alternating direction method of multipliers ‐based approach to solve mixed‐integer nonlinear volt/var optimization problems in distribution systems

Collaborative Distributed AC Optimal Power Flow: A Dual Decomposition Based Algorithm

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