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distributed Finite-Time Economic Dispatch of a Network of Energy Resources

IEEE TRANSACTIONS ON SMART GRID 2017年第2期8卷 822-832页

作者： Chen, Gang Ren, Jianghong Feng, E. Ning Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

The physical power infrastructure is moving from the centralized structure to the distributed structure for enabling integration of distributed energy resources. Due to the large number of distributed energy resources, optimal resource allocation is an important and challenging problem. To solve this problem, a distributed and fast economic dispatch algorithm is provided to share the power generation task in an optimized fashion among a set of distributed energy resources, which can address both generation-demand equality and generation capacity inequality constraints. Different from most existing economic dispatch algorithms, the finite-time convergence to the optimal value is achieved, which makes more sense in real applications. Several case studies are discussed and tested to validate the proposed methods.

关键词： Economic dispatch consensus multi-agent systems resource allocation distributed optimization algorithm

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A distributed stochastic gradient algorithm for economic dispatch over directed network with communication delays

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 110卷 759-771页

作者： Zhang, Hao Li, Huaqing Zhu, Yifan Wang, Zheng Xia, Dawen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Guizhou Peoples R China

Economic dispatch problem (EDP) is one of the fundamental optimization problems in power systems, which involves a coupling linear constraint and several individual box constraints. In this paper, we propose a distributed stochastic gradient descent algorithm based on consensus theory to solve the EDP under directed network, where the convex cost function for each generator only needs to satisfy the condition that the function is strictly convex with Lipschitz continuous gradient. The proposed algorithm utilizes stochastic gradient descent to update values of generators for dealing with the noise which is incurred during the gradient estimation, and the step-sizes are heterogeneous. Under strictly convex assumption on objective functions, the algorithm can seek the exact optimal solution with probability one at the rate of (O(ln K/root K)), where K is the number of iteration. Furthermore, the algorithm is also suitable and effective to the network with communication delays if the communication delays are bounded. Simulation results illustrate the effectiveness of the algorithm.

关键词： Economic dispatch problem (EDP) distributed optimization algorithm Stochastic gradient descent Communication delays Uncoordinated step-sizes Directed network

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Stochastic gradient-push for economic dispatch on time-varying directed networks with delays

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年第0期113卷 564-572页

作者： Yuan, Ye Li, Huaqing Hu, Jinhui Wang, Zheng Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Economic dispatch problem (EDP) is an elementary optimization problem of power systems whose purpose is to minimize the total generation cost while meeting total demands and complying with individual generator output constraints. This paper proposes a distributed optimization algorithm with noisy gradient based on stochastic gradient-push approach to solve the EDP on time-varying directed communication networks potentially with time delays. It shows that the proposed algorithm can be ensured to solve the EDP when the time-varying directed communication networks are uniformly jointly strongly connected. The algorithm with asynchronous step-sizes can deal with the finite time-varying delays on communication links as well. The generation cost functions are considered as strictly convex and strongly convex functions respectively with convergence rate of O((lnk)/root k) and O ((ink)/ k). Simulation results are provided to validate the availability of the algorithm.

关键词： Economic dispatch distributed optimization algorithm Stochastic gradient-push approach Time-varying directed communication networks Time delays

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Bi-level optimal dispatch in the Virtual Power Plant considering uncertain agents number

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NEUROCOMPUTING 2015年 167卷 551-557页

作者： Yu, Jie Jiao, Yiping Wang, Xiaolong Cao, Jinde Fei, Shumin Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Dept Math Nanjing 210096 Jiangsu Peoples R China King Abdulaziz Univ Fac Sci Dept Math Jeddah 21589 Saudi Arabia

In smart grid, virtual power plant (VPP) is a novel solution to distributed Energy Resources (DER) in the power grid. Same for traditional power plants, VPP should provide electricity generation to power grid according to the command electricity generation coming from power grid control center. Different from traditional power plants, VPP controls geographically distributed DER agents, which aggregate various local DER, such as small-scale engines, turbines or storages. Moreover, the number of DER Agents in VPP probably changes in pace with DER Agents plug in and plug out. In this paper, a distributed optimization algorithm is projected to solve VPP bi-level optimal dispatch considering uncertain agents number. Simulation results show that the proposed algorithm can effectively solve bi-level optimal dispatch with changing lower agents. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Virtual Power Plant (VPP) Bi-level optimal dispatch Hierarchical control architecture distributed optimization algorithm Uncertain agents number

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An asynchronous distributed gradient algorithm for economic dispatch over stochastic networks

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 124卷 106240-106240页

作者： Zhang, Hao Liang, Shan Ou, Minghui Wei, Mengli Chongqing Univ Coll Automat Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Southeast Univ Sch Cyber Engn & Sci Nanjing 211189 Peoples R China

The economic dispatch problem (EDP) in power systems is usually formulated as a category of convex optimization problems in which the collective cost function is expressed as the sum of all individual objectives of generators. It is inclined to be solved through a distributed way, which is to minimize the total generation cost while meet the demands under generator capability restrictions. In this paper, we consider this class of convex optimization problems that involve coupling linear constraint and individual box constraints. In order to make information communication of network decentralized, reliable and computationally inexpensive, an asynchronous distributed primal-dual optimization algorithm is proposed over stochastic networks. This algorithm allows nodes to use asynchronous communication strategy to update their state, and the step-sizes for seeking the optimum solution are uncoordinated constants. The asynchronous operation for generators takes link failures of networks into account. Under strongly convex assumption on objective functions, it is proved that the proposed algorithm can seek the exact optimal solution with probability one if the expectation of communication network is undirected connected. The effectiveness of the proposed optimization algorithm over stochastic networks is illustrated by provided simulation results.

关键词： Economic dispatch problem (EDP) distributed optimization algorithm Asynchronous communication Uncoordinated step-sizes Stochastic networks

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distributed quadratic programming under Asynchronous and Lossy Communications via Newton-Raphson Consensus

Distributed quadratic programming under Asynchronous and Los...

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European Control Conference (ECC)

作者： Carli, Ruggero Notarstefano, Giuseppe Schenato, Luca Varagnolo, Damiano Univ Padua Dept Informat Engn Via Gradenigo 6-a I-35131 Padua Italy Univ Salento Dept Engn I-73100 Lecce Italy Lulea Univ Technol Dept Comp Sci Elect & Space Engn S-97187 Lulea Sweden

ISBN: (纸本)9783952426937

Quadratic optimization problems appear in several interesting estimation, learning and control tasks. To solve these problems in peer-to-peer networks it is necessary to design distributed optimization algorithms supporting directed, asynchronous and unreliable communication. This paper addresses this requirement by extending a promising distributed convex optimization algorithm, known as Newton-Raphson consensus, and originally designed for static and undirected communication. Specifically, we modify this algorithm so that it can cope with asynchronous, broadcast and unreliable lossy links, and prove that the optimization strategy correctly converge to the global optimum when the local cost functions are quadratic. We then support the intuition that this robustified algorithm converges to the true optimum also for general convex problems with dedicated numerical simulations.

关键词： Newton-Raphson method convex programming distributed algorithms distributed programming peer-to-peer computing quadratic programming telecommunication control Newton-Raphson consensus asynchronous communication distributed convex optimization algorithm distributed optimization algorithm distributed quadratic programming lossy communication numerical simulation optimization strategy peer-to-peer network quadratic optimization static communication undirected communication unreliable communication algorithm design and analysis Convergence Gold optimization Packet loss Robustness quadratic programming algorithm design and analysis Asynchronous communication Peer to peer computing optimization algorithms Communication and control distributed software Packet loss Newton-Raphson method convex programming distributed algorithms Peer-to-peer network numerical simulation Asynchronous Converge

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distributed Transmission Expansion Planning in Multi-Area Electric Power Systems

Distributed Transmission Expansion Planning in Multi-Area El...

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IEEE Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： Kargarian, Amin Rahmani, Mohsen Falahati, Bamdad Louisiana State Univ Div Elect & Comp Engn Baton Rouge LA 70803 USA Carnegie Mellon Univ Dept Engn & Publ Policy Pittsburgh PA 15213 USA Schweitzer Engn Labs SEL Irvine CA USA

ISBN: (纸本)9781509051670

This paper presents a distributed transmission expansion planning (TEP) approach for multi-area power systems. A local TEP is formulated for each area with respect to local characteristics of that area as well as power flow in existing and candidate tie-lines that interconnect the area with its neighbors. Based on the auxiliary problem principle, a distributed decisionmaking algorithm is presented to coordinates the local TEP problems and find an overall solution for the entire power system. Limited information, only related to the tie-lines and not local lines, is exchanged between transmission areas, so that information privacy of the planners is respected. Numerical results on the Garver Test system show the effectiveness of the proposed distributed TEP algorithm.

关键词： Transmission expansion planning multi-area power systems information privacy distributed optimization algorithm auxiliary problem principle

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Residential PV Settings for MV-LV Networks: A distributed Three-Phase AC OPF

Residential PV Settings for MV-LV Networks: A Distributed Th...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Givisiez, Arthur Goncalves Liu, Michael Z. Ochoa, Luis F. Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9781665435970

The operation of MV-LV networks with high penetrations of residential PV systems will require the active management of PV settings (curtailment). The conventional three-phase AC optimal power flow (OPF) has shown great potential, however, its formulation as a single problem might not be scalable (i.e., solvable fast enough to be relevant). The added complexity due to multiple voltage levels and unbalance can further exacerbate this. An alternative is to break down the single OPF problem into multiple smaller subproblems but considering the interactions at their interfaces. Here, the distributed optimization algorithm alternating direction method of multipliers (ADMM) is adopted to solve a three-phase AC OPF that calculates PV settings in MV-LV networks to avoid voltage and congestion problems. Its performance is assessed for different numbers of LV networks (subproblems). Results show that the proposed algorithm is not only accurate but much faster than the conventional approach for large distribution networks.

关键词： alternating direction method of multipliers distributed optimization algorithm distribution networks residential photovoltaic systems three-phase AC optimal power flow

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KunPeng: Parameter Server based distributed Learning Systems and Its Applications in Alibaba and Ant Financial 17

KunPeng: Parameter Server based Distributed Learning Systems...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhou, Jun Li, Xiaolong Zhao, Peilin Chen, Chaochao Li, Longfei Yang, Xinxing Cui, Qing Yu, Jin Chen, Xu Ding, Yi Qi, Yuan Alan Ant Financial Serv Grp Hangzhou Zhejiang Peoples R China Alibaba Cloud Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450348874

In recent years, due to the emergence of Big Data (terabytes or petabytes) and Big Model (tens of billions of parameters), there has been an ever-increasing need of parallelizing machine learning (ML) algorithms in both academia and industry. Although there are some existing distributed computing systems, such as Hadoop and Spark, for parallelizing ML algorithms, they only provide synchronous and coarse-grained operators (e.g., Map, Reduce, and Join, etc.), which may hinder developers from implementing more efficient algorithms. This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML. Specifically, KunPeng not only encapsulates the characteristics of data/model parallelism, load balancing, model sync-up, sparse representation, industrial fault-tolerance, etc., but also provides easy-to-use interface to empower users to focus on the core ML logics. Empirical results on terabytes of real datasets with billions of samples and features demonstrate that, such a design brings compelling performance improvements on ML programs ranging from Follow-the-Regularized-Leader Proximal algorithm to Sparse Logistic Regression and Multiple Additive Regression Trees. Furthermore, KunPeng's encouraging performance is also shown for several real-world applications including the Alibaba's Double 11 Online Shopping Festival and Ant Financial's transaction risk estimation.

关键词： Parameter Server distributed computing system distributed optimization algorithm

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Sparse Gradient Compression for distributed SGD 24th

Sparse Gradient Compression for Distributed SGD

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24th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Sun, Haobo Shao, Yingxia Jiang, Jiawei Cui, Bin Lei, Kai Xu, Yu Wang, Jiang Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China BUPT Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Tencent Inc Shenzhen Peoples R China

ISBN: (纸本)9783030185794;9783030185787

Communication bandwidth is a bottleneck in distributed machine learning, and limits the system scalability. The transmission of gradients often dominates the communication in distributed SGD. One promising technique is using the gradient compression to reduce the communication cost. Recently, many approaches have been developed for the deep neural networks. However, they still suffer from the high memory cost, slow convergence and serious staleness problems over sparse high-dimensional models. In this work, we propose Sparse Gradient Compression (SGC) to efficiently train both the sparse models and the deep neural networks. SGC uses momentum approximation to reduce the memory cost with negligible accuracy degradation. Then it improves the accuracy with long-term gradient compensation, which maintains global momentum to make up for the information loss caused by the approximation. Finally, to alleviate the staleness problem, SGC updates model weight with the accumulation of delayed gradients at local, called local update technique. The experiments over the sparse high-dimensional models and deep neural networks indicate that SGC can compress 99.99% gradients for every iteration without performance degradation, and saves the communication cost up to 48x.

关键词： distributed computing system distributed optimization algorithm Gradient compression

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