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distributed Secondary Frequency Control algorithm Considering Storage Efficiency

IEEE TRANSACTIONS ON SMART GRID 2018年第6期9卷 6214-6228页

作者： Megel, Olivier Liu, Tao Hill, David J. Andersson, Goran Swiss Fed Inst Technol Power Syst Lab CH-8092 Zurich Switzerland Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Storage technologies, such as batteries, have higher ramping capability than conventional generators, and so they could lead to better dynamic tracking performance when following a frequency control signal. However, storage units are energy constrained, so the energy content of the signal they follow should preferably be low. Our first contribution is a method for decomposing the target signal into a different signal for each generator and each storage unit, depending on their characteristics and on the storage energy levels. Faster units receive signals with higher ramping rates. Storage units with low energy-to-power ratios (tau(i)) receive signals which are energy-neutral (efficiency-corrected) in shorter time frames than the signals for storage units with larger tau(i). Our method is particularly suitable when the tau(i) vary by orders of magnitude over the storage population. It dispatches the units by solving a modified economic dispatch problem. The energy content of the storage unit signals is managed through a term penalizing energy level deviation in the cost function. The second contribution is a distributed implementation of our method, which approaches the dispatch of its centralized counterpart. Our distributed implementation innovates compared to other distributed algorithms in that it considers non-unity storage efficiency. Using a population of 1000 units we show that the distributed implementation is: 1) up to 700 times faster than the centralized implementation, therefore allowing for real-time implementation with a large number of units;and 2) robust to loss of communication links.

关键词： Frequency control storage distributed algorithm storage efficiency

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A fixed-time convergent algorithm for distributed convex optimization in multi-agent systems

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AUTOMATICA 2018年 95卷 539-543页

作者： Chen, Gang Li, Zhiyong 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

This technical paper presents a distributed continuous-time algorithm to solve multi-agent optimization problem with the team objective being the sum of all local convex objective functions while subject to an equality constraint. The optimal solutions are achieved within fixed time which is independent of the initial conditions of agents. This advantage makes it possible to off-line preassign the settling time according to task requirements. The fixed-time convergence for the proposed algorithm is rigorously proved with the aid of convex optimization and fixed-time Lyapunov theory. Finally, the algorithm is valuated via an example. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： distributed algorithm Convex optimization Equality constraint Fixed-time convergence Multi-agent system

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A new multi-objective distributed fuzzy clustering algorithm for wireless sensor networks with mobile gateways

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2018年 89卷 92-104页

作者： Abdolkarimi, Maryam Adabi, Sepideh Sharifi, Arash Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran

In the clustered wireless sensor networks (WSNs) studied, each cluster head (CH) transmits aggregated data from cluster members to the static base station by means of intermediate communication nodes in name mobile gateways. In order to reduce energy consumption along with reducing delays in packet delivery in the mentioned WSNs, two approaches are proposed: (1) designing a completely distributed fuzzy system to determine the eligibility of the node for being CH with two input factors, namely the general state of a sensor node in the WSN (GSoSN) and location of a sensor node relative to mobile gateway nodes (LoSNRtMG). By defining two early discussed factors, not only the accuracy of decision making in selecting the CH node enhances but also the computation overhead decreases;and (2) reducing communication overhead by applying the linear prediction method to estimate the next locations of the moving gateway nodes instead of periodical broadcasting of their location messages. The experimental results demonstrate that the proposed idea has better performance in terms of reducing both energy consumption and delays in packet delivery metrics.

关键词： Wireless sensor networks Clustering distributed algorithm Fuzzy inference system Mobile gateway Multi-objective decision making

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Energy Management Optimization of Microgrid Cluster Based on Multi-Agent-System and Hierarchical Stackelberg Game Theory

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IEEE ACCESS 2020年 8卷 206183-206197页

作者： Dong, Xin Li, Xianshan Cheng, Shan China Three Gorges Univ Coll Elect Engn & New Energy Hubei Prov Res Ctr Microgrid Engn Technol Yichang 443002 Peoples R China

To realize the win-win benefits and resource coordination of the multilevel operating entities of a "microgrid cluster (MGC), microgrid (MG) and user" and improve the self-consumption of new energy in the MGC, this paper proposes an energy trading model and solution algorithm of an "MGC, MG and user" based on a multi-agent-system, incentive demand response, and hierarchical Stackelberg game theory. By analyzing the game objectives and strategies of these participants, the unique Stackelberg equilibrium (SE) of the hierarchical Stackelberg game is proved theoretically. The game optimization process is divided into two levels. In the upper-level game, the MGC as a leader stimulates the MG to participate in intracluster dispatching by establishing an internal price incentive mechanism. As the follower, the MG determines the number of electricity transactions based on the realized internal price to maximize its own profits. In the lower-level game, the MG leads the game by deciding electricity selling prices based on the load demands of users, and the user as follower adjust electricity consumption using strategies to balance expenditure and experience of electricity usage. Simulation results verified the effectiveness and good convergence of the proposed method and demonstrated that the proposed hierarchical game strategy can improve the economic benefits of each participant, which is conducive to the establishment of friendly grid-connected MGC.

关键词： Microgrid cluster hierarchical Stackelberg game multi-agent demand-side response Stackelberg equilibrium distributed algorithm

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A Game Theoretic Approach to Phase Balancing by Plug-In Electric Vehicles in the Smart Grid

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第3期35卷 2232-2244页

作者： Chen, Shibo Guo, Zhenwei Yang, Zaiyue Xu, Yunjian Cheng, Roger S. Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

With the increasing penetration of renewable energy resources and plug-in electric vehicles (PEV) in the smart grid, the problem of phase imbalance in a three-phase distribution system becomes more challenging. In this paper, we propose a phase balancing (PB) scheme performed by PEVs. With PB remunerations introduced as financial incentives, PEV owners are encouraged to assist the distribution system operator in reducing the phase imbalance. This is achieved by exploiting the charging flexibility of PEV batteries. We formulate this problem into a non-smooth non-cooperative game, where each PEV owner autonomously minimizes his/her charging cost minus the PB remuneration, considering decisions of other PEV owners. Through transforming this game into a smooth extended one, we are able to solve this challenging problem. Specifically, existence of its Nash equilibrium (NE) is demonstrated, and a distributed algorithm is developed to achieve the NE. Convergence proof of this algorithm is also provided. Simulation results validate our proposed scheme, and illustrate that both PEV owners and the distribution system operator will benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved.

关键词： Games Batteries Inverters Remuneration distributed algorithms Smart grids Phase balancing plug-in electric vehicles game theory variational inequality distributed algorithm

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Energy Trading and Generalized Nash Equilibrium in Combined Heat and Power Market

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第5期35卷 3378-3387页

作者： Wu, Chenyu Gu, Wei Bo, Rui MehdipourPicha, Hossein Jiang, Ping Wu, Zhi Lu, Shuai Yao, Shuai Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

The increasingly wide application of technologies such as combined heat and power (CHP) systems and micro energy management systems has enhanced the coupling of various types of energy. In this paper, a generalized Nash game model is built in this paper to simulate the multi-round auction in combined heat and power market (CHPM). In this model, the consumer demand curves are deduced from the partial derivatives of their payoff functions, and the thermal comfort of the consumers is considered in their payoff functions. The integrated energy suppliers (IES) compete with each other in the CHPM. The generalized Nash game model is applied to describe their rational economic behavior. First, the existence and uniqueness of the generalized Nash equilibrium is proven in detail. Subsequently, a distributed algorithm based on the augmented Lagrangian approach is designed to solve this generalized Nash game model. Through the distributed algorithm, the participants can protect their privacy without revealing their generation parameters or energy consumption schedule to other participants. Finally, by simulating the multi-round auction, it was found that the proposed method has faster convergence than standard Lagrangian approaches.

关键词： Cogeneration Games Power markets Boilers Home appliances Augmented lagrangian approach distributed algorithm generalized nash game integrated energy demand

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Decentralized PEV Power Allocation With Power Distribution and Transportation Constraints

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第1期38卷 229-243页

作者： Li, Mushu Gao, Jie Chen, Nan Zhao, Lian Shen, Xuemin Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

关键词： Power distribution Resource management Transportation Power grids Reliability Batteries Optimization Electric vehicles charging management range anxiety distributed algorithm alternating direction multiplier method (ADMM) kernel density estimation smart grid

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A Truthful and Efficient Incentive Mechanism for Demand Response in Green Datacenters

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2020年第1期31卷 1-15页

作者： Zhou, Zhi Liu, Fangming Chen, Shutong Li, Zongpeng Sun Yat Sen Univ Sch Data & Comp Sci Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab 1037 Luoyu Rd Wuhan 430074 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Datacenter demand response is envisioned as a promising tool for mitigating operational stability issues faced by smart grids. It enables significant potentials in peak load reduction and facilitates the incorporation of distributed generation. Monetary refund from the smart grid can also alleviate the cloud's burden in escalating electricity cost. However, the current demand response paradigm is inefficient towards incentivizing a cloud service provider (CSP) that operates geo-distributed datacenters. To incentivize CSP participation, this work presents an auction mechanism that enables smart grids to voluntarily submit bids to the CSP to procure diverse amounts of demand response with different payments. To maximize the social welfare of the auction, the CSP that acts as the auctioneer needs to solve the winner determination problemat large-scale. By applying the proximal Jacobian alternating direction method of multipliers, we propose a distributed algorithm for each datacenter to solve a small-scale problemin a parallel fashion. Desirable properties of the proposed auction, such as social welfare maximization and truthfulness are achieved through Vickrey-Clarke-Groves (VCG) payment. Through extensive evaluations based on real datacenter workload traces and IEEE 14-bus test systems, we demonstrate that our incentive mechanism constitutes a win-win mechanism for both the geo-distributed cloud and the smart grid.

关键词： Geo-distributed datacenters smart grid demand response incentive mechanism distributed algorithm

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Low-Cost, Efficient Output-Only Infrastructure Damage Detection With Wireless Sensor Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第3期50卷 1003-1012页

作者： Contreras, William Ziavras, Sotirios New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Structural health monitoring (SHM), which is the process of gauging the health of instrumented structures, is becoming increasingly important as much of the world's transportation infrastructure ages and deteriorates. Wireless sensor networks (WSNs) have the potential to deliver continuous SHM at a low cost. We present a novel WSN system that monitors ambient structural vibrations and looks for deviations from a baseline response to assess the condition of the structure under observation. Efficiency is achieved by implementing our damage assessment process with an innovative wireless-sensor-based technique involving look-up tables. By using look-up tables we are able to minimize computations. Only 4-6 integer and two floating-point operations are required per damage assessment. This is in contrast to the majority of other methods that rely on precise engineering models of the monitored structures. Our look-up table technique is benchmarked against the brute force approach, which involves solving equations, by measuring the execution time of each implementation on a low-power Microchip microcontroller. Here, we were able to achieve a speedup of roughly five. In addition, we investigate a tradeoff between average transmission power and the number of measurements required to make a decision as to the structure's condition. Finally, we benchmark our damage assessment process using a case study to verify its efficacy for bridges.

关键词： Wireless sensor networks Vibrations Monitoring Base stations Clustering algorithms Table lookup Bridges distributed algorithm statistical analysis vibration measurement wireless sensor network (WSN)

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Time-varying multi-objective optimisation over switching graphs via fixed-time consensus algorithms

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第15期51卷 2793-2806页

作者： Li, Zhongguo Ding, Zhengtao Univ Manchester Dept Elect & Elect Engn Sackville St Bldg Manchester M13 9PL Lancs England

This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network-connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are developed for each agent to estimate the global variables and drive all local copies of the decision vector to a consensus. The algorithm with fixed gains is first proposed, where some global information is required to choose the gains. Then, an adaptive algorithm is presented to eliminate the use of global information. The convergence of those algorithms to the Pareto solutions is established via Lyapunov theory for connected graphs. In the case of disconnected graphs, the convergence to the subsets of the Pareto fronts is studied. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.

关键词： Consensus gradient descent optimisation distributed algorithm multi-agent systems

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