Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of...Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.展开更多
This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical learning.These algorithms are suitable for a class of computational problems in...This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical learning.These algorithms are suitable for a class of computational problems in largescale networked systems,ranging from average consensus,sensor fusion,distributed estimation,distributed optimisation,distributed control,and distributed learning.By expressing the underlying computational problem as a sparse linear system,each algorithm operates at each node of the network graph and computes iteratively the desired solution.The behaviours of these algorithms are discussed in terms of the network graph topology and parameters of the corresponding computational problem.A number of examples are presented to illustrate their applications.Also introduced is a message-passing algorithm for distributed convex optimisation.展开更多
Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed conve...Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.展开更多
Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative,eco-friendly,stable,reliable and cost-effi...Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative,eco-friendly,stable,reliable and cost-efficient system as a whole.In order to unlock the full benefits,utilities need now to develop new technologies like distributed optimisation methods to excavate the latent value from the magnanimous data.This paper surveys recent advances of distributed optimisation and game algorithms with applications in power systems.In particular,this paper reviews distributed algorithms for model-based offline optimisation solution of dynamic economic dispatch problems,charging control problems for plug-in electric vehicles and risk-averse energy trading as well as model-free online algorithms for demand response problems.展开更多
In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches an...In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation.By formulating each algorithm in continuous-time,it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set.Therefore,a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method.Furthermore,we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.展开更多
It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex ne...It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.展开更多
文摘Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.
基金supported in part by the of National Natural Science Foundation of China(U21A20476,U1911401,U22A20221,62273100,62073090).
文摘This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical learning.These algorithms are suitable for a class of computational problems in largescale networked systems,ranging from average consensus,sensor fusion,distributed estimation,distributed optimisation,distributed control,and distributed learning.By expressing the underlying computational problem as a sparse linear system,each algorithm operates at each node of the network graph and computes iteratively the desired solution.The behaviours of these algorithms are discussed in terms of the network graph topology and parameters of the corresponding computational problem.A number of examples are presented to illustrate their applications.Also introduced is a message-passing algorithm for distributed convex optimisation.
文摘Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.
基金This research was supported in part by National Priority Research Project through the Qatar National Research Fund(a member of Qatar Foundation)under Grant NPRP 9-166-1-031,in part by the NSFC 61503308,11501070 and 61473326in part by the Natural Science Foundation Projection of Chongqing cstc2017jcyjA0788in part by China Postdoctoral Science Foundation under Grant 2017M620374.
文摘Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative,eco-friendly,stable,reliable and cost-efficient system as a whole.In order to unlock the full benefits,utilities need now to develop new technologies like distributed optimisation methods to excavate the latent value from the magnanimous data.This paper surveys recent advances of distributed optimisation and game algorithms with applications in power systems.In particular,this paper reviews distributed algorithms for model-based offline optimisation solution of dynamic economic dispatch problems,charging control problems for plug-in electric vehicles and risk-averse energy trading as well as model-free online algorithms for demand response problems.
基金The work by M.Egerstedt was funded by The Air Force Office of Scientific Research through[grant number 2012-00305-01].
文摘In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation.By formulating each algorithm in continuous-time,it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set.Therefore,a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method.Furthermore,we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.
基金This work was supported by the National Science Fund for Excellent Young Scholars[grant number 61322302]the National Science Fund for Distinguished Young Scholars[grant number 61025017]+3 种基金the National Natural Science Foundation of China[grant number 61104145],[grant number 61304168]the Natural Science Foundation of Jiangsu Province of China[grant number BK2011581],[grant number BK20130595]the Research Fund for the Doctoral Program of Higher Education of China[grant number 20110092120024]the Fundamental Research Funds for the Central Universities of China,and the Discovery Scheme under[grant number DP140100544].
文摘It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.
