Chaojie Li

Distributed Event-Triggered Scheme for Economic Dispatch in Smart Grids

To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy.

Parallel and Distributed Computation for Dynamical Economic Dispatch

This letter introduces a parallel and distributed computation method for dynamical economic dispatch over a cyber-physical system. To achieve a faster economic dispatch operation, accelerated consensus approach is proposed. The simulation illustrates the better performance of accelerated consensus algorithm.

Optimal economic dispatch by fast distributed gradient

Concerning on optimal economic dispatch, interior point method via 6-logarithmic barrier is employed to reformulate the cost function of power generation. Fully distributed technology-enabled algorithm is developed to solve the economic dispatch. More specifically, the minimum connected dominating set based distributed algorithm aims at efficiently allocating the task of supply-demand balance for the whole power grid. A fast gradient based distributed optimization method is designed to fast converge to optimal solution. The simulations illustrate the effectiveness and good performance of our algorithms.

Noncooperative Game-Based Distributed Charging Control for Plug-In Electric Vehicles in Distribution Networks

Increasing penetration of plug-in electric vehicles (PEVs) has a substantial impact on the operation of power distribution networks. Given the fast-growing load demands from PEVs and unmatched infrastructure investment in transformer and feeder capacity, the PEV charging is subjected to both spatially and temporally security constraints beyond which the network failure may occur. This paper proposes a game-theory-based distributed charging control method to coordinate large-scale PEVs without compromising the security of the distribution network. Under a noncooperative game framework, a price-driven charging model is designed to minimize the cost of each individual PEV customer while satisfying the network loading constraints. Then, a Newton-type method is developed to find a better Nash equilibrium of the game model at a superlinear convergence rate. Furthermore, an accelerated gradient method is proposed to tackle the subproblem for each users best response. The update of the users best response is implemented in a distributed way in order to protect users privacy. The convergence rate of the proposed algorithms is rigorously proved. The effectiveness and efficiency of the proposed methods are tested on the IEEE 13-bus system.

Distributed consensus strategy for economic power dispatch in a smart grid

This paper studies economic power dispatch strategy in a smart grid by using the distributed consensus protocol in multi-agent systems, where there are many generation units working cooperatively under a local neighboring area to achieve a minimum solution for the optimization problem of the total cost function. At first, the analysis indicates that the total cost for generators in a smart grid can reach its minimal value if the incremental cost for all generation units can achieve consensus and the balance between supply and demand of powers is satisfied. Then, by designing a distributed consensus protocol in multi-agent systems for the incremental cost with appropriate initial conditions, incremental cost consensus can be reached for all the generation units and the balance for the powers can also be kept. Thus, the optimization problem for the cost function of all generation units can be solved. Finally, some simulation examples are performed to verify the analysis in this paper.

Economic power dispatch in smart grids: a framework for distributed optimization and consensus dynamics

By using the distributed consensus theory in multi-agent systems, the strategy of economic power dispatch is studied in a smart grid, where many generation units work cooperatively to achieve an optimal solution in a local area. The relationship between the distributed optimization solution and consensus in multi-agent systems is first revealed in this paper, which can serve as a general framework for future studies of this topic. First, without the constraints of capacity limitations, it is found that the total cost for all the generators in a smart grid can achieve the minimal value if the consensus can be reached for the incremental cost of all the generation units and the balance between the supply and demand of powers is kept. Then, by designing a distributed consensus control protocol in multi-agent systems with appropriate initial conditions, incremental cost consensus can be realized and the balance for the powers can also be satisfied. Furthermore, the difficult problem for distributed optimization of the total cost function with bounded capacity limitations is also discussed. A reformulated barrier function is proposed to simplify the analysis, under which the total cost can reach the minimal value if consensus can be achieved for the modified incremental cost with some appropriate initial values. Thus, the distributed optimization problems for the cost function of all generation units with and without bounded capacity limitations can both be solved by using the idea of consensus in multi-agent systems, whose theoretical analysis is still lacking nowadays. Finally, some simulation examples are given to verify the effectiveness of the results in this paper.

Distributed Control of Networked Agent Systems: Theory and Applications

A great number of practical complex systems can bemodeled as networked agent systems. Typical examples include the distributed satellite systems, a group of robots, wireless sensor networks, and power grids. One critical topic within this context is to understand how globally cooperative behaviors of such systems can be emerged as a result of distributed local interactions which has recently receivedmuch attention from various scientific fields. The present special issue mainly focuses on the new distributed control approaches in networked agent systems as well as their potential engineering applications. It tries to not only explore the underlying mechanisms corresponding to various collective behaviors but also manipulate and even control these fascinating collective behaviors. Call for papers has been carefully prepared by the guest editors and posted on the journal’s web page, which has received a lot of attention from researchers in various scientific fields.This special issue has received 30 submissions on networked agent systems. All manuscripts submitted to this special issue went through a thorough peer-refereeing process. Based on the reviewers’ reports and the guest editors’ comments, 10 original research articles are finally accepted. The contents of accepted papers are summarized below.