Shiping Yang

Bio: Shiping Yang is an academic researcher from National University of Singapore. The author has contributed to research in topics: Iterative learning control & Multi-agent system. The author has an hindex of 13, co-authored 27 publications receiving 1053 citations. Previous affiliations of Shiping Yang include Centre for Life.

Consensus Based Approach for Economic Dispatch Problem in a Smart Grid

Abstract: Economic dispatch problem (EDP) is an important class of optimization problems in the smart grid, which aims at minimizing the total cost when generating certain amount of power. In this work, a novel consensus based algorithm is proposed to solve EDP in a distributed fashion. The quadratic convex cost functions are assumed in the problem formulation, and the strongly connected communication topology is sufficient for the information exchange. Unlike centralized approaches, the proposed algorithm enables generators to collectively learn the mismatch between demand and total amount of power generation. The estimated mismatch is then used as a feedback mechanism to adjust current power generation by each generator. With a tactical initial setup, eventually, all generators can automatically minimize the total cost in a collective sense.

Consensus based approach for economic dispatch problem in a smart grid

Abstract: Economic dispatch problem (EDP) is an important problem in the smart grid. Its aim is to minimize the total cost when generating certain amount of power. This paper proposes a novel consensus based algorithm to solve EDP in a distributed fashion. The quadratic cost functions are adopted in the problem formulation, and the strongly connected communication topology is used for the information exchange. Unlike the centralized approach, the proposed algorithm allows generators to learn the mismatch information between demand and total power generation through a distributed manner. The estimated mismatch information is used as a feedback to adjust current power generation by each generator. With a tactical initial setup, generators can automatically minimize the total cost in a collective sense while satisfying power balance equation.

A HOIM-based iterative learning control scheme for multi-agent formation

Abstract: In this paper, a high-order internal model (HOIM) based iterative learning control (ILC) scheme for multi-agent system (MAS) formation is studied. The HOIM-based ILC, which is an effective approach to deal with iteratively varying reference trajectories, provides a suitable framework for derivations and analysis of MAS control in general, and formation control in particular. In this work, the connections between agents are assumed dynamically changing at consecutive formation executions, which can be formulated as a series of structural switches. By employing the HOIM-based ILC, the control signals can be learned directly and tracking error converges asymptotically along the iteration axis.

Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey

Abstract: In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and progressed rapidly in the past few years. Focusing on different kinds of constraints on the controller and the self-dynamics of each individual agent, as well as the coordination schemes, we categorize the recent results into the following directions: consensus with constraints, event-based consensus, consensus over signed networks, and consensus of heterogeneous agents. We also review some applications of the very well developed consensus algorithms to the topics such as economic dispatch problem in smart grid and k -means clustering algorithms.

Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid

Abstract: In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.

Distributed Optimal Resource Management Based on the Consensus Algorithm in a Microgrid

Abstract: A microgrid is a promising approach to provide clean, renewable, and reliable electricity by integrating various distributed generations and energy storage systems into power systems. However, highly intermittent renewable generations and various load demands pose new challenges to the optimal resource management in a microgrid. This paper proposes a fully distributed control strategy based on the consensus algorithm for the optimal resource management in an islanded microgrid. The proposed strategy is implemented through a multiagent system framework, which only requires information exchange among neighboring agents through a local network. The objective is achieved through a two-level control strategy. The upper control level is a consensus-based optimization algorithm that discovers the reference of optimal power generation or demand while maintaining the supply–demand balance. The lower control level is responsible for reference tracking of the associated component. Simulation results in the IEEE 14- and 162-bus systems are presented to demonstrate the effectiveness of the proposed control strategy.

Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication

Abstract: This paper investigates the problem of distributed energy management for both generation and demand side in smart grid. Different from existing works, we formulate a social welfare maximization problem for a more practical scenario by taking transmission losses into account. The formulated problem is non-convex due to the non-convexity of the power balance equality constraint caused by the transmission losses. To solve the problem, we first transform the equality constraint into an inequality constraint and obtain a new convex optimization problem. We then derive a sufficient condition to guarantee that the new problem has the same solution as the original one. Because of the coupling in the constraint, Lagrange duality method is adopted to decompose the problem. Considering the general communication topology among generators and demands, i.e., directed connected topology, we design a consensus-based algorithm to solve the problem in a distributed way. We also prove the convergence and optimality of the proposed algorithm, under which the social welfare maximization is achieved. Extensive simulations validate the theoretical results and demonstrate the effectiveness of the proposed algorithm.