Yinliang Xu

Novel Multiagent Based Load Restoration Algorithm for Microgrids

Once a fault in microgrids has been cleared, it is necessary to restore the unfaulted but out-of-service loads as much as possible in a timely manner. This paper proposes a novel fully distributed multiagent based load restoration algorithm. According to the algorithm, each agent makes synchronized load restoration decision according to discovered information. During the information discovery process, agents only communicate with their direct neighbors, and the global information is discovered based on the Average-Consensus Theorem. In this way, total net power, indexes and demands of loads that are ready for restoration can be obtained. Then the load restoration problem can be modeled and solved using existing algorithms for the 0-1 Knapsack problem. To achieve adaptivity and stability, a distributed algorithm for coefficient setting is proposed and compared against existing algorithms and a particle swarm optimization based algorithm. Theoretically, the proposed load restoration algorithm can be applied to systems of any size and structure. Simulation studies with power systems of different scale demonstrate the effectiveness of the proposed algorithm.

Stable Multi-Agent-Based Load Shedding Algorithm for Power Systems

If generation in a power system is insufficient to power all loads, efficient load shedding operations may need to be deployed to maintain the supply-demand balance. This paper proposes a distributed multi-agent-based load shedding algorithm, which can make efficient load shedding decision based on discovered global information. During the information discovery process, only communications between immediate neighboring agents are used. The information discovery algorithm is represented as a discrete time linear system and the stability of which is analyzed according to average-consensus theorem. According to rigorous stability analysis, convergence of the designed algorithm can be guaranteed. To improve the speed of the algorithm, particle swarm optimization (PSO) is used to optimize the coefficients for information exchange so that the second largest eigenvalue of the iteration matrix is minimized. According to the designed algorithm, total net active power and operating status of loads can be discovered accurately even with faults. Based on the discovered information, coordinated load shedding decision can be made.

Cooperative Control of Distributed Energy Storage Systems in a Microgrid

Energy storage systems (ESSs) are often proposed to support the frequency control in microgrid systems. Due to the intermittency of the renewable generation and constantly changing load demand, the charging/discharging of various ESSs in an autonomous microgrid needs to be properly coordinated to ensure the supply-demand balance. Recent research has discovered that the charging/discharging efficiency of ESSs has remarkable dependence on the charging/discharging rate and state-of-charge of the ESS. This paper proposes a distributed cooperative control strategy for coordinating the ESSs to maintain the supply-demand balance and minimize the total power loss associated with charging/discharging inefficiency. The effectiveness of the proposed approach is validated by simulation results.

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

For a microgrid with high renewable energy penetration to work autonomously, it must maintain its own supply-demand balance of active power. Maximum peak power tracking algorithms, which emphasize high renewable energy utilization, may cause a supply-demand imbalance when the available renewable generation is more than demanded, especially for autonomous microgrids. Currently, droop control is one of the most popular decentralized methods for sharing active and reactive loads among the distributed generators. However, conventional droop control methods suffer from slow and oscillating dynamic response and steady state deviations. To overcome these problems, this paper proposes a distributed subgradient-based solution to coordinate the operations of different types of distributed renewable generators in a microgrid. By controlling the utilization levels of renewable generators, the supply-demand balance can be well maintained and the system dynamic performance can be significantly improved. Simulation results demonstrate the effectiveness of the proposed control solution.

Distributed Dynamic Programming-Based Approach for Economic Dispatch in Smart Grids

In this paper, the discrete economic dispatch problem is formulated as a knapsack problem. An effective distributed strategy based on distributed dynamic programming algorithm is proposed to optimally allocate the total power demand among different generation units considering the generation limits and ramping rate limits. The proposed distributed strategy is implemented based on a multiagent system framework which only requires local computation and communication among neighboring agents. Thus, it enables the sharing of computational and communication burden among distributed agents. In addition, the proposed strategy can be implemented with asynchronous communication, which may lead to simpler implementation and faster convergence speed. Simulation results with a four-generator system and the IEEE 162-bus system are presented to demonstrate the effectiveness of the proposed distributed strategy.

Fully Distributed Coordination of Multiple DFIGs in a Microgrid for Load Sharing

When wind power penetration is high, the available generation may be more than needed, especially for wind-powered microgrids working autonomously. Because the maximum peak power tracking algorithm may result in a supply-demand imbalance, an alternative algorithm is needed for load sharing. In this paper, a fully distributed control scheme is presented to coordinate the operations of multiple doubly-fed induction generators (DFIGs) in a microgrid. According to the proposed control strategy, each bus in a microgrid has an associated bus agent that may have two function modules. The global information discovery module discovers the total available wind generation and total demand. The load sharing control module calculates the generation reference of a DFIG. The consensus-based algorithm can guarantee convergence for microgrids of arbitrary topologies under various operating conditions. By controlling the utilization levels of DFIGs to a common value, the supply-demand balance can be maintained. In addition, the detrimental impact of inaccurate and outdated predictions of maximum wind power can be alleviated. The generated control references are tracked by coordinating converter controls and pitch angle control. Simulation results with a 5-DFIG microgrid demonstrate the effectiveness of the proposed control scheme.

Optimal Distributed Charging Rate Control of Plug-In Electric Vehicles for Demand Management

Plug-in electric vehicles (PEVs) are a promising alternative to conventional fuel-based automobiles. However, a large number of PEVs connected to the grid simultaneously with poor charging coordination may impose severe stress on the power system. To allocate the available charging power, this paper proposes an optimal charging rate control of PEVs based on consensus algorithm, which aligns each PEVs interest with the systems benefit. The proposed strategy is implemented based on a multi-agent system framework, which only requires information exchanges among neighboring agents. The proposed distributed control solution enables the sharing of computational and communication burden among distributed agents, thus it is robust, scalable, and convenient for plug-and-play operation which allows PEVs to join and leave at arbitrary times. The effectiveness of the proposed algorithm is validated through simulations.

Generation of partial medial axis for disassembly motion planning

An efficient approach is presented for determining disassembly motion plans of a subassembly in the free space within its parent subassembly. A two-step approach is presented to generating motion plans. First, all possible paths within any free space of the parent subassembly are generated using a partial medial axis. This is followed by a graph search for an optimal global path. Secondly, a collision-free motion is planned using the global path and the geometry of the moving subassembly. The authors focus on the first problem, i.e. generation of partial medial axis for disassembly motion planning. A method is given to determine a partial medial axis when the parent subassembly is a 2D polygon. A set of critical points within the given polygon is identified, and these critical points are then connected based on geometry constraints.<<ETX>>

Improved H∞ filter design for discrete-time Markovian jump systems with time-varying delay

Abstract This paper is concerned with the H ∞ filter design for a class of discrete-time Markovian jump systems with time-varying delay. By constructing an appropriate Lyapunov–Krasovskii functional and applying Wirtinger-based inequality for discrete-time context combined with reciprocally convex approach, an improved stability criterion is obtained. Then a delay-dependent H ∞ performance condition is achieved for the system. Moreover, a desired filter is constructed based on the performance analysis, which guarantees the filtering error system to be stochastically stable and satisfying a prescribed H ∞ performance level. In the end, some numerical examples are provided to illustrate the effectiveness and superiority of the new criterion.

Robust Real-Time Distributed Optimal Control Based Energy Management in a Smart Grid

With the integration of distributed generations and controllable loads, the power grid becomes geographically distributed with a time-varying topology. The operation conditions may change rapidly and frequently; thus, management and control of the smart grid pose great challenges on traditional centralized control strategies. This paper proposes a distributed algorithm for energy management in a power grid, while dynamically minimizing the adjustment costs. The objective function is designed to optimize the overall social welfare considering generation suppliers and load users simultaneously. The proposed algorithm integrates consensus algorithm and optimal control algorithm, which requires only information exchanging among neighboring units and enables the sharing of computational and communication burden among distributed local controllers. It is robust to communication failures and adaptive to topology changes. Simulation results of the IEEE 9-bus, 39-bus systems, and a 200-unit system demonstrate the effectiveness of the proposed algorithm and indicate the promising applications to practical power systems.