Chuanzhi Zang

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.

Optimal Microgrid Control and Power-Flow Study With Different Bidding Policies by Using PowerWorld Simulator

For a microgrid (MG) to participate in a real-time and demand-side bidding market, high-level control strategies aiming at optimizing the operation of the MG are necessary. One of the difficulties for research of a competitive MG power market is the absence of efficient computational tools. Although many commercial power system simulators are available, these power system simulators are usually not directly applicable to solve the optimal power dispatch problem for an MG power market and to perform MG power-flow study. This paper analyzes the typical MG market policies and investigates how these policies can be converted in such a way that one can use commercial power system software for MG power market study. The paper also develops a mechanism suitable for the power-flow study of an MG containing inverter-interfaced distributed energy sources. The extensive simulation analyses are conducted for grid-tied and islanded operations of a benchmark MG network.

Auction-based Dynamic Coalition for Single Target Tracking in Wireless Sensor Networks

Wireless sensor networks have recently gained a lot of attention. Target tracking is a canonical application of wireless sensor networks and task allocation is an unavoidable problem for target tracking. To improve the tracking quality and save more energy, an auction-based dynamic coalition scheme of task allocation for single target tracking in wireless sensor networks was introduced. Furthermore, the scheme was extended to multiple dynamic coalition scheme to achieve even better performance. The simulations compared the standard deviation of tracking error, detecting ratio and energy consumption of the new schemes with those of case-based reasoning dynamic coalition scheme. The auction-based dynamic coalition scheme saves more energy and improves the tracking quality evidently. The extended scheme achieves better tracking quality and the energy consumed is still far less than that consumed in the case-based reasoning dynamic coalition scheme

Coordinated control for grid integration of PV array, battery storage, and supercapacitor

A substantial increase of solar photovoltaic (PV) power generators have taken place in recent years. The intermittent nature of PV energy and quick fluctuations of load demanding require energy storage units (ESU) which generally consists of storage battery and supercapacitor (SC). The development of coordinated control tools for next-generation PV installations, along with ESU, provides flexibility to distribution system operators. The objective of the control of this hybrid PV and energy storage system is to supply the desired active and reactive power to the grid and at the same time to maintain the stability of the dc-link voltage of the PV and energy storage system through coordinated control of power electronic converters. This paper investigates three different coordinated control structures and approaches for grid integration of PV array, battery storage, and SC. A computational experiment system is developed by using SimPowerSystems and Opal-RT real-time simulation technology. The performance of the coordinated control techniques is evaluated.

A Mobile Agents-Based Real-time Mechanism for Wireless Sensor Network Access on the Internet

Due to the variety of applications and their importance, wireless sensor networks (WSNs) would need to be connected to the Internet. Some approaches have been proposed to connect wireless sensor networks to the existing TCP/IP networks, such as the application-level gateways or overlay networks. However, most existing approaches have to consume network bandwidth and node energy to maintain the static network structure which is neither scalable nor reliable. In this paper, we describe the mobile agent based real-time (MBR) mechanism which use of the mobile software agent (MSA) paradigm to design a dynamic infrastructure for WSNs access on the Internet. We present a agent migration protocol based on reinforcement learning method to reduce the query delay and improve the total performance

A hybrid immune model for unsupervised structural damage pattern recognition

This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve the quality of memory cells, the artificial immune pattern recognition method based on immune learning mechanisms is employed to evolve memory cells. The presented hybrid immune model (combined with fuzzy clustering and the artificial immune pattern recognition) has been tested using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring Task Group. The test results show the feasibility of using the hybrid AIPR (HAIPR) method for the unsupervised structural damage pattern recognition.

Analysis of multi-agent-based adaptive droop-controlled AC microgrids with PSCAD: Modeling and simulation

A microgrid (MG) with integrated renewable energy resources can benefit both utility companies and customers. As a result, they are attracting a great deal of attention. The control of a MG is very important for the stable operation of a MG. The droop-control method is popular since it avoids circulating currents among the converters without using any critical communication between them. Traditional droop control methods have the drawback of an inherent trade-off between power sharing and voltage and frequency regulation. An adaptive droop control method is proposed, which can operate in both the island mode and the grid-connected mode. It can also ensure smooth switching between these two modes. Furthermore, the voltage and frequency of a MG can be restored by using the proposed droop controller. Meanwhile, the active power can be dispatched appropriately in both operating modes based on the capacity or running cost of the Distributed Generators (DGs). The global information (such as the average voltage and output active power of the MG and so on) required by the proposed droop control method to restore the voltage and frequency deviations can be acquired distributedly based on the Multi Agent System (MAS). Simulation studies in PSCAD demonstrate the effectiveness of the proposed control method.

A stochastic programming strategy in microgrid cyber physical energy system for energy optimal operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.

Investigating upper bounds on lifetime for target tracking sensor networks

The distinctive characteristic of target tracking sensor networks is that the delay of data transmission is constrained, which poses a difficult problem for predicting the application lifetime for such sensor networks. In this paper, we first map the delay constraints to the hop bound in routing. By analysing the energy consumption in recurring bounded hop routing, we establish the relationship between individual sensors and the whole sensor network. On the basis of this relationship, we propose a novel model to formally define the lifetime of target tracking sensor networks. The model not only implies the best routing strategy, but also exposes the dependence of lifetime on factors such as hop bound, radio transmission range, sensing range and target behaviour. Finally, we present the results of extensive simulation to investigate the influence of some adjustable parameters in engineering on the lifetime.

Bounding the Lifetime of Target Tracking Sensor Networks

We propose a novel model to formally define the lifetime of target tracking sensor network based on energy by considering the relationship between individual sensors and the whole sensor network, the importance of different sensors based on their roles in delay constrained routing. The model is valuable for two reasons. First, it exposes the dependence of lifetime on factors like hop bound, network density, radio transmission range, sensing range and target behavior within the region. This allows us to see what factors have the most impact on lifetime and consequently where engineering effort is best expended. Secondly, the model implies the best routing strategy. In this paper, we also suggest a simple routing algorithm to achieve the bound.