C. M. Colson

A review of challenges to real-time power management of microgrids

Microgrid systems show great promise for integrating large numbers of distributed energy resource (DER) systems into future power networks. This paper provides a brief overview of current microgrid technologies and issues associated with their implementation. The case is made for a real-time power management and control system that attempts to optimize microgrid systems based on multiple objectives, such as power demands, fuel consumption, environmental emissions, costs, dispatchable loads, etc. A multi-agent based control architecture that can ensure robust, stable, and optimal microgrid operation is also addressed. In addition to the discussion of power management and control topics, a qualitative classification tool has been proposed for assisting system planners in assessing the impact of microgrid systems on broader grid operations.

Real-Time Energy Management of a Stand-Alone Hybrid Wind-Microturbine Energy System Using Particle Swarm Optimization

Energy sustainability of hybrid energy systems is essentially a multiobjective, multiconstraint problem, where the energy system requires the capability to make rapid and robust decisions regarding the dispatch of electrical power produced by generation assets. This process of control for energy system components is known as energy management. In this paper, the application of particle swarm optimization (PSO), which is a biologically inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind-microturbine (MT) energy system, is presented. Results demonstrate that the proposed PSO-based energy management algorithm can solve an extensive solution space while incorporating many objectives such as: minimizing the cost of generated electricity, maximizing MT operational efficiency, and reducing environmental emissions. Actual wind and end-use load data were used for simulation studies and the well-established sequential quadratic programming optimization technique was used to validate the results obtained from PSO. Promising simulation results indicate the suitability of PSO for real-time energy management of hybrid energy systems.

Towards real-time microgrid power management using computational intelligence methods

Microgrids are an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. The microgrid framework offers a means to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the utility grid by generating power close to the consumer. As a critical component to enabling power system diversity and flexibility, microgrids encompass distributed generators and load centers with the capability of operating islanded from or interconnected to the macrogrid. To make microgrids viable, new and innovative techniques are required for managing microgrid operations given its multi-objective, multi-constraint decision environment. In this article, two example computational intelligence methods, particle swarm optimization (PSO) and ant colony optimization (ACO), for application to the microgrid power management problem are introduced. A mathematical framework for multi-objective optimization is presented, as well as a discussion of the advantages of intelligent methods over traditional computational techniques for optimization. Finally, a three-generator microgrid with an ACO-based power management algorithm is demonstrated and results are shown.

Algorithms for distributed decision-making for multi-agent microgrid power management

A key component to future smart grids may be microgrid systems capable of integrating generation, load, and storage assets into an autonomous power system entity. As a potential building block for expanding power networks, microgrids can enable the broad integration of distributed energy resources (DER), as well as providing customers a means to optimize local assets based on multiple objectives. Pivotal to the implementation of microgrids are the power management and control architectures that will operate the microgrid systems. In this paper, a multi-agent based control architecture for microgrids, capable of coordinating and cooperatively achieving user-defined objectives is presented. Herein, unique agents that comprise a distributed multi-agent system (MAS) are developed according to Foundation for Intelligent Physical Agents (FIPA) guidelines. The authors present a method for facilitating the fundamental self-organizing and cooperative behavior amongst the microgrid agents. The MAS formulation developed is intended to lay the groundwork for a power management architecture that incorporates the integrated and robust operation of microgrid assets.

Ant colony optimization for microgrid multi-objective power management

Steadily increasing needs for electrical power, progress in power deregulation, tight construction constraints on new high voltage lines for long distance power transmission, and global environmental concerns have created increased interest in alternative energy (AE) generation. Hybrid combination of AE sources can significantly improve their reliability and better deliver power to customer loads without reliance on centralized electricity production. It is expected that alternative energy distributed generation (AEDG) microgrids that capitalize on diverse energy sources, are controlled in a decentralized way, and reduce the burden on the utility grid by generating power close to the consumer will penetrate the existing grid-infrastructure in the near future. This paper presents a framework for an intelligent supervisory controller that utilizes ant colony optimization (ACO) methods for AEDG microgrid dispatch control. The novelty of this work is the application of ACO to the rapid microgrid power management problem given complex constraints and objectives including: environmental, fuel/resource availability, and economic considerations. Given the compound nature of the multi-objective, multi-constraint energy management problem for integrated AEDG systems, this paper develops a constraint satisfaction problem (CSP) algorithm capable of finding Pareto optimal dispatch solutions. Microgrid power management control is not an easy problem, but its development is critical for widespread AEDG system implementation.

Improving Sustainability of Hybrid Energy Systems Part II: Managing Multiple Objectives With a Multiagent System

Hybrid power systems and microgrids may employ a mixture of dispatchable (conventional) and nondispatchable (renewable) generators alongside storage. Whether in grid-connected or grid-isolated (islanded) modes of operation, these systems may face multiple competing objectives when managing diverse installed assets. Power management of hybrid energy systems, therefore, involves operational tradeoffs amongst Pareto-optimal solutions. These attributes, including the ready implementation of distributed renewable generation and the incorporation of methods to locally manage power-networked assets, make them a unique area of study for pursing better sustainable performance. In part I of this paper, storage system round-trip efficiency and operational cost concepts were formulated for use in real-time dispatch decisions towards yielding improved performance of overall system objectives. In this paper (part II), the concepts of part I are implemented with a decentralized multiagent system (MAS). This MAS is employed for power management of a hybrid (diesel-storage battery) microgrid in grid-connected and islanded modes. This paper highlights the development and implementation of an MAS suitable for hybrid and microgrid system applications, as well as presenting an important discussion about the tradeoffs associated with multiobjective design for power management. The simulation results presented demonstrate improvement in sustainable performance of the hybrid system.

Load-following for wind turbines with permanent magnet synchronous generators

As new wind energy assets are installed at an accelerated pace and wind turbine technology continues to mature, permanent magnet synchronous generators (PMSG) are increasingly being utilized to derive electricity within wind energy conversion systems (WECS). Attractive as renewable sources, unfortunately WECS can introduce electrical instabilities into some power systems due to the intermittent nature of wind. Examples of problematic applications include: islanded or isolated power systems, as well as weak feeders that do not enjoy robust voltage and frequency control. PMSG technology and modern power electronic switch devices offer the opportunity to help overcome this common drawback of WECS applications. In this paper, a load-following mode-of-operation control scheme is presented that facilitates the use of modern PMSG-equipped WECS in applications where generation-to-load matching is desirable. Fundamentals of wind turbine operation are surveyed to support the case for generation control, as well as the development of a PMSG dynamic model and WECS controllers capable of achieving robust load-following capability. Simulation results are given that show the viability of the proposed control system for the PMSG-equipped WECS under real-world wind and load conditions. Finally, the case is made for the need to incorporate load-following controllers into WECS for deployment in future microgrids that require mode-of-operation flexibility depending on varying scenarios.

Improving Sustainability of Hybrid Energy Systems Part I: Incorporating Battery Round-Trip Efficiency and Operational Cost Factors

Storage systems are often employed in hybrid systems alongside generation sources. In the most basic configurations, coupling generation and storage in this manner can improve combined performance. Moreover, in advanced applications, such as for microgrids, the employment of storage offers the opportunity to diversify system objectives and pursue multiple performance goals. In this paper (part I), the authors explore formulations of storage system round-trip efficiency and operational cost, along with a model that can be determined from manufacturer data sheets and used in a real-time simulation environment for evaluation of these objectives. The battery model will be used in a real-time power management study for hybrid systems where a decentralized multiagent system (MAS), developed in part II, addresses the multiobjective tradeoff optimization for a hybrid system.

Power management of a stand-alone hybrid wind-microturbine distributed generation system

As generation and transmission costs, system stability, and security issues continue to be complicated by growing electricity demand that relies primarily on centralized electricity production, alternative distributed generation (DG) systems show promise as a solution. This paper presents a hybrid wind-microturbine generation (MTG) system for stand-alone applications. The Wind-MTG hybrid mainly consists of a wind energy conversion system that utilizes a self-excited induction generator and a split-shaft microturbine that incorporates a self-excited induction generator. The system component models and an overall power management scheme are presented in the paper. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink®. Actual wind data and a practical load profile are used to evaluate the system performance through a simulation case study. The simulation results indicate the suitability of the hybrid DG system for DG applications.

Agent-based power management of microgrids including renewable energy power generation

Future smart grids may rely on microgrid systems that integrate localized generation, load, and storage assets into autonomous power system entities. Seen as an enabling technology, microgrids offer solutions for both customers and system operators as building blocks of expanding power networks capable of broadly integrating distributed energy resources (DER) and providing a means to optimize local assets based on multiple objectives. In this paper, a distributed agent-based microgrid control architecture capable of coordinating and cooperatively achieving user-defined objectives is presented. Key attributes of centralized versus decentralized agent-based control are presented, as well as describing the challenges faced by operating a distributed architecture that must self-organize to appropriately coordinate the cooperative behavior of agents. Further, the demand for a robust, real-time microgrid power management and control framework that achieves effective operation and can optimize its local generation, load, and storage assets is discussed, along with the viability of distributed agent-based microgrid control towards achieving smart grid objectives.