Farshid Shariatzadeh

Real-Time Implementation of Intelligent Reconfiguration Algorithm for Microgrid

Microgrids with renewable distributed generation and energy storage offer sustainable energy solutions. To maintain the availability of energy to the connected loads, considering priority and to interrupt the smallest portion of the microgrid under any abnormal conditions, reconfiguration is critical to restore service to a section or to meet some operational requirements of dropping minimum loads. Reconfiguration is the process of modifying the microgrids topological structure by changing the status (open/close) of the circuit breakers or switches. In this work, constraints are the power balance equation and power generation limits, and we assumed that the system is designed with the entire planning and operational control criterion to meet the voltage violation and line overloading constraints. This paper offers novel real-time implementation of intelligent algorithm for microgrid reconfiguration. Intelligent algorithm is based on the genetic algorithms and has been tested on two test systems including shipboard power system and modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Real-time test bed utilizes real-time digital simulator and commercial real-time controllers from Schweitzer Engineering Lab. Reconfiguration algorithm has been implemented in the real time using real-time test bed, e.g., microgrid system, and satisfactory results were obtained.

Real time implementation of microgrid reconfiguration

Reconfiguration is a control action including topology changes, load/ generation shedding and other measures to redirect power flow to the remaining unaffected loads in a microgrid. In this paper, genetic algorithm (GA) and graph theory based methodologies were used to reconfigure a network, satisfying the operational requirements and priorities of loads. Applications of the GA for reconfiguration were implemented in MATLAB and tested on 8-bus shipboard power system (SPS) and modified CERTS microgrids with consideration of distributed generation and islanding operation. Satisfactory simulations results were obtained for several possible test case scenarios in a real-time and non-real-time case study.

Optimal Control Algorithms for Reconfiguration of Shipboard Microgrid Distribution System Using Intelligent Techniques

The distribution power system in ship is almost similar to an islanded microgrid and supplies energy to navigation, service, and operation system, as well as sophisticated systems of weapons and communications in future ships. After a fault occurs, reconfiguration refers to changing the topology of the shipboard microgrid power system (SMPS) in order to isolate system damage and restore lost loads/or optimize certain characteristics of the system in real time. Reconfiguration problem in shipboard microgrid is nonlinear with numerous discrete variables and additional constraints. Traditional optimization methods are not the best solution due to tendency of getting stuck to a suboptimal solution and/or not providing solution in real time. In this study, intelligent techniques, such as genetic algorithm and particle swarm optimization, have been applied for reconfiguration of SMPS. Proposed methods consider all the operational constraints and load priorities. Graph theory is utilized to model the SMPS and mathematically represent the shipboard system. Proposed intelligent reconfiguration algorithms were implemented using MATLAB and tested on 8-BUS and 13-BUS SMPS models including distributed generations and islanding. Test systems were reconfigured in three different possible scenarios by considering load priority, load magnitude, and by combining these two simultaneously. Developed reconfiguration algorithm was also implemented in real time using controller-in-the-loop with real-time digital simulator. Simulation results show satisfactory performance for several test operating scenarios.

Real time testing and validation of Smart Grid devices and algorithms

With ongoing efforts to upgrade the traditional electric power system into a smart grid, emphasis is on the integration of state-of-the-art computer based online monitoring and control tools along with advanced communication technology. However, testing and validation of these devices and algorithms are required before implementation in a physical grid. New testing and validation method needs to be developed in a simulated environment in a lab. This paper discusses some of the applications of the “smart grid test bed” developed at the “Smart Grid Development and Research Investigation Lab (SGDRIL)” at Washington State University. The specific applications discussed in this paper include synchrophasor device testing, microgrid reconfiguration, voltage stability and vulnerability analysis.

Real time implementation of intelligent reconfiguration algorithm for microgrid

Microgrids with renewable distributed generation and energy storage offer sustainable energy solutions. To maintain the availability of energy to the connected loads, considering priority and to interrupt the smallest portion of the microgrid under any abnormal conditions, reconfiguration is critical to restore service to a section or to meet some operational requirements of dropping minimum loads. Reconfiguration is the process of modifying the microgrids topological structure by changing the status (open/close) of the circuit breakers or switches. In this work, constraints are the power balance equation and power generation limits, and we assumed that the system is designed with the entire planning and operational control criterion to meet the voltage violation and line overloading constraints. This paper offers novel real-time implementation of intelligent algorithm for microgrid reconfiguration. Intelligent algorithm is based on the genetic algorithms and has been tested on two test systems including shipboard power system and modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Real-time test bed utilizes real-time digital simulator and commercial real-time controllers from Schweitzer Engineering Lab. Reconfiguration algorithm has been implemented in the real time using real-time test bed, e.g., microgrid system, and satisfactory results were obtained.

Look-ahead control approach for thermostatic electric load in distribution system

One of the most important goals of smart grid is sustainability. Load management in coordinated manner may help in saving more energy and hence move towards sustainability. Advanced metering infrastructures (AMI) and bidirectional communication between electricity grid and endusers, novel technologies and designs are required to exploit full potential of smart grid investments. New analysis tools and control strategies are needed for distribution systems to augment these investments. Heating, ventilation and air conditioning (HVAC) systems, as thermostatic controllable loads, consume major portion of electric energy. In this work, a detailed load model of thermostatic electric loads is used to develop a novel approach of look-ahead controller over different time frames. Developed controller has been tested for cooling mode operation on a typical summer day and shows satisfactory performance.

Intelligent control algorithms for optimal reconfiguration of microgrid distribution system

The distribution power system in ship is very similar to a microgrid and supplies energy to navigation and operation system as well as sophisticated systems of weapons and communications. After a fault is encountered, reconfiguration refers to changing the topology of the microgrid distribution network in order to isolate system damage and/or optimize certain characteristics of the system. Reconfiguration problem in microgrid is nonlinear with numerous discrete variables and additional constraints. Traditional optimization methods are not the best solution due to tendency of getting stuck to a suboptimal solution. In this work, intelligent methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have been applied for microgrid reconfiguration with shipboard power system (SPS) as an example. Proposed methods are capable to satisfy the operational constraints and consider load priorities. Graph theory is utilized to represent the microgrid network topology. Proposed intelligent reconfiguration algorithms were implemented using MATLAB and tested on 8-BUS and 13-BUS SPS models including distributed generations (DGs) and islands. Test systems were reconfigured in three different possible scenarios by considering load priority, load magnitude, and by combining these two simultaneously. Developed reconfiguration algorithm was also implemented in real time using controller-in-the-loop with real time digital simulator. Simulation results show satisfactory performance for several test case operating scenarios.