Distributed Intelligent Energy Management for Smart Microgrids

Distributed Intelligent Energy Management for Smart Microgrids

by | Nov 13, 2014 | Current

 

IEM_Figure3     IEM_Figure1

Project Description:

The efficiency, reliability, economics and sustainability of the production and distribution of electricity are the major concerns of all the power utilities and customers. Without large investments in new transmission capacity and generation sources to keep up with the growth in demand and corresponding investment, the existing early-twentieth-century-designed power grids would face a lot of challenges. An innovative and economical way of solving these challenges is the introduction of “microgrid” concept. Microgrids are electricity distribution systems consisting of local loads and distributed energy resources such as distributed generators, storage devices, and responsive demands as cheap spinning reserves. This flexibility of generation and demand available in the microgrids along with the access to renewables, can dramatically improve the power quality and reliability of the power networks at the distribution level. However, economically and intelligently managing these energy resources to improve the power quality, maximize the benefit of the unit owners while ensuring their privacy, requires advanced optimization and control technologies.

The objective of ADAC Distributed Intelligent Energy Management (DIEM) project is to design a robust cooperative distributed energy management framework for the microgrids to coordinate and schedule energy generation, energy storage and energy demand at different levels to achieve global optimal in terms of economic operation while satisfying the quality constraints. The envisioned framework is scalable, robust to communication imperfections (such as time delay, packet losses), and robust to single point of failures.

Because of the distributed nature of the controllable devices (e.g., distributed generations and controllable loads), agent-based system modeling is used by utilizing the graph theory. Assuming a two-way communications among distributed controllable devices, the system can be modeled using adjacency matrix based on the availability of communication link between each pair of agents. Based on the communications network, consensus and gossip based algorithm are applied for the development of the DIEM algorithms.

Publications:

Links:

 

Distributed Cooperative Performance Optimization for Large-scale PHEV/PEV Charging

Distributed Cooperative Performance Optimization for Large-scale PHEV/PEV Charging

by | Jul 28, 2014 | Completed

 Picture1

 

Designing efficient demand management policies for charging Plug-in Hybrid Electrical Vehicles (PHEVs) and Plug-in Electrical Vehicles (PEVs) is becoming a vital issue as increasing numbers of these vehicles are being introduced to the power grid. Since these vehicles utilize grid power for charging, this growth could pose potential threats and benefits for the existing power grid. The main challenge is that when large number of PHEVs/PEVs simultaneously connect to the grid, the overall power system quality and stability could be severely affected due the large amount of power consumption. On the other hand, by smartly controlling the charging process, the utility can utilize the flexible nature of these loads for peak shaving and valley filling to improve the quality of power.

Conventionally, optimal managing of the charging process requires gathering data in a single node and performing a central optimization. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach would suffer from vulnerability to single node/link failures as well as computational scalability. In this project, the central demand management unit is eliminated and the global optimal power allocation under all local and global constraints is reached by peer-to-peer coordination of charging stations. This approach is highly efficient in terms of computational and communicational effort, considering that the overall demand management problem is large scale and nonlinear. Moreover, using distributed approach, the demand management system gains robustness against single link/node failures.

Publications:

[1] N. Rahbari-Asr and M.-Y. Chow, “Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks,” IEEE Transactions on Industrial Informatics, vol. 10, no. 3, pp. 1907–1916, 2014.

[2] N. Rahbari-asr, M.-Y. Chow, Z. Yang, and J. Chen, “Network Cooperative Pricing Control System for Large-Scale Optimal Charging of PHEVs / PEVs,” in IECON 2013-39th Annual Conference on IEEE Industrial Electronics Society, 2013, pp. 6148–6153.

Links

AIS Gene Library Based Real-Time Resource Allocation on Time-Sensitive Large-Scale Multi-Rate Systems

by | May 27, 2013 | Completed

ais_gene_lib
When looking forward towards Intelligent Transportation Systems (ITS), driver warning systems are an integral part of an ITS. There is a need for warning systems that can integrate the information that is currently available in the vehicles with the information about the environment in order to make more informed and accurate decisions. These warning systems should be supported by roadside infrastructures for the acquisition and processing of global/environmental information. In such systems, the roadside infrastructures need to communicate a large amount of time-sensitive data to many of the vehicles. In such a large-scale time-sensitive system, real-time information extraction (e.g. determining the risk for each vehicle) and optimal resource (e.g. bandwidth) allocation are crucial yet  computationally demanding.

This project will investigate and develop gene library based real-time information extraction and resource allocation methodology that can be adaptively tuned using the concepts of Artificial Immune System (AIS). This gene library is designed to extract only the relevant information from a vehicle to determine abnormality/risk in vehicle movements at various traffic environments and to provide optimal real-time sampling rate adaptations and emergency interventions based on the information.

Documentations

Links

Intelligent Network-Based Control

by | Sep 27, 2012 | Completed

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Fig. 1 Intelligent Space via Network-Based Control.

Project Description:

The main objective of the Intelligent Network-Based Control Project is to develope a wireless unmanned vehicle to find its way through a platform with obstacles. It should arrive at the destination specified by a user in a remote location using the shortest amount of time, and avoiding any collisions.

Main Focuses:

  • How to recognize the vehicle and obstacles.
  • How to generate a path around the obstacles in the shortest amount of time.
  • How network delays contribute to control errors.

The vision of the vehicle is established by a wireless network camera placed on top of the platform. The vehicle is battery operated and completely controlled by wireless communication via IP network. Image processing technology, as well as hardware and software implementations are utilized to demonstrate the path generation and path tracking algorithms. Gain Scheduler Control algorithm may also be implemented in this project to compensate for any network delay disturbance.

path
Fig. 2 Path generation algorithm.
Publications:

Y. Tipsuwan, M.-Y Chow, “Neural Network Middleware for Model Predictive Path Tracking of Networked Mobile Robot over IP Network,” IEEE IECon’03, Roanoke, VA, Nov 2 – Nov 6, 2003.

Y. Tipsuwan, M.-Y. Chow, “An Implementation of a Networked PI Controller over IP Network,” IEEE IECon’03, Roanoke, VA, Nov 2 – Nov 6, 2003.

Y. Tipsuwan, M.-Y. Chow, “On the Gain Scheduling for Networked PI Controller Over IP Network,” 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Port Island, Kobe, Japan, July 20-24, 2003.

Y. Tipsuwan, M.-Y. Chow, “Gain adaptation of mobile robot for compensating QoS deterioration,” Proceedings of IECon’02, Sevilla, Spain, November 5 – 8, 2002.

Y. Tipsuwan, M.-Y. Chow, “Network-Based Controller Adaptation Based On QoS Negotiation and Deterioration,” IECon01, Denver, CO, Nov.28-Dec.02, 2001, pp. 1794 -1799.

Y. Tipsuwan, M.-Y. Chow, “Network-based control adaptation for network QoS variation,” MILCOM 2001, October 28-31, 2001, McLean, VA, pp. 257-261.

M.-Y. Chow, Y. Tipsuwan, “Gain Adaptation of Networked Dc Motor Controllers on QoS Variations,” IEEE Transactions on Industrial Electronics, Vol. 50, no. 5, October, 2003.

Y. Tipsuwan and M.-Y. Chow, “Control Methodologies in Networked Control Systems,” Control Engineering Practice, vol. 11, 2003, pp.1099-1111.

M.-Y. Chow, “Methodologies in Time Sensitive Network-Based Control Systems,” 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Port Island, Kobe, Japan, July 20-24, 2003.

M.-Y. Chow, Y. Tipsuwan, “Real Time Network-Based Control System,” IEEE IECon 2002 Tutorial, Sevilla, Spain, November 5, 2002.

M.-Y. Chow, Y. Tipsuwan, “Network-Based Control Systems: A Tutorial,” Proceedings of IEEE IECon 2001 Tutorial, November 28 – December 2, Denver, CO, pp. 1593 -1602.

Edge detection and HPF based iSpace – Intelligent Space

by | Sep 27, 2012 | Completed

iSpace_concept

Project Description:

Intelligent space is a concept which fuses global information using sensors and actuators to take intelligent operational decisions for applications like robot navigation, tele-operation, remote surgery, manufacturing plant monitoring etc. iSpace Concept

ADAC lab has developed a prototype of iSpace as a network based integrated navigation system which is a subset of network based multi sensor multi-large scale actuator mechatronic systems. This whole system is used a research platform for many research topics such as network based control systems, path planning for robot navigation, bandwidth allocation, scheduling, network security, collaborative control.

ADACiSpace

The focus of this research is path planning navigation for the unmanned ground vehicle in a network based navigation system. Details of the project and the implementations can be found in the dcument section.

 

Edge Detection

EdgeDetection

 

Harmonic Potential Field

HPF

 

Path Planning

HPFResults
Documentations

Predictive Constrained Gain Scheduling for Robot Path Tracking in a Networked Control System

by | Sep 27, 2012 | Completed

robotQCtracking

Project Description:

In this project a predictive gain scheduler for robot path tracking control in a networked control system with variable delay is being developed. The controller uses the plant model to predict future position and find the amount of travel possible with the global path as a constraint. Based on variable network conditions and vehicle trajectory’s curvature the vehicle is allowed to travel farther with the same control input as long as the vehicle trajectory matches the path constraint. With this method path specific characteristics are used to evaluate the effectiveness of each generated control signal. By scheduling the gain on the control signal the vehicle tracking performance is maintained with an increase in network delay. The tracking time is decreased compared to other methods since the proposed control method allows controller to look farther down the path to evaluate predicted effect of each control signal before scaling it.

The gain scheduling middleware concept can be illustrated using the diagram below. When controlling a remote system over a network the delay caused by the network affects both the control signal and the feedback signal. When feedback arrives at the controller the feedback signals have been delayed by the network. The feedback preprocessor compensates for this by using the remote system model to predict what the feedback values will be when the next control value arrives. This preprocessed, predicted feedback is used by the controller to generate control commands. In this project the controller is a quadratic curve path tracker.

GSM

The control signal is then scaled using a gain table based on certain system parameter such as network delay and path curvature. In predictive constrained gain scheduling the composition of this gain table is uniquely tuned to increase path tracking performance.

Predictive Constrained Motion

When a UGV is tracking a path the motion of the UGV is predicted using the control value and the UGV model. The predicted position is calculated iteratively until the UGV prediction exits a safety region defined around the path. The point where the UGV exits the safety region is the point where the control value loses it’s effectiveness. The predicted position, which is constrained by the future path, is then used to determine how far the UGV is allowed to travel before it needs to get an updated control signal. This distance is then used as another parameter in gain scheduling allowing the UGV to travel further.

track1track2

When the UGV is allowed to travel further the epsilon value increases. A gain table for scaling control signals is computed for the epsilon value so that the UGV is allowed to travel a distance of epsilon. The gain table will decrease the control signal such that the UGV will not begin to deviate from the path while tracking complex paths with network delay. Several gain tables for different epsilon values can be seen below.

gainTables