CDS 270-2, Spring 2003

High Confidence Control of Electric Power Networks using Dynamic
Incentive Mechanisms

Mani Chandy John Ledyard Richard Murray  
Social and Information Systems Laboratory
California Institute of Technology

 

Project Description

In the past one could model the electricity system in terms of a few sources, a transmission network and relatively few sinks (groups of customers with similar service-levels). This system is, however, evolving to one with many sources with widely varying capacities and costs, and a huge number of consumers dynamically adjusting their demand based on prices and environmental conditions. The old model is no longer adequate. New research is needed that brings to bear economics, control theory and distributed systems to address this problem. The goal of this project is to investigate the possibility of developing a framework for designing incentives for electric power networks and their associated markets that provides robust and efficient power generation while rewarding efficiency and green power production. Possible technical thrusts of the project might be take first steps in one or more of the following areas:

  1. Development of prototype economic mechanics for buying and selling power that address non-steady state performance and incorporate engineering considerations such as production efficiency and environmental emissions.

  2. Analysis and synthesis of information fusion and feedback control mechanisms at the component, network, and market levels that provide high performance and robust operation in the presence of uncertainty and faults.

  3. Implementation of economics experiments to test engineering performance and market volatility of representative power networks, using 20--30 human subjects and software agents interacting with a distributed simulation of a large scale power system.

The unique aspect of this project compared with existing work will be the combination of methods from control, computation and economics in a unified framework for market-based systems. While the results will driven by the application to electric power networks, the techniques are also expected to be applicable to other critical infrastructure problems that involve interconnected economic, information, and engineering systems.

Additional Information