Adaptive Load Management: Multi-Layered And Multi-Temporal Optimization Of The Demand Side In Electric Energy Systems.

摘要

Well-designed demand response is expected to play a vital role in operating power systems by reducing economic and environmental costs. However, the current system is operated without much information on the benefits of end-users, especially the small ones, who use electricity. This thesis proposes a framework of operating power systems with demand models including the diversity of end-users' benefits, namely adaptive load management (ALM). Since there are a large number of end-users having different preferences and conditions in energy consumption, the information on the end-users' benefits needs to be aggregated at the system level. This leads us to model the system in a multi-layered way, including end-users, load serving entities, and a system operator. On the other hand, the information of the end-users' benefits can be uncertain even to the end-users themselves ahead of time. This information is discovered incrementally as the actual consumption approaches and occurs. For this reason ALM requires a multi-temporal model of a system operation and end-users' benefits within. Due to the different levels of uncertainty along the decision-making time horizons, the risks from the uncertainty of information on both the system and the end-users need to be managed. The methodology of ALM is based on Lagrange dual decomposition that utilizes interactive communication between the system, load serving entities, and end-users. We show that under certain conditions, a power system with a large number of end-users can balance at its optimum efficiently over the horizon of a day ahead of operation to near real time. Numerical examples include designing ALM for the right types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude that with the right information exchange by each entity in the system over different time horizons, a power system can reach its optimum including a variety of end-users' preferences and their values of consuming electricity.