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

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