Well-designed demand response is expected to play a vital role in operatingpower systems by reducing economic and environmental costs. However,the current system is operated without much information on the benefits ofend-users, especially the small ones, who use electricity. This thesis proposes aframework of operating power systems with demand models including the diversityof end-users’ benefits, namely adaptive load management (ALM). Sincethere are a large number of end-users having different preferences and conditionsin energy consumption, the information on the end-users’ benefits needsto be aggregated at the system level. This leads us to model the system ina multi-layered way, including end-users, load serving entities, and a systemoperator. On the other hand, the information of the end-users’ benefits can beuncertain even to the end-users themselves ahead of time. This information isdiscovered incrementally as the actual consumption approaches and occurs. Forthis reason ALM requires a multi-temporal model of a system operation andend-users’ benefits within. Due to the different levels of uncertainty along thedecision-making time horizons, the risks from the uncertainty of informationon both the system and the end-users need to be managed. The methodologyof ALM is based on Lagrange dual decomposition that utilizes interactive communicationbetween the system, load serving entities, and end-users. We showthat under certain conditions, a power system with a large number of end-userscan balance at its optimum efficiently over the horizon of a day ahead of operationto near real time. Numerical examples include designing ALM for theright types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude thatwith the right information exchange by each entity in the system over differenttime horizons, a power system can reach its optimum including a variety ofend-users’ preferences and their values of consuming electricity.