چكيده به لاتين
Increasing growth of electrical energy consumption and lack of adequate generation resources and investment for power system caused power market to use the activity of the market participants to reduce the above mentioned problems. Consumers' participation in the power market is dependent on the creation of motivation in them. In fact, demand response (DR) is a program that creates the motivation to change power consumption pattern in the end consumer. As the new institutions of the market, DR aggregators play the role of an intermediary between independent network users and utilizers. These institutions execute and manage DR programs on the consumers in the power market and sell again the obtained amount of DR in this way in various power markets. This intermediary role exposes DR aggregators to two main challenges: they should not only consider the behavior of consumers during the implementation of DR programs, but also manage the rate of the risk of unreliability of the power markets at the time of supplying (selling) DR products. These impose costs and the aggregator is required to guarantee the pre-defined profit. Additionally, sometimes, unexpected jumps of uncertainty parameters are used to obtain higher profits. The aim of the present thesis is to present a daily optimal program for risk-averse institutions, as well as risk-taking institutions by modeling uncertainties and using information gap-decision theory. The solution method is in a way that, at first, certain programming is done, and then, three cases are investigated for each of the states. First, consumer participation factor is considered alone as the uncertainty parameter, and then, these participation factors are considered as their predicted values, and the market prices are the uncertainty parameter. In the third case, both participation factors and market prices are investigated in modeling simultaneously. It is worth mentioning that in this Dissertation, DR aggregators receive DR from time of use and reward-based programs.
