هما رشیدی زاده کرمانی

In this paper, a probabilistic model for joint scheduling of energy and reserve (spinning and non-spinning) of an islanded microgrid is proposed considering demand response programs and security constraints. The objective of the problem is to maximize the expected profit of the microgrid operator with considering voltage and frequency security constraints. The presence of stochastic sources such as renewable resources and loads made the nature of the scheduling problem stochastic which is required to be formulated and solved in a scenario-based model. In the proposed method, to minimize the adverse effects of unfavorable scenarios, the cconditional value at risk (CVaR) criteria is used in the probabilistic model. In this method, the sensitivity of operator’s profit and the microgrid security margin in the cases with/ without the participation of responsive loads are assessed with considering risk criteria. Using this method, makes the operator capable to choose a proper risk factor as well as maximizing its expected profit and improving the security margin of voltage and frequency of the microgrid. In addition, in order to assess the system security more precisely and to determine the voltage and frequency deviations in each scenario, the AC optimal power flow (AC-OPF) is applied. Considering the security constraints of voltage and frequency simultaneously (at each hour) is a benefit of this method.

By increasing the number of electric vehicles (EVs) in the power system, it is necessary to manage their interaction with the grid such that to minimize the costs of the owners as well as to maximize the profit of their serving entities. Therefore, in this paper, a stochastic bi-level decision making problem for the participation of EV aggregator in a competitive environment is presented, with considering various sources of uncertainty. The sources of uncertainty for the participation of aggregators in the electricity market include day-ahead (DA) and balancing market prices as well as rival's suggested prices and charging and discharging EVs demand that are modeled with time series. In the proposed bi-level program, the goal of the first level is to maximize the aggregator's profit in interaction with the network, and the goal of the second level is to minimize the payments of the owners. Since the objective function of the first and second levels are in contradiction with each other, with using KKT optimization conditions and the duality theory, the proposed two-level problem has become a linear single-level problem. Finally, the proposed program is implemented in typical test system and the results show that by using this model for the aggregator's decision making, it can offer proper charge and discharge price signals to the EV owners to attract the them in a competitive market and also to maximize its profit.