جستجوی مقالات مرتبط با کلیدواژه « reserve scheduling » در نشریات گروه « فنی و مهندسی »

In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.

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.