a. safdarian

This paper establishes a centralized model to activate residential demand response in order to improve distribution network reliability. The model aims at minimizing the damage cost imposed by load curtailments following occurrence of unexpected events. In this model, distribution system operator (DSO) and responsive customers have already signed a contract authorizing the DSO alters the operation of responsive appliances whenever system reliability is jeopardized. The model addresses consumers’ preferences and guarantees that the operation of appliances is displaced within the bounds defined by the owners. Once an unexpected event occurs, the DSO commits responsive appliances to avoid likely violations in the network operational limits and costly load curtailments. The proposed model is mathematically formulated in the form of mixed integer linear programming (MILP) and its capability is depicted via applying to a real-world distribution network with some residential consumers. The comparison of service reliability indices after and before utilizing demand response potentials illustrates the effectiveness of the model.

Microgrids (MGs) are designed to be able to serve hosting critical load in island-mode during major events. However, during normal condition when they are in grid-connected mode, MGs may have opportunity to achieve monetary profits through optimizing operation of energy resources and their participation in wholesale markets. This paper proposes a model to optimize MGs participation in the markets and operation of energy resources. Since MGs usually host renewable energy resources, making decision without considering the uncertainties may prone MGs to risk. So, the model considers uncertainties associated with generation of renewable DGs, demand, and market prices via robust optimization technique. The model is a max-min problem which is modelled as a bi-level optimization problem. The problem is solved in two iterative steps. In the first step, a genetic algorithm (GA) is applied to obtain the worst case wherein uncertain parameters are determined such that MG profit is minimized. Then, a mixed-integer linear problem is solved to maximize the profit over MG decision variables considering the values determined in the first step. The steps are iterated to converge to the best solution. To verify performance of the approach, it is applied to a typical MG and the results are reported.

The risk imposed by the stochastic nature of wind energy sources has always been a major barrier despite their proliferation in power systems. To further penetrate these sources, this paper draws upon dynamic prices, which realize demand response potentials along with decimating the risk involved. To do so, a model is first established to study the impacts of activating demand response, on the risk index in a system with a high penetration of wind resources. Then, the model is used to estimate the extra wind capacity that can be hosted by the system such that the risk remains within the acceptable range. The well-being indices are calculated via sequential Monte Carlo simulation approach and Fuzzy theory. The demand response with dynamic prices is modeled by self and cross elasticity coefficients of different load sectors. The performance and applicability of the proposed model are verified through simulations on the IEEE Reliability Test System. (IEEE-RTS).