Saber Talari

Optimal Scheduling of Demand Response in Pre-Emptive Markets Based on Stochastic Bilevel Programming Method

This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.

Decentralized Control of DR Using a Multi-agent Method

Demand response (DR) is one of the most cost-effective elements of residential and small industrial building for the purpose of reducing the cost of energy. Today with broadening of the smart grid, electricity market and especially smart home, using DR can reduce cost and even make profits for consumers. On the other hand, utilizing centralized controls and have bidirectional communications Bi-directional communication between DR aggregators and consumers make many problems such as scalability and privacy violation. In this chapter, we propose a multi-agent method based on a Q-learning algorithm Q-learning algorithm for decentralized control of DR. Q-learning is a model-free reinforcement learning Reinforcement learning technique and a simple way for agents to learn how to act optimally in controlled Markovian domains. With this method, each consumer adapts its bidding and buying strategy over time according to the market outcomes. We consider energy supply for consumers such as small-scale renewable energy generators. We compare the result of the proposed method with a centralized aggregator-based approach that shows the effectiveness of the proposed decentralized DR market Decentralized DR market.

A Review of Multi-agent Based Energy Management Systems

This paper proposes a review of Energy Management Systems (EMSs) based on Multi-Agent Systems (MASs). Also, goal, scale, strategy and software are discussed as different characteristics of the EMSs. Then, multi agent-based structure of the EMSs is described. Finally, challenges and future discussions related to the EMSs are presented in this paper.

Assessment of Ancillary Service Demand Response and Time of Use in a Market-Based Power System Through a Stochastic Security Constrained Unit Commitment

In this paper, the impacts of an incentive-based Demand Response, i.e., Ancillary Service DR (ASDR), and a price-based DR, i.e., Time of Use (ToU), are revealed in a restructured power system which has some wind farms. This network is designed based on the pre-emptive market which is a day-ahead market with a balancing market prognosis. It is a proper mechanism to deal with the stochastic nature of non-dispatchable and outage of all units of the network. With Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of the wind farms generation. The impacts of merging ASDR and ToU are investigated through running a two-stage stochastic security constrained unit commitment (SCUC), separately .

The Impacts of Demand Response on the Efficiency of Energy Markets in the Presence of Wind Farms

In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers’ behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

Implementing an Integer Linear Approach to Multi-objective Phasor Measurement Unit Placement

In this paper, an Integer Linear Programming (ILP) problem for a model of Multi-Objective Optimal PMU Placement (MOPP) is proposed. The proposed approach concurrently deals with two objectives. The first objective is the number of phasor measurement units (PMUs) which should be minimized. The second objective function is measurement redundancy which is the number of observable buses in the case of PMU outage. In fact, whatever the amount of second objective increases, the system would be more reliable. Furthermore, some linearized formulations are defined for each nonlinear formula. In fact, the nonlinear nature of formulation related to redundancy is substituted by linear inequality and so there is no nonlinear formula such that the calculation of the problem would be simplified. Finally, a modified 9-bus test system is implemented to show how the proposed method is effective.