Abstract
This paper contrasts the Genetic Algorithm (GA) strategies to solve the problem of Economic Dispatch (ED) in a Microgrid (MG). An environmental ED strategy for MG is proposed through maintaining the system reliability requirements at acceptable level. Two additional cost terms such as the CO2 emission penalties and load curtailment charges are added to the traditional objective function of the ED problem. Also, cost and reserve supply of the network are considered. Since the load curtailment cost is highly dependent to the network reliability indices, specifically those determining network inability to supply demand, the Expected Energy Not Served (EENS) reliability index is used to calculate the curtailment costs. By illustrating the advantages of Inherit Based Genetic Algorithm (IBGA) over the other two strategies, namely Simple Genetic Algorithm (SGA) and 3-matrices Genetic Algorithm (3MGA), IBGA is used to solve the proposed Reliable, Environmental Economic Dispatch (REED) problem in a MG.

Abstract
In this study, a non-probabilistic program is proposed to a trading framework for demand response (DR) aggregators. Both sides of the aggregator, including upper side and down side of this entity, have been taken into account. In the down-side of the aggregator, two popular programs are considered such as reward-based program and time-of-use (TOU) program, where DR is obtained from these resources. The acquired DR is being sold to the purchasers in the other side of the aggregator through DR options and fixed DR contracts. To the aim of increasing the desired target profit of risk-seeker aggregator, an opportunity function of information-gap decision theory (IGDT) is used to handle the uncertainty, which is solved in General Algebraic Modeling System (GAMS) software. This model is implemented in a realistic case study.

Abstract
The proposed model analyzes the profit of a demand response (DR) aggregator from trading DR in the day-ahead electricity market in a way that it tends to gain profit from the favorable deviations of the uncertain parameters. Two types of DR programs are implemented in this model, i.e., time-of-use and reward based DR program. The information-gap decision theory is being employed as a risk measure to address the uncertainties. Two uncertain parameters from both sides of the aggregator have been taken into account in this model, such as the participation rate of the consumers in reward-based DR program in the consumer-side of the aggregator and the day-ahead market prices in the wholesale-side of it. The program is simulated in GAMS software using the available commercial solver. Real data is considered to check the feasibility of the proposed program.

Abstract
During recent decades with the power system restructuring process, centralized energy sources are being replaced with decentralized ones. This phenomenon has resulted in a novel concept in electric power systems, particularly in distribution systems, known as Distributed Generation (DG). On one hand, utilizing DG is important for secure power generation and reducing power losses. On the other hand, widespread use of such technologies introduces new challenges to power systems such as their optimal location, protection devices' settings, voltage regulation, and Power Quality (PQ) issues. Another key point which needs to be considered relates to specific DG technologies based on Renewable Energy Sources (RESs), such as wind and solar, due to their uncertain power generation. In this regard, this paper provides a comprehensive review of different types of DG and investigates the newly emerging challenges arising in the presence of DG in electrical grids.

Abstract
This paper investigates the optimal allocation of Spinning Reserve (SR) for power systems in the presence of Renewable Energy Sources (RES) and Electrical Energy Storage (EES) devices. This is done in order to reduce the system's dependency on thermal generation units and the decrease total daily operational cost. A Security Constrained Unit Commitment (SCUC) model for a typical power system was used, which includes thermal and renewable generation units and EES devices in the form of batteries. In the proposed model, the hourly operation strategy is determined by adopting a predetermined level of SR. In order to optimize SR requirements, the Independent System Operator (ISO) runs the SCUC problem and determines the minimum SR that should be provided by generation units and EES devices. The simulation results illustrate that by optimizing the operation of batteries, the ISO can effectively reduce the required capacity of thermal units. Therefore, optimal SR allocation under RES uncertainty is determined in this study.

Abstract
Congestion in transmission lines is an important topic in power systems and it continues to be an area of active research. Various approaches have been proposed to mitigate congestion especially immediate ready ones such as Congestion Management (CM) and Transmission Switching (TS). Using either of the two or their combination (CMTS) may have undesirable consequences like increasing operational costs or increasing the number of switching of transmission lines. More switching aggravates system reliability and imposes extra costs on the operator. In this paper, a multi-objective model is introduced which reduces overall operation costs, the number of switching in transmission lines, and the congestion of lines, compared to available approaches which employ congestion management and TS simultaneously. To verify the performance of the proposed model, it is implemented using GAMS and tested on 6- and 118- bus IEEE test systems. A benders' decomposition approach was employed.