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.

Abstract
This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision making of energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by such uncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework and the energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained.

Abstract
This paper demonstrates day-ahead operation of power systems in the presence of a Demand Response Program (DRP) for serving exact amounts of demanded energy over the operational horizon. The proposed two-stage model features a here-and-now framework for shaping the aggregated demands during operation. First, the day-ahead scheduling problem is solved by adopting Unit Commitment (UC) to determine the generation level of power generation units as well as the Locational Marginal Prices (LMPs). Afterwards, the obtained LMPs are considered as the Time of Use (ToU) for the second step of the scheduling and reshaping the demanded loads of each aggregator. A new methodology is provided in this paper to estimate the reaction of consumers behavior in terms of encouraging their participation in DRPs. Unlike classical models which adopt load reduction over the operational horizon, this model ensures that the total demanded loads will be served. Therefore, the total supplied energy for the operational period before and after DRP implementation remains unchanged. Meanwhile, the total payment of consumers will be considerably reduced by adopting this strategy. The simulation results on the 6-bus test system clarify that the proposed model can reduce the total operational cost as well as smoothen the load profile and nodal prices over the operational horizon.