Abstract
While compressed air energy storage (CAES) has many applications in the field of generation and transmission power systems based on the state-of-the-art, this paper proposes the application of smallscale CAESs (SCAESs) in form of a storage aggregator in the daily operation of an active distribution system (ADS), joining the distribution system operator (DSO) for the participation in the day-ahead (DA) wholesale market. An innovative two-agent modeling approach is formulated. The first agent is responsible for aggregating SCAES units and the profit maximization of the aggregator is based on the distribution local marginal price. The DSO as the second agent receives the DA scheduling from the independent SCAES aggregator and is thus responsible for the secure operation of the ADS, utilizing solar and dispatchable distributed generation (DG) as well as purchasing power from the wholesale market. Linear programming is used for the formulation and optimization of the SCAES aggregator, while a biobjective optimization algorithm (with the objectives of minimum operating cost as well as minimum power loss and emissions in different scenarios) is employed for DSO scheduling. The results show that the CAES aggregator can offer a considerable impact for power loss reduction, specifically, when diesel generators are not committed in the system operation (i.e., where emission has very low values between 10,000 and 12000 kg). Additionally, the CAES aggregator could reduce the operation costs of the grid in a wide range of operations, even though for the scenario in which the CAES units are not under the control of the DSO anymore and also are scheduled to maximize their own profit. Moreover, results demonstrated that CAES units can be a significant voltage control device for a distribution grid with different objectives. Finally, some conclusions are duly drawn.

Abstract
This paper presents a novel iterative three-level optimization framework for the optimal resilient operational scheduling of active multi-carrier energy generation and distribution systems. The main contribution of this paper is that the proposed framework simulates the day-ahead and real-time pre-event preventive and post-event corrective actions for external shocks and explores the effectiveness of risk-averse operational strategies on the system's costs. The solution methodology is another contribution of this paper that finds the optimal scheduling of distributed energy resources and switching of electrical switches and district heating and cooling control valves. At the first stage, the optimal day-ahead scheduling of distributed energy resources and the initial value of the risk control parameter are determined using robust optimization. At the second stage, the optimal realtime market scheduling of distributed energy resources is performed. Finally, at the third stage, different extreme shock scenarios are considered, the effectiveness of corrective actions are investigated, and the value of risk control parameter is modified. The proposed method was successfully applied to the modified 123-bus test system and 600 scenarios of external shocks were considered. The proposed process successfully reduced the expected cost of the. 123-bus system by about 74.59% for the worst-case external shock. Further, the algorithm reduced the aggregated expected values of operational and interruption costs by about 57.73% for all of the 600 cases of the considered external shocks.

Abstract
Natural disasters in recent years have highlighted the need for enhancing the resilience of the power systems against these events. Dynamic microgrid (MG) formation using distributed energy resources (DERs) is the common approach in restoring the critical loads (CLs). On the other hand, vehicle-to-grid (V2G) and grid-tovehicle (G2V) capabilities in electric vehicles (EVs), as well as the presence of high-powered engine-generators (EGs) embedded in plug-in hybrid electric vehicles (PHEVs) provide a new capability for using electric and fossil energy stored in EVs simultaneously to restore the CLs during an outage. In this regard, the outage management system (OMS) cooperates with aggregators and uses EVs in the form of a public parking lot (PL) or residential parking (RP), besides other resources such as diesel generators and photovoltaic (PV) units. The approach presented in this paper shows the procedure of load restoration and energy management of available resources under a two-stage stochastic framework. Also, a new method is introduced for restoring CLs in the mesh network by using the load control and the master-slave control techniques. The problem is formulated as mixed-integer linear programming (MILP), and simulations are performed on IEEE 123-buses test system and a real distribution network.

Abstract
Energy demand in the world is mostly met by conventional sources that cause carbon emissions. Considering environmental problems and the depletion of these sources in the near future, there is a trend towards renewable energy sources (RESs). Also, countries are implementing policies such as investment support, production support, quantity target, and limiting carbon emissions to increase the number of RESs. When these policies are compared, one of them can be superior to another in different countries. Also, superficial supports can cause an excessive financial burden on the governments. RESs have inherently intermittent power generation and in this respect, it is important to correctly estimate the RESs whose production changes with environmental conditions and to offer to the electricity markets optimally. For this reason, it is also important to know the structures of the electricity markets in bidding. Besides, RESs can come together to take an effective position in the market in terms of price and manage their imbalances. These structures can take names such as aggregator, virtual power plant (VPP), and portfolio. Considering the above-mentioned issues, this study aims to investigate in detail the methods applied to increase the number of RESs and the ways these resources participate in the electricity markets. In this context, subjects of policies promoting RESs, electricity market structures, development of the electricity market, optimum bidding strategy and ways of collective participation of RESs in the electricity markets are comprehensively examined under different sections.

Abstract
The proliferation of demand response programs in the smart grid provides the system operator unique opportunities to reduce the load peak and alleviate network congestions. This paper considers the economic dispatch problem with elastic demands which flexibly respond to the locational marginal prices (LMPs). However, LMP is the dual variable of optimal power flow (OPF) problem and thus is unknown before the OPF problem is solved. Without LMP, the elastic demand is unclear, and the OPF problem cannot be set up. Given this interactive nature, it is difficult to acquire the dispatch strategy as well as the LMP according to the traditional OPF method. This paper thoroughly addresses this problem. Specifically, the limitation of the traditional LMP scheme in the described situation is analyzed. An equilibrium solution may not exist because the demand function and the discontinuous LMP may not have an intersection. To overcome this difficulty, LMP at the discontinuity point is redefined, so that the dispatch problem always has an equilibrium solution. A mixed-integer linear programming model for the economic dispatch problem with LMP-dependent load is proposed, and the equilibrium solution simultaneously offers the dispatch strategy and LMPs. Case studies demonstrate the difficulties of traditional approaches and the effectiveness of the proposed method.

Abstract
The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit.