Abstract
There are significant changes occurring both in the electricity system and the natural gas system. These two energy carries can be combined to form what is known as an energy hub. These energy hubs can play a significant role in the energy system and thus understanding of their optimization, especially their costs, is important. This paper proposes a risk management framework for an energy-hub through the utilization of the information-gap decision theory (IGDT). The uncertainties introduced from the various load profiles, such as the electric and heating loads, are considered in this risk management framework. The modeled energy-hub consists of several distributed generation systems such as a microcombined heat and power (mu CHP), electric heat pump (EHP), electric heater (EH), absorption chiller (AC) and an energy storage system (ESS). A demand response (DR) program is also considered to shift a percentage of electric load away from the peak period to minimize the operational cost of the hub. A feasible test system is also applied to demonstrate the proposed model's effectiveness.

Abstract
This paper focuses primarily on the flexibility of active prosumers in an islanded microgrid operation. The main objective is finding the best strategy to implement on an existing medium voltage grid, with several consumers, with the capability of producing some power for the grid operation, via Renewable Energy Resources (RES), or thermal Units, generally gas turbines, also there is the capability of some energy storage through batteries. Since power output of RES has a cost per kw of zero, it is greatly important to find the best combination of these resources who best suit the test system. For the purposes of these tests, the available investment funds are unlimited, although, there are some constraints regarding maximum RES penetration and ESS capacity.

Abstract
This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

Abstract
Yearly, the number of Distributed Energy Resources (DER) integrated into the power grid increases has increased, having a large impact on power generation globally, promoting the introduction of renewable energy resources (RER). To increase the flexibility of the power system with integrated RER, the introduction of energy storage systems (ESS) is essential. Demand response (DR) programs also help to increase grid flexibility, resulting in increased grid reliability as grid congestion and losses decrease. However, this new paradigm shift needs further research and careful analysis. In this work, two types of DR programs are addressed to promote greater participation by different consumers features. To interconnect the different consumers, DR aggregators are inserted to ensure that individual consumers have influence on the power market. All these aspects, if accompanied by information, measurement, communication, and control systems, give rise to the smart grids, playing an essential role. The results show, considering the worst uncertainty case scenario, that there is a suitable total RER of 2151.50 kW, against 3227.30 kW, by not considering the RER uncertainty.

Abstract
The penetration of electric vehicles (EVs) in vehicle markets is increasing; however long charging time in battery charging stations is an obstacle for larger adoption of EVs. In order to address this problem, battery swap stations (BSSs) have been introduced to exchange near-empty EV batteries with fully charged batteries. Refilling an EV in BSS takes only a few minutes. With decentralization of power systems, BSSs are typically connected to the microgrid (MG) in their neighborhood. Although the location of BSS in MG affects MG operation cost, to the best knowledge of the author, optimal placement of BSS has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro pumped hydro storage (PHS), photovoltaic, wind and geothermal units, while reactive power dispatch and all network constraints are considered by AC optimal power flow. The effect of BSS capacity and maximum charging/discharging power, BSS to MG link capacity, PHS capacity and maximum power of PHS unit on MG operation and optimal BSS location are investigated. DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem.

Abstract
This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users? bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs.