Abstract
In this paper, operation management of microgrids is performed. To do so, some contingencies including outage of distributed generators (DG), energy storage (ES) and the upstream network are considered. Since the microgrids have suitable capabilities in terms of control and communication, demand response reserve can be applied to improve the operation management. Using Monte Carlo simulation method and Markov chain, several scenarios are generated to show the possible contingencies in various hours. Then, a scenario reduction method is used for reducing the number of scenarios. Finally, a two-stage stochastic model is applied to solve a day-ahead scheduling problem in mixed-integer linear programming by GAMS. Consequently, the effect of demand response in the reduction of operation cost is demonstrated.

Abstract
Electric vehicle (EV) technology with a vehicle to grid (V2G) property is used in power systems to mitigate greenhouse gas emissions, reduce peak load of the distribution system, provide ancillary service, etc. In addition, demand response (DR) programs as an effective strategy can provide an opportunity for consumers to play a significant role in the planning and operation of a smart distribution company (SDISCO) by reducing or shifting their demand, especially during the on-peak period. In this paper, the optimal operation of a SDISCO is evaluated, including renewable energy resources (RERs) along with EV parking lots (PLs). RER and PL uncertainties and a suitable charging/discharging schedule of EVs are also considered. Furthermore, price-based DR programs and incentive-based DR programs are used for operational scheduling. To achieve this aim, a techno-economic formulation is developed in which the SDISCO acts as the owner of RERs and PLs. Moreover, DR programs are prioritized by using the technique for order preference by similarity to ideal solution method. In addition, a sensitivity analysis is carried out to investigate different factors that affect the operational scheduling of the SDISCO. The proposed model is tested on the IEEE 15-bus distribution system over a 24-h period, and the results prove the effectiveness of the model.

Abstract
Optimum operation of the energy consumption of end-users gains more importance to reduce total electricity bills and in order to more efficiently use energy resources thanks to smart grid concept. Electrical railway stations are one of the best places to take into account for in this manner. This study proposes a railway station energy management (RSEM) model. As the main contribution to the literature, regenerative braking energy (RBE) recovered during the operation of a metro line is assumed to meet the station load demand in daily operation. The RSEM model composed of RBE usage, energy storage system (ESS), and grid support is formulated as a mixed-integer linear programming (MILP) framework. RSEM model is tested by introducing the impact of passengers change on RBE for such cases that whether RBE and ESS are considered or neglected.

Abstract
This paper deals with a synchronous active proportional resonant-based (SAPR) control technique for interfaced converters, enhancing the stable operation of the power grid under high penetration of distributed generation sources. By considering the grid specifications and load currents, both d and q axis of converter currents are obtained in terms of active and reactive power and also angular speed using small-signal linearization method. Then, swing equation is analyzed in detail to achieve the reference current components in the current control loop of the interfaced converter. By using the obtained swing equation and a non-ideal proportional resonant (PR) controller, a new control technique is proposed, which introduces the behavior of synchronous power generators based on power electronic converters in distributed generation (DG) technology. The effectiveness of the proposed control technique is verified through stringent simulation studies in MATLAB/SIMULINK.

Abstract
This paper aims to present a novel control strategy for modular multilevel converters (MMC) based on differential flatness theory, in which instantaneous active and reactive power values are considered as the flat outputs. To this purpose, a mathematical model of the MMC taking into account dynamics of the ac-side current and the dc-side voltage of the converter is derived in a d-q reference frame. Using this model, the flat outputs-based dynamic model of MMC is obtained to reach the initial value of the proposed controller inputs. In order to mitigate the negative effects of the input disturbance, model errors, and system uncertainties on the operating performance of the MMC, the integral-proportional terms of the flat output errors are added to the initial inputs. This can be achieved through defining a control Lyapunov function which can ensure the stability of the MMC under various operating points. Moreover, the small-signal linearization method is applied to the proposed flat output-based model to separately evaluate the variation effects of controller inputs on flat outputs. The proficiency of the proposed method is researched via MATLAB simulation. Simulation results highlight the capability of the proposed controller in both steady-state and transient conditions in maintaining MMC currents and voltages, through managing active and reactive power.

Abstract
Bundled generation and transmission expansion planning (BGTEP) aims to solve problems related to ascendant demand of power systems. A BGTEP model is considered in this paper and the optimal planning for a long-term period is obtained such that the cost of installation and operation would be minimized. Also, due to the recent orientation toward renewable energy sources, the influence of wind farms is involved in the methodology. An important aspect of load and wind power is their uncertain nature and the characteristic of being unforeseen. This matter is under consideration by a bounded and symmetric uncertainty optimization approach. In fact, the combination of two uncertainty methods, i.e., robust and stochastic optimization approaches are utilized and formulated in this paper. Besides, to cope with this uncertainty, Weibull distribution (WD) is considered as wind distribution, while load distribution is counted by a normal distribution (ND). A unique approximation approach for WD to be considered as ND is presented. In addition, a linear formulation is obtained by alternative constraints in order to drastically reduce the level of complexity of the formulation. Accordingly, a mixedinteger linear programming formulation is proposed to solve the BGTEP problem. The modified 6-bus and IEEE 24-bus RTS test systems are used to prove the applicability of the proposed method.