Abstract
The smart grid paradigm has provided great opportunities to decrease energy consumption and electricity bills of end users. Among a wide variety of end users, electrical railway systems with their huge installed power capacity should be considered as a vital option in order to avoid wasted energy, provided that an energy management system is utilized. In this study, a mixed-integer linear programming model of a railway station energy management (RSEM) system is formulated by a stochastic approach, aiming to utilize the emerged regenerative braking energy (RBE) during the braking mode in order to supply station loads. Furthermore, the proposed RSEM model is composed of an energy storage system (ESS), RBE utilization, photovoltaic (PV) generation units, and an external grid in this paper. The passengers' impact on RBE as well as the stochastic behaviour of the initial state-of-energy of ESS along with uncertainty of PV generation by the RSEM model are also evaluated. The model is tested under a bunch of case studies formed considering several combinations of the cases that an ESS or PV are available or not and using RBE is possible or not.

Abstract
This paper addresses the wind farm contribution in frequency control during the integration in the power grid. In the proposed model, the wind farm utilizes inertia control and droop control techniques with the purpose of improving the frequency regulation. In order to achieve optimal results, all the parameters of the controllers for the different units in the power grid are obtained by using a particle swarm optimization algorithm (PSO) and by introducing a modified objective function instead of a conventional objective function e.g., Integral Time-weighted Absolute Error (ITAE). Also, different constraints such as reheat turbine, time delay, governor dead band and generation rate constraint (GRC) are considered for thermal and hydro units with the aim of studying a more realistic power system, which is the main contribution of this paper when compared to the other works in this field. It is shown that, in case of a perturbation in power demand, the system frequency will recover quickly and effectively in comparison with the traditional approaches. In addition, a sensitivity test is carried out in a single power grid area in order to examine the effectiveness of the proposed approach. Then, the system is extended to a multi-area power system using a multi-terminal HVDC for further investigation of the suggested strategy. Simulation results are presented in order to assess the performance of the proposed approach in the power system.

Abstract
The energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG) and other air pollutants emissions. Since home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper, a comprehensive comparison is made between the thermostat (ON/OFF), proportional-integral-derivative (PID) and Model Predictive Control (MPC) control models of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. A power interface that adjusts the MPC dynamic range of the output command signal into a discrete two level control signal is proposed, as a new contribution to earlier studies. The model of the house with local solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and 5 Time-of-Use (Toll) electricity rates applied on an entire week of August 2016. The purpose of the optimisation is to achieve the best compromise between temperature comfort levels and energy costs and also to assess which is the best electricity ToU rate option provided by the electricity retailer for the residential sector. Also, for each electrical load of the HVAC system, the energy and cost are calculated and the results are presented by varying the different MPC weight combination in order to obtain the best possible solution and increase the quality of the model. Finally, after the best tariff and controller are determined, the impact of the solar generation is assessed.

Abstract
The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.

Abstract
In this paper, a new decomposing strategy is proposed to solve the power flow problem in the large-scale multienergy carrier (MEC) systems, including gas, electrical, and heating subnetworks. This strategy has been equipped with a novel noniterative method named holomorphic embedding (HE) to solve the energy flow of the electrical subnetwork. Moreover, it benefits from the less-computational graph method for solving the energy flows of the heating subnetwork. The HE method unlike initial-guess iterative methods guarantees to find the power flow solution, if there is a solution. In addition, it finds only the operational power flow solution without concern about the convergence of the solution. In the proposed strategy, the decomposing method decouples various energy flows of subnetworks without losing the major benefits of the simultaneous analysis of the subnetworks and losing accuracy. Moreover, the proposed decomposing strategy has more reliability and faster computation time than the Newton-Raphson technique. In order to demonstrate the efficiency and superiority of the proposed decomposing strategy on solving large-scale MEC systems, the strategy is tested on three large-scale case studies.

Abstract
The stochastic nature of the renewable generators and price-responsive loads, as well as the high computational burden and violation of the generators' and load aggregators' privacy can make the centralized energy market management a big challenge for distribution network operators. In this paper, we first formulate the centralized energy trading as a bilevel optimization problem, which is nonconvex and includes the entities' optimal strategy to the price signals. We tackle the uncertainty issues by proposing a probabilistic load model and studying the down-side risk of renewable generation shortage. To address the nonconvexity of the centralized problem, we apply convex relaxation techniques and design proper price signals that guarantee zero relaxation gap. It enables us to address the privacy issue by developing a decentralized energy trading algorithm. For the sake of comparison, we use the dual decomposition and proximal Jacobian alternating direction method of multipliers for the algorithm design. Extensive simulations are performed on different standard test feeders to compare the CPU time of the proposed algorithm with the centralized approach and evaluate its performance in increasing the load aggregators' and generators' profit. Finally, we compare the impact of load and generation uncertainties on the optimality of the results.