Abstract
Several governments are phasing out coal fired generation power plants to reduce greenhouse gas emission. At the same time, nuclear generating facilities are reaching the end of their life and in the wake of the Fukushima disaster, developed countries have chosen to phase out nuclear energy early in the 2020s, removing 15% of the most stable and reliable portion of their energy mix. These two reasons create an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is the use of renewable energy resources, which can inject power to the grid without greenhouse gas emissions. But, the capacities of renewable energy resources are not enough to supply all the required power from the load side. All of these facts are leading to the proposal of novel approaches to reduce the utilization of energy in different sectors i.e. in residential, commercial, agricultural and/or industrial sectors to reduce the customer's total energy costs, energy demand, especially during on-peak, and greenhouse gas emissions, while taking into account the end-user preferences. The main objective of this paper is to demonstrate the impact of optimization technologies on energy savings of residential households. In this regard, a model-based predictive control approach is proposed for home cooling and heating systems. Its effectiveness is compared to thermostat conventional control by providing simulations upon 24 hours in a household.

Abstract
Smart local energy networks provide an opportunity for more penetration of distributed energy resources. However, these resources cause an extra dependency in both time and carrier domains that should be considered through a comprehensive model. Hence, this paper introduces a new concept for internal and external dependencies in Smart Multi-Energy Systems (SMES). Internal dependencies are caused by converters and storages existing in operation centers and modeled by coupling matrix. On the other hand, external dependencies are defined as the behavior of multi-energy demand in shifting among carriers or time periods. In this paper, system dependency is modeled based on energy hub approach through adding virtual ports and making new coupling matrix. Being achieved by SMESs, the dependencies release demand-side flexibility and subsequently enhance system efficiency. Moreover, a test SMES that includes several elements and multi-energy demand in output is applied to show the effectiveness of the model.

Abstract
Smart local energy networks represent a key option for more penetration of sustainably developed facilities. These facilities can cause an extended dependency in both time and carrier domains which should be considered through a comprehensive model. This paper introduces a new concept of internal and external dependencies. The concept is related to penetration of energy converters on demand side and the effects they bring to the system. Being achieved by implementation of smart grid, dependencies release operational flexibility and subsequently enhance the system efficiency. The model contains, carrier based demand response which preserves consumers satisfaction by utilizing the flexibility in exchanging the input energy carrier instead of changing end-usage pattern. The paper develops the coupling matrix model for smart multi-energy systems considering the external dependency as an added module to the overall model.

Abstract
New technologies such as Plug-in Electric Vehicles or PEVs bring new trends in areas where multi entities interact. One of these areas is the facilities in cities for better manipulation of technologies. Encouraging the utilization of PEVs necessitates the provision of charging stations. In this regard, PEV Parking Lots (PLs) can be a great help as they can solve both urban and electrical problem of PEVs. On the other hand, gathering numerous PEVs in one point like PLs provides the system operator with the opportunity of PEVs batteries that can be used as potential storages. Therefore, in this paper, the optimum behavior of PEV PLs is modeled while they contribute their batteries' capacity in both energy and reserve market. The charging schedule of PLs with various numbers of stations has been reported as well as their share in the market as an energy source during peak hours.

Abstract
In this paper, a mixed-integer linear programing ( MILP) model for the traffic behavior of plug-in electric vehicles ( PEVs) in an urban environment is proposed. It is assumed that any environment can be categorized into different zones based on their urban functions ( e. g. industrial, residential, and commercial). Therefore, the interaction of PEVs that travel between these zones has to be modeled. Besides, it is assumed that in each zone a parking lot ( PL) and individual charging stations exist to provide the required state of charge ( SOC) for PEVs during their daily travel. As a result, the amount of power that these PEVs consume ( rather in PL or charging stations) and the amount of SOC that PEVs carry with them should be precisely computed. The proposed MILP model is applied on two zones urban area with residential and industrial districts and the numerical results prove the proficiency of the model.

Abstract
Distribution Systems (DS) are usually structured as weakly-meshed but the majority of them operate with a radial topology, mainly in order to accommodate the protection coordination. Obtaining the optimal radial configuration under several criteria has been an active research topic for more than two decades. Because of the computational burden and the non-linearity of the problem, the majority of the proposed methods and techniques, single or multi-objective, use various meta-heuristics. The DS reconfiguration problem, respecting the radiality constraints, is formulated in this paper as a multi-objective Mixed-Integer Linear Programming (MILP) problem. An adequate representation of the Pareto set is produced using an improved implementation of the epsilon-constrained method. The objective is to determine the optimal radial configuration during several time intervals, minimizing the active power losses and the cost emerging from the switching operations. The proposed methodology is tested using a 16-node sample system.