Abstract
Even renewable energy sources provide several advantages, especially from an environmental point of view, where the world has faced great challenges in the last few decades; several negative issues also exist regarding the integration of renewable resources-based power production units in electric power systems. One of the main problems related to pivotal renewable energy resources such as solar, wind, etc., is their stochastic and uncontrollable nature in terms of power production. Therefore, this stochasticity in the supply side of the power system may pose many challenges for system operators. This issue is also problematic for smaller applications where the stochastic production by a main resource, such as a roof-top photovoltaic system, and load demand may not match perfectly at each time instant and therefore should be compensated by additional resources such as battery-based energy storage systems. Herein, the economic considerations to ensure minimum costs for such a hybrid system design are vital so as to increase the penetration of such systems. Therefore, the optimal sizing and planning of hybrid systems have recently gained increasing importance to enhance power system operation in the context of the smart grid paradigm. From a different perspective, harmonics are one of the most important power quality problems in system operations caused by widespread integration of power electronic loads with non-linear characteristics that should be considered. Thus, a new approach for grid-connected hybrid renewable energy system sizing is provided. In order to determine optimal capacities for photovoltaic (PV) and energy storage system (ESS) units for covering residential consumer demand, a mixed integer linear programming (MILP)-based formulation is presented. The main objective is minimizing total costs of the system consisting of investment, capital and maintenance cost functions. A daily power curve is created accurately with real measurements of non-linear loads considering harmonic contents of smart home appliances in Yildiz Technical University, Istanbul, Turkey. In addition, real radiation and temperature values are used in PV production as well as dynamic pricing schemes for realistic evaluations. Moreover, optimal sizing results are compared for both the harmonic-based power curve and rated power curve in terms of satisfying objective function.

Abstract
This paper presents a two-level optimization problem for optimal day-ahead scheduling of an active distribution system that utilizes renewable energy sources, distributed generation units, electric vehicles, and energy storage units and sells its surplus electricity to the upward electricity market. The active distribution system transacts electricity with multiple downward energy hubs that are equipped with combined cooling, heating, and power facilities. Each energy hub operator optimizes its day-ahead scheduling problem and submits its bid/offer to the upward distribution system operator. Afterwards, the distribution system operator explores the energy hub's bids/offers and optimizes the scheduling of its system energy resources for the day-ahead market. Further, he/she utilizes a demand response program alternative such as time-of-use and direct load control programs for downward energy hubs. In order to demonstrate the preference of the proposed method, the standard IEEE 33-bus test system is used to model the distribution system, and multiple energy hubs are used to model the energy hubs system. The proposed method increases the energy hubs electricity selling benefit about 185% with respect to the base case value; meanwhile, it reduces the distribution system operational costs about 82.2% with respect to the corresponding base case value.

Abstract
Economic development and changing lifestyles are leading to the extensive use of energy-intensive technologies by consumers. As a result, this has led to a dramatically increased demand for electricity. In addition, the consumers' increasing demand for a more reliable and uninterrupted energy supply is posing enormous challenge for service providers. This necessitates the development of novel solutions that should be at the system operators' disposal, particularly at distribution levels. One way to partly address this concern is by automating distribution systems and equipping them with intelligent technologies-a transformation to smart distribution systems. Such a transformation should improve system reliability and operational efficiency because such systems will be capable of operating and immediately restoring discontinued service to consumers. To facilitate this, it is necessary to replace manual switches by remotely controlled ones, improving the system restoration capability, which is one of the key features of smart grids. This paper presents a new framework to determine the minimal set of switches that have to be replaced or optimally allocated in order to automate the system. This is supported by a sensitivity analysis. Different topologies are also assessed taking into account various reliability indices and power losses in system operation following the system's automation. Such an optimization work is done under a massive integration of renewable energy sources and energy storage systems. All this simultaneously addresses the economic and functional requirements of the automated system, ultimately improving system's reliability. The standard IEEE 119-bus standard system is used as a case study, where different types of loads are considered (residential, commercial, and industrial).

Abstract
Electrical, heating and cooling energy demands of the end users are increasing day by day. For the sake of using fewer fossil fuels, decreasing the energy costs and gas emissions as well as increasing the efficiency and flexibility of the traditional energy systems, multi-energy systems (MESs) have begun to be used. In this study, a MES structure which also includes renewable-based generation units as suppliers together with combined heating and power (CHP) and heat pumps (HPs) is presented. The proposed MES structure is modelled as a mixed integer linear programming (MILP) problem with the objective of minimizing total gas and electricity costs in daily operation. Furthermore, electric vehicles (EVs) as a new type of electrical load with inherently different characteristics are evaluated considering different end-user types as residential and commercial together with the capability of offering operational flexibility. In order to tackle with the intermittent structure of the renewable energy sources, a scenario oriented stochastic programming concept is taken into account by addressing real radiation, temperature, and wind data. Moreover, actual time-of-use (TOU) tariffs for electricity prices along with the real gas prices are evaluated. The simulation results of the devised model are given for different case studies and the effectiveness of the system is demonstrated via a comparative study. As a result, it is found that the operational costs are decreased nearly 5.49% by integrating only photovoltaic (PV) production according to the case which has no additional sources. Also, a substantial reduction of 13.45% is achieved by considering both PV and wind generation. Moreover, the flexibility is increased with taking EVs into account on the demand side and this leads to a cost reduction of 8.81% even if EVs are integrated to the system as an extra load.

Abstract
In security-constrained unit commitment (SCUC) problems, one approach to decrease operation costs is using a transmission switching (TS) tool. In SCUC problems with TS, one of the main challenges is that there is no limitation for the number of switching of circuit breakers (CB) in the system. In this article, the reliability of CB is merged into the SCUC problem with the TS and is considered as a limiting factor for switching. With a more reliable CB, the overall reliability of the system will be increased. So, it can be concluded that the reliability of a CB affects the amount of load shedding. Reliability of a CB is a nonlinear equation based on the number of switching in a period. An approach is presented to linearize the switch reliability equation. In this article, the power flow model uses an improved linear ac optimal power flow and a dynamic thermal line rating (DTLR) model, which considers the weather conditions. Other than CB reliability, DTLR in SCUC problems affects the number of switching and, as a result, operation costs will be significantly decreased. The proposed model is empowered by Bender's decomposition and is tested on 6-bus and 118-bus IEEE test systems.

Abstract
The decarbonization of the economy, for which the contribution of power systems is significant, is a growing trend in Europe and in the world. In order to achieve the Paris Agreement's ambitious environmental goals, a substantial increase in the contribution of renewable sources to the energy generation mix is required. This trend brings about relevant challenges as the integration of this type of sources increases, namely in terms of the distribution system operation. In this paper, the challenges foreseen for future power systems are identified and the most effective approaches to deal with them are reviewed. The strategies include the development of Smart Grid technologies (meters, sensors, and actuators) coupled with computational intelligence that act as new sources of data, as well as the connection of distributed energy resources to distribution grids, encompassing the deployment of distributed generation and storage systems and the dissemination of electric vehicles. The impact of these changes in the distribution system as a whole is evaluated from a technical and environmental perspective. In addition, a review of management and control architectures designed for distribution systems is conducted. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy Infrastructure > Economics and Policy