Abstract
This paper investigates the optimal allocation of Spinning Reserve (SR) for power systems in the presence of Renewable Energy Sources (RES) and Electrical Energy Storage (EES) devices. This is done in order to reduce the system's dependency on thermal generation units and the decrease total daily operational cost. A Security Constrained Unit Commitment (SCUC) model for a typical power system was used, which includes thermal and renewable generation units and EES devices in the form of batteries. In the proposed model, the hourly operation strategy is determined by adopting a predetermined level of SR. In order to optimize SR requirements, the Independent System Operator (ISO) runs the SCUC problem and determines the minimum SR that should be provided by generation units and EES devices. The simulation results illustrate that by optimizing the operation of batteries, the ISO can effectively reduce the required capacity of thermal units. Therefore, optimal SR allocation under RES uncertainty is determined in this study.

Abstract
Secure and reliable operation is one of the main challenges in restructured power systems. Wind energy has been gaining increasing global attention as a clean and economic energy source, despite the operational challenges its intermittency brings. In this study, we present a formulation for electricity and reserve market clearance in the presence of wind farms. Uncertainties associated with generation and line outages are modeled as different system scenarios. The formulation incorporates the cost of different scenarios in a two-stage short-term (24-hours) clearing process, also considering different types of reserve. The model is then linearized in order to be compatible with standard mixed-integer linear programming solvers, aiming at solving the security constrained unit-commitment problem using as few variables and optimization constraints as possible. As shown, this will expedite the solution of the optimization problem. The model is validated by testing it on a case study based on the IEEE RTS1, for which results are presented and discussed.

Abstract
In this study, the operation of an energy system composed of a battery energy storage system (BESS) and a conventional generator to compensate the forecasting error of renewable power production has been analyzed. A scenario with low forecasting error and another with high forecasting error have been synthetically modeled and incorporated to a computational model of the energy system. The results obtained from a case study suggest that a low forecasting error could be compensated by a single BESS. However, a high forecasting error would require the installation of a controllable power source such as a conventional generator.

Abstract
New energy market players such as micro-grid aggregators (MGA), distributed energy resource aggregators (DERA), and load aggregators (LAs) have all emerged to facilitate the integration of DERs into power systems. These players can participate in wholesale markets either individually or through distribution companies (Discos). In both cases, several operational challenges emerge for transmission system operators (TSOs) and distribution system operators (DSOs). Meanwhile, a transition is occurring from centralized wholesale markets into local energy markets (LEMs). A literature review shows that these LEMs are mostly modeled focusing on the coordination between DSOs and TSOs to meet demand in real-time operation using ancillary service markets and balancing markets. The main contribution of this paper is to model a local day-ahead energy market (LDEM) for optimal operation of a distribution network. This LDEM is cleared by the DSO with the aim of maximizing the social welfare of market players while satisfying the technical constraints of the network. To investigate the effectiveness of the proposed model, it is applied on the IEEE 33-bus network. Moreover, the effect of technical constraints of the network on the distribution locational marginal price (DLMP) is studied.

Abstract
To counter the intermittent nature of variable Renewable Energy Sources (vRESs), it is necessary to deploy new technologies that increase the flexibility dimension in distribution systems. In this framework, the current work presents an extensive analysis on the level of energy storage systems (ESSs) in order to add flexibility, and handle the intermittent nature of vRES. Moreover, this work provides an operational model to optimally manage a distribution system that encompasses large quantities of vRESs by means of ESSs. The model is of a stochastic mixed integer linear programming (WILY) nature, which uses a linearized AC optimal power flow network model. The standard IEEE 119-bus test system is used as a case study. Generally, numerical results show that ESSs enable a much bigger portion of the final energy consumption to be met by vRES power, generated locally.

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).