Abstract
Distribution networks are more active due to demand response programs which causes flexible behavior of end-users. This paper proposes an iterative algorithm to transact electricity based on interplay between aggregators and the Distribution Company (DisCo) considering the amount which the bottom-layer of a distribution system can provide from the aggregated end-users. The performance of the proposed trading algorithm was tested on a 33-bus test system for a distribution network. Similations for different scenarios were made to analyze the impact of different flexibility constraints on sustainability of the system and expected cost on distribution grid's player.

Abstract
Integration of renewable generation in distribution systems aims to reduce consumption of energy from conventional sources such as coal and oil in order to minimize the negative impacts of the human ecological footprint. Massive incorporation of renewables can produce reverse power flow at distribution substations, which is against the operating philosophy and design of energy systems. To deal with this problem, the installation of a battery energy storage system (BESS) is proposed in this work. Incorporation of BESS at distribution substations can manage the excess of renewable power generation flowing in reverse, adding flexibility to the power system and allowing increased distributed generation capacity to be installed. Optimal sizing of vanadium redox flow batteries (VRFBs) is carried out by using golden section search algorithm considering capital costs as well as operating and maintenance costs over the project lifetime. The effectiveness of the proposed technique is evaluated through the analysis of a case study. A significant reduction of both reverse flow and the power to be supplied by the substation has been observed.

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
In this paper, a new methodology to unleash the potential of demand response (DR) in real-time is presented. Customers may tend to apply their DR potential in the real-time market in addition to their scheduled potential in the day-ahead stage. Thus, the proposed method facilitates balancing the real-time market via DR aggregators. It can be vital, once the stochastic variables of the network such as production of wind power generators do not follow the forecasted production in real-time and have some distortions. Two-stage stochastic programming is employed to schedule some DR options in both day-ahead and real-time markets. DR options in real-time are scheduled based on possible scenarios that reflect the behaviors of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study and in the IEEE RTS-96, which shows a notable reduction in total operation cost.

Abstract
Enhanced utilization of the existing transmission grid is a cheaper and paramount way to have a high penetration of large-scale wind energy resources. Smart wire devices (SWDs) are a new technology to enhance the transfer capability through power flow control. The SWDs are distributed, cheap, self-governing smart assets of FACTS technologies, which can adjust the power flow in an interconnected transmission network. Accordingly, this paper presents a comprehensive three-stage robust SWD placement model, which minimizes the generation and investment costs while guaranteeing that the adaptive and secure robust solution is accustomed to cover the wind uncertainty interval. Since the proposed robust model is not solvable via an off-the-shelf optimization package and to reduce the computation burden of the solution process, an effective solution strategy is proposed to solve it. Detailed simulation results on the IEEE 24-bus system verify the effectiveness of the proposed robust SWD placement model.

Abstract
In this paper, demand-side management (DSM) is performed through demand response aggregators (DRAs) in an uncertain environment within zonal price market framework. The proposed scheme aims to allow cross-border electricity trading and optimize interconnections usage as well as to obtain optimum DR volume from the perspective of the Market Coupling Operator (MCO). The market consists of several zonal price markets as Nominated Electricity Market Operators (NEMO) who run their day-ahead and balancing market internally and communicate the information to the MCO to provide the cooperation with other NEMOs. To this end, a stochastic two-stage model is formulated in which the total operation cost from MCO's viewpoint is minimized. Accordingly, the model aims to consider day-ahead decisions in the first stage and balancing decisions in the second stage. Furthermore, the intermittent nature of renewable sources generation is handled by scenario generation with Monte-Carlo Simulation (MCS) method. NEMOs are physically connected as radial network. Therefore, all relative network constraints are taken into account as a linear power flow for radial networks. The results of the implementation of the proposed model demonstrate the effectiveness of various DR biddings on hourly DR volume, hourly DR cost and power exchange between different NEMOS. © 2019 IEEE.