Abstract
This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision making of energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by such uncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework and the energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained.

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
Microgrids are often designed as energy systems that supply electrical and thermal loads with local resources such as combined heat and power units, renewable energy sources, diesel generators, and others. However, increasing interaction between natural gas and electrical systems, in addition to increased penetration of natural gas fired units gives rise to both opportunities and challenges in microgrid operation scheduling. In this paper, the energy hub concept is used to construct a scenario-based model for the optimal scheduling of electrical and thermal resources in a microgrid with integrated electrical and natural gas infrastructures. The objective function of the proposed model minimizes the expected operation costs while considering all network constraints and uncertainties. The natural gas and electricity flow equations are linearized and formulated as a mixed-integer linear programming problem. Scenarios associated with stochastic variables such as renewable generation and electrical and thermal loads are generated using the corresponding probability distribution functions and reduced using a scenario reduction technique. The proposed model is applied to an energy hub-based microgrid and the simulation results demonstrate the effectiveness of the approach. Furthermore, the benefits of implementing electrical and thermal demand response schemes are quantified. Also, more in-depth analyses for this network-constrained model are performed, including natural gas flow rate variations, natural gas pressures, power flow, and nodal voltages.

Abstract
In this paper, a new method for optimal sizing of distributed generation (DG) is presented in order to minimize electricity costs in smart microgrids (MGs). This paper presents a study of the effect of wholesale electricity market on smart MGs. The study was performed for the Ekbatan residential complex which includes three smart MGs considering high penetration of renewable energy resources and a 63/20 kV substation in Tehran, Iran. The role of these smart MGs in the pool electricity market is a price maker, and a game-theoretical (GT) model is applied for their bidding strategies. The objective cost function considers different cost parameters in smart MGs, which are optimized using particle swarm optimization (PSO). The results show that applying this method is effective for economic sizing of DGs.

Abstract
This paper presents an AC optimal power flow (AC-OPF) model including flexible resources (FRs) to handle uncertain wind power generation (WPG). The FRs considered are thermal units with up/down re-dispatching capability, corrective topology control (CTC), and thyristor-controlled series capacitors (TCSC). WPG uncertainty has been modeled through a proposed interval-based robust approach, the goal of which is to maximize the variation range of WPG uncertainty in power systems while maintaining an adequate reliability level at a reasonable cost with the aid of FRs. However, utilization of FRs (especially CTC and TCSC devices) is limited due to the difficulty of their incorporation in the AC-OPF. The optimization framework of the full FR-augmented AC-OPF problem is a mixed-integer nonlinear programming (MINLP) in which the solution for large-scale systems is very hard to obtain. To solve this issue, this paper uses a two-stage decomposition algorithm to decompose the MINLP representation into a mixed-integer linear program (MILP) and a nonlinear program (NLP). Finally, the robust AC-OPF model with FRs is implemented and tested on a 6-bus and the IEEE 118-bus test systems to evaluate its efficiency and performance.

Abstract
Distributed energy resources (DERs) change the supply-demand balance of power systems. To better manage these resources, they are operated as micro-grids (MGs) in both grid-connected and standalone modes. In the presence of MGs, the decision making framework in power systems is changing from the centralized structure into the decentralized one. In such framework, modeling the operation problem of MGs with the participation in energy and reserve markets in the presence of uncertainties is considered as an important challenge. Most of the studies use probability distribution functions (PDFs) to model the uncertain parameters. In this paper, the operation problem of a grid-connected MG is modeled while the MG operator (MGO) faces with uncertainties of renewable energy sources (RESs) without considering their PDFs. For this purpose, an information gap decision theory (IGDT)-based approach is employed to model the uncertain behavior of RESs as well as to control the risk level of the MGO on the optimal scheduling of DERs. To investigate the effectiveness of the model, a modified 15-bus low voltage MG us used as a test system. The results show that optimal decisions of the risk-averse MGO are different from those of a risky one. © 2019 IEEE.