Abstract
Energy hubs (EHs) are units in which multiple energy carriers are converted, conditioned and stored to simultaneously supply different forms of energy demands. In this research, the objective is to develop a new stochastic model for unit commitment in EHs including an intelligent electric vehicle (EV) parking lot, boiler, photovoltaic (PV) module, fuel cell, absorption chiller, electric heat pump, electric/thermal/ cooling storage systems, with electricity and natural gas (NG) as inputs and electricity, heat, cooling and NG as demands. The uncertainties of demands, PV power and initial energy of EV batteries are modeled with Monte Carlo Simulation. The effect of demand response and demand participation factors as well as effect of EVs and storage systems on EH operation are investigated. The results indicate that thermal demand response is more effective than electric and cooling demand response; as it decreases EH operation cost by 12%, while electric demand response and cooling demand response decrease it respectively by 9.3% and 4.2%. The results show that at low electric/thermal/cooling demand participation factors, an increase in participation factor sharply decreases EH operation cost, while the same amount of increase at higher participation factors leads to a smaller decrease in operation cost. The results also indicate that thermal storage system and cooling storage system have significant effect on reduction of EH operation cost, while the effect of electric storage system is trivial.

Abstract
Increasing the penetration of renewables on prosumers' side brings about operational challenges in the distribution grid due to their variable and uncertain behavior. In fact, these resources have increased the distribution grid net load fluctuation during recent years. In this article, the flexibility-oriented stochastic scheduling of a microgrid is suggested to capture the net load variability at the distribution grid level. In this scheduling, the flexibility limits are set to manage the net load fluctuation at a desirable level for the main grid operator. The uncertainties of load and renewables are considered, and their uncertainties are under control by the risk-averse strategy. Moreover, multiperiod islanding constraints are added to the problem, preparing the microgrid for a resilient response to disturbances. The model is examined on a typical distribution feeder consisting of prosumers and a microgrid. The numerical results are compared for both flexibility-oriented and traditional scheduling of a microgrid at the distribution level. The proposed model reduces the net load ramping of the distribution grid using an efficient dispatch of resources in the microgrid. A sensitivity analysis is also carried out to show the effectiveness of the model.

Abstract
Energy hubs (EHs) are units wherein multiple energy carriers can be converted, stored and conditioned to simultaneously supply different energy demands. In this paper, a new model is proposed for unit commitment in renewable EHs with electric, thermal and cooling demands, different storage systems, combined heat and power (CHP) unit, boiler, electric chiller, absorption chiller, PV module, wind turbine and battery charging station (BCS). Using information gap decision theory (IGDT), day-ahead EH scheduling is done from risk-neutral, risk-averse and risk-seeking perspectives, considering the un-certainties of electric demands, BCS demands, heat demands, cooling demands, PV and wind power and electricity prices. Comprehensive models are used for storage systems considering their degradation, charging loss, discharging loss and storage loss; the ramp-up and ramp-down rate limits, start-up and shut-down costs of CHP, boiler and cooling components are considered. The effect of risk as well as effect of critical cost deviation factor and target cost deviation factor on EH operation cost and schedule of EH components is investigated. The findings indicate that the sensitivity of EH operation cost may be very different with respect to different sets of uncertain input data. The findings also show the significant effect of risk-awareness on schedule of EH components and its operation cost.

Abstract
System flexibility has been introduced as one of the most significant concepts in energy systems, and accordingly it has captured attention. It should be noted that various parameters and equipment, directly and indirectly, affect system flexibility, among which, demand response (DR) programs, distributed energy resources (DERs), and storage systems, are some important examples. In this respect, a comprehensive review of DR and integrated demand response (IDR) programs has been conducted in this chapter, and the impact of such programs on enhancing the flexibility of local energy systems has been thoroughly investigated. The local energy systems, studied in this chapter, include three residential, commercial, and industrial energy hubs, located in a 33-bus network, equipped with renewable energy sources (RES), as well as electrical and thermal energy storage systems. It should be noted that to evaluate the flexibility of the system, the operation problem of energy hubs has been investigated through simulating six different case studies, and the impact of DR/IDR programs, energy storage systems, RESs, and operation mode has been evaluated on operating costs, emissions, and flexibility. The results showed that each of the hubs will have a different reaction to the presence/absence of the mentioned items.

Abstract
This paper presents a scenario-based framework for energy hub (Ell) design considering the variable efficiencies of gas-fired converters, wind turbines and integrated demand response (MR) programs. The proposed hub is able to meet the electrical, heating and cooling demands and is also equipped with a power-to-gas (P2G) system. Electrical, cooling, and heating loads uncertainties have been taken into account and the final problem is modeled as a mixed-integer non-linear programming (MINLP) problem. The P2G system is precisely modeled and its impacts on hub planning, emission, and the efficiency of gas-fired converters are thoroughly investigated. The results demonstrate that the P2G system reduced CO2 emissions by 37.4% by consuming CO2 emitted by gas-fired units. In addition, the results indicate that the P2G system injects hydrogen into the gas-fired units and increases their efficiencies. Therefore, the generation rate of these units has increased and consequently a smaller capacity has been installed for them. Numerical results illustrate that the presence of the P2G system has led to a reduction of 7.7% and 16.2% of investment and operation costs, respectively. Finally, the results indicate that the implementation of the IDR program reduces the installed capacity of the equipment, thereby reducing 3.3% of total cost. Overall, the results prove that the implementation of IDR programs along with the installation of the P2G system lead to reduce costs and CO2 emissions.

Abstract
Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem.