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.

Abstract
Phasor measurement unit (PMU) provides beneficial information for dynamic power system stability, analysis, and control. One main application of such useful information is data-driven analysis and control. This article presents an approach for optimal signal selection and controller structure determination in PMU-based power system stabilizer (PSS) design. An algorithm is suggested for selecting the optimal input and output signals for PSS, in which a combination of system clustering, modal analysis, and principal component analysis techniques is used. The solution for the optimal PSS input-output selection is determined to increase the observability and damping of the power system. The approach can efficiently reduce the number of input-output signals, while the overall performance is not deteriorated. Then, a linear matrix inequality-based technique is elaborated to design the PMU-based PSS parameters. The stabilizer design approach is formulated as a convex optimization problem and the appropriate stabilizer for pole allocation of the closed-loop model is designed. This method is simulated on two sample power systems. Also, to compare the results with the previous methods, the system is simulated and the results of two previously developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which decreases the number of required PMUs, while the system damping is not affected.

Abstract
Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The com-bination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is pre-sented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator. Superscript/Subscript Available