Abstract
Increasing penetration of variable nature wind energy sources (WES) due to environmental issues, impose several technical challenges to power system operation as it is difficult to predict its output power because of wind intermittency. Power generation based on gas turbine with fast starting fitness and high ramping could better deal with inherent uncertainties comparing to other power generation sources. Considering natural gas network constraints impacts flexibility and participation of gas-fueled generation units on reserve and energy markets. Hence, the use of flexible energy storage system can reduce renewable sources alternation and the gas network limitation effects on power system operation cost. This paper proposes a two-stage stochastic network-constrained unit commitment based market clearing model for energy and reserve products in coordinated power and gas networks with the integration of compressed air energy storage (CAES) and WES. A six-bus electric system with a six-node gas system and IEEE reliability test system (RTS) 24-bus electric system with a ten-node gas network are considered to perform numerical tests and demonstrate the performance of the proposed model. The effect of including the constraints of the gas system on the power system operation cost in day-ahead co-optimization of energy and reserve products is evaluated using numerical studies. Also, including CAES reduces the power system operation cost, load shedding and wind spillage. Crown Copyright

Abstract
Exposure to extreme weather conditions increases power systems' vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.

Abstract
Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

Abstract
The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across thetwo systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

Abstract
Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price-elastic demand curve that relies on several factors such as estimation of a customer's elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price-elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer's disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

Abstract
Demand response (DR) programs offer tremendous opportunities to those who have concerns about the future of energy. Since the DR strategies facilitate new technologies to take part in the power systems, the idea of spreading of electric vehicles (EVs) attracts researchers around the world. In this study, an optimal energy management strategy for EV parking lots considering peak load reduction based DR programs is built in stochastic programming framework, denoted by EV parking lot energy management (EV-PLEM). The proposed EV-PLEM aims to maximize the load factor during the daily operation of an EV parking lot taking into account the uncertain behavior of EVs, such as arrival and departure times together with the stochasticity of the remaining state-of-energy of EVs when they reach the parking lot. A set of case studies is conducted to validate the effectiveness of the suggested EV-PLEM concept, and credible results and useful findings are reported for the cases in which the EV-PLEM is implemented.