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Using energy hub concept has been increased as a way of implementing multi-carrier energy systems in recent years. It still needs more study and examination in both modeling and operating concerns. In this regard, in this paper optimal operation of an energy hub in restructured power system is investigated. Operation includes decision making to procure large consumer energies. A model is proposed considering an energy hub with electrical and gas energy as inputs and electrical and heat energy as outputs. Electrical energy is procured in various ways including: buying from bilateral contract, buying from pool market and generating by a Combined Heat and Power (CHP) unit. Heat demand is also procured by a CHP and furnace. Buying from pool market is faced with uncertainty. In addition, electrical loads have uncertainty, constantly. These uncertainties complicate the decision making process and cause unfavorable conditions. Conditional Value at Risk (CVaR) as a known risk index is used in order to decrease unfavorable states. Real data are utilized to simulate, as much as possible. Efficiency of proposed model is verified by various simulations.

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Optimal reactive power dispatch plays an important role in economic operation of network by reducing the loss. In this paper, the teaching learning based optimization algorithm is used to optimal reactive power dispatch and voltage control. This algorithm is an evolutionary and population based algorithm which has a great capability to solve the nonlinear problems. This problem is formulated as a mixed integer nonlinear problem including both continues and discrete variables. Optimal solution of problem contains set of generators voltage, tap changers and compensative reactive components. In the proposed approach, using real wind speed data and considering the wind uncertainty, the two points estimate power flow is used to model the uncertainties. The proposed method has been implemented on 57-bus IEEE test case. A comparing has been done between the teaching learning based optimization algorithm and particle swarm optimization and differential evolution algorithms in order to verify the efficiency of the proposed algorithm. The results demonstrate the efficiency of the proposed method in reducing losses and handling the constraints.

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Energy hub is a concept relates various energy carriers which has been expanded in recent years. Operation of energy hub is often attended with uncertainties. Therefore, this paper presents a linear model based on information gap decision theory (IGDT) to solve the energy hub problem. This approach can consider plenty of uncertainties, simultaneously. Energy hub under study includes a combined heat and power (CHP) unit, a boiler, a wind turbine and trades electricity with day ahead and real time markets as well as natural gas network. Different uncertainties affect the problem. Here, the uncertainties of real time and day ahead electricity prices, wind turbine generation and natural gas prices are taken into account. Since, nature of IGDT is a bi-level optimization problem, it has been linearized using the Karush Kohen Tucker (KKT) conditions and strong duality theorem and then, it is solved. The simulations have been done on the mentioned case study verifying the efficiency of the proposed model.