Abstract
This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem. In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal scheduling of generating units can improve the economic operation of the multi-carrier energy hub while meeting the system's constraints. © 2017 IEEE.

Abstract
This paper proposes a long-term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into master and slave subproblems where the master subproblem is solved using a heuristic optimization algorithm and slave subproblems are solved using general algebraic modeling system, which is a well-known software with powerful mathematical solvers. The proposed heuristic algorithm is a combination of the intelligent particle swarm optimization and chaotic searching technique. Finally, the proposed model is simulated using 3 case studies including 6-bus Garver test system, IEEE 24-bus, and modified IEEE 118-bus test systems to validate the effectiveness of the long-term planning framework while the simulation results are compared to those obtained from classic genetic algorithm (GA-Classic) and classic particle swarm optimization (PSO-Classic) to verify the efficiency of the technique used in this paper. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract
This paper presents a robust optimisation framework for long-term composite generation and transmission expansion planning problem which considers inherent uncertainties such as load growth, fuel cost and renewable energy output uncertainties. In this paper, a bi-level robust optimisation framework is proposed to accommodate wind output uncertainty in line with the uncertain demanded loads and uncertain fuel cost. The addressed optimisation problem is modelled as a mixed-integer optimisation framework with the objective of providing a robust expansion plan while maintaining the minimum cost expansion. In order to evaluate the robustness of each plan, an off-line Lattice Monte Carlo simulation technique is adopted in this study. The validity of the proposed method is examined on a simple six-bus and modified IEEE 118-bus test system as a large-scale case study. The simulation results show that the presented method is both satisfactory and consistent with expectation. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

Abstract
This paper proposes a robust optimization framework for energy hub management. It is well known that the operation of energy systems can be negatively affected by uncertain parameters, such as stochastic load demand or generation. In this regard, it is of high significance to propose efficient tools in order to deal with uncertainties and to provide reliable operating conditions. On a broader scale, an energy hub includes diverse energy sources for supplying both electrical load and heating/cooling demands with stochastic behaviors. Therefore, this paper utilizes the Information Gap Decision Theory (IGDT) to tackle this uncertainty as an efficient robust optimization tool with low complexity to ensure the optimal operation of the system according to the priorities of the decision maker entity. The proposed optimization framework is also implemented on a benchmark energy hub which includes different energy sources and evaluated under different working conditions. © 2017 IEEE.

Abstract
The concept of the water, energy, food nexus is extremely relevant to Kazakhstan as the country faces population growth, economic progress and environmental challenges such as water scarcity, desertification, and climate change. Furthermore, poor sectoral coordination and inadequate infrastructure have caused unsustainable resource use and threaten the long-term water, energy and food security in Kazakhstan. This study presents the key elements required to implement a nexus-based resource management approach in Kazakhstan, by identifying linkages between water resources, energy production and agriculture. A case study illustrates how this methodology can be applied to quantify linkages between the water and energy sectors.

Abstract