Abstract
The smart city seeks a highly interconnected, monitored and globally optimized environment to profit from the synergies among systems such as energy, transports or waste management. From an energy perspective, transport systems and facilities are among the bigger energy consumers inside cities. However, despite the research available on such systems, few works focus on their interactions and potential synergies to increase their efficiencies. This paper address this problem by assessing the benefits of the interconnection and joint management of different energy systems in a smart city context. This is done using a linear programming problem, modelling a district with residential loads, distributed energy resources (DER) and electric vehicles (EV), which are also connected to an electrical metro substation. This connection allows to store the metro regenerative braking energy into EVs' batteries to be used later for other trains or for the EVs themselves. The objective of the linear programming model is to find the optimal planning and operation of all the considered systems, achieving minimum energy costs. Therefore, the main contributions of this paper are the assessment of synergies of the interconnection of these systems and the detailed analysis of the impact of different EV penetration levels. Results show important economic benefits for the overall system (up to 30%) when the investments and its operation are globally optimized, especially reducing the metro energy costs. Also, analysing the energy transfers between metro-EV, it is evident that the metro takes advantages of the cheaper energy coming from the district (through the EVs), showing the existence of “opportunistic” synergies. Lastly, EV saturation points (where extra EVs represent more load but do not provide additional useful storage to the system) can be relatively small (200–300 EVs) when the energy transfer to the metro electrical substation is restricted, but it is also reduced by the presence of DER systems. © 2017 Elsevier Ltd

Abstract
Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results.

Abstract
This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation.

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.