Abstract
Cascaded utilization of natural gas, electric power, and heat could leverage synergetic effects among these energy resources, precipitating the advent of integrated energy systems. In such infrastructures, energy hub is an interface among different energy systems, playing the role of energy production, conversion and storage. The capacity of energy hub largely determines how tightly these energy systems are coupled and how flexibly the whole system would behave. This paper proposes a data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee. Renewable generation and load uncertainties are modelled by a family of ambiguous probability distributions near an empirical distribution in the sense of Kullback-Leibler (KL) divergence measure. The objective is to minimize the sum of the construction cost and the expected life-cycle operating cost under the worst-case distribution restricted in the ambiguity set. Network energy flow in normal operating conditions is considered; demand supply reliability in extreme conditions is taken into account via robust chance constraints. Through duality theory and sampling average approximation, the proposed model is transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm which entails solving only linear program. Case studies demonstrate the effectiveness of the proposed model and method. © 2019 IEEE.

Abstract
The global economic and energy situation implies that all contributions for reducing energy consumption are important and relevant in terms of study and investment analysis. In electrical installations, energy consumption can be reduced by reducing losses in the conductors, associated with the use of electronic ballasts in fluorescent illumination, allowing a better use of the energy and of the installed power, which can be an important issue, particularly when using renewable energies. In this sense, this study presents a new software application that compares and chooses the best investment in the acquisition and installation of electronic ballasts in fluorescent illumination.

Abstract
This paper proposes a stochastic framework to augment the integration of variable renewable energy sources (VRESs) in power system scheduling. In this way, the fast-response capability of gas-fired generator units (GFGUs) and vehicle-to-grid (V2G) capability of electric vehicles (EVs) can play important roles in large-scale integration of VRESs. However, the growth of GFGUs utilization can increase the grade of interdependency between power and natural gas systems. In this condition, the power system tends to demand more reliability and flexibility from the natural gas system, which creates new challenges in power system scheduling. The likely significant growth of EVs can solve this challenge and reduce the correlation between power and natural gas systems, bringing new opportunities for power system scheduling. However, a considerable literature in the field of operation of GFGUs and EVs has only focused on using the hourly discrete time model (HDTM). Undoubtedly, the major limitation of HDTM is its inability to handle the fast sub-hourly dispatch of GFGUs and energy storage capability of EVs. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). The reliability test system with a ten-node gas transmission system has been analysed to show the effectiveness of the proposed problem.

Abstract
The work reported in this paper jointly addresses two major challenges in modern power systems: 1) systematically maximizing wind power generation (WPG) utilization under worst-case uncertainty and 2) employing mixed integer-nonlinear programming (MINLP) in the co-optimization of variable reactance devices (VRD) and transmission switching (TS) in an AC optimal power flow problem (ACOPF). The first challenge is solved by proposing an interval based robust approach to identify the worst-case WPG uncertainty. Similarly, to overcome the second challenge, a tri-level decomposition algorithm is used to decompose the MINLP representation into one consisting of one mixed-integer linear programming (MILP) and two nonlinear programming (NLPs) problems. Finally, the effectiveness and efficiency of the proposed model is shown by analysing results from testing on the modified RTS-96 system.

Abstract
One of the most important reasons of smart grids (SGs) is renewable energies (REs) increase and demand response (DR). Present methods are not practical in the case of high REs presence in SGs and should be revised. The flexibility of production is low in SGs with high REs penetration, and loads should follow the production. The increase in the domestic controllable loads (DCLs) flexibility compensates the low flexibility in production. As a result, DR is essential in SGs with high share of REs. This study presents a new method for DR with high REs penetration. Also, a new formulation for electrical appliances is provided in this study, including washing machine, dish washer and heating/cooling system. In the proposed method, the RE resources produced power is considered in addition to loads condition. The controllable loads are transferred to the time when the difference between load and RE generated power is maximum (considering the consumers' welfare). The results show that applying this method will increase the flexibility of consumption and REs penetration in SGs. The study is performed for the Ekbatan residential complex that includes three smart microgrids and a 63/20 kV substation in Tehran, Iran.

Abstract
During the last decade wood pellets consumption grew rapidly. In this paper the development of the wood pellet production factors for markets in Germany, Sweden and Portugal are compared. Domestic market prices for pellet production factors as well as domestic market prices for pellets variation are analysed. The analysis are based on two model plants, representing the first common technologies for small scale production based on dry sawdust sources, and the second represents large scale production based on wet materials. The results show how differences in costs of raw materials, energy and labour affects wood pellets production. The economic sustainability for European pellet producers depends on their domestic markets as internationally traded pellets are priced lower than their production costs, being Portugal an exception due to lower labour and raw materials costs. Future pellet production will be based on wet raw materials such as logs and wet sawdust. These raw materials are also demanded by pulp and paper or fiberboard industries. The transition from smaller wood pellets plants using dry raw materials to larger plants using wet raw materials, can be expected to follow comparative advantages regarding raw materials, energy costs and economies of scale.