Abstract
In recent years there has been a series of documents such as the European Strategy 20-20-20 to address the issue of energy efficiency in various sectors of activity. The objective is to reduce 20% of energy consumption, 20% of GHG emissions (Greenhouse Gases) and 20% of the energy consumed from renewable sources. Public lighting participates with 2.3% in global electricity consumption, so all contributions to the reduction in energy consumption will be relevant. Decision support in the investment analysis on efficient and sustainable street lighting allows a better use of the installed power. Hence, this paper deals with the reduction of losses in cables of a street lighting installation, depending on the luminaire used, presenting both simulation and experimental results. The economic choice of cables losses will allow improving the efficiency of the street lighting in general, providing also an optimal cost/benefit relationship. Moreover, real-time data acquisition systems of the equipment's consumption can be integrated into a collective awareness system.

Abstract
A successful deployment of electrical energy storage (EES) in current electricity grid systems is a plausible episode in several power systems given the outstanding technical, economic and environmental benefits that EES provides. Also, more distributed resources are becoming key actors in remotely located renewable energy based and poorly interconnected island grid systems. Healing these fragile grid systems in islands requires devising and promoting a robust grid system operation. Such move should go hand in hand with an increasing distributed energy resource use to guarantee sustainable renewable energy integration into these systems. In the present document, EES technologies and applications have been compiled, where special emphasis has been given on islands, studying their particular requirements and technology appropriateness on operating project experiences around the world, from which some lessons can be learned. Conclusions about EES technologies' general suitability on island systems are duly drawn throughout the content of this paper.

Abstract
The growing trend of variable energy source integration in power systems (especially at a distribution level) is leading to an increased need for flexibility in all levels of the energy flows in such systems: the supply, the network and the demand sides. This paper focuses on a viable flexibility option that can be provided by means of a dynamic network reconfiguration (DNR), an automatic changing of line statuses in response to operational conditions in the system. The ultimate aim is to assess the impacts of such flexibility on the utilization levels of variable power sources (mainly, solar and wind) integrated at a distribution level. To perform this analysis, a stochastic mixed integer linear programming (S-MILP) operational model is developed in this work. The objective of the optimization problem is to minimize the sum of the most relevant cost terms while meeting a number of model constraints. The proposed model dynamically finds an optimal configuration of an existing network system in accordance with the system's operational conditions. The operation scale in the current work is one day, but with the possibility of an hourly reconfiguration. The standard IEEE 41-bus system is employed to test the proposed model and perform the analysis. Numerical results generally show that DNR leads to a more efficient utilization of renewable type DGs integrated in the system, reduced costs and losses, and a substantially improved system performance especially the voltage profile in the system.

Abstract
It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations.

Abstract
Multi-energy systems (MES) are considered various energy carriers and energy players in an integrated energy model. Vast amount of decision making data is gathered in these systems that cannot be processed by conventional methods. Cloud-based computing is an opportunity to develop these kinds of integrated and efficient approaches. Developing mathematical models that can be compatible with cloud-based engineering systems will help decision makers to enhance the system agendas in short to long term studies. In this paper, the energy hub approach is developed to consider electric reserve ancillary service in MES. The reserve is modeled as a virtual energy output that can be injected into the upstream network. The reserve service is defined for electric energy converters and storages, comprehensively. Therefore, the energy hub mathematical model is developed and new elements are added to the input and output vectors and system conversion matrix. For energy converters, reserve is defined as the capability of the converter to increase its output service to its maximum operational limits. Moreover, for electric storages this capability is also restricted by storages' state of charges. The numerical results demonstrate the importance of reserve considerations in MESs and allow assessing the proficiency of the proposed model.

Abstract
In this paper, the impacts of type, size and placement of renewable energy resources (RERs) on the offering strategy of Plug-in Electric Vehicle (PEV) parking lots is investigated. To this end, a stochastic programming model is proposed to optimize the behavior of a PEV parking lot in energy and reserve markets, considering the interaction with RESs. On this basis, the PEV parking lot is modeled in a distribution system consisting of wind and photovoltaic power producers. Moreover, the stochastic model consists of uncertainty characteristics of PEV owners' behavior, wind and photovoltaic power generation. Several numerical studies are analyzed to indicate how the factors of the RESs affect the optimal offering strategy of the parking lots.