Abstract
Over the last few years, mechanical biological treatment systems for municipal solid waste have been introduced in many European countries. In most cases, this was driven by the European Union Landfill Directive, which requires the diversion of biodegradable municipal waste from landfill to alternative processes. Although this type of treatment allows energy recovery from municipal solid waste, the process of mechanical biological treatment appears to be an intensive energy consumer, due to high demand of electricity consumed by process equipment. This paper presents the main results of an energy audit performed to a Mechanical Biological Treatment facility in Portugal, which due to the amount of energy consumed must comply with the Portuguese Program called Intensive Energy Consumption Management System – SGCIE. The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. To implement actions that improve energy efficiency, it is necessary for the facilities operation to be associated with an effective energy management methodology, as well as an efficient facilities management procedure. The implementation of any energy management system should start with an energy audit, which was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns and sector/equipment load profiles. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. Results shown that there is a considerable potential for reducing energy consumption and greenhouse gases emissions on Mechanical Biological Treatment units. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.

Abstract
This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM. © 2014 IEEE.

Abstract
This study introduces a new theoretical interruption model for assessing more accurately the moment in time when interruptions of electricity customers are likely to occur. Recordings of short and long interruptions from two power supply systems are analysed and the similarity between their patterns is identified and then used to introduce a general interruption probability distribution model, defined in stages as multi-zone theoretical curves. The effectiveness of the proposed theoretical interruption model is firstly verified for a basic test system supplying an aggregate load point whose power profiles (residential, commercial, industrial and mixed load) are engaged in assessing the energy not supplied, and afterwards for a typical UK power supply system consisting of about 15 000 electricity customers. The results show that a correct representation of the moment of interruption performed with the proposed model leads to completely different results than those obtained based on the conventional assumption that the time when interruption occurs is given by a known probability distribution. Moreover, comparisons against reported figures of reliability indices determine the most suitable probability distribution that shall be used to model the initial conditions of the Monte Carlo simulation and accompany the proposed theoretical model throughout the simulation process. © The Institution of Engineering and Technology 2014.

Abstract
Legislation defined to protect domestic and non-domestic customers from long durations of interruptions includes additional requirements to system's reliability-related performance that distribution network operators (DNOs) must consider in planning the operation and maintenance process of power supply systems. DNOs are required to restore the supply to interrupted customers that fall into "unprotected" customer class within a given period of time, otherwise penalties are applied. In order to meet these requirements, comprehensive strategies must be defined based on upfront analyses. Accordingly, this paper proposes a deterministic algorithm for estimating DNOs' risk of experiencing interruptions with durations above imposed targets. Besides the Regulator-defined legislation, security of supply requirements are engaged in the development of the proposed methodology. Failure analysis of network components is used to identify interrupted customers that are grouped into power demand classes such that the duration of interruptions can be addressed following the security of supply requirements. Moreover, the penalty times defined by the Energy Regulator are engaged in the analysis and used as thresholds to quantify the penalty risk that DNOs are exposed to. The proposed methodology is applied to a typical UK distribution system, whose average reliability performance is also considered in the analysis. © 2013 Elsevier Ltd. All rights reserved.

Abstract
Tins paper presents a multi-energy modelling environment developed to simulate and optimize urban energy strategies, with a focus on urban energy infrastructure planning. A multi-scale approach is applied for modelling urban energy networks, considered as the backbone of urban energy infrastructure. This is complemented by the modelling of energy demand (to consider the costs and impacts of demand-side measures. The model is also linked to a set of optimization techniques in order to provide answers to urban energy infrastructure planning issues. Two case study applications follow the presentation of the chosen modelling principles to illustrate the type of answers that can be provided by the proposed modelling and optimization approach. Copyright © 2011 by IPAC'11/EPS-AG.

Abstract
The recent development of electric vehicles (EVs) has brought a new set of problems regarding their integration in power networks, particularly in terms of the potential growth of peak load. The peak growth leads to the increase of losses and braches charging and to voltage drops. Conversely, optimizing EV charging policy creates new opportunities for both network safety and energy trading through the markets. This paper presents a multi-level framework combining two representations of a medium voltage (MV) network in order to optimize the EV charging policy. A minimizing cost approach is set, modeling day-ahead markets, and taking into account losses. The proposed methodology is tested on a typical MV network.