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Utilities are increasingly interested in integrating energy efficiency and demand response measures as a strategic approach that aims to achieve an increasingly reliable, dynamic, and integrated electric system. The concepts of energy efficiency (EE), demand response (DR), and system reliability (ESR) have been mainly studied from a different perspective, dealing only with the interaction between EE and DR and between EE and ESR. This paper aims to improve knowledge and demonstrate the validity of EE interventions (EEIs) through the use of a tool for evaluating the benefits of energy efficiency measures (EEMs) in the environmental sphere and in terms of DR and ESR. In order to evaluate this interaction, a quantitative method is proposed and validated on real results in an Italian case scenario, taking into account the analysis of smart meters data for residential users located in southern Italy in Calabria. Based on the collected data, an aggregation of users was evaluated with real hourly profiles covering the period from January 2020 to August 2020. These results show how efficient energy use may affect DR resource availability and power systems reliability. In conclusion, the authors justify that a more reliable electric system is ready for new types of EE financing systems based on pay-for-performance contracts. [ABSTRACT FROM AUTHOR]

Electricity from renewable energy sources represents the most promising way to decarbonize energy systems. A grid connection of car Electricity Storage Systems (ESSs) represents an opportunity to tackle issues regarding electricity production non-programmability, only if sufficiently smart bi-directional Vehicle to Grid technologies (V2G) are widely implemented. Fully Bi-directional grid capabilities are still poor and must be increased, both physically and in terms of management and billing possibilities (in the so-called smart-grid paradigm). However, some V2G technologies may be already implemented in smaller individual contexts: so-called Vehicle to Home, V2H technologies. Starting from these considerations, within the frame of an Italian publicly funded research project, the authors categorized and described many possible application contexts and developed an open-source dynamic simulation (fully available under request for the scientific community) to identify most promising conditions. To this aim, they also synthetized and tested an effective energy optimization algorithm which will soon be implemented on a prototypal wireless V2H device, built by ENEA in cooperation with Cassino University, in Italy. The performances of the system were assessed evaluating electricity auto-consumption and home auto-feeding ratios. Simulations show that very relevant performances can be obtained, up to the values 69% for electricity auto-consumption and 82% of home auto-feeding. [ABSTRACT FROM AUTHOR]

Abstract: Government initiatives in several developed and developing countries to roll-out smart meters call for research on the sustainability impacts of these devices. In principle smart meters bring about higher control over energy theft and lower consumption, but require a high level of engagement by end-users. An alternative consists of load controllers, which control the load according to pre-set parameters. To date, research has focused on the impacts of these two alternatives separately. This study compares the sustainability impacts of smart meters and load controllers in an occupied office building in Italy. The assessment is carried out on three different floors of the same building. Findings show that demand reductions associated with a smart meter device are 5.2% higher than demand reductions associated with the load controller. [Copyright &y& Elsevier]

One of the most common demand side management programs consists of time-of-use (TOU) tariffs, where consumers are charged differently depending on the time of the day when they make use of energy services. This paper assesses the impacts of TOU tariffs on a dataset of residential users from the Province of Trento in Northern Italy in terms of changes in electricity demand, price savings, peak load shifting and peak electricity demand at sub-station level. Findings highlight that TOU tariffs bring about higher average electricity consumption and lower payments by consumers. A significant level of load shifting takes place for morning peaks. However, issues with evening peaks are not resolved. Finally, TOU tariffs lead to increases in electricity demand for substations at peak time. [ABSTRACT FROM AUTHOR]

The reduction of water losses is a crucial aspect to be addressed in Water Distribution Networks (WDNs) since they account for almost 50% of the water supply globally. To have a better figure of the involved water volumes, the District Metered Area (DMA) approach has been used in a WDN of a town located in the center of Italy (Osimo). In particular, a complete methodology for calculating and then detecting the water losses is described and applied using the end-users' water volumes consumptions obtained by inspecting water bills and the supplied water through on-field measurements. These values have been subsequently used to develop and calibrate a hydraulic model of the district under investigation through EPANET, thus allowing the WDN management to estimate both flow rates and pressures to assess the zones where smart meters have to be installed. Water losses have been controlled by monitoring the flow rates through smart meters with an innovative 868 MHz remote reading infrastructure, thus leading to 5,558.8 m3 of avoided water losses (12.5% less than the initial case) that corresponds to an energy reduction and an economic saving of 7,332 kWh and 1,857 € in the last three-month period of the year 2020, respectively. • The district metered area approach was used to reduce water leakages in WDNs. • On-field measurements of a WDN in Italy were performed to validate the method. • A numerical model was also built in EPANET® in order to resemble the analysed WDN. • Smart meters and a fixed infrastructure were used in the selected water district. • Energy (7.3 MWh) and (2 k€) economic savings in the last three month-period of 2020. [ABSTRACT FROM AUTHOR]

Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level. We take inspiration from the technology adopted in Italy, where the national regulatory authority actively supported the definition of a solution agreed upon by all the involved stakeholders. In this context, smart meters are enabled to convey data to low voltage end users through a power line communication channel (CHAIN 2) in near real-time. The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99% success rate). On the other hand, it defines a classification framework (I2MA) for post-metering value-added services, in order to categorize each use case based on both level of service and expected benefits, and understand its maturity level. As an example, we apply the methodology to the 16 use cases defined in Italy. The lessons learned from the regulatory, technological, and functional approach of the Italian experience bring us to the provision of recommendations for researchers and industry experts. In particular, we argue that a well-functioning post-metering value-added services' market can flourish when: (i) distribution system operators certify the measurements coming from smart meters; (ii) national regulatory authorities support the technological innovation needed for setting up this market; and (iii) service providers create customer-oriented solutions based on smart meters' data. • Smart meters' data can open a market of post-metering value-added services. • Access to electricity exchange data must be granted through a dedicated channel. • CHAIN 2 is a reliable and effective PLC-based communication channel. • Post-metering use cases can be classified based on level of service and benefits. • The Italian post-metering market structure can be regarded as a successful example. [ABSTRACT FROM AUTHOR]

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results. [ABSTRACT FROM AUTHOR]