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Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy. [ABSTRACT FROM AUTHOR]

For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. This paper presents an integrated platform, constituted of two main components: a PV simulator and a real-time distribution network simulator. The first, designed and developed following the microservice approach and providing Representational State Transfer web services, simulates real-sky solar radiation on rooftops and estimates the PV energy production. The second, based on a digital real-time power systems simulator, simulates the behaviour of the electricity network under the simulated generation scenarios. The platform is tested on a case study based on real data for a district of the city of Turin, Italy. In the results, the authors show possible applications of the platform for power flow forecasting during real-time operation and for detecting possible voltage and transformers' capacity problems during planning due to high penetration of renewable energy sources. In particular, the results show that the case study distribution network, in the actual configuration, is not ready to accommodate all the generation capacity that can be installed, as, in certain hours of the day and on certain days of the year, the capacity of some transformers is exceeded. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances. [ABSTRACT FROM AUTHOR]

The article discusses the design evolution of electrical outlets and attempts to define the concept of a smart grid. Topics include how electrical outlets were invented to provide lighting fixtures with electricity and then were developed to accommodate multiple electrical devices simultaneously while presenting a standardized way to distribute electricity, why the author considers early Italian outlets as proof that they are used for both function and aesthetic, and how the concept of "The Internet of Things" impacted the design of smart grids.

Highlights: [•] Two GIS driving patterns databases are analysed. [•] The urban travel behaviour in two Italian provinces is derived. [•] Two BEV models and five recharging strategies are assumed. [•] Results show that an urban fleet share between 8% and 28% can be driven only electric. [•] The electric energy demand of this fleet share stays below 5% of that of the province. [Copyright &y& Elsevier]

Highlights: [•] The photovoltaic penetration in the distribution grids is subject to technical limits. [•] The distribution-grid upgrading with high level of PV penetration increases the tariff. [•] The fixed costs in the tariff must be included in the analysis of PV grid parity. [•] The PV grid parity is reality in Germany for commercial and residential users. [•] The PV grid parity is impossible in Italy for apartment-blocks with the current tariff. [Copyright &y& Elsevier]

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]

The integration of Dispersed Generation (DG) is by far the most important and challenging issue that modern power systems are facing nowadays, and is the only way of exploiting Renewable Energy Sources (RES) for electric production. This revolution is running particularly fast in Europe, where significant incentive schemes have been promoted by many Member States in order to match the targets decided by the European institutions. As a consequence of the important share of RES already connected (especially to low voltage and medium voltage networks), new technical challenges have to be faced both at a distribution network level and at a transmission system level. Some of these challenges are covered by Smart grids that represent a new framework for improved management of distribution and transmission networks with attention to interoperability, security, resilience problems, and quality of service (QoS). It is recognized that an intelligent use of Information and Communication Technology (ICT), as enabling technology, is the only approach able to solve new problems arising on energy networks due to larger DG penetration, without hindering system security and QoS.The paper focuses on the Italian case and in particular on the Italian regulatory framework for developing Smart Grids, and describes the technical foundations of the regulatory innovations introduced by the Italian energy regulatory authority (Autorità per l'energia elettrica e il gas - AEEG). After a selection process based on cost/benefit assessment, some demonstration projects for Smart Grid proposed by Distribution System Operators have been awarded with special capital cost remuneration (extra WACC of 2% for 12 years, on top of the ordinary WACC equal to 7% for distribution investments). The smart grid demonstration projects founded by AEEG introduce and test a new advanced management of DG in order to avoid the problems coming from reverse power flowing and maintain the necessary level of security, availability and quality of service. [ABSTRACT FROM AUTHOR]

To foster the development, multiple sources of uncertainty associated with benefits and costs of innovation portfolios from low carbon investment need to be considered. We apply Modern Portfolio Theory to construct efficient portfolios of different low-carbon technology fields. The empirical framework is applied to four countries (i.e., Germany, the United Kingdom, Italy, and France) for six low-carbon technology fields (i.e., renewable energy, smart grid, energy efficiency, sustainable transport, carbon capture and storage, and nuclear power) categorized based on priority areas of the EU's 2008 Strategic Energy Technology Plan. The four main findings are: (1) the highest priority technology fields with the minimum risk portfolio are nuclear power in Germany, renewable energy in the United Kingdom, and energy efficiency in Italy and France, (2) the highest priority technology field with the maximum risk portfolio is sustainable transport in Germany, the United Kingdom, and France, and smart grid in Italy, (3) sustainable transport declines in priority as risk decreases in the portfolios of Germany, the United Kingdom, and France, while smart grid declines in priority as risk decreases in the portfolio in Italy, and (4) the presence of negative correlations over time among expected Return on Investments across energy technology fields improves using the Modern Portfolio Theory framework as risk diversification strategy. Our analysis provides efficient portfolios of low-carbon technologies, which can help shape the overall strategy and coordinate each country's comparative advantage by proposing new laws and policies, monitoring existing ones, and managing budget. • We apply Modern Portfolio Theory to construct efficient portfolios of technologies. • We find the highest priority technology fields with the min and max risk portfolios. • A country has its own strategy for R&D investments based on past information. • The EU should adopt efforts to manage risk inherent in the uncertainties of R&D. [ABSTRACT FROM AUTHOR]

Islands are widely recognised as ideal pilot sites that can spearhead the transition to clean energy and development towards a sustainable and healthy society. One of the assumptions underpinning this notion is that island communities are more ready to engage with smart grids (SGs) than people on the mainland. This is believed to be due to the high costs of energy on islands and the idea that the sense of community and collective action is stronger on islands than on the mainland. This paper presents findings from a survey conducted to assess people's perception of, and readiness to engage with, SG and demand response (DR) in the communities of three islands taking part in a H2020 project called REACT. The main objective of the survey, conducted in 2020, was to inform the recruitment of participants in the project, which is piloting different technologies required for SGs and DR with communities on the three islands. The results show that many island residents are motivated to take part in SG, to engage with energy saving, and are willing to change some energy-related behaviours in their homes. However, the results also indicate that levels of ownership of, and knowledge and familiarity with, the SG and DR related technologies are extremely low, suggesting that the expected uptake of DR in islands might not be as high as anticipated. This brings into question the readiness of island dwellers for the SG, their role in the deployment of such schemes more widely and the validity of the assumptions often made about island communities. This has significant implications for the design of SGs and DR solutions for islands, including devoting sufficient efforts to build knowledge and awareness of the SG, investing in demonstration projects for that purpose and tailoring interventions based on island communities' motivations. [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]

Recent strategic policies and regulations dealing with market liberalization and decarbonization plans, such as the European directives contained in the recent EU Clean Energy for All Europeans Package, are seeking to promote new roles for citizens in the management of the self-produced renewable energy and the development of local energy markets. In this context, this paper aims at presenting the planning actions for the transition of the current passive distribution system of the Municipality of Berchidda (Italy) towards a smart local energy community. This planning study represents the first stage of a development action financed by the Sardinian Region, whose Regional Energetic and Environmental Plan identifies the Municipality of Berchidda as a priority area to focus the experimental actions for innovative smart grids and intelligent energy management. The project, named "Berchidda Energy 4.0", focuses on increasing the energy efficiency of the community by boosting local renewable generation production and maximizing its self-consumption, also with the support of storage systems, as well as increasing the active involvement of the consumers that will be equipped with a smart home automation system for demand response applications. [ABSTRACT FROM AUTHOR]