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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.

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

The hygrothermal behaviour of an internally insulated historic wall is still hard to predict, mainly because the physical characteristics of the materials composing the historic wall are unknown. In this study, the hygrothermal assessment of an internally thermal insulated masonry wall of an historic palace located in Ferrara, in Italy, is shown. In situ non-destructive monitoring method is combined with a hygrothermal simulation tool, aiming to better analyse and discuss future refurbishment scenarios. In this context, the original U-value of the wall (not refurbished) is decreased from 1.44 W/m2K to 0.26 W/m2K (10 cm stone wool). Under the site specific conditions of this wall, not reached by the sun or rain, it was verified that even in the absence of vapour barrier, no frost damage is likely to occur and the condensation risk is very limited. Authors proposed further discussion based on simulation. The results showed that the introduction of a second gypsum board to the studied technology compensated such absence, while the reduction of the insulation material thickness provides a reduction of RH peaks in the interstitial area by 1%; this second solution proved to be more efficient, providing a 3% RH reduction and the avoidance of further thermal losses.

This study presents the results of the biomass pyrolysis process focusing on biochar production and its potential energetic (as solid fuel) and material (as adsorbent) applications. Three kinds of biomass waste were investigated: wheat straw, spent coffee grounds, and brewery grains. The pyrolysis process was carried out under nitrogen atmosphere at 400 and 500 °C (residence time of 20 min). A significant increase in the carbon content was observed in the biochars, e.g., from 45% to 73% (at 400 °C) and 77% (at 500 °C) for spent coffee grounds. In addition, the structure and morphology were investigated using scanning electron microscopy. Thermal properties were studied using a simultaneous thermal analysis under an oxidising atmosphere. The chemical activation was completed using KOH. The sorption properties of the obtained biochars were tested using chromium ion (Cr3+) adsorption from liquid solution. The specific surface area and average pore diameter of each sample were determined using the BET method. Finally, it was found that selected biochars can be applied as adsorbent or a fuel. In detail, brewery grains-activated carbon had the highest surface area, wheat straw-activated carbon adsorbed the highest amount of Cr3+, and wheat straw chars presented the best combustion properties.

The European Union’s energy policy favors increasing the share of renewable energy in total energy production. In this context, the co-digestion of various waste streams seems an interesting option. This study aimed to determine the effect of selected pretreatment methods on the efficiency and kinetics of the co-digestion process of poultry manure with sewage sludge and organic waste. This research was carried out in four stages: (1) the selection of the third component of the co-digestion mixture; (2) the determination of the most favorable inoculum-to-substrate ratio for the co-digestion mixture; (3) the selection of the most favorable pretreatment parameters based on changes in volatile fatty acids, ammonium nitrogen, extracellular polymers substances (EPS) and non-purgeable organic carbon (NPOC); and (4) the evaluation of anaerobic co-digestion based on the result of the BMP tests and kinetic studies. All the pretreatment methods increased the degree of organic matter liquefaction as measured by the NPOC changes. Waste with a high fat content showed the highest methane potential. The addition of grease trap sludge to feedstock increased methane yield from 320 mL/g VSadd to 340 mL/g VSadd. An optimal inoculum-to-substrate ratio was 2. The pretreatment methods, especially the thermochemical one with NaOH, increased the liquefaction of organic matter and the methane yield, which increased from 340 mL/g VSadd to 501 mL/g VSadd (trial with 4.5 g/L NaoH).

In fusion devices, such as European Demonstration Fusion Power Reactor (EU DEMO), primary neutrons can cause material activation due to the interaction between the source particles and the targeting material. Subsequently, the reactor’s inner components become activated. For safety and safe performance purposes, it is necessary to evaluate neutron-induced activities. Activities results from divertor reflector and liner plates are presented in this work. The purpose of liner shielding plates is to protect the vacuum vessel and magnet coils from neutrons. As for reflector plates, the function is to shield the cooling components under plasma-facing components from alpha particles, thermal effects, and impurities. Plates are made of Eurofer with a 3 mm layer of tungsten, while the water is used for cooling purposes. The calculations were performed using two EU DEMO MCNP (Monte Carlo N-Particles) models with different breeding blanket configurations: helium-cooled pebble bed (HCPB) and water-cooled lithium lead (WCLL). The TENDL–2017 nuclear data library has been used for activation reactions cross-sections and nuclear reactions. Activation calculations were performed using the FISPACT-II code at the end of irradiation for cooling times of 0 s–1000 years. Radionuclide analysis of divertor liner and reflector plates is also presented in this paper. The main radionuclides, with at least 1% contribution to the total value of activation characteristics, were identified for the previously mentioned cooling times.