• Energy Research
• IT close

Municipal wastewater treatment plants (MWWTPs) are essential infrastructures in any urban context, but they may be considered as a potential source of greenhouse gas (GHG) emissions and should be coherent with European Union (EU) policy on energy efficiency. This study presents a sustainability evaluation of four Italian and Romanian MWWTPs in terms of energy efficiency and greenhouse gas emissions using Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool software. The obtained results indicated that biogas recovery improved energy performances, while the largest contributions in terms of GHG emissions were in all cases caused by energy consumption and methane produced during wastewater treatment. The Romanian plants exhibited higher GHG emissions, compared to the Italian plants, mainly because of the different values of national conversion factors for grid electricity (0.41 kg CO2/kWh for Italy and 1.07 kg CO2/kWh for Romania). Two scenarios aimed at enhancing the overall sustainability were hypothesized, based on increasing the serviced population or energy efficiency, achieving significant improvements. A sustainability assessment of MWWTPs should be adopted as a useful tool to help water utilities to introduce low-energy, low-carbon management practices as well as being useful for policy recommendations.

Regional and local decision-makers still require relevant information and training in order to establish long-term strategies and to contribute to national and supranational energy and climate targets. As an example, a widespread participation of local authorities to comply with the Italian long-term building renovation strategy has not occurred so far. Thus, the overall target, annual 1% floor area of new or deeply renovated buildings to the nearly zero-energy building (nZEB) standard by 2020 (PanZEB 2015), proves to have been disregarded to date. Evidence-based, data-enabled assessment of the building stock and of its relationship with the energy system as a whole at a capillary level is crucial to this extent. In Italy, various building databases are already being used with the ultimate purpose of EPBD implementation and to track and record incentives for public and private building renovation. These datasets have an untapped potential for local energy planning that could be released from wider integration, also including energy consumption data and smart-metering data. Moreover, the regulatory landscape is changing toward an interaction of the building with the user, the energy grid and other buildings in a dynamic and functional way. Within this context, the paper will investigate how integrated data could unlock the value of a more evidence-based planning starting from the DIPENDE integrated dataset, a REQUEST2ACTION (IEE 2014–2017) pilot project combining data from energy performance certificates (EPCs) with bottom-up information on building renovation, and other data in order to support decision making at different territorial scales.

This paper focuses on the experimental validation of a building energy performance simulation tool by means of a comparative analysis between numerical results and measurements obtained on a real test room. The empirical tests were carried out for several months under variable weather conditions and in free-floating indoor temperature regime (switched off HVAC system). Measurements were exploited for validating an in-house simulation tool, implemented in MatLab and called DETECt, developed for dynamically assessing the energy performance of buildings. Results show that simulated indoor air and surface room temperatures resulted in very good agreement with the corresponding experimental data; the detected differences were often lower than 0.5 °C and almost always lower than 1 °C. Very low mean absolute and percentage errors were always achieved. In order to show the capabilities of the developed simulation tool, a suitable case study focused on innovative solar radiation high-reflective coatings, and infrared low-emissivity materials is also presented. The performance of these coatings and materials was investigated through a comparative analysis conducted to evaluate their heating and cooling energy saving potentials. Simulation results, obtained for the real test cell considered as equipped with such innovative coatings and material, show that for the weather zone of Naples a 5% saving is obtained both in summer and in winter by simultaneously adopting a high-reflectance coating and a low- emissivity plaster for roof/external walls and interior walls, respectively.

Abstract In this work we develop an agent-based model that offers an alternative to standard, computable general equilibrium integrated assessment models (IAMs). The Dystopian Schumpeter meeting Keynes (DSK) model is composed of heterogeneous firms belonging to capital-good, consumption-good and energy sectors. Production and energy generation lead to greenhouse gas emissions, which affect temperature dynamics. Climate damages are modelled at the individual level as stochastic shocks hitting workers' labour productivity, energy efficiency, capital stock and inventories of firms. In that, aggregate damages emerge from the aggregation of losses suffered by heterogeneous, interacting and boundedly rational agents. The model is run focusing on a business-as-usual carbon-intensive scenario consistent with a Representative Concentration Pathway 8.5. We find that the DSK model is able to account for a wide ensemble of micro- and macro-empirical regularities concerning both economic and climate dynamics. Simulation experiments show a substantial lack of isomorphism between the effects of micro- and macro-level shocks, as it is typical in complex system models. In particular, different types of shocks have heterogeneous impact on output growth, unemployment rate, and the likelihood of economic crises, pointing to the importance of the different economic channel affected by the shock. Overall, we report much larger climate damages than those projected by standard IAMs under comparable scenarios, suggesting possible shifts in the growth dynamics, from a self-sustained pattern to stagnation and high volatility, and the need of urgent policy interventions.

Economic support policies are widely adopted in European countries in order to promote a more efficient energy usage and the growth of renewable energy technologies. On one hand these schemes allow us to reduce the overall pollutant emissions and the total cost from the point of view of the energy systems, but on the other hand their social impact in terms of economic investment needs to be evaluated. The aim of this paper is to compare the social cost of the application of each incentive with the correspondent CO2 emission reduction and overall energy saving. A Mixed Integer Linear Programming optimization procedure is used to evaluate the effect of different economic support policies on the optimal configuration and operation of a distributed energy supply system of an industrial area located in the north-east of Italy. The minimized objective function is the total annual cost for owning, operating and maintaining the whole energy system. The expectation is that a proper mix of renewable energy technologies and cogeneration systems will be included in the optimal solution, depending on the amount and nature of the supporting policies, highlighting the incentives that promote a real environmental benefit.

Microalgae represent a carbon-neutral source of bulk biomass, for extraction of high-value compounds and production of renewable fuels. Due to their high metabolic activity and reproduction rates, species of the genus Chlorella are highly productive when cultivated in photobioreactors. However, wild-type strains show biological limitations making algal bioproducts expensive compared to those extracted from other feedstocks. Such constraints include inhomogeneous light distribution due to high optical density of the culture, and photoinhibition of the surface-exposed cells. Thus, the domestication of algal strains for industry makes it increasingly important to select traits aimed at enhancing light-use efficiency while withstanding excess light stress. Carotenoids have a crucial role in protecting against photooxidative damage and, thus, represent a promising target for algal domestication. We applied chemical mutagenesis to Chlorella vulgaris and selected for enhanced tolerance to the carotenoid biosynthesis inhibitor norflurazon. The NFR (norflurazon-resistant) strains showed an increased carotenoid pool size and enhanced tolerance towards photooxidative stress. Growth under excess light revealed an improved carbon assimilation rate of NFR strains with respect to WT. We conclude that domestication of Chlorella vulgaris, by optimizing both carotenoid/chlorophyll ratio and resistance to photooxidative stress, boosted light-to-biomass conversion efficiency under high light conditions typical of photobioreactors. Comparison with strains previously reported for enhanced tolerance to singlet oxygen, reveals that ROS resistance in Chlorella is promoted by at least two independent mechanisms, only one of which is carotenoid-dependent.

Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.