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This paper presents a multi-objective optimization model for the integration of polygeneration systems into energy communities (ECs), by analyzing a case study. The concept of ECs is increasingly seen as beneficial for reducing global energy consumption and greenhouse gas emissions. Polygeneration systems have the potential to play a crucial role in this context, since they are known for producing multiple energy services from a single energy resource, besides the possibility of being fed also by renewable energy sources. However, optimizing the configuration and operation of these systems within ECs presents complex challenges due to the variety of technologies involved, their interactions, and the dynamic behavior of buildings. Therefore, the aim of this work is developing a mathematical model using a mixed integer linear programming (MILP) algorithm to optimally design and operate polygeneration systems integrated into ECs. The model is applied to a case study of an EC comprising nine buildings in a small city in the northeast of Italy. The work rests on the single- and multi-objective optimization of the polygeneration systems taking into account the sharing of electricity among the buildings (both self-produced and/or the purchased from the grid), as well as the sharing of heating and cooling between the buildings through a district heating and cooling network (DHCN). The main results from the EC case study show the possibility of reducing the total annual CO2 emissions by around 24.3% (about 1.72 kt CO2/year) while increasing the total annual costs by 1.9% (about 0.09 M€/year) or reducing the total annual costs by 31.9% (about 1.47 M€/year) while increasing the total annual CO2 emissions by 2.2% (about 0.16 kt CO2/year). The work developed within this research can be adapted to different case studies, such as in the residential–commercial buildings and industrial sectors. Therefore, the model resulting from this work constitutes an effective tool to optimally design and operate polygeneration systems integrated into ECs. [ABSTRACT FROM AUTHOR]

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]

The use of biomass can be a strategic way to realize a carbon-neutral energy plan, ensuring a fuel feedstock. Residual biomass arising from pruning is demonstrated to be an important energy resource in terms of quantity and quality. In the Salento peninsula, Apulia Region, in the south of Italy, a dramatic outbreak of Xylella fastidiosa has decimated olive trees since 2013, gaining a considerable amount of wood biomass. This paper, starting from the need to find a way to optimize the use of this available stock, reviews the main technologies on the utilization of olive wood for energy purposes. In particular, processes and products are here described, and an energy analysis compares lower heating value (LHV), higher heating value (HHV), mass yield, process operating conditions, and energy generated and spent by the process in order to find the most effective technology in order to optimize the energy use of olive biomass. The conclusions show the advantages and disadvantages of each technology. Pyrolysis performs well, showing the best results for both char HHV and syngas yield under different operating conditions. Gasification seems to be the most appropriate among conversion technologies to optimize olive tree pruning for energy purposes, as it can be used to produce both electrical and thermal energy. In terms of economic valorization, char is the most promising material representing a value-added product, the quality and versatility of which ranges from fuel to soil improvers and additives for the construction of supercapacitors. Conversely, its disadvantages are mainly represented by high ash content, which can slightly decrease the boiler efficiency. Finally, the amount of alkali metals can produce several problems, such as fouling, slagging, corrosion, etc., posing a challenge for combustion control and pollutant minimization. [ABSTRACT FROM AUTHOR]

Carbon Capture Utilization and Storage (CCUS) is a set of technologies aimed at capturing carbon dioxide (CO2) emissions from point-source emitters to either store permanently or use as a feedstock to produce chemicals and fuels. In this paper, the potential benefits of CCUS integration into the energy supply sector are evaluated from a Life Cycle Assessment (LCA) perspective by comparing two different routes for the CO2 captured from a natural gas combined cycle (NGCC). Both the complete storage of the captured CO2 and its partial utilization to produce dimethyl ether are investigated. Moreover, the assessment is performed considering the region-specific features of two of the largest CO2 emitters in Europe, namely Italy and Poland. Results shows that the complete storage of the captured CO2 reduces Global Warming Potential (GWP) by ~89% in Italy and ~97%, in Poland. On the other hand, the partial utilization of CO2 to produce dimethyl ether leads to a decrease of ~58% in Italy and ~68% in Poland with respect to a comparable reference entailing conventional dimethyl ether production. A series of environmental trade-offs was determined, with all the investigated categories apart from GWP showing an increase, mainly connected with the higher energy requirements of CCUS processes. These outcomes highlight the need for a holistic-oriented approach in the design of novel implemented configurations to avoid burden shifts throughout the value chain. [ABSTRACT FROM AUTHOR]

Featured Application: The results of this paper can support the design of nearly zero-energy single-family houses in the Italian context. The provided methodological framework can support the design of nearly zero-energy buildings and be replicated in different contexts. The effective design of nearly zero-energy buildings depends on a large set of interdependent variables, which affect both energy demand and supply. Considering them simultaneously is fundamental when searching for optimal design of nearly zero-energy buildings, as encouraged by the EU in the second recast of the Energy Performance of Building Directive (EPBD). This paper presents the application of the new energy demand and supply simultaneous optimization (EDeSSOpt) methodology to optimize the design of a single-family house in the Italian context. Both primary energy optimization and financial optimization are carried out in the context of European regulations. Robustness of the resulting optimal solution is studied through analysis of optimum neighborhoods. The resulting cost-optimized solution relies on a moderately insulated envelope, a highly efficient system, and 34% of coverage from renewables. The energy-optimized solution requires a higher level of insulation and a higher coverage from renewables, demonstrating that there is still a gap between energy and cost optimums. Beyond the results, integrated optimization by means of EDeSSOpt is demonstrated to better minimize cost functions while improving the robustness of results. [ABSTRACT FROM AUTHOR]

COVID-19 is likely to be the 2020s' deadliest pandemic according to the World Health Organization (WHO). There have been more than 3.7 million confirmed deaths after 15 months spread. Besides the loss of human lives, COVID-19 has other unfavorable impacts on society, education, and the economy. Due to successive lockdowns and the continuous quarantines, the demands on power resources have reduced. Therefore, there is a need to investigate the impacts of the COVID-19 pandemic on electricity prices (EP). In this paper, a set of six economic factors that are affected by COVID-19 and affect EP are considered. These factors were fed into a functional link artificial neural network (FLANN) to model the relationships between them and the EP. An empirical equation was formulated to help decision makers and strategic developers in the electricity markets come up with more appropriate plans. Italy, the Czech Republic, and China were used as case studies in this research. [ABSTRACT FROM AUTHOR]

Microgrids are composed of distributed energy resources (DERs), storage devices, electric vehicles, flexible loads and so on. They may either operate connected to the main electricity grid (on-grid operation) or separated from the grid (islanded operation). The outputs of the renewable energy sources may fluctuate and thus can cause deviations in the voltage magnitudes especially at islanded mode. This may affect the stability of the microgrids. This paper proposes an optimization model to efficiently manage controllable devices in microgrids aiming to minimize the voltage deviations both in on-grid and islanded operation modes. RSE Distributed Energy Resources Test Facility (DER-TF), which is a low voltage microgrid system in Italy, is used to verify the algorithm. The test system's data is taken through an online software system (REDIS) and a harmony search based optimization algorithm is applied to control the device parameters. The experimental results show that the harmony search based optimization approach successfully finds the control parameters, and can help the system to obtain a better voltage profile. [ABSTRACT FROM AUTHOR]

This paper contributes to the climate policy discussion by focusing on the challenges and opportunities of reaching net zero emissions by 2050 in Italy. To support Italian energy planning, we developed energy roadmaps towards national climate neutrality, consistent with the Paris Agreement objectives and the IPCC goal of limiting the increase in global surface temperature to 1.5 °C. Starting from the Italian framework, these scenarios identify the correlations among the main pillars for the change of the energy paradigm towards net emissions by 2050. The energy scenarios were developed using TIMES-RSE, a partial equilibrium and technology-rich optimization model of the entire Italian energy system. Subsequently, an in-depth analysis was developed with the sMTISIM, a long-term simulator of power system and electricity markets. The results show that, to achieve climate neutrality by 2050, the Italian energy system will have to experience profound transformations on multiple and strongly related dimensions. A predominantly renewable-based energy mix (at least 80–90% by 2050) is essential to decarbonize most of the final energy consumption. However, the strong increase of non-programmable renewable sources requires particular attention to new flexibility resources needed for the power system, such as Power-to-X. The green fuels produced from renewables via Power-to-X will be a vital energy source for those sectors where electrification faces technical and economic barriers. The paper's findings also confirm that the European "energy efficiency first" principle represents the very first step on the road to climate neutrality. [ABSTRACT FROM AUTHOR]

This paper focuses on the relationship between the European Union Emission Trading System allowances' prices and the Italian electricity price, aiming at assessing whether such a mechanism has been a driver for the decarbonization of the power sector. To this aim, we calculate the long-run relationships between energy prices, natural gas prices and allowances' prices, through a VECM model, distinguishing between peak and off-peak prices. The analysis is carried out for the third phase of the EU-ETS, which started in 2013, and for two-year rolling windows that account for changes over time of the pass-through rates. It is shown that the natural gas price has a high pass-through rate of roughly 70%, which is increasing over time. On the contrary, the pass-through rate of the allowances' price is as low as 7% for the wholesale electricity price, being slightly more and less for the peak and off-peak prices, respectively. However, this rate has been substantially changing over time, starting from a high level and falling significantly, becoming negative in the recent years. This could signal that the EU-ETS has been increasingly more effective in endogenizing emission costs for power producers, inducing them to reduce their production costs associated with emissions by means of a change in technologies. However, the analysis of the impulse response functions hardly supports this finding, eventually casting doubts on the effectiveness of the EU-ETS in Italy to drive the transition toward a less carbon-intensive power supply. [ABSTRACT FROM AUTHOR]

A detailed review of wave energy resource assessment and the state-of-the-art of deployed wave energy converters (WECs) in real environmental conditions in the Mediterranean Sea have been analysed in this study. The installed power of the several deployed WECs in the Mediterranean Sea varies between 3–2500 kW. Ten project cases of deployed WECs in the basin are presented, with their analysis of the essential features. Five different types of WEC have already been tested under real environmental conditions in Italy, Greece, Israel and Gibraltar, with Italy being the Mediterranean country with the most deployed WECs. The main questions of the relevant studies were the ongoing trends, the examination of WECs in combination with other renewable sources, the utilising of WECs for desalination, and the prospects of wave energy in the Mediterranean islands and ports. This paper is the first comprehensive study that overviews the recent significant developments in the wave energy sector in the Mediterranean countries. The research concludes that the advances of the wave energy sector in the Mediterranean Sea are significant. However, in order to commercialise WECs and wave energy exploitation to become profitable, more development is necessary. [ABSTRACT FROM AUTHOR]

Due to our increasing dependence on resources and energy, climate change is among the most tangible issues we are facing today. The construction industry has been identified as a key sector to intervene in, but mitigation strategies must focus on more than just operational energy. As current climate policies recommend, designers should consider the broader framework of environmental impacts characterizing the entire building lifecycle. Life Cycle Assessment (LCA) considers the mentioned aspects. Nevertheless, the complexity of LCA and the criticalities of related tools prevent the application of the approach during decisive early stages. Additionally, LCA allows the estimation of environmental issues, but fails to describe other dimensions of sustainability. The article proposes an evaluation method to support decision-making during the design of sustainable buildings. The method exploits the LCA tool and the assessment framework provided by a Green Sustainable Building Certification (Active House) integrated with BIM. The approach was applied and proven through a case study: a residential single-family building in northern Italy, comparing two technological options. The study confirmed the effectiveness of the multicriteria evaluation framework that, from the early design stage, can be used to measure the environmental impact of buildings alongside implications for the final users' comfort. [ABSTRACT FROM AUTHOR]

Renewable energy sources are environmentally friendly and cost-efficient. However, the problem with these renewable resources is their heavy reliance on weather conditions. Thus, at times, these solutions are not guaranteed to meet the required demand all the time. For this, hybrid microgrids are introduced, which have a combination of both renewable energy sources and non-renewable energy resources. In this paper, a cost-efficient optimization algorithm is proposed that minimizes the use of non-renewable energy sources. It maximizes the use of renewable energy resources by meeting the demand for utility grids. Real data based on the load and demand of the utility grids in Italy is used, and a system that determines the optimal sizing of the microgrid and a daily plan is introduced to optimize the renewable resources operations. As part of the proposal, the objective function for the operation and planning of the microgrid in such a way to minimize cost is formulated. Moreover, a variant of the PSO algorithm named recurrent PSO is implemented. The recurrent PSO algorithm solves the proposed optimization objective function by minimizing the cost for the installation and working of the microgrid. Afterwards, the energy management system algorithm lays out a plan for the daily operation of the microgrid. The performance of the system is evaluated using different state-of-the-art optimization methods. The proposed work can help minimize the use of diesel generators, which not only saves financial resources but also contributes toward a green environment. [ABSTRACT FROM AUTHOR]

Pruning residues could represent an important biomass resources for energy production. Only in Italy it has been estimated that an annual quantity of biomass of over 2600 kt of dry matter could be obtained from olive residues. Several machines developed for pruning harvesting are available in the market, most of which are tractor-driven, while limited knowledge is available on performance, quality of work and costs of harvesting logistics based on stationary chippers. The aim of the present paper is to analyze machine performance of a forestry stationary chipper applied to pruning harvesting for what concerns work productivity, quality of the comminuted product and harvesting operating costs. This system is actually applied by Fiusis Company, an Italian enterprise which manages a biomass power plant exclusively powered by olive trees' pruning residues, and it has never been analyzed in literature. The results obtained showed consistent work productivity, which resulted the highest ever found in olive pruning harvesting systems and equal to 5.23 ± 0.81 tdm·h−1. This high work productivity allowed also to obtain a little economic gain from a matter, which is actually considered a problem for olive groves' owners and not a potential source of income. In particular, the use of a stationary chipper seemed very efficient in olive groves with a consistent amount of wooden residues to be processed and with big branches not harvestable by the most common towed pruning harvester. In addition, the stationary chipper has the advantage of avoiding the preliminary raking operation, which results in reduced costs for the farmer. [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]

Worldwide, the use of fossil fuels covers almost 80% of the entire energy needs. In the European Union (EU), 2020 represents a watershed: for the first time, renewables were the main source of electricity. In Italy, the latest surveys demonstrate an increase in the use of alternative energy sources. European legislative framework highlights the importance of these new trends encouraging (and imposing) the use of renewables. The necessity to become more proactive in the energy production–consumption process and in the achievement of sustainability targets brings people to create Energy Communities (ECs) to manage their own energy supply chain. The authors present an overview of the main legislative framework in the EU and Italy in relation to ECs, analyzing the energy consumption, the electricity and heat energy production potentiality, and the energy balance in a portion of an Italian middle-size city in the Milan metropolitan area. Moreover, they underline the technical, regulatory, and planning possibilities to achieve energy independence, exchanging energy among the selected urban district when there is a surplus in production. Lastly, the authors underline the strengths and barriers to the development of ECs. [ABSTRACT FROM AUTHOR]

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud, and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver, and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost, and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration, and number of devices. The results are analyzed and discussed for packet delivery ratio, energy consumption, localization accuracy, battery discharge measurement, and delay. [ABSTRACT FROM AUTHOR]

Combined heat and power systems (CHP) produce heat and electricity simultaneously. Their resulting high efficiency makes them more attractive from the energy managers' perspective than other conventional thermal systems. Although heat is a by-product of the electricity generation process, system operators usually operate CHP systems to satisfy heat demand. Electricity generation from CHP is thus driven by the heat demand, which follows the variability of seasonal temperature, and thus is not always correlated with the fluctuation of electricity demand. Consequently, from the perspective of the electricity grid operator, CHP systems can be seen as a non-controllable energy source similar to other renewable energy sources such as solar, wind or hydro. In this study, we investigate how 'non-controllable' electricity generation from CHP systems combines with 'non-controllable' electricity generation from solar photovoltaic panels (PV) and run-of-the river (RoR) hydropower at a district level. Only these three energy sources are considered within a 100% renewable mix scenario. Energy mixes with different shares of CHP, solar and RoR are evaluated regarding their contribution to total energy supply and their capacity to reduce generation variability. This analysis is carried out over an ensemble of seventeen catchments in North Eastern Italy located along a climate transect ranging from high elevation and snow dominated head-water catchments to rain-fed and wet basins at lower elevations. Results show that at a district scale, integration of CHP systems with solar photovoltaic and RoR hydropower leads to higher demand satisfaction and lower variability of the electricity balance. Results also show that including CHP in the energy mix modifies the optimal relative share between solar and RoR power generation. Results are consistent across the climate transect. For some districts, using the electricity from CHP might also be a better solution than building energy storage for solar PV. [ABSTRACT FROM AUTHOR]