Search

The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

This paper aims at evaluating the best allocation of potential biomethane generation for the decarbonization of the transport system, presenting a case study in Italy. The country has some peculiar features, such as several operating biogas plants, additional potential feedstock for biogas/biomethane generation, a well-developed natural gas network and established relevant natural gas uses in different final sectors, including transport. Based on current estimates for sustainable biomethane potential by 2030, ranging from 2.3 to 7.6 billion cubic meters depending on the scenario, the analysis compares technologies for the generation, distribution and final use of biomethane. The results of the analysis confirm the potential interesting contribution of biomethane in decarbonizing the Italian transport system: a billion cubic meters of biomethane can lead to 2.33–4.37 Mt CO 2 e savings, depending on the feedstock mix and the application. On a national basis, annual climate emission savings in 2030 range from 10.0 to 26.7 Mt CO 2 e , depending on the scenario. Additional 3.1–8.1 Mt CO 2 of emissions can be avoided if the CO 2 captured during the biomethane upgrading can be stored or reused. The proposed methodology could be used to extend the analysis to other countries, and to the European context. • We analyze the climate emissions of biomethane for different supply chains. • We evaluate the effects of feedstock, distribution strategy and electricity mix. • The results confirm the important role of biomethane for transport decarbonization. • We discuss the best allocation in transport segments based on emissions savings. • We compare alternative biomethane scenarios for Italy in 2030. [ABSTRACT FROM AUTHOR]

So far, energy planning methods faced several challenges in achieving a proper assessment due to the extreme variability of input conditions. To considerably decrease the computational efforts in achieving solutions, most of the analyses reported in the scientific literature assume constant costs and demand over the entire planning horizon in a typical year. However, results might not be perfectly aligned with real ones because of the incapability of some models to address several aspects like energy demand/production variability, energy and technology costs, efficiency degradation, and use of different energy carriers. This paper proposes a novel methodology that optimises both the long-term planning and short-term scheduling decisions in the management of a multi-energy carries community by means of a Mixed Integer Linear Programming model that also considers the modular design of technologies (e.g., technological devices selection from a discrete set of variants). Such a model has been applied to the case study of a University campus in Italy, whose historical demand data were used for its energy planning with a time horizon of 30 years. Three scenarios have been analysed: (i) the Business As Usual, (ii) the Sector-coupling scenario, and, finally, (iii) the Hydrogen deployment one. The results are obtained under different energy scenarios, showing the effectiveness of the methodology in dealing with multi-investment stages at different planning levels in a reasonable computational time. In particular, they showed that deploying more sustainable technologies would increase the cost of the electricity (between 43%–89%), while reducing other energy carriers' cost (about 60%) and lowering all energy carriers' carbon footprint (between 50%–80%). From a long-term perspective, (i) the use of sector-coupling technologies is beneficial from both economic and environmental points of view, and ii) dynamic variations of some parameters can strongly affect the deployment of high-cost technologies to be installed beyond 2030. • A novel multi-investment stage approach for multi-energy-carriers community is proposed. • Methodology validated on different energy-policy-driven scenarios with up to five energy-carries. • Manufacturer's datasheet-based size enables realistic modular design. • Automatic energy systems expansion aligned with fluctuating parameters. • Electricity cost increment (40%–80%) for the economic and environmental benefit of the other carriers (50%–90%). [ABSTRACT FROM AUTHOR]