• Energy Research

To face environmental and energy security issues, planning an energy system with high penetration of renewables is becoming increasingly important. The EPLANopt model couples a multi-objective evolutionary algorithm to EnergyPLAN simulation software to study the future best energy mix. In this study, EPLANopt is applied to the case study of Niederösterreich, an Austrian region, to inspect the best configurations of the energy system at 2050. This model is used to inspect the competition between different renewable energy integration options. Storage systems, power to gas, power to heat or power to mobility are all integration options taken into account to study their competition in presence of electricity excess from renewables. The results show that in order to decarbonize the energy system the increase of the installed power of renewables is not enough to reach the CO2 reduction objective. Integration methods like the already mentioned storage systems, power to gas, power to heat or power to mobility become relevant. In particular the results show a deep energy efficiency refurbishment coupled to power to heat through heat pumps. Power to gas presents a relevant role in the integration of the excess of electricity from renewables. However, at the increase of electric mobility penetration the available excess of electricity is reduced and the deployment of power to gas decreases. International Journal of Sustainable Energy Planning and Management, Vol. 27 (2020): Special Issue from the 5th International Conference on Smart Energy Systems

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.

Abstract The interest in the assessment of performance loss rate (PLR) of Photovoltaic (PV) modules and arrays has been increasing as long as the global installed power expands and ages. Reliable performance metrics, statistical methods and filtering techniques exploiting continuous outdoor measurements are therefore needed, in order to foster solar bankability of PV systems. This work presents an improved estimation method to decrease the uncertainty associated to PLR assessment by (a) using the array generated power metric corrected to Standard Test Conditions (STC), namely P max , STC , to minimize seasonal oscillations, (b) applying a filtering technique to eliminate outliers and (c) performing linear interpolation on P max , STC monthly averages series. Estimated PLR and its uncertainty are assessed using three-years data from twenty-four grid-connected PV arrays representing nine different PV technologies and results are compared with two other widely-recognized performance metrics, namely: the Array Performance Ratio ( PR a ) and the Array Photovoltaic for Utility Systems Applications ( PVUSA a ). Results show (a) that adding spectral correction to irradiance and temperature correction reduces the uncertainty of 25% on average and (b) that the uncertainty associated to P max , STC metric is reduced to more than 60% on average with respect to the other investigated metrics for crystalline silicon-based technologies, while it is comparable in the case of thin-film technologies. Finally, two procedures estimating the first year PLR and the the first five months PLR are presented and discussed.

Abstract In 2021 the European Commission has proposed the Fit-for-55 policy package, requiring European countries to reduce their CO2 emissions by 55% with respect to 1990 by the year 2030, a first step to achieve carbon neutrality by 2050. Energy system modeling can be a valuable tool for national policymakers to choose the most appropriate technologies to achieve these goals efficiently. This article presents a model of the Italian power system realized employing the open energy modeling framework, Oemof. A linear programming optimization is implemented to evaluate how to minimize system costs at decreasing CO2 emissions in 2030. The developed tool is applied to evaluate different research questions: (i) pathway towards full decarbonization and power self-sufficiency of the electricity sector in Italy, (ii) relevance of flexibility assets in power grids: li-ion batteries, hydrogen storage and transmission lines reinforcement. A 55% CO2 emissions reduction for the actual Italian power sector can be achieved through an increase of 30% of the total annual system cost. Achieving complete decarbonization and self-sufficiency increases significatively annual expenditures. However, cost mitigation is plausible through the integration of sector coupling methodologies or the adoption of a broader spectrum of technological solutions. Flexibility measures appear instrumental for decarbonization, particularly transmission lines, demanding a substantial expansion beyond the stated plans for 2030. This infrastructure is crucial in Italy to facilitate the transfer of renewable electricity generated in the Southern regions to the Northern areas, where a large portion of the electricity demand is located.

The transition to sustainable waterways transport is imperative in the face of environmental and climate challenges. Local lakes, often overlooked, play a significant role in regional transportation networks and ecosystems. This study focuses on Orta lake, Italy, and aims to facilitate its transition to sustainable inland waterways transport by substituting its diesel-based fleet with electric vessels. Firstly, a comprehensive market analysis was conducted to understand the available electric vessel models and their technical characteristics. This included parameters such as capacity, range, and charging time. Based on the market analysis, an optimization model was developed to determine the minimum number of electric vessels required to completely replace the existing diesel-based fleet. This model considers various constraints and objectives, such as meeting transport demand, minimizing the number of vessels, and reducing environmental impact. The developed model was then applied to the case study of Orta lake using the collected market data. The results indicate an optimal fleet configuration and provide insights into the feasibility and implications of the transition. This study contributes to the growing body of knowledge on sustainable inland waterways transport and offers a methodology that can be replicated and adapted for other local lakes or maritime settings.

Abstract Energy system modelling supports decision-makers in the development of short and long-term energy strategies. In the field of bottom-up short-term energy system models, high resolution in time and space, the implementation of sector coupling and the adoption of a multi-objective investment optimization have never been achieved simultaneously because of the high computational effort. Within this paper, such a bottom-up short-term model which simultaneously implements (i) hourly temporal resolution, (ii) multi-node approach thus high spatial resolution, (iii) integrates the electric, thermal and transport sectors and (iv) implements a multi-objective investment optimization method is proposed. The developed method is applied to the Italian energy system at 2050 to test and show its main features. The model allows the evaluation of the hourly curtailments for each node. The optimization highlights that the cheapest solutions work towards high curtailments and low investments in flexibility options. In order to further reduce the CO2 emissions the investments in flexibility options like electric storage batteries and reinforcement and enlargement of the transmission grid become relevant.