• Energy Research
• 2025-2025close
• IT close
• US close
• Applied Energy close

A realistic pursuit of decarbonization targets requires planning and designing new configurations of “multi-energy systems” to identify the optimal number, type, location and size of the energy conversion and storage units and their interconnections with the end users of different forms of energy. The common approach in the literature is to treat the optimization problem of energy conversion and storage separately from that of energy networks, and the few attempts to address the two problems simultaneously have led to oversimplifications due to the very large number of decision variables involved. To fill this gap, this study introduces “DOMES” (Design Of Multi-Energy Systems), a general optimization method for the integrated synthesis, design and operation of a multi-energy system in its entirety. With the goal of minimizing costs and reducing carbon emissions, DOMES can simultaneously find the location, type, size and operation of the energy conversion and storage units, as well as t...

Green hydrogen holds potential for decarbonizing the energy sector, but high production costs are a major barrier. This study provides a comprehensive techno-economic-financial-environmental analysis of PV-based grid-connected hydrogen production plants, targeting hard-to-abate industries having constant hydrogen demand across all Italy. Using real hourly data, the Multi Energy System Simulator (MESS), an in-house developed rule-based tool, was employed and integrated with Genetic Algorithm for optimal plant sizing. The aim is to minimize the Levelized Cost of Hydrogen (LCOH) while complying with regulatory frameworks for green hydrogen incentives access. Key findings show that hydrogen storage is more advantageous than battery storage for supply-side flexibility, and the optimal PV-to-electrolyzer size ratio ranges from 1.8 in Southern Italy to 2.1 in Northern Italy, with hydrogen tank designed for daily storage. Considering photovoltaic, electrolyzer and battery aging models grid dependence increases by 60 % when comparing the first and worst year of operation and leads to a 7 % increase in LCOH. Transitioning from the strictest (hourly) to the least stringent (annual) temporal correlation increases certified green hydrogen by 22 %, while LCOH decreases by only 3 %, suggesting that the environmental benefits of stringent temporal requirements outweigh their moderate economic drawbacks. These findings underscore the need for additional national-level incentives to allow the deployment of this technology and achieving cost parity with grey hydrogen.

This study presents sector-coupled PyPSA-Earth: a novel global open-source energy system optimization model that incorporates major demand sectors and energy carriers in high spatial and temporal resolution, to enable energy transition studies worldwide. The model includes a workflow that automatically downloads and processes the necessary demand, supply and transmission data to co-optimize investment and operation of energy systems of countries or regions of Earth. The workflow provides the user with tools to forecast future demand scenarios and allows for custom user-defined data in several aspects. Sector-coupled PyPSA-Earth introduces novelty by offering users a comprehensive methodology to generate readily available sector-coupled data and model of any region worldwide, starting from raw and open data sources. The model provides flexibility in terms of spatial and temporal detail, allowing the user to tailor it to their specific needs. The capabilities of the model are demonstrated through two showcases for Egypt and Brazil. The Egypt case quantifies the relevant role of PV, exceeding 35 GW, and electrolysis in Suez and Damietta regions, for meeting 16% of the EU hydrogen demand. Complementarily, the Brazil case confirms the model's ability in handling hydrogen planning infrastructure, including repurposing of existing gas networks which results in 146 M€ lower costs than building new pipelines. The results prove the suitability of sector-coupled PyPSA-Earth to meet the needs of policymakers, developers, and scholars in advancing the energy transition. The authors invite the interested individuals and institutions to collaborate in the future developments of the model within PyPSA meets Earth initiative.

Power tower concentrated solar power systems integrated with thermal energy storage systems offer promising solutions for reliable and cost-effective energy production. This research applies Artificial Intelligence techniques to enhance the operational efficiency, reliability, and economic performance of a power tower system. A comprehensive real-time data-driven optimization model was developed incorporating an AI-based machine learning technique - Random Forest Regressor combined with grid search cross-validation to accurately predict output power. Furthermore, an interdependent dual-parameter optimization was conducted to optimize critical system parameters, including mirror angles and heat transfer fluid flow rates. The proposed model facilitates energy forecasting, performance optimization, and operational decision-making, as well as economic, weather impact, and sensitivity analysis. Economic feasibility was evaluated using Net Present Value and Levelized Cost of Energy calculations, while sensitivity analysis highlighted the system's resilience to variations in fuel prices, discount rates, and technology cost. The results indicate a highly accurate prediction, with a Mean Squared Error of 0.0676 and an R2 score of 0.9999, featuring the model's robustness. Additionally, a weather impact and correlation analysis was conducted to analyze the system's operational capabilities under varying weather conditions. Moreover an environmental impact assessment illustrated the sustainability advantages of integrating thermal energy storage (TES) with the concentrated solar power (CSP) system, particularly in improving energy dispatch and reducing emissions. Overall, integrating the TES significantly enhanced dispatch capabilities, particularly under varying weather scenarios.

The production of biogas for energy generation through the anaerobic digestion is seen as an effective way to exploit local renewable resources as a substitute of fossil fuels. The two main applications that are currently adopted are the electricity production through biogas internal combustion engines, potentially combined with heat recovery, and the biogas upgrading to biomethane, to be supplied to the natural gas infrastructure. This research work contributes to the discussion by analyzing the performance of a real biogas plant in Italy, based on the anaerobic digestion of the organic fraction of municipal solid waste, that has shifted from power generation to biomethane generation. The performance of the two configurations is compared by means of the expected CO2 emissions savings against the current average electricity in Italy and natural gas carbon intensities, including upstream emissions. The results show that, based on the assumptions of our analysis for the current context of Italy, 1 MWh of biogas from organic fraction of municipal solid waste can lead to 152 kgCO2,eq savings if upgraded to biomethane and injected into the grid, but only to 120 kgCO2,eq when used in engines running in full-electric mode. If the engines are also producing useful heat, emission savings increase, reaching a trade-off with biomethane if 31% of the annual heat production can be recovered. However, considering the expected 2030 electricity mix in Italy, biomethane production would still be the best solution to maximize emission savings. Performance data from real plants are an important resource to develop reliable and effective energy system models, that can support policy makers in defining local energy plans and decarbonization strategies.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

This study presents a techno-economic assessment of dynamic wireless power transfer for long-haul freight transport, focusing on the fleet operator’s perspective. In particular, we compared three different powertrain technologies: a conventional powertrain and a battery-electric with or without a dynamic charger installed. For all three technologies, we developed a cost model to assess the total cost of ownership for a fleet operator using different scenarios. Notably, dedicated cost models were devised to estimate energy carrier costs and costs related to time loss incurred by fleet operators due to extended delivery times of electric trucks compared to conventional ones. The novelties in the cost model are twofold. First, dedicated cost models have been devised to estimate the costs related to the energy carriers (including the cost of infrastructure) and to the time loss incurred by fleet operators due to the extended delivery times of electric trucks compared to conventional ones. Second, the energy consumption by source and travel time were derived from an ad-hoc developed simulation approach models longitudinal dynamics of the case-study as well as the powertrain’s performance on the basis of experimentally derived look-up tables provided by manufacturers as well as by previous research projects. The simulation results provided by this model are instrumental to our enhanced cost model as it provides the required inputs and it allowed us to tailor the results to a specific delivery mission. Our results provide valuable insights for fleet operators considering the adoption of zero-emission trucks and to policy-makers and other infrastructure stakeholders regarding the conditions required for the cost-effectiveness of electric road systems.