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Abstract As main contributors to greenhouse gas emissions, power and transportation are crucial sectors for energy system decarbonization. Their interaction is expected to increase significantly: plug-in electric vehicles add a new electric load, increasing grid demand and potentially requiring substantial grid upgrade; hydrogen production for fuel cell electric vehicles or for clean fuels synthesis could exploit the projected massive power overgeneration by intermittent and seasonally-dependent renewable sources via Power-to-Hydrogen. This work investigates the infrastructural needs involved with a broad diffusion of clean mobility, adopting a sector integration perspective at the national scale. The analysis combines a multi-node energy system balance simulation and a techno-economic assessment of the infrastructure to deliver energy vectors for mobility. The article explores the long-term case of Italy, considering a massive increase of renewable power generation capacity and investigating different mobility scenarios, where low-emission vehicles account for 50% of the stock. First, the model solves the energy balances, integrating the consumption related to mobility energy vectors and taking into account power grid constraints. Then, an optimal infrastructure is identified, composed of both a hydrogen delivery network and a widespread installation of charging points. Results show that the infrastructural requirements bring about investment costs in the range of 43–63 G€. Lower specific costs are associated with the exclusive presence of FCEVs, whereas the full reliance on BEVs leads to the most significant costs. Scenarios that combine FCEVs and BEVs lie in between, suggesting that the overall power + mobility system benefits from the presence of both drivetrain options.

Abstract As main contributors to greenhouse gas emissions, power and transportation are crucial sectors for energy system decarbonization. Their interaction is expected to increase significantly: plug-in electric vehicles add a new electric load, increasing grid demand and potentially requiring substantial grid upgrade; hydrogen production for fuel cell electric vehicles or for clean fuels synthesis could exploit the projected massive power overgeneration by intermittent and seasonally-dependent renewable sources via Power-to-Hydrogen. This work investigates the infrastructural needs involved with a broad diffusion of clean mobility, adopting a sector integration perspective at the national scale. The analysis combines a multi-node energy system balance simulation and a techno-economic assessment of the infrastructure to deliver energy vectors for mobility. The article explores the long-term case of Italy, considering a massive increase of renewable power generation capacity and investigating different mobility scenarios, where low-emission vehicles account for 50% of the stock. First, the model solves the energy balances, integrating the consumption related to mobility energy vectors and taking into account power grid constraints. Then, an optimal infrastructure is identified, composed of both a hydrogen delivery network and a widespread installation of charging points. Results show that the infrastructural requirements bring about investment costs in the range of 43–63 G€. Lower specific costs are associated with the exclusive presence of FCEVs, whereas the full reliance on BEVs leads to the most significant costs. Scenarios that combine FCEVs and BEVs lie in between, suggesting that the overall power + mobility system benefits from the presence of both drivetrain options.

The chemical and petrochemical sector is by far the largest industrial energy user, accounting for 30% of the industry's total final energy use. However, due to its complexity its energy efficiency potential is not well understood. This article analyses the energy efficiency potential on a country level if Best Practice Technologies (BPT) were implemented in chemical processes. Two approaches are applied and an improved dataset referring to Europe has been developed for BPT energy use. This methodology has been applied to 66 products in fifteen countries that represent 70% of chemical and petrochemical sector's energy use worldwide. The results suggest a global energy efficiency potential of 16% for this sector, excluding savings in electricity use and by higher levels of process integration, combined heat and power (CHP) and post-consumer plastic waste treatment. The results are more accurate than previous estimates. The results suggest significant differences between countries, but a cross-check based on two different methods shows that important methodological and data issues remain to be resolved. Further refinement is needed for target setting, monitoring and informing energy and climate negotiation processes. For the short and medium term, a combination of benchmarking and country level analysis is recommended.

The chemical and petrochemical sector is by far the largest industrial energy user, accounting for 30% of the industry's total final energy use. However, due to its complexity its energy efficiency potential is not well understood. This article analyses the energy efficiency potential on a country level if Best Practice Technologies (BPT) were implemented in chemical processes. Two approaches are applied and an improved dataset referring to Europe has been developed for BPT energy use. This methodology has been applied to 66 products in fifteen countries that represent 70% of chemical and petrochemical sector's energy use worldwide. The results suggest a global energy efficiency potential of 16% for this sector, excluding savings in electricity use and by higher levels of process integration, combined heat and power (CHP) and post-consumer plastic waste treatment. The results are more accurate than previous estimates. The results suggest significant differences between countries, but a cross-check based on two different methods shows that important methodological and data issues remain to be resolved. Further refinement is needed for target setting, monitoring and informing energy and climate negotiation processes. For the short and medium term, a combination of benchmarking and country level analysis is recommended.

Thermo-economically optimal design = optimal molecule + optimal process + optimal equipment.

Thermo-economically optimal design = optimal molecule + optimal process + optimal equipment.

This paper presents a stochastic bi-level approach for tariff design in the electricity market where the leader is represented by a retailer and the follower by a residential prosumager, i.e. a consumer equipped with an energy system consisting of photovoltaic panels and a battery storage device. Both players solve an optimization problem subject to uncertainty in market prices, weather-related variables and electricity demand. To account for the retailer’s attitude towards risk, the upper level problem includes a safety measure to maximize. The model allows to determine a dynamic pricing scheme with time-variant rates delivering the average profit that can be gained in a given percentage of unfavorable realizations of the uncertain parameters and the optimal load pattern that minimizes the expected prosumager's electricity bill. The stochastic bi-level problem is reformulated as a single level model and different approaches to deal with the lower level problem and the non linearity in the objective function are analyzed and empirically investigated. A large number of numerical experiments have been carried out on real test cases. The results have shown the efficacy of the proposed approach as a tool to define pricing schemes that reflect the retailer's risk attitude underlining the importance of explicitly dealing with uncertainty.

This paper presents a stochastic bi-level approach for tariff design in the electricity market where the leader is represented by a retailer and the follower by a residential prosumager, i.e. a consumer equipped with an energy system consisting of photovoltaic panels and a battery storage device. Both players solve an optimization problem subject to uncertainty in market prices, weather-related variables and electricity demand. To account for the retailer’s attitude towards risk, the upper level problem includes a safety measure to maximize. The model allows to determine a dynamic pricing scheme with time-variant rates delivering the average profit that can be gained in a given percentage of unfavorable realizations of the uncertain parameters and the optimal load pattern that minimizes the expected prosumager's electricity bill. The stochastic bi-level problem is reformulated as a single level model and different approaches to deal with the lower level problem and the non linearity in the objective function are analyzed and empirically investigated. A large number of numerical experiments have been carried out on real test cases. The results have shown the efficacy of the proposed approach as a tool to define pricing schemes that reflect the retailer's risk attitude underlining the importance of explicitly dealing with uncertainty.