Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost/profit allocation of ECs, this paper aims to identify an allocation method that allows for an appropriate weighting of both the interests of an EC as a whole and those of all its members. The novelty is in comparing different optimization approaches and cooperative allocation criteria, satisfying different notions of fairness, to assess which one may be best suited for an EC. Thus, a cooperative model is used to optimize the operation of an EC that includes two consumers and two solar PV prosumers. The model is solved by the “Social Welfare” approach to maximizing the total “incremental” economic benefit (i.e., cost saving and/or profit increase) and by the “Nash Bargaining” approach to simultaneously maximize the total and individual incremental economic benefits, with respect to the “disagreement” case. Since the “Social Welfare” approach could lead to an unbalanced benefit distribution, the Shapley value and Nucleolus criteria are applied to re-distribute the total incremental economic benefit, leading to higher annual cost savings for consumers with lower electricity demand. Compared to “Social Welfare” without re-distribution, the Nash Bargaining distributes 39–49% and 9–17% higher annual cost savings to consumers with lower demand and to prosumers promoting the energy sharing within the EC, respectively. However, total annual cost savings drop by a maximum of 5.5%, which is the “Price of Fairness”.

Reduced-order electrothermal models play a key role in the design and control of lithium-ion cell stacks, calling for accurate model parameter calibration. This paper presents a complete electrical and thermal experimental characterization procedure for the coupled modeling of cylindrical lithium-ion cells in order to implement them in a prototype Formula SAE hybrid racing car. The main goal of the tests is to determine how the cell capacity varies with the temperature and the discharge current to predict the open-circuit voltage of the cell and its entropic component. A simple approach for the characterization of the battery equivalent electrical circuit and a two-step thermal characterization method are also shown. The investigations are carried out on four commercial 18650 NMC lithium cells. The model was shown to predict the battery voltage with an RMS error lower than 20 mV and the temperature with an RMS error equal to 0.5 °C. The authors hope that this manuscript can contribute to the development of standardized characterization techniques for such cells while offering experimental data and validated models that can be used by researchers and BMS designers in different applications.

Planning the best path for the energy system decarbonization is currently one of the issues of high global interest. At the European level, the recent policies dealing with the transportation sector have decided to ban the registration of light-duty vehicles powered by internal combustion engines fed by fossil fuels, from 2035. Regardless of the official positions, the major players (industries, politicians, economic and statistical institutions, etc.) manifest several opinions on this decision. In this paper, a mathematical model of a nation’s energy system is used to evaluate the economic impact of this decision. The model considers a superstructure that incorporates all energy conversion and storage units, including the entire transportation sector. A series of succeeding simulations was run and each of them was constrained to the achievement of the decarbonization level fixed, year by year, by the European community road-map. For each simulation, an optimization algorithm searches for a less costly global energy system, by including/excluding from the energy system the energy conversion units, storage devices, using a Mixed Integer Linear approach. Three optimization scenarios were considered: (1) a “free” scenario in which the only constraint applied to the model is the achievement of the scheduled decarbonization targets; (2) an “e-fuels” scenario, in which all new non-battery-electric light-duty vehicles allowed after 2035 must be fed with e-fuels; (3) a “pure electric” scenario, in which all new light-duty vehicles allowed after 2035 are battery-electric vehicles. The comparison of the optimum solutions for the three scenarios demonstrates that the less costly transition to a fully renewable energy system decarbonizes the transportation sector only when the share of renewable energy sources exceeds 90%. E-fueled light-duty vehicles always turn out to be a less expensive alternative than the electric vehicles, mainly because of the very high cost of the energy supply infrastructure needed to charge the batteries. Most of all, the costs imposed to society by the “e-fuels” and “pure electric” light-duty-vehicle decarbonizing scenarios exceed by 20% and 60%, respectively, the “free” transition scenario.

The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO₂ emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO₂ emissions by up to 30%.