• Energy Research

Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the “best” scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the “best” set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the “stochastic forecasts” of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the “best” scenarios. The proposed novel procedure shifts the “present moment” back in time to split historical data into “past” and “future” periods used to identify the “best” scenarios and compare the “stochastic forecasts” with the utopic “perfect forecasts” based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2 %) and sizes (3–13 %) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of “stochastic forecasts” are fairly distributed among users by applying the Shapley value mechanism.

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.

Abstract In the literature, there is a lack of tools able to optimize contextually the design and operation of a multi-energy system in its entirety, encompassing both (i) the number, type, and size of the energy conversion and storage plants supplying the end users of the system with the required energy and (ii) the geometry and capacity of the distribution networks delivering that energy to the users. Moreover, rarely the retrofit design problem is considered, where “retrofit design” refers to the addition of new capacity to components initially available in existing systems. Here, a general method is proposed to simultaneously optimize the retrofit design and operation of a multi-energy system and the associated energy networks. The goal consists of finding the additional capacity to be added to the already available components—energy conversion and storage plants, energy networks—and the new components to be installed in order to comply with given reduction targets in carbon emissions while keeping the life cycle cost of the system at a minimum. A district composed of commercial and residential buildings operating in a microgrid is considered as a case study. Heat can be provided to the end users via a district heating network, while electricity can be either generated on-site or imported from the national power grid. Results of the retrofit design problem show a contextual reduction of 35% in CO2 emission and 20% in life cycle cost with respect to the original system configuration.

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...

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.

The reduction of anthropogenic emissions of greenhouse gases requires decreasing the overall consumption of primary energy. Thus, waste heat recovery at medium-to-high temperature is an opportunity for generating electricity while reducing the need for primary resources. Recently, supercritical carbon dioxide power cycles (S-CO2) are emerging as a promising solution. However, a method lacks to simultaneously optimize their layout and design parameters, without relying on superstructures defined a priori. To this end, this paper suggests a novel extension of the superstructure free SYNTHSEP methodology, a bottom-up approach for the optimal synthesis and design of thermodynamic cycles, to handle also super-and transcritical cycles. An Evolutionary Algorithm combining elementary cycles makes it possible to define optimal S-CO2 configurations without limiting the search space of the optimization problem. The objective consists in finding the S-CO2 topology and design pa-rameters that maximize the mechanical power extractable from waste heat streams in the temperature range from 200 to 700 degrees C, typical of the industrial sector. Results demonstrate the capability of the method to find optimal cycle layouts for any given waste heat temperature, and to achieve, at the same conditions, cycle effi-ciencies up to 5 % higher in relative terms than the best ones in the literature.

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”.

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%.