136 research outputs found
Including fairness and uncertainties in the design and operation optimization of local multi-energy systems
No full textIn the context of “local-to-regional” Multi-Energy Systems (MES), this Thesis aims to i) evaluate the weight of uncertainties of input data and boundary conditions in the design optimization of local MES and ii) analyse the aspects influencing the optimal aggregation of end users in Energy Communities (ECs).
According to the second objective of this Thesis, the optimal aggregation of end users is studied in two different reference cases, neglecting uncertainties. In the first reference case, Mixed Integer Linear Programming (MILP) models are developed to optimize the daily operation of the local power systems of the Citizen Energy Community (CEC) and the Renewable Energy Community (REC). Results shows that the complementarity between the energy demand and generation profiles of the EC members leads to relevant cost savings (e.g., daily cost savings are 15-20 % higher in ECs with a higher degree of complementarity between members). A novel cost allocation mechanism is also proposed to encourage end users using free-of-charge and non-dispatchable RES to join the EC. The second reference case goes beyond the optimal aggregation of users and examines the “fairness” of the distribution of the total economic benefit among the members of an EC. A MILP model is developed to optimize the daily operation of the local power system of a REC, and the “Shapley value” mechanism is applied to fairly allocate the optimal total profit. This mechanism distributes higher economic benefits to the prosumers than to the consumers, as they contribute significantly to increasing the total economic benefit of the system.
The third case study, in line with the first objective of this Thesis, focuses on the weight of uncertainties associated with RES and electricity demands in the design optimization of a local MES serving a REC. A novel framework is developed to perform the design-operation optimization of the system in the absence of and under uncertainties. The framework includes MILP and Stochastic Programming (SP) optimization models without and with uncertainties, solved for each day and for a set of daily stochastic scenarios of the uncertain parameters in one year, respectively. Different sets of stochastic scenarios are obtained by applying a clustering technique for each year of a training dataset. The “best” set of stochastic scenarios is then found by searching for the minimum standard deviation of errors between the solutions of the MILP and SP models without and with uncertainties, respectively, over the years of the training dataset. The SP model with the “best” set of stochastic scenarios is solved for each year of a testing dataset, resulting in “stochastic forecast” solutions. Finally, the “stochastic forecasts” are compared with the “perfect forecast” solutions obtained by solving the MILP model in the absence of uncertainties for each year of the testing dataset. A key finding is that the “stochastic forecasts” predict the optimal life cycle cost of the system quite accurately, with an average error of 4 % compared to the “perfect forecasts”.
Overall, this Thesis shows interesting results in the design-operation optimization of local energy systems, despite some necessary assumptions such as the geographical boundaries of the systems and the type of EC members. Several optimizations are performed to achieve general guidelines that lead to the optimal aggregation of end users in ECs, also ensuring a possibly fair distribution of the total economic benefit. The proposed framework for optimizing the design of local MES under uncertainties provides a reliable approach to assess the accuracy of the results under uncertainty with respect to those in the absence of uncertainty, allowing uncertainties to be weighted in the optimal design of the system
Domestication process of two Solanum (section Lasiocarpa) species among Amerindians in the Upper Orinoco, Venezuela with special focus on Piaroa indians
No full textVolpato, Gabriele, Rossella Marcucci, Noemi Tornadore, and Maurizio G. Paoletti (Department
of Biology, University of Padua, 325100 Padova, Italy; email corresponding author:
[email protected]). DOMESTICATION PROCESS OF TWO SOLANUM SECTION LASIOCARPA SPECIES
AMONG AMERINDIANS IN THE UPPER ORINOCO, VENEZUELA, WITH SPECIAL FOCUS ON PIAROA
INDIANS. Economic Botany 58(2):000–000, 2004. Two semi-cultivated Solanum species (S. sessiliflorum
Dunal and S. stramonifolium Jacq.) are utilized by the Amazonian Indians of the
Upper Orinoco Basin in Venezuela. The manner by which they have become partially domesticated
by the Piaroas and other native tribes of this rain forest region is elucidated in the
following text. Both species have two varieties, with and without prickles, the latter the result
of human selection. Patterns of indigenous utilization of these species by the selection of morphologic
forms and to the differentiation of karyotypes of varieties, and exploitation of the
species reflects also in the perception of them among users. S. sessiliflorum is cultivated in
swiddens and has an economic role, whereas S. stramonifolium is grown in dooryards. This
difference is detectable to the Piaroas, as they recognize in their folk taxonomy three different
varieties of the S. sessiliflorum and one of S. stramonifolium, this according to the stage of
domestication of the species and the way in which they are utilized
A moralidade vinculada à ação comunicativa e ao direito em Habermas
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Filosofia e Ciências Humanas, Programa de Pós-graduação em Filosofia, Florianópolis, 2010Neste trabalho investigar-se-á a moralidade proposta por Jürgen Habermas no sentido de determinar a relação desta com a ação comunicativa e com o sistema jurídico defendidos por este autor. Para isso, inicialmente, se verificará por que esse autor considera que sua ação comunicativa é capaz de obter consensos racionais como solução para conflitos, entre eles, conflitos morais. O que implica, entre outras coisas, na demonstração do princípio universal de sua ética e o que defende por princípio do discurso. Este último definido por ele como necessário de ser respeitado não só na solução de conflitos morais, mas também na solução do que essa teoria compreende por conflitos éticos e por conflitos pragmáticos. Tal defesa, conforme se observará aqui, é um dos quesitos que essa teoria respeita para vincular os conteúdos justificados por meio dela à condição histórica dos indivíduos sobre os quais se aplica. Investigar-seá a análise habermasiana de críticas de Hegel a Kant para verificar por que essa ética considera essa vinculação necessária para a efetivação dos seus consensos morais, bem como também para verificar por que Habermas considera que realiza essa vinculação. Porém, a pluralidade cultural presente nas sociedades pós-tradicionais obriga a este autor lançar mão de um sistema jurídico para a moralidade se realizar nessas. Analisar-se-á aqui esse sistema de modo a se verificar como esse possibilita o uso da argumentação racional para a obtenção de suas normas, o que implica na compreensão do princípio democrático habermasiano, e de como, por meio da coação jurídica, contribui para a efetivação e para a própria obtenção dessas normas de modo que a moralidade seja respeitada nesses processos.This thesis investigated the morality as proposed by Jürgen Habermas in order to determine its relation to communicative action and the legislative system, as defended by the author. To achieve that, at first, the author's observation was verified to comprehend why he believes communicative action can obtain rational consensus as conflict solution, such as, moral conflicts. What has been brought about as a consequence to Habernas demonstration of ethics universal principle, which he defends as discourse principle: defined as necessary of respect, not only to solve moral conflicts, but also, to solve what the theory comprehends as ethical and pragmatical conflicts. Such argument has been observed as one of the aspects employed in the theory to bond the defended contents over which it is applied. Hebermas analysis on Hegel's and Kant's critiques was also investigated to verify why does ethics consider the correlation necessary to moral consensus effectiveness, as well as to verify why does it consider and performs this correlation. However, post-traditional societies cultural plurality has required the author to account for a legislative system to moralitys achievement on such contexts. This system was analyzed in order to verify the formulation of possibilities of rational argumentation to obtain its rules, which implied on the understanding of Habermas democratic principle as well as on how judicial coercion contributes to these rules effectiveness and achievement so that morality came to be respected in these processes
The long reach of commodity frontiers: social reproduction and food procurement strategies among migrant workers in Kenya’s flower farms
No full textConceptualizing global floriculture as a commodity frontier, this article explores rural-urban transfers and in loco production and exchange of food by migrant workers at Naivasha flower farms in Kenya. It highlights how food procurement strategies are central to the reproduction of a cheap labour force and are supported by multi-local family networks. Distant livelihoods and rural ecologies are thus tied to the frontier's interests and are embedded into global chains of cut flowers. We argue that considering reproductive labour strategies is critical to understand the functioning and expansion of commodity frontiers and their impact on peasant families and food circulation
How Uncertainty Affects the Optimal Design and Operation of Renewable Energy Communities: An Italian Case Study
No full textRenewable Energy Communities (RECs) are local aggregations of energy users who can share electricity generated from Renewable Energy Sources (RES) to increase onsite self-consumption. The resulting increased use of RES makes the local balance between energy generation and demand a more challenging task. Indeed, RES have a strong variability associated with their spatial and temporal availability, and an inherent uncertainty associated with variations in weather conditions. This uncertainty can affect any choice on the design and operation of a REC, depending on which of all possible scenarios will occur. This paper aims at evaluating the impact of uncertainty in weather variables (i.e., solar irradiance and ambient temperature) on the optimal life cycle cost (i.e., investment and operation costs) of a REC. The novelty consists of assessing the accuracy of the optimal solutions under uncertainty for different locations of the REC, corresponding to Italian cities with different climatic conditions (i.e., Padova and Palermo). First, the 'best' set of daily stochastic scenarios (i.e., the most representative) of the weather variables is obtained by applying a clustering technique on a 'past' dataset (2005-2008). A Stochastic Programming (SP) model with the 'best' set of scenarios is then used to find the design and operation of the system that minimizes its life cycle cost in a 'future' dataset (2009-2010). The optimal 'stochastic forecasts' solutions are compared with the state-of-the-art 'deterministic forecasts', obtained by solving a Mixed-Integer Linear Programming (MILP) model for average seasonal days. Results show the higher accuracy of the 'stochastic forecasts' in predicting the life cycle costs (errors of 3.5% and 5% for Padova and Palermo, respectively, versus errors of 30% of the 'deterministic forecasts'), taking as reference the 'perfect forecasts' obtained by solving the MILP model with 'perfect knowledge' of data occurred (i.e., for 365 days of a year
How clustering approaches affect the optimal design of future multi-energy systems
No full textThis paper focuses on the correct assessment of the total cost and sizes of the energy conversion and storage units of multi-energy systems operating under uncertain energy market conditions. The objectives are to: i) find the best set of representative days of uncertain electricity and natural gas prices, global solar irradiance and air temperature, with a special focus on prices, influenced by unpredictable socio-economic events; ii) assess the impact of the representative days, including the extreme ones, on the optimal total cost and unit sizes of a multi-energy system. An available historical dataset (2010-2022) of time series is divided by shifting the “present” moment back to 2018 to use the previous period as “past” training dataset (2010-2018) and the subsequent period as two (independent) “future” testing
datasets, featured with different price stability, corresponding to a pre- (2019-2020) and a post- (2021-2022) Russia-Ukraine war scenario, respectively. The novel methodology consists in comparing the “annual” and “seasonal” clustering approaches to obtain, respectively, sets of representative days of the uncertain variables in the entire training dataset or in the training dataset divided into seasons, also
considering different criteria to evaluate the extreme days of electricity price and thermal demand. A Mixed-Integer Linear Programming model of the system is used to optimize the sizes and operation of its units, minimizing the total investment and operational costs in the training dataset. Subsequently, the optimal sizes are fixed to optimize the operation in the two testing datasets. The optimal total cost and sizes are compared with “perfect knowledge” solutions obtained considering all the time series
really occurred in the two testing datasets. Key results highlight that the error of the optimal total cost with respect to the “perfect knowledge” solutions is about 2.5-4% with clustering, compared to 9-17% with the “state-of-the-art” hourly profiles averaged on months or seasons, respectively. This error is higher using seasonal clustering than annual clustering for a number of representative days higher than 12. Moreover, the extreme days of electricity price do not bring a relevant gain in the solution accuracy
because they are very similar to the typical ones in the pre-war scenario and their weight is small in the post-war scenario, given the higher values of the real prices in the period 2021-2022. In contrast, the extreme days of thermal demand are necessary to guarantee a feasible solution
On the Different Fair Allocations of Economic Benefits for Energy Communities
No full textEnergy 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 Effect of Disruptive Events on Optimal Design and Operation of an Energy Community
No full textThe price of energy carriers follows very complex dynamics that are not only closely related to markets but can be significantly influenced by socio-political or environmental events. This paper examines how disruptive events of these types affect the total energy supply costs of multi-energy systems and explore potential actions to mitigate their effects.
A stochastic model based on a CIR (Cox-Ingersoll-Ross) process with jumps is first developed to represent energy price trends featuring sudden peaks. The CIR process is chosen for its mean-reverting behavior, which drives prices back toward a long-term average, and for its ability to ensure price positivity, as observed in electricity markets like the Italian one. The models are used to generate stochastic scenarios of natural gas and electricity prices considering different jump frequencies.
The multi-energy system of a renewable energy community is then considered as a case study. A stochastic optimization problem for the design and operation of this system is formulated and solved, using the generated stochastic scenarios as input, along with the other required input data (treated as deterministic). Results show that accounting for a single energy price peak during the design phase reduces total costs by 2.3%, primarily by increasing PV capacity. This strategic increase in installation costs enables a substantial reduction in net operating costs associated with electricity exchange with the grid when a price peak is expected to occur
MESCO: a Clustering framework for the design Optimization of future Multi-Energy Systems
No full textClustering techniques are the standard to identify representative days of annual trends of energy demand, prices and climatic conditions in Multi-Energy Systems (MES) design. However, the literature lacks guidelines for clustering techniques leading to the ‘best’ design solution of a MES, usually neglecting a complete testing phase on multi-year datasets. This paper presents MESCO, a MES Clustering-Optimization framework to (1) generate representative days using different clustering algorithms (k-means, substitution, k-medoids) and extreme days criteria (null, replacing, adding, iterative); (2) validate the clustering-based design solutions on a ‘past’ dataset (2010-2018) and assess their robustness against two ‘future’ scenarios (Covid-19 pandemic, 2019-2020; Russia-Ukraine war, 2021-2022). Kmeans−iterative clustering-based solutions with 7–9 representative days lead to the lowest relative error in total cost compared to perfect knowledge design solutions based on full time series, with errors of +4% and +25% for 2019-2020 and 2021-2022 scenarios, respectively. While results in other cases may differ, the application of the proposed general framework remains effective in evaluating the accuracy of different clustering algorithms and extreme day criteria in MES design
Is Banning Fossil-Fueled Internal Combustion Engines the First Step in a Realistic Transition to a 100% RES Share?
No full textPlanning 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
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