1,720,989 research outputs found
Including fairness and uncertainties in the design and operation optimization of local multi-energy systems
Full text linkIn 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
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
On the Different Fair Allocations of Economic Benefits for Energy Communities
Full text linkEnergy 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”
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
DOMES: A general optimization method for the integrated design of energy conversion, storage and networks in multi-energy systems
Full text linkA 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..
A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community
Full text linkOptimizing 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
Guidelines for minimum cost transition planning to a 100% renewable multi-regional energy system
Full text linkThe paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system
From exergoeconomics to Thermo-X Optimization in the transition to sustainable energy systems
Full text linkExergoeconomics has played an important role in the study of new energy systems in the last decades. The use of exergy as a “carrier of value” has made it possible to define an unambiguous criterion for allocating costs among energy systems products. The paper shows how exergoecomic procedures of analytical optimization and design improvement on heuristic basis have been progressively replaced by more efficient procedures aimed at minimizing an objective cost function, leaving exergoecomic cost evaluation as the final step. It also highlights how growing concerns about climate change and the continued growth of inequalities in energy availability have broadened the set of objectives in the search for the optimal integration of the design and operation of energy conversion units and network with intelligent methodologies to reduce energy demands. The objective of the article is twofold: i) present the evolution of the main Exergoeconomic methods and show how they have paved the way for Thermo-X Optimization methods; and ii) outline the path for developing a general model of society's entire energy system that includes a broader set of objectives and constraints in addition to economic ones to help build the energy system of the future with a more sustainable perspective
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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