Scalfati, Andrea (2017) Optimal sizing of distributed energy resources in microgrids. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Optimal sizing of distributed energy resources in microgrids
Creators:
CreatorsEmail
Scalfati, Andreaandrea.scalfati@unina.it
Date: 9 December 2017
Number of Pages: 132
Institution: Università degli Studi di Napoli Federico II
Department: dep10
Dottorato: phd034
Ciclo di dottorato: 30
Coordinatore del Corso di dottorato:
nomeemail
Riccio, Danieledaniele.riccio@unina.it
Tutor:
nomeemail
Fantauzzi, MaurizioUNSPECIFIED
Iannuzzi, DiegoUNSPECIFIED
Date: 9 December 2017
Number of Pages: 132
Uncontrolled Keywords: Microgrids; Distributed Energy Resources; Optimization
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-IND/32 - Convertitori, macchine e azionamenti elettrici
Area 09 - Ingegneria industriale e dell'informazione > ING-IND/33 - Sistemi elettrici per l'energia
Date Deposited: 26 Jan 2018 12:29
Last Modified: 02 Apr 2019 11:09
URI: http://www.fedoa.unina.it/id/eprint/12106

Abstract

This work is focused on the optimal sizing of the Distributed Energy Resources included in a Microgrid. In recent years Microgrids are one of the most relevant research topics in electrical power systems. They are electricity distribution systems containing loads and distributed energy resources that can be operated in a controlled, coordinated way either while connected to the main power network or while islanded, and they are considered a key component of the smart grid scenario, aimed at obtaining better integration of distributed energy resources, increasing energy efficiency and reliability of the whole system, and providing the possibility to improve power quality and to achieve grid-independence to individual end-user sites. Despite the strong consensus existing among researchers and stakeholders on the variety and importance of the advantages deriving from the implementation of the Microgrid paradigm in modern electrical distribution systems, their widespread diffusion is hindered from cost considerations and from the difficulties in conducting a comprehensive cost-benefit analysis and in identifying qualified modalities for system design and management. Although the accurate evaluation of the economic results originating from the deployment of a µG is a demanding task, due to considerable uncertainties affecting the required input data, the complexity of system model and market dynamics, difficult representation of the economic value for some outcomes, the identification of efficient methodologies for the optimal system design is important to allow appropriate analyses and informed choices on the opportunity and feasibility of µG realizations. A fundamental aspect involved in the Microgrid design process, which constitutes the object of this work, is the choice and sizing of Distributed Energy Resources to be installed, including both Distributed Generators (DGs) and Electrical Energy Storage Systems (ESSs). The thesis includes four chapters: - Chapter 1 is an introduction to Microgrids, outlining their definition and main characteristics, the role they can play in present and future power systems, expected benefits and challenges related to their adoption and diffusion. - Chapter 2 introduces different approaches applicable to the problem of optimally sizing the distributed energy resources included in a microgrid; a categorization is made distinguishing analytical approaches, mathematical programming approaches and heuristic approaches, then various techniques used to deal with the uncertainty affecting design parameters are presented: sensitivity analyses, Stochastic Optimization, Sample Average Approximation, Robust Optimization and Decision Theory. - Chapter 3 presents a new analytical approach aimed at the optimal sizing of energy storage systems in DC microgrids, pursuing the objective to improve the efficiency of energy supply through the minimization of line losses; the DC µG under study is characterized by the presence of loads, fossil and renewables based generation units and storage devices; numerical applications show the effectiveness of the method and allow implementing sensitivity analyses to identify ratios between costs of energy and cost of storage devices which make their installation convenient. - Chapter 4 is focused on the application of mathematical programming approaches to the problem of optimally sizing, from an economic perspective, the Distributed Energy Resources included in a Microgrid; the proposed procedure is based on Mixed Integer Linear Programming and allows to determine the optimal sizes of Distributed Energy Resources, i.e. distributed generators and storage devices, which minimize the Microgrid Total Cost of Ownership, given location and load characteristics, also considering the opportunities of Load Management related to the presence of different quotes of controllable loads. Two variants of the sizing procedure are presented: the first uses a deterministic approach, not considering the uncertainties that affect design parameters, while the second uses a Robust Optimization approach to deal with them. In both cases, the performance of the sizing results against uncertainty is evaluated a-posteriori by means of Monte Carlo simulations. Numerical applications to a case study, referring to a DC µG with PV generation, storage and a certain amount of flexible load, are reported to show the effectiveness of the proposed methodology and allow different useful considerations. Three appendices accompany the above mentioned chapters: - Appendix A, linked to Chapter 3, presents an application of the superposition principle to derive a simple analytical expression of the power losses caused by the circulation of currents through the resistances of branch lines connecting the nodes of a DC network, depending on the nodal currents and the conductance matrix of the network; - Appendix B details the calculations and operations made to adapt the standard AC LV CIGRE distribution network to be used as a DC test grid in the numerical applications of Chapter 3; - Appendix C describes a well-established methodology of mathematical programming, useful to force different variables not to being simultaneously different from zero while preserving the linearity of the problem formulation, used in Chapter 4 to prevent solutions where energy is sold to the grid and bought from the grid at the same time (which is physically impossible on a single PCC).

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