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Research Papers: Energy Conversion/Systems

Upper Level of a Sustainability Assessment Framework for Power System Planning

[+] Author and Article Information
Sergio Cano-Andrade

Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061;
Department of Mechanical Engineering,
Universidad de Guanajuato,
Salamanca, Guanajuato 36885, Mexico
e-mails: sergioca@vt.edu;
sergio.cano@ugto.mx

Michael R. von Spakovsky

Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061
e-mail: vonspako@vt.edu

Alejandro Fuentes

Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061

Chiara Lo Prete

John and Willie Leone Family Department
of Energy and Mineral Engineering,
The Pennsylvania State University,
University Park, PA 16802

Lamine Mili

Bradley Department of Electrical
and Computer Engineering,
Northern Virginia Center,
Virginia Tech,
Falls Church, VA 22043

A “superstructure” or “superconfiguration” is a system configuration that contains all the possible components and interconnections from which the optimal system configuration is found [21]. The optimal system configuration is obtained by synthesizing, i.e., extracting a subset from, this superstructure or superconfiguration.

Synthesis refers to the reduction of a superstructure or superconfiguration by means of optimization in order to obtain the optimum configuration of a system defined by its components and their interconnections [21]. Design refers to finding the optimum component characteristics of the synthesized system at the most constrained point [21].

By “detailed” it is meant that the design of every component within each producer or storage technology is optimized as that technology competes within the MG network and the optimal capacity for each technology is determined.

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received March 6, 2015; final manuscript received March 11, 2015; published online April 8, 2015. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 137(4), 041601 (Jul 01, 2015) (11 pages) Paper No: JERT-15-1104; doi: 10.1115/1.4030154 History: Received March 06, 2015; Revised March 11, 2015; Online April 08, 2015

This paper describes the upper level of a two-tiered sustainability assessment framework (SAF) for determining the optimal synthesis/design and operation of a power network and its associated energy production and storage technologies. The upper-level framework is described, and results for its application to a test bed scenario given by the Northwest European electricity power network presented. A brief description of the lower level of the SAF is given as well. In order to analyze the impact of microgrids (MGs) in the main network, two different scenarios are considered in the analysis, i.e., a network without MGs and a network with MGs. The optimization is carried out in a multi-objective, quasi-stationary manner with producer partial-load behavior taken into account via nonlinear functions for efficiency, cost, and emissions that depend on the electricity generated by each nonrenewable or renewable producer technology. Results indicate for the particular problem posed and for the optimal configurations found that including MGs improves the network relative to reductions in capital and operating costs and to increases in network resiliency. On the other hand, total daily SO2 emissions and network exergetic efficiency are not improved for the case when MGs are included.

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Figures

Grahic Jump Location
Fig. 1

Schematic representation of the SAF

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Fig. 2

Schematic representation of the Northwest European electricity network [34,35]

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Fig. 3

Sizes of the optimum network configurations in the Pareto set for scenario 1

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Fig. 4

Sizes of the optimum network configurations in the Pareto set for scenario 2: (a) main grid configurations and (b) MG configurations (residential, commercial, and industrial)

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Fig. 5

Optimal daily SO2 emissions versus optimal total daily costs (capital and O&M)

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Fig. 6

Optimal daily exergy use versus optimal total daily costs (capital and O&M)

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Fig. 7

Optimal resiliency (penetration of MGs) versus optimal total daily costs (capital and O&M)

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