Energy technology allocation for distributed energy resources: A strategic technology-policy framework

被引:37
|
作者
Mallikarjun, Sreekanth [1 ]
Lewis, Herbert F. [2 ]
机构
[1] SUNY Stony Brook, Coll Engn & Appl Sci, Dept Technol & Soc, Stony Brook, NY 11794 USA
[2] SUNY Stony Brook, Coll Business, Stony Brook, NY 11794 USA
关键词
Distributed generation; Distributed energy resources; Optimal allocation; Multi-objective optimization; Data envelopment analysis; United States energy policy; MULTICRITERIA DECISION-MAKING; DATA ENVELOPMENT ANALYSIS; GREENHOUSE-GAS EMISSIONS; MULTIOBJECTIVE OPTIMIZATION; OPTIMAL-DESIGN; TRIGENERATION SYSTEMS; PROGRAMMING APPROACH; THERMOENVIRONOMIC OPTIMIZATION; ENVIRONMENTAL IMPACTS; DISPERSED GENERATION;
D O I
10.1016/j.energy.2014.05.113
中图分类号
O414.1 [热力学];
学科分类号
摘要
Distributed energy resources (DER) are emerging rapidly. New engineering technologies, materials, and designs improve the performance and extend the range of locations for DER. In contrast, constructing new or modernizing existing high voltage transmission lines for centralized generation are expensive and challenging. In addition, customer demand for reliability has increased and concerns about climate change have created a pull for swift renewable energy penetration. In this context, DER policy makers, developers, and users are interested in determining which energy technologies to use to accommodate different end-use energy demands. In this paper, we present a two-stage multi-objective stiategic technology-policy framework for determining the optimal energy technology allocation for DER. The framework simultaneously considers economic, technical, and environmental objectives. The first stage utilizes a production frontier estimation model for each end-use to evaluate the performance of each energy technology based on the three objectives. The second stage incorporates factor efficiencies determined in the first stage, capacity limitations, dispatchability, and renewable penetration for each technology, and demand for each end-use into a bottleneck multi-criteria decision model which provides the Pareto-optimal energy resource allocation. For demonstration purposes, we apply the framework to a dataset constructed for a typical commercial building located in the Northeastern United States and discuss the results. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:783 / 799
页数:17
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