Calculating systems-scale energy efficiency and net energy returns: A bottom-up matrix-based approach

doi:10.1016/j.energy.2013.09.054

Energy

Volume 62, 1 December 2013, Pages 235-247

https://doi.org/10.1016/j.energy.2013.09.054 Get rights and content

Highlights

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An improved bottom-up mathematical method for computing net energy return metrics is developed.
•
Our methodology allows arbitrary numbers of interacting processes acting as an energy system.
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Our methodology allows much more specific and rigorous definition of energy return ratios such as EROI or NER.

Abstract

In this paper we expand the work of Brandt and Dale (2011) on ERRs (energy return ratios) such as EROI (energy return on investment). This paper describes a “bottom-up” mathematical formulation which uses matrix-based computations adapted from the LCA (life cycle assessment) literature. The framework allows multiple energy pathways and flexible inclusion of non-energy sectors. This framework is then used to define a variety of ERRs that measure the amount of energy supplied by an energy extraction and processing pathway compared to the amount of energy consumed in producing the energy. ERRs that were previously defined in the literature are cast in our framework for calculation and comparison. For illustration, our framework is applied to include oil production and processing and generation of electricity from PV (photovoltaic) systems. Results show that ERR values will decline as system boundaries expand to include more processes. NERs (net energy return ratios) tend to be lower than GERs (gross energy return ratios). External energy return ratios (such as net external energy return, or NEER (net external energy ratio)) tend to be higher than their equivalent total energy return ratios.

Introduction

The efficiency of primary energy resource extraction and processing often receives less attention than the efficiency of energy end use. For example, the efficiency of extracting and refining crude oil into gasoline and diesel fuel receives rather less attention than the efficiency of gasoline-powered automobiles. Unfortunately, inefficient energy extraction necessitates larger capital and labor inputs, as well as larger environmental impacts, per unit of final energy consumed. Inefficiencies also result in less useful energy supplied to society per unit of energy resource extracted from the earth.

The concept of energy efficiency is usually applied to a specific process, facility, or technology. The efficiency of supplying energy is more complex: it is fundamentally a property of a system of multiple interacting technologies, not of any individual technology. A device-focused perspective on efficiency ignores the systems-scale aspects of energy transformations from resource acquisition to ultimate degradation to waste heat.

Consider the example of generating electricity with a gas turbine. Gas turbine technical specifications might indicate an operating efficiency of η on a LHV (lower heating value) basis (i.e., for every megajoule (MJ LHV) of natural gas combusted by the turbine, η MJ of electricity will be produced). However, energy is used in natural gas production, processing and transport. Thus, the same gas turbine might produce significantly less than η MJ of electricity for every MJ of natural gas consumed in the full fuel cycle. If one also includes embodied natural gas consumed in manufacturing and supporting the natural gas infrastructure (e.g., steel or cement inputs), as well as the fuel used to support institutions and services required by the gas industry, then the systems-scale efficiency of providing electricity from a gas turbine (defined most comprehensively) would be lower still.

Systems-scale efficiency calculations can ideally permit quantitative comparisons between widely varying energy types, resource locations, and extraction methods. One type of metric obtained from systems-scale analyses is ERRs (energy return ratios). ERRs compare the amount of energy supplied by an extractive industry to the energy consumed in extracting and processing the energy source. Since more energy is generally supplied by extractive industries than consumed, ERRs are generally greater than 1 (rather like a coefficient of performance for a heat pump). Systems of technologies with ERRs ≥1 will supply net energy to society in excess of that which they consume [1].

Commonly used ERRs include the EROI (energy return on investment) and the NER (net energy ratio) [1], [2], [3], [4]. Many ERRs exist. Different ERRs can produce varying insights into system characteristics [3], [4], [5].

In this paper, we present a framework for bottom-up ERR calculations. The method is general and flexible, and is based on a method previously developed in the LCA (life cycle assessment) literature. Following a brief background discussion on ERR methodology, we introduce our mathematical framework. Next we demonstrate this framework with a series of examples of increasing complexity for the oil industry. We next provide a second example for the solar PV (photovoltaic) industry. Lastly we discuss limitations, possible extensions, and potential directions for improvement.

Section snippets

Development of methods for assessing systems-scale energy efficiency

Exploration of systems-scale energy efficiency started in the 1970s [1], [2], [3], [6], [7], [8], [9], [10], [11], [12]. These methods have been labeled NEA (net energy analysis). NEA worked to understand the efficiency of primary resource extraction and processing, and to examine if shifting from one energy resource to another would have significant impacts on the availability of energy resources to society. NEA studies typically emphasize the calculation of an ERR (energy return ratio) such

Matrix based methodology

In this work, we develop a framework for calculating ERRs. Our method advances the literature on NEA by standardizing the terminology used to refer to different NEA ratios and providing an unambiguous mathematical method, rooted in the LCA literature, for computing each ERR from process-level data.

Our framework adapts methods from the LCA literature that use systems of linear equations to model production pathways. Such systems can model complex systems of interacting energy processes and

Application of method – the example of PV manufacture and installation

We now demonstrate our framework with a PV electricity production system. The vector of product flows for the PV system, contains 12 flows and 11 environmental interventions (eq. (43)). For simplicity, we have simplified the numerous material inputs to the PV energy production system to include silicon, aluminum, glass, copper wire, steel and concrete. The system is 90% efficient at converting DC (direct current) PV system output into AC (alternating current) grid power.

PV data are from

Uncertainty and sensitivity

The uncertainties associated with this method are similar in scope and nature to the uncertainties in bottom-up, process modeling-based LCA. These have been well explored in the LCA literature [15], [16], [17], [18]. A key challenge with modeling modern production processes is that data are required for thousands of processes in the economy. Life cycle databases (e.g., GaBi and EcoInvent databases) have been developed to aid in the constriction of rigorous assessments. Another approach is to

Conclusions

Extending on the work of mathematical formulation of process-based LCA, this study presents the first comprehensive, complete framework for process-based computation of ERRs such as NER (net energy return) or EROI (energy return on investment). This framework removes the ambiguity about embodied energy encountered in many previous approaches, and allows for computation of energy consumption across the economy, including direct consumption and an arbitrary number of indirect consumption

Acknowledgments

Matthew Huen, David Murphy, and Carey King provided helpful insights into this work.

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View full text

Calculating systems-scale energy efficiency and net energy returns: A bottom-up matrix-based approach

Highlights

Abstract

Introduction

Section snippets

Development of methods for assessing systems-scale energy efficiency

Matrix based methodology

Application of method – the example of PV manufacture and installation

Uncertainty and sensitivity

Conclusions

Acknowledgments

Energy Policy

Renew Energy

Technol Forecast Soc Change

Energy Convers Manag

Energy

Renew Energy

Ecol Model

Energy

Energy Policy

Energy

Int J Greenh Gas Control

Energy Policy

Energy and resource quality: the ecology of the economic process

Energy analysis workshop on methodology and conventions

A general mathematical framework for calculating systems-scale efficiency of energy extraction and conversion: energy return on investment (EROI) and other energy return ratios

Energies

Year in review; EROI or energy return on (energy) invested

Report of the NSF-Stanford workshop on net energy analysis

Net energy analysis: an energy balance study of fossil fuel resources

Energy and employment impacts of policy decisions

Order from chaos: a preliminary protocol for determining the EROI of fuels

Sustainability

A preliminary investigation of energy return on energy investment for global oil and gas production

Energies

Life cycle environmental and economic assessment of willow biomass electricity: a comparison with other renewable and non-renewable sources