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Environmental multi-objective optimization of the use of biomass resources for energy Carl Vadenbo, Davide Tonini, and Thomas Fruergaard Astrup Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06480 • Publication Date (Web): 17 Feb 2017 Downloaded from http://pubs.acs.org on February 18, 2017

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Environmental Science & Technology

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Environmental multi-objective optimization of the

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use of biomass resources for energy

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Carl Vadenbo†,*, Davide Tonini‡, Thomas Fruergaard Astrup‡

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†

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Zürich, Switzerland

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‡

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2800 Kgs. Lyngby, Denmark

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*Corresponding author: [email protected], telephone +41 44 633 70 66, fax +41 44 633

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ETH Zürich, Institute of Environmental Engineering, John-von-Neumann-Weg 9, CH-8093

Technical University of Denmark, Department of Environmental Engineering, Miljoevej 115,

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KEYWORDS: biomass, biofuels, renewable energy, LCA, multi-objective environmental

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optimization, fuzzy intervals

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ACS Paragon Plus Environment

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ABSTRACT

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Bioenergy is often considered an important component, alongside other renewables, to mitigate

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global warming and to reduce fossil fuel dependency. Determining sustainable strategies for

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utilizing biomass resources, however, requires a holistic perspective to reflect a wider range of

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potential environmental consequences. To circumvent the limitations of scenario-based life cycle

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assessment (LCA), we develop a multi-objective optimization model to systematically identify

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the environmentally-optimal use of biomass for energy under given system constraints. Besides

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satisfying annual final energy demand, the model constraints comprise availability of biomass

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and arable land, technology- and system-specific capacities, and relevant policy targets.

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Efficiencies and environmental performances of bioenergy conversions are derived using

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biochemical process models combined with LCA data. The application of the optimization

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model is exemplified by a case aimed at determining the environmentally-optimal use of biomass

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in the Danish energy system in 2025. A multi-objective formulation based on fuzzy intervals for

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six environmental impact categories resulted in impact reductions of 13-43% compared to the

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baseline. The robustness of the optimal solution was analyzed with respect to parameter

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uncertainty and choice of environmental objectives.

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INTRODUCTION

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Member states of the European Union, as well as many other countries worldwide, have

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committed to promote the use of renewable energy.1-2 Besides harvesting wind, solar, hydro, and

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geothermal energy, the use of biomass is frequently put forward as an important supply-side

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component to meet this objective.3 But a narrow focus on the energy system and on reducing the

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contribution to global warming (GW) may compromise other environmental impact categories,

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causing burden shifting (e.g., through increased stress on water and land resources, nutrients

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enrichment, particulate emissions, etc.). A more holistic perspective is therefore needed to reveal

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conflicting objectives.4-5 For this purpose, effects induced beyond the boundary of the energy

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system should also be considered, including the direct impacts of (bio-)energy provision and the

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indirect environmental consequences caused by, for example, competition with food/feed sector

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for limited agricultural production capacity, which in turn may induce indirect land use changes

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(iLUC).6-9

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Environmental assessments of bioenergy systems are commonly performed using a scenario-

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based life cycle assessment (LCA) approach comparing a limited set of pre-defined combinations

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of biomass feedstock, conversion pathways, biofuels, and final energy services supplied.10-11 In

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this context, the choices related to scope, methodology and reference system of the assessment

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have been found to play a critical role.12-15 The application of scenario-based LCA to complex

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bioenergy systems faces a number of important challenges, including: (i) the definition of a set of

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alternative scenarios to be assessed out of a large number of feasible conversion pathways, (ii)

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methodological choices to consistently deal with the multi-functional character of these

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systems,13 (iii) the consideration of system constraints, for example in terms of balancing overall

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supply and demand of energy or the influence of system-wide policy targets, (iv) the


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interdependencies between the different sub-systems of the energy system, and (v) the analysis

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of conflicting objectives and the systematic identification of efficient trade-offs. LCA-based

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multi-objective optimization shares many features with scenario-based LCA, but may overcome

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several of the aforementioned challenges16. For example, multi-objective optimization

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encompasses ‘all’ feasible system configurations (thereby lowering scenario uncertainty) and

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explicitly captures the consequences of multi-functional product systems and other constraining

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dependencies (thus reducing model uncertainty). On the other hand, covering these aspects

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inflates data requirements, which in turn introduces additional sources of parameter uncertainty.

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To explore the potential for environmentally-optimal regional/national biomass and bioenergy

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strategies, multi-objective optimization formulations including consistent uncertainty analysis

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are needed. Recent studies have advanced the understanding of the influence of life cycle

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inventory/impact assessment (LCI/LCIA) parameter uncertainty for bioenergy/biofuels.11, 17 But

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other relevant aspects remain largely unexplored, such as the influence of the choice of

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environmental impact categories considered.

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Attempts to integrate a fully consequential LCA perspective in an environmental multi-objective

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optimization problem formulation are scarce in the literature. The aim of this work is to advance

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the use of LCA for bioenergy systems to systematically identify robust environmentally-optimal

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biomass utilization strategies for energy under imposed system constraints. We propose to

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combine substrate-specific biochemical process modelling, consequential LCA, and

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mathematical multi-objective optimization techniques in order to (i) capture the effect of biomass

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characteristics on resource consumption, emission levels, and byproduct recovery, (ii) explicitly

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reflect resource availability, system- and process-specific constraints, and policy targets imposed

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upon the solutions, and (iii) systematically identify the optimal strategies that minimize the


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system-wide environmental impact out of all feasible options. For illustration, we apply the

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model to the Danish energy system, building on preceding consequential scenario-based LCA

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studies.8-9, 18-20

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METHODOLOGY

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Model formulation. To enable a systematic identification of the environmentally-optimal use of

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biomass in a given energy system, a static multi-objective optimization model is formulated

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based on a linear program (LP) extension of matrix-based LCA.21-22 The goal is to determine the

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optimal activity levels of a comprehensive set of process options for conversion and provision of

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energy that minimize environmental impacts while satisfying annual final energy demand

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(Figure 1). The conversion of domestic biomass resources and imported biofuels into

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intermediate products and subsequently final energy services (highlighted in orange) are

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indicated on the left of Figure 1, and other renewable and fossil energy product systems on the

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right side.

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*INSERT FIGURE 1 HERE

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The detailed model formulation is provided in section S1 of the Supporting Information (SI) on

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the Web. Annual final energy demand represents the functional unit of the analysis and acts as

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the driving constraint in this LP-LCA model. The final energy demand, reflecting either current

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conditions or a future energy scenario, is subdivided into a set of demand categories (electricity,

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centralized/decentralized district heat, individual heat, process heat, and various transport

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services). Further model constraints considered include:


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technologies, based on biochemical process modeling combined with LCA data.8-9

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Technical process constraints and interdependencies, e.g., meeting a suitable carbon-tonitrogen ratio of the substrate mix for anaerobic co-digestion.

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Substrate-dependent conversion efficiencies and environmental performance of feasible combinations of biomass substrates (or intermediate energy carriers) and conversion

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Availability of own or imported biomass substrates or biofuels and domestic arable land that may be dedicated to energy crop production.

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Aggregated current or foreseen capacities of the conversion technologies throughout the energy system.

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Balance constraints to couple supply and demand of specific combinations of conversion

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technologies, e.g. high-pressure supply of biomethane for large-scale installations vs.

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high-to-low-pressure supply for residential consumption.

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Minimum share of electricity supplied (annually) by flexible technologies to reflect the need for regulating capacity to ensure grid stability, cf. 23.

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Minimum acceptable share of energy from renewables per final energy demand category.

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Diurnal or seasonal fluctuations/variations and the geographical distribution of demand and

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supply of energy are not considered explicitly; typical load factors and average transport

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distances are instead used as proxies to derive the effective availability of plants and transport

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requirements, respectively. Model constraints formulated with upper/lower bounds that are

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satisfied by equality (i.e., without slack) in an optimal solution are referred to as tight constraints.


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The marginal (or dual) values of tight constraints24 (see also section S1.3 in the SI) can be

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interpreted as lost opportunities for further improvement caused by, for example, capacity

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limitations or imposed policy targets forcing resources to be diverted from potentially more

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beneficial uses.

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To improve model flexibility, we disaggregate the bioenergy conversion pathways described in

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Tonini, et al. 8, 9 based on the assumed substitutability of the generated products (Figure 1).

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Primary bioenergy conversions include pretreatment for liquid/solid separation (e.g., for slurry

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manures), pelletization, bioethanol biorefineries, anaerobic digestion, gasification, and pyrolysis.

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Options for intermediate products (e.g., biogas, bioethanol, syngas, solid biofuel, bioethanol-

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molasses, biomethanol, biochar, etc.) include direct utilization (mainly for heat-only or

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combined-heat-and-power, CHP) or upgrading to higher-quality energy carriers (e.g.,

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biomethane, biodiesel, etc.). Upgrading of intermediate bioenergy carriers is referred to as

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secondary conversions, whereas activities intended for final service provision (i.e., supply of

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electricity, heat, and transport services) are denoted tertiary conversions.

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Environmental impact assessment. In order to capture the environmental impacts arising from

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increasing or altering the use of biomass for energy purposes, we adopt a consequential (change-

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oriented) perspective. For each substrate, the induced or avoided impacts expected when

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diverging it from its current use (status quo) to bioenergy supply are accounted for by

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conforming with the approach of Tonini, et al. 9. Similarly, expanding domestic energy crops is

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assumed to displace marginal crop production, causing global demand for arable land to increase

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and ultimately inducing iLUC effects.8 Agricultural products are considered to be traded on

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global markets with full long-term elasticity of supply and that the response to increased demand

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for arable land consists of a combination of intensified production and expansion into nature.


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Following common practice in consequential LCA,25 we also assume that any change in the

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demand for energy- and protein-feed due to the diversion of biomass from feed to energy sector

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(or conversely, by supplying additional feed to the market) is compensated by increased (or

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decreased) production of marginal feed, here assumed to be maize and soymeal, respectively.8

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The LCIs of other (non-bio-based) renewable and fossil energy technologies are sourced from

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the consequential system model of the ecoinvent LCI database (v3.1).26

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Multi-objective formulation. To determine Pareto-efficient solutions to the multi-objective

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optimization problem and to facilitate the analysis of environmental tradeoffs between

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conflicting objectives, we apply the fuzzy linear program (LP) extension for matrix-based LCA

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proposed by Tan, et al. 22. For each objective (i.e., environmental impact category), a fuzzy

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interval is defined by an upper and a lower bound representing completely unacceptable and

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fully acceptable levels of performance, respectively. This formulation aims to maximize the joint

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degree of satisfaction (λ) that can be achieved over a set of objectives simultaneously, according

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to equations (S2-S3) in the SI. Each objective must be satisfied to at least λ over its respective

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fuzzy interval. We define the upper bound (i.e., highest allowable impact) as the impact level in

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the current situation (the baseline year), and the lower bound as the single-objective optimal

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solution for each environmental category (i.e., the lowest achievable impact level, see also

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section S1.2 of the SI).

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Sensitivity analysis. In order to assess the robustness of the multi-objective optimal solution,

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two aspects are addressed: (i) to analyze the influence of model parameter uncertainty on the

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resulting multi-objective optimal solution and (ii) to investigate the sensitivity of the optimal

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outcome to the subjective choice of impact categories considered in the multi-objective

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formulation. For the parameter uncertainty, e.g., in terms of biomass availability, crop yields,


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conversion efficiencies and resource consumption, impacts of background product systems, etc.,

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we apply a Monte Carlo simulation (analogous to the commonly used approach for uncertainty

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analysis in LCA).27-28 For this, the optimization problem is solved iteratively with input

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parameter values sampled from their respective probability distributions. The Monte Carlo

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simulation can be run with two alternative setups, representing different degrees of freedom: in a

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first instance the decision variables (i.e., the scaling vector) are left free, while in the second

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setup the decision space is limited to the choice of bioenergy conversion pathways of the initial

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optimal solution, i.e., based on expected parameter values. The first setup allows for comparing

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the performance of different conversion pathways competing for biomass under uncertainty

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(section S2.3 in the SI). The second lets us evaluate the robustness of the recommendations

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derived from the original multi-objective optimal solution based on the likelihood that they

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would lead to environmentally favorable outcomes.

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Next, we investigate the sensitivity of the optimal outcome to the choice of environmental

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objectives, that is, aspect (ii) above. This is achieved by omitting each impact category once,

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resolving the optimization problem, and comparing the results to the original multi-objective

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solution. This final step not only enables for assessing the sensitivity of the overall outcome

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arising from the selection of objectives, but also provides insights into the tradeoffs between

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conflicting objectives.

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Case study. The Danish energy system is used as an illustrative case due to the data available on

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national biomass resource potential29 and the political ambitions for expanding bioenergy use30.

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Forecasted energy demand, based on a ‘frozen policy’ scenario for the year 2025,31 is split into

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eleven final demand categories (Table S1 in the SI). The 39 biomass substrates considered for

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the case study are ordered into five main categories (with reference/counterfactual scenarios


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indicated in brackets), namely agricultural residues (otherwise left/applied on-field), domestic

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and imported woody biomass (left to decay and inducing LUC, respectively), municipal biomass

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waste (requiring treatment), domestic energy crops and imported biofuels (competing for arable

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land), and industrial food-processing residues (competing on the feed market). The domestic

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biomass potential in 2025 is assumed to be unchanged from that of the current situation. A

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longer time perspective or the application to other geographical locations might require that the

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expected effects of changing climatic conditions or prevailing crop regimes on biomass

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availability are reflected, in turn introducing additional scenario/modelling uncertainty. The

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availability and respective counterfactual treatment/management considered for each biomass

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substrate is presented in Tables S2-S3. We assume that up to 10% of the area currently used for

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barley (representing the marginal crop based on 19, 25, 32-34) may be dedicated to energy crop

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production. Further system constraints include upper bounds on biomass/biofuel imports and

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minimum shares of 20% renewable energy in electricity and heat generation and 10% in road

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and rail transport. Expected availabilities or capacities of different energy carriers and

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technologies are presented in section S.2.1.3 in the SI. A baseline is derived from national energy

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statistics for the year 2013.35

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Six environmental impact categories are considered for the case study, namely global warming

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(GW),36 water footprint (WF; based on water scarcity),37 marine eutrophication (EP),38

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acidification (AC),39-40 particulate matter (PM),41-42 and cumulative energy demand (CED) for

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non-renewable fossil resources 43. Each impact category is first addressed individually and

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subsequently jointly for the multi-objective optimization problem. The parameter uncertainty

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considered for the sensitivity analysis encompasses the availability of domestic biomass

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substrates, crop yields, the efficiencies and auxiliary resource/energy consumption of conversion


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processes, and the aggregated LCIA results of the bioenergy conversions. Other renewable and

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fossil energy technologies are assigned a default uncertainty with standard deviations set to 10%

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of the mean values. Similarly, an uncertainty of 10% is considered for the biomass availability in

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Table S2. Final energy demand was assumed to be fixed, i.e., no uncertainty was considered for

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this model parameter.

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RESULTS & DISCUSSION

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The results of the single- and multi-objective optimal solutions are summarized in Table 1 and

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illustrated in Figure S4 in the SI. A first observation from Table 1 is that every single-objective

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solution performs worse than the 2013 baseline in at least one other impact category. This

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implies that none of these solutions result in an overall improvement, which highlights the

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relevance of a multi-objective formulation to avoid burden-shifting. The characteristics and key

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findings of the single-objective optimal solutions are described in section S2.2.1 of the SI and

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illustrated in Figures S5-S10. A detailed account of the multi-objective optimal results is

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presented in the next section. The overall shares of renewable energy and the contribution from

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bioenergy were determined as the weighted average of all renewables and bioenergy over all

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demand categories.

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*INSERT TABLE 1 HERE

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Multi-objective optimal solution. In the multi-objective formulation of the optimization

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problem, the fuzzy intervals prevent solutions performing worse than the baseline in any of the

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impact categories from being considered. It can be observed from Table 1, however, that none of

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the impact categories are at their respective upper bound in the optimal solution, with the

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objective value for the joint degree of satisfaction (λ) reaching 0.46. This configuration


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outperforms the baseline (2013) across all environmental impact categories simultaneously, with

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the improvement potential ranging from 13% to 43%. This implies that a ‘win-win’-solution is

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feasible for the set of six environmental objectives considered. This multi-objective optimal

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solution is characterized by a high degree of utilization of domestic biomass resources (Figure

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2). Overall, 52% of the energy demand is met by renewables, with 34% from bioenergy and the

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rest predominantly by wind power. Relying mainly on biomass combustion and anaerobic

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digestion, the resulting system configuration has several similarities to the solutions that

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minimize contributions to GW and AC. The potential for domestic energy crops is likewise used

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for willow. Liquid biofuels are not imported due to the implications for WF, EP-marine and PM.

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Instead, the minimum share of renewables in road transport is satisfied by upgrading biogas and

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syngas to biomethane and liquid biofuels, respectively. The set of conflicting objectives

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ultimately defining the optimal multi-objective solution, and the related trade-offs, are further

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analyzed and discussed in the following sections.

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Sensitivity analysis – robustness of the optimal solution. The multi-objective solution under

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model parameter uncertainty is shown in Figure 3a-b as the relative frequency of optimality (i.e.,

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the share of the 500 iterations in which an activity is utilized) for each option of (a) primary and

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(b) secondary/tertiary bioenergy conversions. Focus is here placed on the robustness of the

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original solution to the multi-objective optimization problem, and the likelihood that

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environmentally favorable outcomes may be expected thereof. For this purpose, we use the

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second Monte Carlo simulation setup outlined above, i.e., with choice of conversion pathways

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limited to the selection of the original multi-objective solution. (The sensitivity results for single


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objectives and for the first, less constrained, model setup are provided in Figures S12-S25 in the

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SI.)

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The results in Figure 3a indicate that the recommendations for whether or not to utilize a specific

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biomass for energy is generally rather robust (with frequencies >90%) for woody biomass and

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several agricultural residues. Direct combustion dominates for woody and most lignocellulosic

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biomass and some waste streams, whereas anaerobic digestion (including pretreatment)

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represents the primary conversion pathway for the manures, sewage sludge, and wild grass.

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Willow was chosen among the domestic energy crops in the original multi-objective optimal

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solution and is also favorable in 69% of the iterations. Substrates otherwise used for animal feed

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(representing about 10% of overall potential) remain excluded, as in the original solution.

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The robustness of the optimal use of intermediate and final bioenergy carriers is shown in Figure

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3b. Solid and residual biofuels are predominately used for heat-only, with a fraction of the solid

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biofuels sent to combustion for CHP in 20% of the iterations. Biogas and syngas are generated in

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all iterations. The first is upgraded to biomethane to enable gas grid distribution and the second

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is utilized for the production of liquid biofuel. The biomethane is subsequently split between

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utilization for CHP, heat-only, or in transport. Bio-oil is used solely for heat. The renewable

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energy targets in the transport sector are mainly met by liquid biofuels produced domestically

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from syngas or transesterification of used cooking oil. Additional biodiesel is imported in 52% of

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the iterations. Imported bioethanol, in contrast, was not utilized in the original solution and hence

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also excluded here (in agreement with the results of the less constrained model setup in Figure

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S25a). Wood chips and pellets are imported in 53% and 91% of the iterations, respectively.

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These results indicate that the environmental benefits of importing wood chips and biodiesel, as


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first suggested by the multi-objective optimal solution, represent the most ambiguous outcomes

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of the analysis.

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*INSERT FIGURE 3 (top AND bottom) HERE

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Sensitivity analysis – multi-objective formulation. The fuzzy interval formulation of the multi-

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objective optimization problem can accommodate a large number of objectives. For the case

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study, the selection of the six environmental impact categories to be considered was made based

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on expected relevance and data availability. The influence of including each environmental

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impact category is presented in Figure 4. The results show that omitting impact categories GW or

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AC will not affect the performance of the multi-objective optimal solution, as the overall λ-value

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remains unaffected (λ =0.46) in these cases. GW and AC are hence not compromised in the

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determining tradeoffs. It should be noted, however, that the solutions minimizing GW and AC

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share several key features (compare Figures S5 and S8), e.g., in terms of the contribution of solid

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biofuel combustion, anaerobic digestion, or woody biomass imports. In contrast, the remaining

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four impact categories (WF, EP-marine, PM, and CED-fossil) cannot be further improved

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without compromising the performance of each other. It can be observed from Figure 4 that

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leaving out EP-marine from the multi-objective formulation leads to the highest achievable λ-

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value (0.62), followed by the cases without CED-fossil (0.61), WF (0.52) and PM (0.49). In all

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but the solution excluding CED-fossil, the resulting performance of the omitted impact

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categories exceeds the levels of the 2013 baseline (i.e., above 100% in Figure 4), whereas the

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impact on CED-fossil increases from 87% to 90% of the baseline level. Eliminating WF from the

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multi-objective formulation leads to a shift in the cultivation of domestic energy crops from

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willow to sugar beet for bioethanol production. Excluding EP-marine favors import of biodiesel,

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direct anaerobic co-digestion of manures with lignocellulosic residues, and woody and waste


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biomass are either combusted or digested. Most of these pathways, indeed, involve N-emissions

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to water bodies due to leaching from mineral/organic fertilizer application. Omitting PM from

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the multi-objective formulation leads to a higher degree of straw/stover combustion, originally

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utilized in anaerobic co-digestion. Removing CED-fossil mainly results in less woody biomass

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being imported for heat generation, thereby slightly increasing the contributions to GW and AC,

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and decreasing the contributions to WF, EP-marine, and PM compared to the original solution

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(Figure 4).

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Discussion – case study findings. The marginal values of the renewable targets imposed on the

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case study suggest that the final demand categories light and heavy road transport are associated

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with an environmental lost opportunity, i.e., that the overall performance of this optimal solution

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may be improved by omitting these targets. Relaxing the corresponding constraints results in an

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improvement of the mutual degree of satisfaction from λ=0.46 to 0.48 for the multi-objective

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optimal solution. This improves the performance over the whole set of impact categories

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(between -0.6% and -3.3%). These results indicate that the use of constrained biomass resources

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for electricity and/or heat is preferable over the transport sector, as long as the former demand

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categories would otherwise be met by coal. The results of the sensitivity analysis suggest that the

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impact category EP-marine, followed by CED-fossil, WF, and PM, exercises the largest

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individual influence on the overall performance of the multi-objective solution. By

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compromising the performance of each other, these impact categories also represent decisive

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tradeoffs, e.g., in terms of the choice between direct combustion versus conversion pathways

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involving nutrient recovery (due to N-leaching), or the extent of biomass/biofuel imports

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(causing higher water-related impacts). In contrast, GW and AC appear to be non-essential


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objectives to obtain this optimal solution, meaning that they can be omitted without introducing

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any approximation error in the formulation (see Guillén-Gosálbez 44). This can here be explained

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by the correlation between GW, AC, and CED-fossil, with the latter representing the limiting

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tradeoff in conjunction with the remaining objectives. Biogenic CO2-emissions were here

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considered climate-neutral, but recent advances in assessing the climate relevance of forest

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bioenergy45 might reduce the level of correlation between GW and CED-fossil.

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Small and statistically insignificant differences in the underlying LCIA results may, in a

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deterministic optimization model, lead to an overly unambiguous picture of the optimality of

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end-uses and substitution options for bioenergy.11 The influence of parameter uncertainty was

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therefore analyzed through Monte Carlo simulations using two complementary model setups, as

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described above. Given the large number of feasible conversion pathways for each biomass

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substrate, it may be expected that a solution that performs better than the original multi-objective

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optimal solution can be found in each iteration. By constraining the decision variables to the

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reduced set of pathways, it was found that the recommendations for these biomass resources can

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be considered robust, although the original conversion pathways might not consistently represent

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the most favorable solution under the considered parameter uncertainty.

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Discussion - modelling approach. The described modelling approach circumvents several

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short-comings of scenario-based LCA studies, most notably the ability to cover a vast amount of

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feasible combinations and to identify the global optimum or Pareto-efficient solutions thereof.

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Specifically, by combining mass/energy flow analysis, consequential LCA, and mathematical

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optimization techniques, we are able to (i) capture the effect of biomass characteristics on

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resource consumption, emission levels, and byproduct recovery, (ii) explicitly reflect resource

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limitations, process-specific constraints, and policy targets imposed upon optimal solutions, and


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(iii) systematically identify the configurations that minimize the system-wide environmental

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impact or represent efficient trade-offs between conflicting environmental objectives.

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Furthermore, and as demonstrated in the case study, an optimization approach also provides a

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consistent and transparent format for considering uncertainties and for assessing the related

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sensitivities. This would also be possible in scenario-based LCA by tracking the effects through

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multiple single-outcome assessments. This, however, is hardly practical. Thus, while general

349

frameworks for quantitative uncertainty assessment of LCA28 apply to our optimization model, it

350

also offers enhanced opportunities to capture the effects of dependencies.

351

It should be noted with respect to (i) that increasing biomass exploitation may be associated with

352

increasing efforts per additional unit of supply, e.g., from transport requirements.46 Instead of

353

considering a few highly aggregated biomass categories with a non-linear supply-cost, we assess

354

39 substrates individually on the basis of their specific biochemical properties. This leads to a

355

piece-wise linear supply-cost for bioenergy over the full range of substrates. Given the relatively

356

limited geographical scope of the case study, we consider a linear response an appropriate

357

approximation. The unit processes of the feasible conversion pathways were aggregated up to the

358

point where key intermediate bioenergy carriers or final energy services are provided. The

359

definition of a common pool of (perfectly substitutable) interchangeable intermediate bioenergy

360

carriers of the same kind represents a generic modelling choice. Allowing for more flexibility to

361

introduce or modify conversions at any stage throughout the model building, this approach also

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meant that any qualitative differences among these same-kind bioenergy carriers are disregarded

363

in subsequent conversion steps. This may introduce higher uncertainty for the input-dependent

364

emissions or the resource requirements for emission abatement.


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365

It has been questioned whether a price on fossil carbon emission alone will be sufficient to boost

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biomass/bioenergy utilization due to the highly uncertain future development of these markets.14

367

Establishing and expanding bioenergy systems are hence likely to require additional financial

368

incentives. On this basis, public spending to promote increased or altered use of biomass

369

resources should be based on robust sustainability criteria, e.g., consequential LCA or ecosystem

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services as illustrated for the UK biogas sector.5 Our modelling approach offers a powerful tool

371

to provide decision makers with holistic environmental information that supplements market and

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energy system analyses, e.g., 2, 47, as a basis for identifying potentially constraining factors in

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policy design. The results of our static optimization model should be interpreted as an upper limit

374

for the environmental improvement potential under the constraints considered for a given energy

375

system, as referred to in (ii) above. Further insights may be gained by increasing the complexity,

376

e.g., by adding spatial or temporal resolutions. This may enable logistic constraints, diurnal and

377

seasonal fluctuations in demand and supply, the interaction between intermittent and grid-

378

stabilizing energy technologies, and importance of transmission capacities for exchange with

379

adjacent energy systems to be analyzed.47-51 A too narrow focus on the energy system may cause

380

alternative (potentially more sustainable) uses of biomass to be neglected, e.g., for feed, or

381

biogenic waste anyway requiring treatment, cf. Jablonski, et al. 52. The information obtained

382

from energy systems analysis can serve as input for detailed LCAs,53 as demonstrated by

383

Turconi, et al. 54. To further close this gap, the integration of dynamic and spatial aspects of the

384

energy system into LCA and environmental optimization problems hence represents an

385

important area for future research.

386

Regarding point (iii) above, it should be noted that the multi-objective approach based on fuzzy

387

intervals chosen here does not represent an explicit weighting of the individual objectives.


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388

Nevertheless, the upper and lower bounds of the intervals introduce a relative importance of the

389

different environmental impact categories.22 The use of a particular reference year as the basis

390

for the upper bound of the fuzzy objective formulation (above which any outcome is considered

391

completely unacceptable) represents a subjective modelling choice, and the sensitivity

392

introduced by this step may consequently be analyzed by shifting the baseline year.

393

Alternatively, the upper bounds could be defined based on more ambitious or stringent targets.22

394

These bounds could reflect levels set by binding political commitments, or require adhering to

395

the concept of planetary boundaries. Fuzzy intervals based on political targets offer an alternative

396

approach to aggregated distance-to-target LCIA methods, e.g., the Swiss ecological scarcity

397

method,55 with the advantage that they enable efficient trade-offs between conflicting impact

398

categories to be identified systematically. Multi-objective optimization techniques, such as the

399

methods based on fuzzy intervals22 or objective reduction44 represent important opportunities for

400

dealing with the multitude of environmental issues, and any potential tradeoffs thereof, which is

401

essential for a holistic perspective of environmental optimization problems.

402


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403

FIGURES AND TABLES

404

Figure 1. Scope (superstructure) of the static optimization problem with the goal to identify the

405

environmentally-optimal utilization of biomass resources in a regional or national energy system.

406

Processes are indicated in framed grey boxes, final energy demand categories are highlighted in

407

orange, and the arrows represent the main energy flows. Ancillary energy inputs to the

408

conversion processes (e.g., electricity, transport requirements, etc.) have been omitted for clarity.

409

Figure 2. Optimal system configuration maximizing overall degree of satisfaction over six

410

impact categories (using the performance of the 2013 baseline scenario as upper bound for each

411

respective objective) for the 2025 scenario.

412

Figure 3. Relative frequency of optimality for the utilization of biomass substrates among the

413

primary conversions (top), and of the intermediate bioenergy carriers among secondary

414

(upgrading) and tertiary (provision of final energy service) conversions (bottom), respectively,

415

from the Monte Carlo simulation. The selection of feasible conversion pathways is fixed that of

416

the multi-objective optimal solution for based on expected parameter values. Dedicated energy

417

crops are indicated with an asterisk (*; top graph). Abbreviations: AD: anaerobic digestion; LF:

418

liquid fraction; L/S-sep.: liquid/solid separation; UoL: use-on-land.

419

Figure 4. Sensitivity analysis of the multi-objective solution with respect to the choice of

420

environmental objectives (i.e., impact categories) considered. The performance of individual

421

impact categories is expressed relative to the respective impact levels of the 2013 baseline

422

(100%). The resulting optimal λ-values (i.e., mutual degree of satisfaction) are shown on the

423

secondary axis.


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424

Table 1. Comparison of the environmental performance of the optimized 2025 scenario

425

expressed as relative to the impacts of the 2013 baseline scenario (indicated as 100%) for single-

426

objective optimal solutions (GW, WF, EP-marine, AC, PM, CED-fossil) and the multi-objective

427

optimal solution. The color-shade highlights the best (light) to worst (dark) performance per

428

impact category, i.e., column-wise.

Year

Scenario/objective

GW

2013

baseline min global warming (GW) min water footprint (WF) min marine eutrophication (EP) min acidification (AC) min particulate matter (PM) min fossil resources (CED) multi-objective (overall λ = 0.46) category-specific λ

100% 67% 100% 98% 77% 88% 73% 78% 0.66

2025

Environmental impact category EPWF AC PM marine 100% 100% 100% 100% 261% 99% 82% 156% 7% 115% 130% 203% 153% 7% 96% 121% 167% 81% 71% 99% 15% 112% 87% 53% 324% 195% 96% 137% 57% 57% 81% 78% 0.46 0.46 0.68 0.46

CEDfossil 100% 79% 99% 99% 84% 93% 72% 87% 0.46

429 430 431

ASSOCIATED CONTENT

432

Additional information on the optimization model formulation and supplementary case study

433

data, results, and sensitivity analysis are provided in the Supporting Information (SI), freely

434

available at http://pubs.acs.org.

435

AUTHOR INFORMATION

436

Corresponding Author

437

*Carl Vadenbo, [email protected], telephone +41 44 633 70 66, fax +41 44 633 10 61

438

Present Addresses


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439

†

440

Zürich, Switzerland

441

‡

442

2800 Kgs. Lyngby, Denmark

443

Author Contributions

444

The study design was developed by all of the authors, C. Vadenbo and D. Tonini carried out the

445

analysis, and all authors contributed to the interpretation. All authors have contributed and given

446

approval to the final version of the manuscript.

447

Funding Sources

448

This work was conducted within the IRMAR project (grant no. 11-116775) funded by the Danish

449

Council for Strategic Research. The authors declare no competing financial interest.

450

ACKNOWLEDGMENT

451

The authors are grateful for the financial support from the IRMAR project (grant no. 11-116775)

452

funded by the Danish Council for Strategic Research.

453

ABBREVIATIONS

454

AC: acidification; AD: anaerobic digestion; CED: cumulative energy demand; CHP: combined

455

heat-and-power; EP: eutrophication; GW: global warming; LCA: life cycle assessment; LF:

456

liquid fraction; L/S: liquid/solid (separation); PM: particulate matter (respiratory inorganics);

457

UoL: use-on-land; WF: water footprint

ETH Zürich, Institute of Environmental Engineering, John-von-Neumann-Weg 9, CH-8093

Technical University of Denmark, Department of Environmental Engineering, Miljoevej 115,


22

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Environmental Multiobjective Optimization of the ... - ACS Publications

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