Assessing the performance of biogas plants with multi-criteria and data envelopment analysis
Summary (4 min read)
- Over the last two decades, a growing environmental awareness has changed the focus of energy planning processes from an almost exclusive concern with cost minimization of supply-side options to the need of explicitly including multiple and potentially conflicting aspects, such as cost and environmental issues, in decision support models.
- The Kyoto Protocol, the EU Renewables Directive 2001/77/EC, and the European Biomass Action Plan are examples of ambitious political goals fostering the development of energy conversion technologies based on RES.
- With these intentions in mind, this paper also addresses the challenge of determining how MCDA methods can be used in the context of efficiency evaluation, trying to keep the spirit behind DEA, while being able to use MCDA’s capabilities of explicitly incorporating the preferences of a decision-maker, not necessarily in the form of trade-off restrictions.
- Uncertainty is an intrinsic characteristic of real-world problems arising from multiple sources of distinct nature.
- In Section 5 the findings from the analysis are discussed and some conclusions are drawn.
- The attainment of high levels of performance is a key issue for the success of every organization.
- DEA models use these inputs and outputs to compute an efficiency score for a given DMU when this particular DMU is compared with all the other DMUs considered.
- The weights are chosen by the LP model such that a DMU is ‘‘shown in its best light”, i.e., that its efficiency score is maximized.
- The CCR model was presented in the seminal work of Charnes et al. (1978).
- In fact, the inputs and outputs are not generally equally relevant and some preference information must be included in the analysis.
- The above-mentioned considerations about DEA led us to envisage the use of MCDA models to perform efficiency evaluation.
- In assessing the performance of DMUs in which technical, economic and environmental aspects are at stake, it is often important to use known standards (or theoretical maxima) and efficiency profiles.
- The ELECTRE TRI method belongs to the ELECTRE family of multi-criteria methods developed by Bernard Roy and his co-workers (Roy, 1996).
- Also, a set of indifference (qj), preference (pj) and veto (vj) thresholds for each criterion j and reference profiles can be defined.
- Information about these parameter values can be provided through the introduction of intervals, linear constraints, or even sorting examples (which are translated into constraints on the parameters that guarantee that those example results are reproduced).
3. Case study
- In Austria, an effective promotion of renewable energy technologies has been pursued in recent years, driven by the need to achieve ambitious energy and climate policy goals.
- 1 Note that in 2006 a revised Green Electricity Act and Ordinance entered into force, with amended feed-in tariffs and budget restrictions (BGB1.
- Please cite this article in press as: Madlener, R. et al., Assessing the perf Journal of Operational Research (2008), doi:10.1016/j.ejor.2007.12.051.
- As a result, many different technologies, design concepts, and specific applications occurred on the market, some of which were either not very productive, energy-efficient, or reliable.
- Due to the attractive feed-in tariffs granted, anaerobic digestion of energy crops currently mainly aims at the generation of electricity, and much less so at heat generation (or the feed-in of purified biogas into the natural gas grid, if available).
4.1. Description of the data and parameters used
- The DMUs considered are a representative set of energy crop digestion plants in Austria, aimed at covering the whole spectrum of existing plant types and operating conditions.
- Cooling, safe transport and appropriate storage were also scrutinized.
- The sampled installations are geographically well distributed over the country.
- The main groups of evaluation aspects at stake for assessing the efficiency of energy crop digestion plants are: (1) substrate provision, storage and pre-treatment; (2) biogas production (by means of anaerobic digestion); (3) net utilization of heat and electricity; (4) digestate handling and disposal; and (5) greenhouse gas (GHG) emissions.
- For further details on data collection see Braun et al. (2005, 2007) and Laaber et al. (2005), and for further details about the various inputs and outputs and DEA model specifications scrutinized see Madlener (2006).
- In the first DEA model considered in this paper, the authors have used substrate (i2) and labor (i1) as inputs and the amount of net electricity (o1) and external heat (o2) as outputs.
- See Scheel, 2001; Seiford and Zhu, 2002), and although the choice influences the results, there does not seem to be an undisputed ideal method to handle undesirable outputs (Dyson et al., 2001).
- A scale transla- Please cite this article in press as: Madlener, R. et al., Assessing the perf Journal of Operational Research (2008), doi:10.1016/j.ejor.2007.12.051 tion was used to account for the negative net values, another modeling option known to have an influence on the results (Lovell and Pastor, 1995).
- On the other end of the spectrum, DMUs 5, 13, 14, 25, 26, 33, and 39 appear as some of the worst-performing plants, irrespective of whether GHG emissions are considered or not.
- Keeping the same inputs and outputs presented here, the main implications of using the variable returns to scale version (BCC-O) would be to add DMU 15 to the set of efficient units, without GHG emissions, and adding DMUs 33 and 38 to the set of efficient units, with GHG emissions considered.
- In the MCDA approach the objective was to identify groups of DMUs that could be assigned to different efficiency labels, rather than computing a precise efficiency score or deriving a complete ranking.
- Each plant has to be assigned to one of these ordered categories, according to the multiple evaluation criteria.
- The indicators would then be labor/ODS (i1/i2), electricity/ODS (o1/i2), heat/ODS (o2/i2), and GHG/ODS (o3/i2).
- Note that although this approach leads to a high number of indicators as the number of criteria increases, it mimics the spirit of DEA: to allow each DMU to be evaluated according to multiple indicators and to choose the most favorable indicators (within the constraints that the decision-maker may impose, as the authors will illustrate further below).
- DMUs in the best categories, several options can be envisaged: (1) to make the category bound more demanding; (2) to require the support of more than one indicator (e.g., the support of half of the indicators, as depicted in Fig. 4); and/or (3) to add some information about the relative power of the indicators.
4.4. Comparing the results
- The DMUs in Fig. 4 are ranked by order of their DEA efficiency score (for the case with GHG emissions), from the worst (DMU 26) to the best (the last six DMUs – 12, 15, 17, 18, 20, and 28 – have an efficiency score of 1).
- The DMUs in Figs. 5 and 6 have similar profiles with few exceptions, and four of them are among the worst according to both approaches (DMUs 5, 13, ormance of biogas plants with multi-criteria and data ..., European 0.00 0.25 0.50 0.75 1.00 Lab. ODS GHG Elec.
- Heat D EA fa ct or s (n or m al iz ed ) (best) 0.00 0.25 0.50 0.75 1.00 Elec./Lab. Elec./ODS.
- Fig. 7 depicts the profiles of the DMUs with the best performance according to DEA, the efficient ones; Fig. 8 depicts the profiles of the best-performing DMUs according to MCDA which the authors have defined to be those that reach C4 on half of the indicators (at least) and are not placed in C1 by any indicator (i.e., ‘‘pessimistic” classification is C2 or better).
- The difference between the approaches can be diminished as the number of categories increases, as the discrimination among DMUs would increase in the MCDA analysis.
4.5. Further analyses with IRIS
- The choice of the best and worst DMUs in the MCDA study was performed without making any distinction between the indicators.
- This implies that the importance of g2 (electricity/ODS) cannot be lower than the importance of g1 (electricity/labor).
- Optionally, the ELECTRE TRI models also allow incorporating veto thresholds, such that, for instance, a DMU that is classified as C1 according to a given indicator will not be able to reach category C4 in a multi-criteria evaluation.
- A form which is easily perceived by managers is to ask for intervals for some of the parameters (for instance, the weights), aimed at capturing information that is not precisely known but can be taken as bounded within some acceptable limits.
- The MCDA analysis may complement the DEA analysis by providing another perspective from which the conclusions of DEA may be either strengthened or weakened.
5. Discussion and conclusions
- DEA is a data-oriented approach that requires no a priori specification of the functional form of the production model converting inputs into outputs.
- Moreover, managerial preference information is often required, since inputs and outputs do not generally have the same importance in assessing the efficiency of operational units.
- This has been the main motivation for the use of MCDA techniques, in order to assess the extent by which these could overcome those characteristics of DEA, and what adaptations would be needed to improve the quality of the assessment.
- An authority certifying sustainable development practices may use this type of MCDA to label energy production plants according to their efficiency, taking into account the inputs they consume, the energy and other desirable outputs they produce, as well as greenhouse gas emissions and other undesirable outputs.
- DEA, on the other hand, can be particularly suited to identify DMUs with efficiency gaps relative to the state of the art, given the observed efficiency frontier.
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Cites background or methods from "Assessing the performance of biogas..."
...In the referred literatures, the listed MCDA methods in Table 5 were mainly applied in all kinds of sustainable energy DM problems [11,13,29,30,32,33,35–47,49,50,52,54,55,57,60,65,66,71–91], such as the selection of CCHP alternatives, the comparisons of renewable energy plants and the DM of energy policy....
...Elimination et choice translating reality (ELECTRE) [32,46,47,65,66,75,82,88,90] 9...
...Such methods have been widely used in energy DM [32,46,47,65,66,75,82,88,90]....
...The energy issues applyingMCDA includes energy planning and selection [11–13,30–47], energy resource allocation [48–54], energy exploitation [55,56], energy policy [57–59], building energy management [60–67], transportation energy systems [68,69] and others [70–75]....
Cites background from "Assessing the performance of biogas..."
...Madlener  performed a multi-criteria study with the aim of evaluating the performance of a large number of agricultural biogas plants in order to determine their relative performance in terms of economic, environmental, and social criteria and corresponding indicators....
... Madlener R, Antunes CH, Dias LC....
"Assessing the performance of biogas..." refers methods in this paper
...For the cases where the constant returns to scale assumption is dropped, Banker et al. (1984) proposed a variable returns to scale (VRS) version of the CCR model, referred to as the BCC model....
"Assessing the performance of biogas..." refers background or methods in this paper
...Three basic DEA models are generally distinguished: CCR model, BCC model, and Additive model (see Cooper et al., 2000, 2004; for a presentation and comparative analysis of these models)....
...DEA is a non-parametric performance measurement technique, based on linear programming (LP), for assessing the efficiency of DMUs (e.g., Charnes et al., 1985; Cooper et al., 2000) relative to an observed set of production possibilities....
...It is now widely recognized that the largest source of atmospheric pollution stems from fossil fuel combustion, upon which current energy production and use patterns throughout the world rely heavily....
...From an interdisciplinary point of view, the assessment of the global performance of different entities (potential solutions, courses of action, decision alternatives) can no longer be based on a single-dimensional axis of evaluation, such as cost or benefit....
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