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Task 26 – Multiple Benefits of Energy Efficiency
In commercial and administrative spaces, and in manufacturing, energy cost is usually a small portion of the total production cost and, therefore, it receives relatively little attention. Even when energy cost is high, core business considerations come first.
Replacing old equipment by a new more energy-efficient one, translates into energy benefits, i.e. the energy savings induced by the change, translated into monetary terms. But new equipment very often also induces non-energy benefits. Non-energy benefits (NEBs) include all benefits entailed by new equipment which are not an energy benefit. Examples often observed of NEBs include maintenance cost reduction, increase in workplace comfort or safety (for instance when an old oven is replaced by a new one better insulated), increase in industrial productivity (thanks to lower production time or a reduction of the rejection rate), product quality improvement. A reduction in GHG emissions is another frequently observed NEB of an energy-efficiency project. Similarly to energy benefits, NEBs translate into financial benefits for the investor.
According to the International Energy Agency, if current trends continue in the years to come, two thirds of the economic potential to improve energy-efficiency will remain untapped until 2035, including 55% of the energy efficiency opportunities in the industrial sector (Benoît et al., 2014). In 2014, the IEA, in an effort to activate this huge untapped potential of energy-efficiency, issued a report on the “multiple benefits of Energy Efficiency” (IEA, 2014). As emphasized in the report, “identifying the multiple benefits that may be linked to energy-efficiency measures in industry could enhance the business case for action” (IEA, 2014:134).
 To gather research around a common and non-“negatively-defined” concept, the IEA report suggests using “multiple” instead of “non-energy” to describe the numerous benefits of energy-efficiency.
Figure 1: Multiple Benefits of Energy Efficiency.
Download the IEA report: Capturing the Multiple Benefits of Energy Efficiency
The IEA report divides the multiple benefits of energy efficiency into five categories:
1. macroeconomic impacts;
2. public budget impacts;
3. health and well-being impacts;
4. industrial sector impacts; and
5. energy-delivery impacts.
This categorization, as usually in the NEBs literature (to the exception of Russell, 2015), does not distinguish between public and private benefits (or macro and micro benefits according to economics theory terminology). Macroeconomic and public budget are clearly public benefits; health and well-being and energy-delivery impacts can be public or private, although the financial benefits do not fall into the same cash-boxes. Industrial sector impacts are more clearly private benefits, although they can translate into nation-wide competitiveness increase and thus into GDP increase and into additional tax revenues.
Identifying and assessing NEBs is not an easy matter. “Hundreds of different benefits for industry have already been identified in past studies and surveys of energy efficiency project implementation, making it challenging to produce a definitive list of the most important ones” (IEA, 2014:134). “Because so few studies have been undertaken in this area, methodologies for quantifying wider benefits from energy efficiency measures in industry are still at the inception stage” (IEA, 2014:137). NEBs vary in terms of the time horizon in which they occur, as well as in terms of their measurability (which has to be made in physical, monetary and strategic terms). In addition, NEBs are not constant in time (as equipment efficiency usually decreases with time), and all the costs and benefits associated with an investment in energy efficiency need to be compared to a counter-factual, which could range from “no investment” to “an alternative less energy efficiency investment”. All this complicates NEBs assessment.
NEBs can be identified upstream (to inform energy-efficiency investment decisions) or downstream (after investment decision-making, in a retrospective analysis). It seems that most NEBs reported have been found incidentally, i.e. ex post after implementation of energy-savings measures. To reinforce the business case of energy efficiency and increase acceptance of energy-efficiency investments, a method is needed to identify and analyze NEBs upstream, i.e. ex-ante in early analyses of projects (energy audit analyses, technical, financial and strategic analyses), and to include them in investment calculations.
NEBs pose different types of challenges:
- a methodology is needed to categorize NEBs (existing categorization is still too vague) and to assess them along different perspectives (technical, human, financial, strategic);
- this methodology must be capable to take into account NEBs time variations and measurability requirements.
- In order to inform practitioners and decision-makers, reliable data must be identified and collected (when possible) throughout industry worldwide;
- data collected must be anonymized and organized in a global public data base.
- a convincing way to communicate about NEBs towards businesses and policy-makers has to be developed.
The work made by the IEA secretariat must now be taken further in two respects in order to make NEBs operational:
- Deepening the knowledge about issues and actors concerned and do so in a way that takes into consideration different applications in different countries and different planning environments. This to make NEBs relevant for applications locally
- Actual quantification that also has to take into consideration the difficulties of multiple actors and the fact that benefits may not show on the balance sheet of the investor.
The DSM-Programme is well situated to take on the supervisory task “Multiple Benefits in Action” and to do so in co-operation with other relevant IEA Agreements.
Task ZERO – Running the DSM Energy Technology Initiative
Task Zero is a suggestion to in a formal way create a comprehensive and coherent overview and to ensure that resources are used in the most efficient manner.
The mission of the IEA DSM-Programme is to deliver to its stakeholders, materials that are readily applicable for them in crafting and implementing policies and measures.
In order to do so we have several tools that we need to maintain but also develop to ensure that results are disseminated in ways that are useful for people in everyday practice.
This concerns our:
- Informational tools
- Our networks and in particular the local ones run by ExCo-participants
- Dissemination and the extension with the “DSM-University”
To ensure that the activities are coherent it is proposed to gather all these actions in a context that we call “Task ZERO”. A Task that is mandatory for participants and builds on both cost-sharing and task-sharing.
The management of the IEA DSM-Programme requires the following responsibilities to be executed (text deducted from the Strategic work plan delivered to the IEA EUWP and CERT in September 2014).
|Output and visibility (technical facilities and content|
|Improved dissemination by development and running of the DSM University||X||X|
|Local dissemination and “anchoring” within the areas of the participants and to support them in recruiting the experts necessary for Task in which they have decided to participate, but also to gather material of interest for other tasks who need local points of contact for their work (Task-sharing)||X|
The Task is lead by the Chair who may delegate responsibilities in particular to the secretary and the vice-chairs and who has the Project Preparatory Committee, PPC, as “steering committee”.
To ensure that different activities are coherent the Programme secretary is the coordinator for task ZERO. The co-ordinator gathers the necessary information from those concerned with functions, “subtasks”, as described above in order to produce a work-plan and a budget for the ExCo to decide upon annually.
By treating and dealing with these functions as a Task Zero it will be possible to have a comprehensive and coherent overview and to ensure that resources are used in the most efficient manner.
The value of work put into the DSM University is approximately 45,000 USD per year. Part of this is covered by in-kind contributions, part of it is covered within the budget.
The common fund presently, with 16 participants paying 8000 USD each, receives an income of 128,000 USD per year.
The expenses between years fluctuates widely, partly because of fluctuations in exchange rates. The Programme has managed to meet rising costs and rising expectations during its life-time with rationalising the work not the least by making full use of the IT-development. During these years we have also managed to build a common fund that has been touching the limit of 300,000 USD. This has allowed the Programme to facilitate upstart of new tasks from a seed-fund.
It would however be irresponsible to base the budget by use of this fund for running costs. A total income of approximately 190,000 USD per year is required to safely cover all obligations as described. Divided on 17 participants the yearly fee to the common fund would be 11,000 USD per participant.
 The fee to the Common Fund has been 8000 USD per annum since the programme started more than 20 years ago.
Big Data and Energy Efficiency, a research area for the DSM ETI ?
During the EXCO meeting in October 2014 the EXCO delegates discussed topics for potential future work. One of the topics was Big Data. Based on internet search, this paper provides an overview on what ‘big data’ is in general and why it could be an interesting area for research work for the IEA DSM Agreement. I also provides some topics that could be selected.
In our modern society the number of available data is exploding. Companies like Google, Facebook and Apple are storing (and analysing) search action on the internet, activities in social media; shops, airline companies, credit card organisations and banks are storing the data from users of their loyalty programs, their cards and transactions; navigation systems use the driving information etc.
Big Data is a container concept and is determined by the so called 3 V:
- Volume: large number of data
- Varity: data related to very different topics
- Velocity: quick availably of data
Not only the amount of data is increasing, but also the production of these data is continues speeding up. These two developments make it possible that one can (re)-act earlier to changes.
Big Data is often the combination of data from different sources as well as the use of data for other purposes then those were these data were collected for.
One cause of concern in the Big Data community is uneasiness about sharing data. Privacy is one obvious obstacle.
Big Data and energy use and energy efficiency
Big Data is an enormous opportunity for making energy-efficiency savings. People do not seem to adopt efficient technologies that appear financially attractive. One of the commonly cited reasons is that information about how to save energy is hard or time consuming to collect, or that the efficient devices are hard to use. The use of improved metering information on processes as well as the combination with other (big) data in industrial companies, can generate information to be used by the management to improve energy efficiency. Companies may do it there self, but firms that employ Big Data can help better to overcome these challenges.
The introduction of smart meters can result in a large amount of data on the energy use of consumers, detailed over the time of use and (almost) real-time.
Smart and intelligent metering technology allows electricity customers to keep an eye on their current and previous energy consumption at all times. The pivotal role of consumers in energy management will be greatly facilitated by their ability to access their usage data. Such transparency helps end-users to better control their consumption, use energy more efficiently, protect the environment and potentially save money. Organisations can make huge energy efficiency gains, for instance through intelligent lighting and heating systems which only activate when facilities are in use.
Utilities are approaching the Big Data differently. e.g. EDF begins its plan to roll out 35 million smart meters across France and it will need to start incorporating that flood of data into its way of doing business. Options under research are to use data from the smart meter to better estimate the state of the grid, or use that data to better assess the material lifetime duration” of power lines, transformers and other distribution grid equipment. Or you can basically– and this is the main important part – try to manage the energy consumption: to push the people, or to find the best condition, in which the consumer will consume less, or will consume on a different timescale. This is an area where data will help and that will be an important part of managing grid assets in a world where new customer loads such as plug-in electric vehicles, as well as the increasing share of power being generated by customers themselves, are altering supply-demand balances.
Most of the U.S. utilities data analytics focus to date has been on improving grid reliability and outage response, as well as lowering the cost of distribution operations.
The data ‘explosion’ in energy created a new potential for evaluation, measurement and verification of energy efficiency programmes. The data can be used to get quicker and cheaper answers on the impact of polices; more accurate savings estimates; and allow new kind of analysis. While at the moment most impact evaluations has to work with a number of assumptions on the energy use and related items, not only energy use data are more frequently and easier available but also the data to relate changes in energy use with other changes related to the programme or not.
Items for the Executive Committee:
- Decide whether Big Data is a topic the Agreement wants to deal with
- When Big data are a relevant topic, decided on the focus for one or more Big Data topic, e.g.
- Tools to improve the knowledge of real time energy use in companies and organisations; e.g. Changing energy management systems
- Use of big data for improved evaluation, monitoring and verification of EE programs
- Big data as a tool to improve the customer relationship of the energy providing company/utility
- Big data as a tool for network operators for smarter operation of the grid
- Decide whether the selected topic(s) should be handled as
- A new subtask within an existing Task
- A new Task