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Proving savings: case history using fine-grained energy data

This case history shows how fine-grained energy measurements enabled savings to be verified in the face of assorted practical problems.

THE STORY concerns a large air-conditioned establishment in the middle east which had been fitted with adiabatic cooling sprays on its central chillers. It is one of a number belonging to an international chain and my purpose was to establish whether similar technology should be contemplated elsewhere in the chain.

I had originally been commissioned to give a second opinion on the savings claims made by the equipment supplier. Although the claims were quite plausible, they lacked some credibility because they were based on an extremely short evaluation. So in an initial effort at  independent checking I obtained several years’ worth of monthly consumption data at the whole-site level and analysed it against local cooling degree days. The results were ambiguous because there appeared to be some unrelated phenomenon at play which resulted in the site toggling on a seasonal cycle between two performance characteristics, one significantly worse than the other, with an impact sufficient to mask the beneficial effect of the energy conservation measure (ECM).

Without reliable evidence I declined to verify the supplier’s assessment, and recommended a deliberate test using a ‘retrofit isolation’ approach based on three existing electricity submeters (one per chiller) and a new heat meter in the common chilled-water circuit. Because nobody wanted to pay for a proper heat meter, a clamp-on ultrasonic flow element was specified. At this point the pandemic struck.

Because we had data-logged metering, and because the adiabatic cooling system sounded as if it would lend itself to being turned on and off, the measurement and verification plan was based on day-on, day-off testing. I had proposed a ten-week testing campaign which would give us 35 observations in each state. The plan called for two regression models to be developed: one for the ECM ‘on’ days and the other for the ‘off’ days. Extrapolation from the regression formulae would indicate a percentage difference and variance within each would confirm how much uncertainty there was.

Then came the collision with reality. The ECM supplier said disabling it wasn’t as simple as turning the cooling sprays off; he insisted that the mesh screens which were part of the installation should come off as well and be reinstated when the spray was re-enabled. This was a quite reasonable stance because it gave a more valid comparison, but unfortunately he couldn’t afford to send a man to site every day to do what was necessary. Meanwhile the establishment’s manager had got wind of the project and put pressure on the site engineer not to disable the ECM, which he was convinced was saving him a lot of money which (thanks to lockdown) the business could not afford to lose. He also had a point. Luckily we agreed a compromise: in exchange for them coming down to a three-day on-off cycle, I promised to monitor things closely and terminate the test as soon as conclusive results emerged.

Needless to say, the ultrasonic meter let us down. The technician responsible for downloading its data reported that some of its hourly totals looked suspiciously high, and he proposed filtering them out. But I feared that he might not be able to capture all the rogue points. Some of them might only be slightly wrong, but wrong nonetheless. When we drilled down we discovered that the raw data were stored at one-minute intervals, with the measurement fault manifesting itself as gross errors confined to occasional one-minute records. You can see this from this figure, which spans several months but plots every single one-minute record:

Figure 1: one-minute interval flow measurements fell into three bands during June and July. Only values in the bottom band can be trusted.

Valid readings at the one-minute interval clearly fell below a threshold of about 450 m3/hr, and abnormal readings were (a) very clear and (b) sparsely distributed through time, so my colleague Daniel Curtis was able to pushed the records through a sieve, take out the lumps and thereby cleanse the data (and that included reinstating hourly values which the meter software had censored because they appeared to be too low). We were helped in this by the flow normally being relatively constant, so that simple interpolation was an accurate gap-filling strategy. When we compared reported flow measurements with corrected values at eight-hour intervals we saw that in reality almost every reported value had been wrong:

Figure 2: raw flow measurements in eight-hour intervals compared with their cleansed values. In the absence of errors all the points would fall on the 1:1 line

Analysis then proceeded using cleaned-up eight-hour interval data, but we were still not out of the woods. From the individual chillers’ electricity meters we could see that the site operations team were changing the chiller sequence from time to time. Fortunately their manager agreed that barring emergencies they could leave the same chillers in service, with the same one in the lead, until the test finished. They also agreed not to change the chilled-water set point temperature, which we discovered they were in the habit of tweaking. Of course in a textbook measurement and verification exercise these static factors would have emerged during planning but this project was being conducted in a hurry on a shoestring and managed remotely. That wasn’t the end of it: later in the test we would have interruptions because the chiller maintenance firm was scheduled to clean the condenser coils. More on that later.

Nor were those the only complications. A few weeks after we started testing in anger in June, the plant did one of its random, unexplained and unexpected changes in energy performance. In Figure 3 you can see the deviation from predicted consumption for intervals when the ECM was active:

Figure 3: deviation from expected consumption, based on performance observed in June

The chillers change their behaviour about two-thirds of the way through this history. If we look at the regression model for the ‘ECM on’ condition we can see the unexplained shift very clearly:

Figure 4: with the ECM active, performance in July was consistently different from what it was in June

So we ended up, in effect, with two tests: one for June and one for July. Looking now at the comparison between ‘on’ and ‘off’ performance in July alone we saw a clear difference:

A similar picture was obtained from observations made in June and the conclusions were that savings of 16.1% and 17.5% were attributable to the ECM in June and July respectively. However, as a further bonus we observed:

  • The chiller installation’s performance spontaneously deteriorated by 8% at the end of June, echoing behaviour first witnessed in 2019. Identifying the cause will probably save money quite easily; and
  • Cleaning the condenser coils made no difference to performance. They were probably clean and it was a waste of money, so I suggested not cleaning them until condenser temperatures started to rise.

What lessons would I draw from this episode? That the verifier needs to be vigilant, sceptical, and cautious but flexible. In fact flexibility is needed on all sides, and that is best served by developing trust; trust in this case was built up through openness and continuous communication.

Audits and surveys, Uncategorized

Energy audits and surveys: rule of three

ENERGY surveys and audits – deliberate studies to find energy-saving opportunities – can be done with three levels of depth and thoroughness, can look at three broad aspects of operations, and will generally adopt one of three approaches.

Depth and thoroughness

Let’s take depth and thoroughness first. Level 1 would be an opportunity scan. This typically has a wide scope, and is based on a walk-though inspection. It will use only readily-available data, and provide at most only rough estimates of savings with no implementation cost estimates. It will yield only low-risk recommendations (the “no-brainers”) but should identify items for deeper study.

Level 2 is likely to have a selective scope (based perhaps on the findings from a Level 1 exercise). It is best preceded by a desktop analysis of consumption patterns and relationships, which means first collecting additional data on consumption and the driving factors which influence it. It should yield reasonably accurate assessments of expected savings but probably at best only rough cost estimates. It can therefore provide some firm recommendations relating to ‘safe bets’ and otherwise identify possible candidates for investment.

Level 3 is the investment-grade audit. This may have a narrow scope – perhaps one individual project – and will demand a sketch design and feasibility study, with accurate assessments of expected savings, realistic quotations for implementation, sound risk evaluation and (I would recommend) a measurement and verification plan.

Aspects covered

Next we will look at the three broad aspects of operations that the audit could cover. These are ‘technical’, ‘human factors’, and ‘procedural’.

Technical aspects will encompass a spectrum from less to more intrusive (starting with quality of automatic control and set points through energy losses to component efficiencies). In manufacturing operations the range continues through process layouts, potential for process integration and substitution of alternative processes.

Human-factors aspects meanwhile will cover good housekeeping, compliance with operating instructions, maintenance practices, training needs and enhanced vigilance.

Thirdly, procedural aspects will include the scope for improved operating and maintenace instructions, better plant loading and scheduling, effective monitoring and exception handling, and ensuring design feedback.

Approaches to the audit

The final three dimensions relate to the audit style, which I characterise as checklist-based, product-led, or opportunity-led.

The checklist-based approach suits simple repetitive surveys and less-experienced auditors.

Product-led audits have a narrow focus and exploit the expertise of a trusted technology supplier. Because the chosen focus is often set by advertising or on a flavour-of-the-month basis, the risk is that the wrong focus will be chosen and more valuable opportunities will be missed. Or worse still, the agenda will be captured by snake-oil merchants.

Finally we have the ‘opportunity-led’ style of audit. This is perhaps the ideal, although not always attainable because it needs competent auditors with diverse experience and will include the prior analysis and preliminary survey mentioned earlier.

These ideas, together with other advice on energy auditing, are to be covered in a new optional add-on module for my “Energy efficiency A to Z” course which explais a wide range of technical energy-saving opportunities. Details of all my forthcoming training and conferences on energy saving can be found at https://vesma.com/training.

Uncategorized

Remote measurement and verification

Note: this guidance was developed because of the Covid-19 pandemic. However, by its nature, the pandemic is an unexpected major non-routine event which will in many cases completely invalidate baselines developed before March 2020.  Readers should not expect this advice to remedy the resultant disruption to evaluations. It may prove to be applicable only to ‘retrofit isolation’  assessments and trials using extended sequences of on-off mode changes.

THIS GUIDANCE proposes enhancements to standard measurement and verification protocols to cover the situation where, for reasons beyond the control of the parties involved, a measurement and verification practitioner (MVP) is unable to participate in person.

Firstly the proposal for the energy-saving project should indicate not only the quantity or proportion by which consumption is expected to be reduced, but wherever possible the nature of the expected reduction. For example where data are to be analysed at weekly or monthly intervals, it may be possible to say whether reductions are expected in the fixed or variable components of demand or both; while for data collected at intervals of an hour or less it may be possible to define the expected change in daily demand profile or other parameters related to the pattern of demand.

Setting the expectations more precisely in this manner will help the MVP to detect whether post-implementation results may have been affected by unrelated factors.

Secondly a longer period than usual of pre-implementation data should be collected and analysed. This is necessary in order not only to establish prevailing levels of measurement and modelling uncertainty, but potentially to expose pre-existing irregularities in performance. Such monitoring should employ the same metering devices which will be used for post-implementation assessment.

The causes of historical irregular performance should be traced as they could provide clues about foreseeable non-routine events (NRE) which would then need to be allowed for in post-implementation assessment in case they recur. If NREs are not adequately allowed for, they will at best degrade the analysis and at worst lead to incorrect conclusions.

Thirdly, all parties should remember that as well as foreseeable NREs, there will be unforeseen ones as well. Dealing with is part of standard practice but because the appointed MVP is unable to visit the subject site and interview key personnel, he or she is likely to miss important clues about potential NREs which otherwise would have been evident based on his or her professional experience. It is therefore imperative that a planning teleconference takes place involving local personnel who are thoroughly conversant with the operation and maintenance of the facility. As part of this meeting a knowledgeable client representative should provide the MVP with a clear account of how the facility is used with particular emphasis on non-standard situations such as temporary closures. Pertinent input from client representatives at large would include (to give some examples) control set-points, time schedules, plant sequencing and duty-cycling regimes, occupation patterns, the nature and timing of maintenance interventions and so on. Information about other projects—both active and contemplated—should be disclosed. The MVP has a duty to probe and ask searching questions. It should never be assumed that something is irrelevant to the matter in hand and as a general rule no question asked by the MVP should go unanswered.

It may be helpful to provide the facility of a walk-through video tour for the benefit of the MVP, which can of course be on a separate occasion.

We will be holding this year’s measurement and verification conference, MAVCON20, as a weekly series of two-hour sessions in October and November.  Follow this link for details and booking information

Uncategorized

Monitoring solar installations

In a recent newsletter I suggested that somebody wishing to monitor the health of their solar PV installations could do so using ‘back-to-back’ comparisons between them. Reader Ben Whittle knows a lot more about these matters and wrote to put me right. His emails are reproduced here:

I would point out that whilst it may possibly be interesting to compare solar installations and account for cloud cover, personally I wouldn’t bother!

  1. variable cloud cover is variable – you can’t control it and generally it is fair to say that annual variation in the UK rarely deviates +/- 5% annually
  2. if you have a monitoring system, it will be capable of telling you when there is a fault immediately by email rather than waiting to do analysis

In the case of inverter  manufacturers’ own monitoring systems they will directly report faults immediately , usually based on

  1. an actual fault code being generated by the inverter – typically either being switched off and failing to report at all, string insulation resistance faults or other major failures
  2. output not matching other inverters in the same installation or sometimes against a base case / prediction based on yield expected due to weather forecasts
  3. possibly against a self defined target, and a failure to meet it

Third-party monitoring manufacturers will typically do the same as inverter manufacturer monitoring (with the exception of not reporting actual fault codes), but they have the advantage of being able to report on installations with mixed inverter manufacturers being used (possibly new + historic installations in one location or a portfolio of installations from different installers)

One classic mistake made with solar monitoring information is having no clear idea of what and how you are going to deal with all the information! It is time consuming to do and takes a bit of experience to make sense of it all.

So I asked Ben if there was a cost attached and this was his reply:

Most inverter manufacturers provide a solution. Three of the biggest brands (SMA, Fronius, SolarEdge) all have very competent systems, which are hosted for free, but you can get additional services by paying extra.

A typical domestic setup (which could cover any installation to any size in theory) would have basic info on annual, monthly and daily yield, and may also display self consumption rates assuming you have bought the requisite sub meter. Other info can include energy sent to a battery if you have one. It would also notify you if you lost grid connection, or communication faults. Communication is typically managed over wifi for domestic set ups and ethernet in commercial set ups. Remote solar farms do all this over 3g or 4g if there is no nearby telephone infrastructure.

Where you would pay money for a service is for an enterprise solution: this would allow you to also compare multiple installations and give you more detailed performance info, possibly also automating equipment replacement or engineer visits if malfunctions were being detected. (You would only get this from the major manufacturers with a dedicated team in this country, or an O&M service provider who was being paid to keep an eye on performance).

Third party systems typically only work using generation meter and export meter info, but a surprising amount of knowledge can be gleaned from this – you are after all only trying to find anomalies and once you have defined the expected performance initially this is quite straightforward. The advantage of this is that if you are managing lots of different installations with different inverters then you can pull the data all into one database. Big O&M companies may insist on this being added where a service level is being defined – such as 98% availability or emergencies responded to  in under 24 hours. The service will also include additional data points such as pryanometer info and other weather data, depending on the scale of the installation.

The companies who operate big solar farms are often hedge funds and they don’t like leaving systems down and not running for any length of time given the income from feed in tariffs. Though they quite often don’t manage the farms as well as they could do…

Ben Whittle (07977 218473, ) is with the  Welsh Government Energy Service

Uncategorized

Heat meters using ultrasonic flow measurement

Clamp-on ultrasonic flow meters are tricky things to deploy and I always get a sinking feeling when somebody says they’re going to use them. In this case they were fitted to measure cooling energy as part of a measurement and verification project.  Provisional analysis in the early weeks of the project showed that all was not well: there were big apparent swings in performance, which were unrelated to what we knew was going on on the plant.

Data from the meters, which were downloaded at one-minute intervals, contained computed kWh values which I was consolidating into hourly totals. The person sending me the data was extracting the kWh figures into a spreadsheet for me but some instinct prompted me to request the raw data, which I noticed contained the flow and temperature readings as well as the kWh results. My colleague Daniel wrote a fast conversion routine which saved our friend the trouble and we discovered that there were occasional huge spikes in the one-minute kWh records which were caused by errors in the volumetric flow rates. The following crude diagram of the minute-by minute flows over several weeks shows that as well as plausible results (under 500 cubic metres per hour) there were families of high readings spaced at multiples of about 750 above that:

The discrepancies were sporadic, rare, and clearly delineated so Dan was able to modify his software to skip the anomalous readings and average over the gaps. We were lucky that flow rates and temperatures were relatively constant, meaning that the loss of an occasional minute per hour was not fatal. He also discovered that the heat meter was zeroing out low readings below a certain threshold, and he plugged those holes by using the flow and differential-temperature data to compute the values which the meter had declined to output.

The next diagram shows the relationship between the meter’s kWh output, aggregated to eight-hourly intervals (on the vertical axis) with what we believe to be the true readings (on the horizontal axis). The straight line represents a 1:1 relationship and shows that, quite apart from the gross discrepancies, readings were anomalously high in almost every eight-hour interval.

Relationship between raw eight-hourly kWh and the estimated true values

The effect on our analysis was dramatic. Instead of erratic changes in performance not synchronised with the energy-saving measure being turned on and off, we were able to see clear confirmation that it was having the required effect.

 

Uncategorized

Sankey diagram software

Sankey diagrams depict the flows of energy through a system or enterprise using arrows whose widths are proportional to the magnitudes of the flows.

My friend and former colleague Kevin Cardall recently challenged my newsletter readers to come up with improvements on an Excel-based Sankey Diagram generator which he had devised (illustrated right, and attached here). His work was inspired by this website. Reader David B. responded rapidly with a neat enhancement but we also had a number of alternatives suggested by other readers.

Readers’ recommendations

When the subject of Sankey diagram software came up in 2003 one of my clients recommended SDRAW.  It is a commercial product but there is a free demonstration version.

Readers Colin G., Gary C. and Chris S. mentioned a free online tool, http://sankeymatic.com/ and reader Andy drew my attention to this toolkit for those who want to do it in Excel.

 

 

Energy technologies, Reference

Product guidance

The Government’s Energy Technology List (ETL) is a highly recommended resource. The ETL was devised to complement the Enhanced Capital Allowances scheme (a meagre tax concession for installing energy-efficient equipment) but it is useful in its own right because products cannot get onto the list without rigorous scrutiny from expert assessors. This makes it useful for filtering out bogus products.

Of course not every manufacturer, supplier or product that could be on the list is necessarily there. But the ETL itself, which lists products, is complemented by an Energy Technology Criteria List which lays down the criteria for inclusion within each product category. This will help you establish what to look for if you are doing your own evaluation of unlisted products (or if you just want to know what attributes are important, or what performance thresholds separate good products from the rest).

The ETL website has recently been revamped and can be accessed at https://etl.beis.gov.uk/.

Uncategorized

“Energy Service Company in a Box”

ESCO in a box is a concept being developed with government support with the aim of improving takeup of energy-saving measures among small and medium enterprises.

In essence, the project is designed to enable respected local community organisations (for example) to set up as providers of energy-saving services to the SMEs in their area, by equipping them with a complete package of technical, analytical, legal and financial components.

The originators of the idea, EnergyPro Ltd, have produced an  overview of the scheme  and I interviewed their managing partner, Steven Fawkes, about it on 15 April. The recording will be available until 14 May at http://bit.ly/ESCObox  (apologies that the recording is missing the first couple of minutes).

April fools

Software-defined electricity

DATELINE 1 APRIL, 2020:  Some readers will remember the so-called ‘signature meter’ that was marketed ten or so years ago. This was a kWh meter which could reportedly discriminate between over 83 different connected electrical appliances being turned on and off. It did not take off at the time but thanks to the current transition from big data to immense data (ID) the concept has returned in the guise of ‘software-defined electricity’.

With software-defined electricity your power supply waveform would be sampled more than 37 times a microsecond, processed in the cloud and dynamically corrected to remove spikes, holes and resonant ramps so that connected equipment uses less energy, runs cooler, and enjoys extended life. Phase-shifting arrays (similar to those used in multi-antenna 5G beam-forming transmitters) will allow a parallel-wired power conditioner to target individual loads on a customer’s premises so that they operate as an internet-of-things (IoT), each at its own unique current and frequency, thereby running cooler and enjoying extended life.

With people increasingly working from home, it even allows their additional energy expenditure to be monitored and charged back to their employer. In effect, every connected device will act as its own microgrid with blockchain technology embedded in the optimisation algorithm to give near-real-time trading and market reconciliation up to the seventh harmonic, using less energy, running cooler and enjoying extended life. Exciting stuff and all thanks to the Immense-Data Internet of Things (IDIoT).

Energy analysis and reporting

Energy targeting for humidity-control systems

The amount of moisture in the atmosphere varies through the year because the amount of water vapour that the air can hold is temperature-dependent. We human beings are sensitive to the relative humidity (RH), which is the ratio between the actual moisture content and the maximum that the air could hold at its prevailing temperature: generally, we don’t feel uncomfortable if the RH is between 30% and 70%. Problems will obviously occur in very hot, humid weather (when the RH will be high) but they can also occur in the depths of winter. This is because, if you take some cold outside air and heat it up for use in your building, its relative humidity falls as the temperature increases without the addition of any moisture. In an air-conditioned building, hot outside air is chilled to the comfort temperature, and the RH rises. Suppose that you want to maintain 20°C inside, with RH in the range 40% and 60%. If the ambient air contains less than 0.006 kg of water vapour per kg of dry air (regardless of its temperature), it will need humidifying; but should it exceed 0.009, moisture will need to be removed. The demand for moisture addition or removal will be proportional to the deficit or surplus in the mixing ratio. For example, ambient air at 0.013 kg/kg needs twice as much dehumdification as air at 0.011 kg/kg (0.013-0.009 is double 0.011-0.009).

Figures 1 and 2 show typical weekly histories of moisture deficit and excess for a site in Ireland. Notice how the demand is seasonal, with warmer summer air able to hold more moisture.

Figure 1
Figure 2

How does this affect energy demand? Well, to dehumidify air you need to chill it to its saturation temperature at the required moisture content. Excess moisture condenses out, and the partially dried air is then reheated to take it back to the required target temperature. This where the extra energy demand comes from.

You can see the difference between cooling-only and full air conditioning in figures 3 and 4. Figure 3 shows a case where the relationship between chiller electricity and cooling degree days is evidently a straight line: this building has no humidity control and chiller demand is effectively driven only by the outside dry-bulb temperature.

Figure 3

Figure 4, by contrast, is curved; this building has humidity control. The curve occurs because as the weather gets hotter, the amount of moisture in the air increases, and with it the demand for dehumidification.

Figure 4

Figure 5 shows the deviation from expected consumption that results when one tries to model electricity demand with the single straight-line relationship of figure 4 in which predicted consumption is

23,151 kWh per week, plus
162 kWh per cooling degree day
Figure 5

The model can be improved by accounting for the dehumidification demand: Figure 6 shows the history of deviations when a two-factor model is used, in which predicted consumption is

24,259 kWh per week, plus
107 kWh per cooling degree day, plus
16.4 kWh per unit of dehumidification demand
Figure 6

The reduced error in the calculation of expected electricity consumption makes overspend alerts more reliable and the monitoring and targeting scheme more effective.

To implement this scheme one needs local dry-bulb and relative humidity readings at frequent intervals, which are used to calculate the ambient mixing ratio. Two running totals are then kept: one of the accumulated atmospheric moisture deficit, and one of the accumulated excess. The procedure is not unlike the “accumulated temperature deficit” which is used in the calculation of heating degree days.

For details of training courses in energy management visit vesma.com/training