# Heat Transport: What I Wrote So Far.

Don’t worry, The Subversive Elkement will publish the usual silly summer posting soon! Now am just tying up loose ends.

In the next months I will keep writing about heat transport: Detailed simulations versus maverick’s rules of thumb, numerical solutions versus insights from the few things you can solve analytically, and applications to our heat pump system.

So I checked what I have already written – and I discovered a series which does not show up as such in various lists, tags, categories:

[2014-12-14] Cistern-Based Heat Pump – Research Done in 1993 in Iowa. Pioneering work, but the authors dismissed a solar collector for economic reasons. They used a steady-state estimate of the heat flow from ground to the tank, and did not test the setup in winter.

[2015-01-28] More Ice? Exploring Spacetime of Climate and Weather. A simplified simulation based on historical weather data – only using daily averages. Focus: Estimate of the maximum volume of ice per season, demonstration of yearly variations. As explained later (2017) in more detail I had to use information from detailed simulations though – to calculate the energy harvested by the collector correctly in such a simple model.

[2015-04-01] Ice Storage Challenge: High Score! Our heat pump created an ice cube of about 15m3 because we had turned the collector off. About 10m3 of water remained unfrozen, most likely when / because the ice cube touched ground. Some qualitative discussions of heat transport phenomena involved and of relevant thermal parameters.

[2016-01-07] How Does It Work? (The Heat Pump System, That Is) Our system, in a slide-show of operating statuses throughput a typical year. For each period typical temperatures are given and the ‘typical’ direction of heat flow.

[2016-01-22] Temperature Waves and Geothermal Energy. ‘Geothermal’ energy used by heat pumps is mainly stored solar energy. A simple model: The temperature at the surface of the earth varies sinusoidally throughout the year – this the boundary condition for the heat equation. This differential equation links the temporal change of temperature to its spatial variation. I solve the equation, show some figures, and check how results compare to the thermal diffusivity of ground obtained from measurements.

[2016-03-01] Rowboats, Laser Pulses, and Heat Energy (Boring Title: Dimensional Analysis). Re-visiting heat transport and heat diffusion length, this time only analyzing dimensional relationships. By looking at the heat equation (without the need to solve it) a characteristic length can be calculated: ‘How far does heat get in a certain time?’

[2017-02-05] Earth, Air, Water, and Ice. Data analysis of the heating season 2014/15 (when we turned off the solar/air collector to simulate a harsher winter) – and an attempt to show energy storages, heat exchangers, and heat flows in one schematic. From the net energy ‘in the tank’ the contribution of ground can be calculated.

[2017-02-22] Ice Storage Hierarchy of Needs. Continued from the previous post – bird’s eye view: How much energy comes from which sources, and which input parameters are critical? I try to answer when and if simple energy accounting makes sense in comparison to detailed simulations.

[2017-05-02] Simulating Peak Ice. I compare measurements of the level in the tank with simulations of the evolution of the volume of ice. Simulations (1-minute intervals) comprise a model of the control logic, the varying performance factor of the heat pump, heat transport in ground, energy balances for the hot and cold tanks, and the heat exchangers connected in series.

(Adding the following after having published this post. However, there is no guarantee I will update this post forever ;-))

[2017-08-17] Simulations: Levels of Consciousness. Bird’s Eye View: How does simulating heat transport fit into my big picture of simulating the heat pump system or buildings or heating systems in general? I consider it part of the ‘physics’ layer of a hierarchy of levels.

Planned episodes? Later this year (2017) or next year I might write about the error made when considering simplified geometry – like modeling a linear 1D flow when the actualy symmetry is e.g. spherical.

# On Photovoltaic Generators and Scattering Cross Sections

Subtitle: Dimensional Analysis again.

Our photovoltaic generator has about 5 kW rated ‘peak’ power – 18 panels with 265W each.

South-east oriented part of our generator – 10 panels. The remaining 8 are oriented south-west.

Peak output power is obtained under so-called standard testing condition – 1 kWp (kilo Watt peak) is equivalent to:

• a panel temperature of 25°C (as efficiency depends on temperature)
• an incident angle of sunlight relative to zenith of about 48°C – equivalent to an air mass of 1,5. This determines the spectrum of the electromagnetic radiation.
• an irradiance of solar energy of 1kW per square meter.

Simulated spectra for different air masses (Wikimedia, User Solar Gate). For AM 1 the path of sunlight is shortest and thus absorption is lowest.

The last condition can be rephrased as: We get 1 kW output per kW/minput. 1 kWp is thus defined as:

1 kWp = 1 kW / (1 kW/m2)

Canceling kW, you end up with 1 kWp being equivalent to an area of 1 m2.

Why is this a useful unit?

Solar radiation generates electron-hole pairs in solar cells, operated as photodiodes in reverse bias. Only if the incoming photon has exactly the right energy, solar energy is used efficiently. If the photon is not energetic enough – too ‘red’ – it is lost and converted to heat. If the photon is too blue  – too ‘ultraviolet’ – it generates electrical charges, but the greater part of its energy is wasted as the probability of two photons hitting at the same time is rare. Thus commercial solar panels have an efficiency of less than 20% today. (This does not yet say anything about economics as the total incoming energy is ‘free’.)

The less efficient solar panels are, the more of them you need to obtain a certain target output power. A perfect generator would deliver 1 kW output with a size of 1 m2 at standard test conditions. The kWp rating is equivalent to the area of an ideal generator that would generate the same output power, and it helps with evaluating if your rooftop area is large enough.

Our 4,77 kW generator uses 18 panels, about 1,61 m2 each – so 29 m2 in total. Panels’ efficiency  is then about 4,77 / 29 = 16,4% – a number you can also find in the datasheet.

There is no rated power comparable to that for solar thermal collectors, so I wonder why the unit has been defined in this way. Speculating wildly: Physicists working on solar cells usually have a background in solid state physics, and the design of the kWp rating is equivalent to a familiar concept: Scattering cross section.

An atom can be modeled as a little oscillator, driven by the incident electromagnetic energy. It re-radiates absorbed energy in all directions. Although this can be fully understood only in quantum mechanical terms, simple classical models are successful in explaining some macroscopic parameters, like the index of refraction. The scattering strength of an atom is expressed as:

[ Power scattered ] / [ Incident power of the beam / m2 ]

… the same sort of ratio as discussed above! Power cancels out and the result is an area, imagined as a ‘cross-section’. The atom acts as if it were an opaque disk of a certain area that ‘cuts out’ a respective part of the incident beam and re-radiates it.

The same concept is used for describing interactions between all kinds of particles (not only photons) – the scattering cross section determines the probability that an interaction will occur:

Particles’ scattering strengths are represented by red disks (area = cross section). The probability of a scattering event going to happen is equal to the ratio of the sum of all red disk areas and the total (blue+red) area. (Wikimedia, User FerdiBf)

# Half a Year of Solar Power and Smart Metering

Our PV generator and new metering setup is now operational for half a year; this is my next wall of figures. For the first time I am combining data from all our loggers (PV inverter, smart meter for consumption, and heat pump system’s monitoring), and I give a summary on our scrutinizing the building’s electrical power base load.

For comparison: These are data for Eastern Austria (in sunny Burgenland). Our PV generator has 4.77kWp, 10 panels oriented south-east and 8 south-west. Typical yearly energy production in our place, about 48° latitude: ~ 5.300 kWh. In the first 6 months – May to November 2015 – we harvested about 4.000kWh.
Our house (private home and office) meets the statistical average of an Austrian private home, that is about 3.500 kWh/year for appliances (excl. heating, and cooling is negligible here). We heat with a heat pump and need about 7.200kWh electrical energy per year in total.

In the following plots daily and monthly energy balances are presented in three ways:

1. Total consumption of the building as the sum of the PV energy used immediately, and the energy from the utility.
2. The same total consumption as the sum of the heat pump compressor’s input energy and the remaining energy for appliances, computers, control etc.
3. Total energy generated by PV panels as the sum of energy used (same amount as contributing to 1) and the energy sold to the utility.

In summer there is more PV  energy available than needed and – even with a battery – the rest would needed to be fed into the grid. In October, heating season starts and more energy is needed by the heat pump that can be provided by solar energy.

This is maybe demonstrated best by comparing the self-sufficiency quota (ratio of PV energy and energy consumed) and the self-consumption quota (ratio of PV energy consumed and PV production). Number ‘flip’ in October:

In November we had some unusually hot record-breaking days while the weather became more typical at the end of the month:

This is reflected in energy consumption: November 10 was nearly like a summer day, when the heat pump only had to heat hot water, but on the colder day it needed about 20kWh (resulting in 80-100kWh heating energy).

In July, we had the chance to measure what the building without life-forms needs per day – the absolute minimum baseline. On July 10, 11, and 12 we were away and about 4kWh were consumed per day160W on average.

Note that the 4kWh baseline is 2-3 times the energy the heat pump’s compressor needs for hot water heating every day:

We catalogued all devices, googled for data sheets and measured power consumption, flipped a lot of switches, and watched the smart meter tracking the current consumption of each device.

Consumption minus production: Current values when I started to write this post, the sun was about to set. In order to measure the consumption of individual devices they have been switched an of off one after the other, after sunset.

We abandoned some gadgets and re-considered usage. But in this post I want to focus on the base load only – on all devices that contribute to the 160W baseline.

As we know from quantum physics, the observing changes the result of the measurement. It was not a surprise that the devices used for measuring, monitoring and metering plus required IT infrastructure make up the main part of the base load.

Control & IT base load – 79W

• Network infrastructure, telephone, and data loggers – 35W: Internet provider’s DSL modem / router, our router + WLAN access point, switch, ISDN phone network termination, data loggers / ethernet gateways for our control unit, Uninterruptible Power Supply (UPS).
• Control and monitoring unit for the heat pump system, controlling various valves and pumps: 12W.
• The heat pump’s internal control: 10W
• Three different power meters: 22W: 1) Siemens smart meter of the utility, 2) our own smart meter with data logger and WLAN, 3) dumb meter for overall electrical input energy of the heat pump (compressor plus auxiliary energy). The latter needs 8W despite its dumbness.

Other household base load – 39W

• Unobtrusive small gadgets – 12W: Electrical toothbrush, motion detectors, door bell, water softener, that obnoxious clock at the stove which is always wrong and can’t be turned off either, standby energy of microwave oven and of the PV generator’s inverter.
• Refrigerator – 27W: 0,65 kWh per day.

Non-essential IT networking infrastructure – 10W

• WLAN access point and router for the base floor – for connecting the PV inverter and the smart meter and providing WLAN to all rooms.

These are not required 24/7; you don’t lose data by turning them off. Remembering to turn off daily might be a challenge:

Non-24/7 office devices – 21W. Now turned off with a flip switch every evening, and only turned on when needed.

• Scanner/Printer/Fax: 8W. Surprisingly, there was no difference between ‘standby’ and ‘turned off’ using the soft button – it always needs 8W unless you really disconnect it.
• Server in hibernated state 4W. Note that it took a small hack of the operating system already to hibernate the server operating system at all. Years ago the server was on 24/7 and its energy consumption amounted to 500kWh a year.

Stuff retired after this ‘project’ – 16W.

• Radio alarm clock – 5W. Most useless consumption of energy ever. But this post is not meant as bragging about the smartest use of energy ever, but about providing a realistic account backed up by data.
• Test and backup devices – 7W. Backup notebooks, charging all the time, backup router for playground subnet not really required 24/7, timer switch most likely needing more energy than it saved by switching something else.
• Second old Uninterruptable Power Supply – 4W. used for one connected device only, in addition to the main one. It was purchased in the last century when peculiarities of the local power grid had rebooted  computers every day at 4:00 PM.

In total, we were able to reduce the base load by about 40W, 25% of the original value. This does not sound much – it is equivalent to a small light bulb. But on the other hand, it amounts to 350kWh / year, that is 10% of the yearly energy consumption!

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Logging setup:

• Temperature / compressor’s electrical power: Universal control UVR1611 and C.M.I. as data logger, logging interval 90 seconds. Temperature sensor: PT1000. Power meter:  CAN Energy Meter. Log files are exported daily to CSV files using Winsol. Logging interval: 90 seconds.
• PV output power: Datamanager 2.0 included with PV inverter Fronius Symo 4.5-3-M, logging interval 5 minutes.
• Consumed energy: Smart meter EM-210, logging interval 15 minutes.
• CSV log files are imported into Microsoft SQL Server 2014 for analysis and consolidation. Plots are created with Microsoft Excel as front end to SQL Server, from daily and monthly views on joined UVR1611 / Fronius Symo / EM-210 tables.

# The Impact of Ambient Temperature on the Output Power of Solar Panels

I have noticed the impact of traversing clouds on solar power output: Immediately after a cloud has passed, power surges to a record value. This can be attributed to the focusing effect of the surrounding clouds and/or cooling of the panels. Comparing data for cloudless days in May and June, I noticed a degradation of power – most likely due to higher ambient temperatures in June.

We had a record-breaking summer here; so I wondered if I could prove this effect, using data taken at extremely hot days. There is no sensor on the roof to measure temperature and radiation directly at the panels, but we take data taken every 90 seconds for:

• Ambient air temperature
• Global radiation on a vertical plane, at the position of the solar thermal collector used with the heat pump system.

I was looking for the following:

• Two (nearly) cloudless days, in order to rule out the impact of shadowing at different times of the days.
• These days should not be separated by too many other days, to rule out the effect of the changing daily path of the sun.
• Ideally, air temperature should be very different on these days but global radiation should be the the same.

I found such days: August 1 and August 12 2015:

Daily output of the photovoltaics generator (4,77 kW peak), compared to average and maximum air temperatures and to the global radiation on a vertical plane. Dotted vertical lines indicate three days nearly without clouds.

August 12 was  a record-breaking day with a maximum temperature of 39,5°C. August 1 was one of the ‘cool’ but still perfectly sunny days in August. The ‘cold day’ resulted in a much higher PV output, despite similar input in terms of radiation. For cross-checking I have also included August 30: Still fairly hot, but showing a rather high PV output, at a slightly higher input energy.

August 2015 in detail:

Same data as previous plot, zoomed in on August. Dotted lines indicate the days compared in more detail.

Overlaying the detailed curves for temperature and power output over time for the three interesting days:

Detailed logging of ambient air temperature and output power of the photovoltaic generator on three nearly cloudless days in August 2015.

The three curves are stacked ‘in reverse order’:

The higher the ambient air temperature, the lower the output power.

Note that the effect of temperature can more than compensate for the actually higher radiation for the middle curve (August 30).

I have used global radiation on a vertical plane as an indicator of radiation, not claiming that it is related to the radiation that would be measured on the roof – or on a horizontal plane, as it is usually done – in a simple way. We measure radiation at the position of our ribbed pipe collector that serves as a heat source for the heat pump; it is oriented vertically so that it resembles the orientation of that collector and allows us for using these data as input for our simulations of the performance of the heat pump system.

Our house casts a shadow on the solar collector and this sensor on the afternoon; therefore data show a cut-off in the afternoon:

Global radiation in W per square meter on a vertical plane, measured at the position of the solar collector. The collector is installed on the ground, fence-like, behind the house, about north-east of it.

Yet, if you compare two cloudless days where the sun traversed about the same path (thus days close in the calendar) you can conclude that solar radiation everywhere – including the position on the roof – was the same if these oddly shaped curves are alike.

This plot shows that the curves for these two days that differed a lot in output and temperature, August 1 and 12, were really similar. Actually, the cooler day with higher PV output, August 1, even showed the lower solar radiation due to some spikes. Since the PV inverter only logs every 5 minutes whereas our system’s monitoring logs every 1,5 minutes those spikes might have been averaged out in the PV power curves. August 30 clearly showed higher radiation which can account for the higher output energy. But – as shown above – the higher solar power could not compensate for the higher ambient temperature.

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Logging setup:

# Economics of the Solar Collector

In the previous post I gave an overview of our recently compiled data for the heat pump system.

The figure below, showing the seasonal performance factor and daily energy balances, gave rise to an interesting question:

In February the solar collector was off for research purposes, and the performance factor was just a bit lower than in January. Does the small increase in performance – and the related modest decrease in costs of electrical energy – justify the investment of installing a solar collector?

Monthly heating energy provided by the heat pump – total of both space heating and hot water, related electrical input energy, and the ratio = monthly performance factor. The SPF is in kWh/kWh.

Daily energies: 1) Heating energy delivered by the heat pump. Heating energy = electrical energy + ambient energy from the tank. 2) Energy supplied by the collector to the water tank, turned off during the Ice Storage Challenge. Negative collector energies indicate cooling of the water tank by the collector during summer nights. 200 kWh peak in January: due to the warm winter storm ‘Felix’.

Depending on desired pay-back time, it might not – but this is the ‘wrong question’ to ask. Without the solar collector, the performance factor would not have been higher than 4 before it was turned off; so you must not compare just these two months without taking into account the history of energy storage in the whole season.

Bringing up the schematic again; the components active in space heating mode plus collector are highlighted:

(1) Off-the-shelf heat pump. (2) Energy-efficient brine pump. (3) Underground water tank, can also be used as a cistern. (4) Ribbed pipe unglazed solar collector (5) 3-way valve: Diverting brine to flow through the collector, depending on ambient temperature. (6) Hot water is heated indirectly using a large heat exchanger in the tank. (7) Buffer tank with a heat exchanger for cooling. (8) Heating circuit pump and mixer, for controlling the supply temperature. (9) 3-way valve for switching to cooling mode. (10) 3-way valve for toggling between room heating and hot water heating.

The combination of solar collector and tank is ‘the heat source’, but the primary energy source is ambient air. The unglazed collector allows for extracting energy from it efficiently. Without the tank this system would resemble an air heat pump system – albeit with a quiet heat exchanger instead of a ventilator. You would need the emergency heating element much more often in a typical middle European winter, resulting in a lower seasonal performance factor. We built this system also because it is more economical than a noisy and higher-maintenance air heat pump system in the long run.

Our measurements over three years show that about 75%-80% of the energy extracted from the tank by the heat pump is delivered to it by the solar collector in the same period (see section ‘Ambient Energy’ in monthly and yearly overviews). The remaining energy is from surrounding ground or freezing water. The water tank is a buffer for periods of a few very cold days or weeks. So the solar collector is an essential component – not an option.

In Oct, Nov, and March typically all the energy needed for heating is harvested by the solar collector in the same month. In ‘Ice Months’  Dec, Jan, Feb freezing of water provides for the difference. The ice cube is melted again in the remaining months, by the surplus of solar / air energy – in summer delivered indirectly via ground.

The winter 2014/2015 had been unusually mild, so we had hardly created any ice before February. The collector had managed to replenish the energy quickly, even in December and January. The plot of daily energies over time show that the energy harvested by the collector in these months is only a bit lower than the heating energy consumed by the house! So the energy in the tank was filled to the brim before we turned the collector off on February 1. Had the winter been harsher we might have had 10 m3 of ice already on that day, and we might have needed 140kWh per day of heating energy, rather than 75kWh. We would have encountered  the phenomena noted during the Ice Storage Challenge earlier.

This post has been written by Elke Stangl, on her blog. Just adding this in case the post gets stolen in its entirety again, as it happened to other posts tagged with ‘Solar’ recently.

# Solar Power: Some Data for the First Month.

On May 4, 2015, we started up our photovoltaic generator. Here are some numbers and plots for the first month – and what I plan to do next.

Our generator has a rated power of 4,77 kWp (kilowatt peak), one module has 265 Wp. The generator would deliver 4,77 kW of electrical power under so-called standard testing conditions: An irradiance of 1000 W/m2 of light from the sun, a module temperature of 25%, and a standard spectrum of wavelengths determined by the thickness of the atmosphere light has to traverse (Air mass – AM 1,5, equivalent to sunlight hitting the earth at an angle of about 48° from the zenith).

Our 18 panels are mounted on two different roof areas, 10 of them (2,65 kWp) oriented south-east and 8 modules (2,12 kWp) south-west. The inclination relative to the surface of the earth is 30°, the optimum angle for PV at our latitude:

Positions of our PV panels on the roof.

We aimed at using our 30° upper roof spaces most efficiently while staying below the ‘legal threshold’ of 5 kW, avoiding a more complicated procedure for obtaining a permit to install them.

The standard conditions are typically met in spring here – not in summer – as the efficiency of solar panels gets worse with increasing temperature: for our panels -0,44% of rated power per °C in temperature difference. If the temperature is 60°C, peak power (for otherwise same irradiance and spectrum) would drop by 15% . We can already see this effect, when comparing two nearly cloudless days in May and in June. The peak power is lower in the first days of June when maximum daily air temperatures were already about 30°C:

Total output power (AC) of the PV generator and input power (DC) for each string as a function of time for two days. 1) May 11 – maximum ambient air temperature 23°C. 2) June 5 – maximum ambient air temperature 30,5°C.

The temperature-dependence of performance might in part explain impressive spikes in power you see after clouds have passed: The modules have a chance to cool off, and immediately after the cloud has gone away the output power is then much higher than in case of constant irradiance. Here is a typical example of very volatile output:

Output power of our PV generator when clouds are passing. The spikes (clear sky) show a peak power much higher than the constant value on a cloudless day in May; the troughs correspond to clouds shadowing the panels. The data logger included with the inverters only logs a data point every 5 minutes, so I parsed the inverter’s website instead to grab the current power displayed there every second (Using the inverter’s Modbus TCP interface would be the more professional solution, but parsing HTTP after reverse engineering the HTML structure is usually a quick and dirty ‘universal logging interface’.)

The maximum intermittent power here was about 4,4 kW!

Another explanation for the difference is local ‘focussing’ of radiation by specific configuration of clouds reflecting more radiation into one direction: Consider a cloudless region surrounded by clouds – a hole in the clouds so to speak. Then radiation from above might be reflected at the edges of that hole at a very shallow angle, so that at some place in the sunny spot below the power might be higher than if there were no clouds at all. Here is another article about this phenomenon.

A PV expert told me that awareness of this effect made recommendations for sizing the inverter change: From using one with a maximum power about 20% lower than the generator’s power a few years ago (as you hardly ever reach the rated power level with constant radiation) to one with matches the PV peak output better.

The figures from May 11 and June 5  also show that the total power is distributed more evenly throughout the day as if we would have had a ‘perfect’ roof oriented to the south. In the latter case the total energy output in a year would be higher, but we would not be able to consume as much power directly. But every kWh we can use immediately is worth 3 times a kWh we have to sell to the utility.

The next step is to monitor the power we consume in the house with the same time resolution, in order to shift more loads to the sunny hours or to identify some suckers for energy. We use more than 7000 kWh per year; more than half of that is the heat pump’s input energy. Our remaining usage is below the statistical average in Austria (3700 kWh per 2-person household) as we already did detective work with simpler devices.

Smart meters are to be rolled out in Austria in the next years, by 2020 95% of utilities’ clients should be equipped with them. These devices measure energy consumption in 15-minute intervals; they send the data to the utility daily (which runs a web portal where clients can access their data) but must also have a local interface for real-time logging given to clients on request. As a freshly minted owner of a PV generator I got a new ‘smart’ meter by the utility; but this device is just a temporary solution, not connected to the utility’s central system. It will be replaced by a meter from another vendor in a few years. Actually, in the past years we could read off the old analogue Ferraris meter and submit the number at the utility’s website. This new dumb smart meter, in contrast, requires somebody to visit us and read off the stored data once a year again, using its infrared interface.

I did some research on all possible options we have to measure the power we consume, the winner was another smart meter plus integrated data logger and WLAN and LAN interfaces. It has been installed yesterday ‘behind’ the official meter:

Our power distribution cabinet. The official (Siemens) smart meter is the rather large box to the left; our own smart meter with integrated data logger is is the small black one above it – the one with the wireless LAN antenna.

We will combine its data with the logging of ‘PV energy harvested’ provided by the inverter of the PV panels – an inverter we picked also because of the wealth of options and protocols for accessing it [*]

For the first month we can just have a look at daily energy balances from two perspectives (reading off the display of the dumb smart meter manually every day):

1. The energy needed by appliances in the house and for hot water heating by the heat pump – 11 kWh per day: On average 56,5% in the first month come from the solar panels (self-sufficiency quota), and the rest was provided by the grid.
2. The daily energy output of the solar generator was 23 kWh per day on average – either consumed in the house – this is the same cyan bar as in (1) – or fed into the grid. In this month we consumed 27% of the PV power directly (self-consumption quota).

Daily energy balance: 1) The energy we consume in the house – partly from PV, partly from the grid (left axis) and 2) The energy harvested by the PV generator – party used directly, partly fed into the grid (right axis).

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[*] For German-speaking readers: I wrote a summary about different solutions for metering and logging in this case in this German article called ‘The Art of Metering’ – options are to use the official meter’s IR interface with yet another monitoring ‘server’, your own unrelated meter (as we did), a smart meter integrated with the inverter and using the inverter’s own data logging capabilities), or building and programming your own smart meter from scratch.

# Two Weeks After Lift-Off

After a little delay our photovoltaic generator went online – we had been waiting for the delivery of this sophisticated addition to our office decoration:

People on G+ had very cool suggestions, such as a rotating alien-fighting device throwing darts. Closest to the truth were: fuse box and fire alarm.

The box containing two knobs (actually the large box does not contain a lot):

Two switches that are connected to that big red button downstairs, positioned next to the inverter for our PV panels:

We have two strings of modules, oriented perpendicular to each other; so irradiation on these is different. I add an overlay to a screenshot from Google Maps:

Solar panels subject to different irradiance are connected in different strings – serial connections of modules; otherwise output power would suffer. The inverter has two inputs for two such strings and two MPP trackers that try to find the Maximum Power Point for each generator, by constantly probing each string’s current versus voltage curve.

Each strings is connected to one of the little red knobs, which are part of yet another safety mechanism. The inverter converts DC current from the panels to standard 3-phase AC output voltage (230 V each phase). It has surge protection (another grey boy, but downstairs) and can shut off power at its DC and AC connectors – but then there is still a voltage drop across the DC cable from the roof to the inverter.

DC voltages supplied by our PV generators are about 400V, but generally they can be close to 1000V. This is a risk for firefighters connecting themselves to the circuit via a jet of water. You ‘cannot turn the panels off’ as long as there is sunlight! In order to make sure that the voltage drops to zero as close as possible to the panels, those switches are installed.

That ‘firefighters’ switch is semi-mandatory here. Lightning protection is not mandatory too, but we decided we should finally have one. Since safety standards and costs of such protection have grown exponentially in recent years, we can brag with a Faraday cage with tighter meshes and taller antenna-style tips than all our neighbours.

I am sure it protects us not only from lightning but also from alien attacks (see image below) and EMP guns – and the wiring goes well with the surface-mounted aluminium tube for the DC and AC cables for the PV generator.

The big red button is in the tech gadget closet on the left side of the driveway.

Firefighters will pull or push the red button in case of a fire. We decided for the pull option as you are less likely to pull than push something accidentally.

What we did not know before installation: The switch will also be activated automatically in case of a power outage – this means: about every 2 years for a few minutes. but when the big red button has been activated you need to switch power on again upstairs in the roof, too!

Normally, the switch box would be tucked away in an attic, above a dropped ceiling. We have no attic anymore – this is all office space, 3,5 high in the center. We could have squeezed the box into the insulation. But then after every power outage we would have needed to climb up there, remove roof tiles and switch on power again. So we spontaneously decided to have it installed on the ceiling, above the Chief Engineer’s desktop:

Last Monday The Metering Guy from the utility finally installed a smart meter, capable of metering both consumption and feed-in to the grid. He had to disconnect from the grid to do so. We switched on the inverter in bright daylight – and there was no power! Panic – what happened? I fetched the laptop and the inverter’s manual, ready for troubleshooting – until The Chief Engineer walked by, carrying a ladder, and grinning mischievously:

Have you perhaps triggered the firefighters’ switch when disconnecting from the grid?

I had forgotten about the switch only about 15 minutes after I putting big signs for firemen! But at least we knew it worked!

After one more controlled test of a power outage we were finally online. This is what power generation looks like on a nearly perfect sunny day now (2015-05-11).

Since May 5 we have consumed 11kWh / day on average; about 55% of this have been provided directly by the solar panels. Daily energy generation was about 23kWh; we used 27% of the power generated.