I'm more and more convinced of this. Control theory appears to be like lightsabers, "a refined weapon for a more civilized age". It's really unfortunately that the controls literature is so opaque.
>> Control theory is better if you know what you’re doing.
The "if you know what you’re doing" here does not refer to the ability to understand control theory. It means that if you know the underlying dynamics, there is mathematically nothing better than controlling those dynamics. Flying a plane, oscillating a circuit, etc. are all things we can do very well without ML because we have exact models of the physical phenomena. Playing chess has no dynamics, control theory is useless. Anything where the dynamics are not "nice" differential equations, ML is probably easier at learning the dynamics than coming up with an ansatz.
There are areas of control theory where you can learn the dynamics ("adaptive control"). The advantage over RL is that in control theory, you generally assume the dynamics are described by differential equations (sometimes difference equations), not by Markov decision processes. MDPs are more general, but basically any physical mechanism you're going to control doesn't need that generality.
There is a surprising amount of structure imposed by the assumption that the dynamics are differential equations, even if you don't know what the differential equations look like. As a consequence, adaptive control laws generally converge a lot faster (like, orders of magnitude faster) than MDP-based RL approaches on the same system being controlled.
The other advantage is that you can prove stability and in some cases have an idea of your performance margin with control theory. THis is important if you eg want your system to receive any sort of accreditation or if you want to fit it into the systems engineering of a more complex system. There's a reason autopilots don't use RL, and it isn't that RL can't be made to work. It's that you can't rigorously prove how robust the RL policy is to changes in the airplane dynamics.
You certainly could, but that doesn't entirely account for shading / system degradation / site-specific diffuse light opportunities (consider a huge amount of light reflecting off the side of a mountain at some time of day). Those are both really difficult and time-intensive to model for, so there's a desire to have an AI that can simply learn those things specific to the system it's optimizing without humans having to do it. I see the larger impact of RL as scaling humanity's problem solving capability. If we have to use N human hours per installation to get to 97% optimality per installation but RL can use N/10000000 per installation to get to 95%, we could free up all those N human hours for things that RL still struggles with. Just my 2 cents though, it's a very fair question
I remember there was this science barge that had a solar panel that passively tracked the sun by being mounted on a metal tube that had some gas in it that expanded and somehow tilted the panel more towards the sun.
I wonder how much more accurate your system is and whether the tradeoff is worth the added expense of a motor + the additional maintenance cost of moving parts.
I wonder why solar farms don't use active tracking, is that added maintenance + equipment cost just not worth it?
So large solar farms are usually single-axis tracking, which provides a huge energy production benefit over static panels. Consider this panel's application more for a standalone installment, where it could make sense to use a dual-axis tracking panel over a single-axis or static panel. Re: solar farms and cost, I actually learned that around ~70% of the cost of solar installation is soft costs, not the actual panels/solar cells etc. Applying RL to much larger installations would be about finding non-obvious ways to leverage the single-axis tracking when the sun is not directly overheard. A fun problem for the future :)
> around ~70% of the cost of solar installation is soft costs
This is incorrect, especially for solar farms
Panels are indeed only about 1/3 of the cost.
Additional components, labour, inverter, mounting - another 1/3.
The last third is indeed soft(ish) cost, but this includes profit (duh..), certification, survey, tax, fees, etc. This can be reduced, but it is not going to magically disappear ...
Another fun fact : newer mega farms in low-altitude deserts are considering no-axis no-mounting zero-tilt - just laying the panels on the ground ... It all comes down to cost vs yield
A nice project by the way. Did you ever compare the results to pre-calculated angles based on time/location/season?
Farmers are placing them mounted vertically, like fences, in productive fields, running north/south, far enough apart to run a tractor between. They pick up power in morning and afternoon. The crops benefit from reduced heat stress and water loss, and yield more.
Panels that work with light from either side are preferred for this use.
Doesn't this model fail to account for seasonal variations in the locus of the sun? The optimal angle will vary across the year, whatever the latitude.
Maybe I'm missing something, but i would use a simpler algorithm which doesn't need ML. On day 0, plug in the latitude and allow the system to traverse the range of angles, finding the optimal one at the time - ie: yielding maximum power. Let it run 3-5 times during the day, then fit those points to the theoretical path of the sun across the sky. Now your system is calibrated, without needing any other input. As the seasons change, the system will always know which angle to face for optimal power.
I'd like to see a comparison with a dumb as a box of hammers controller like PI or extremum seeking on the local gradient. I can't imagine that it is learning much more than a simple strategy like this, but maybe that's my lack of imagination...
Correct. Basically all the gimbal is doing is minimizing the cosine loss. This is, literally, the simplest loss function possible. Even if you don't know where the sun is, damped gradient descent will solve this problem every time, with no learning required (albeit with some jitter because you're having to estimate the gradient by moving the panel around). If you treat it as an iterative learning control (ILC) problem, though, you can learn the correct trajectory for the panel in just a couple of iterations and take the jitter out.
You can do this without jitter by having the panel with a bend in it and comparing power from the 2 sides. The difference drives a motor controller directly.
I raced solar cars in college. One car we built had a solar concentrator system made up of 1D parabolic mirrors. This was a several hundred thousand dollar system due to the use of specialized concentrator cells, and required multiple team members to temporarily move to LA for months to delicately manufacture the cells in a Spectrolab clean room.
The concentrator mirrors had to be aimed within about 2 degrees to aim the light onto the active area of the cells. I tried using a pair of photodiodes as the sensor, and it worked well in early prototypes. We flew out to Australia before the system was fully integrated into the car, though. In the real setup it just wasn't precise enough. On top of that, the aerodynamically shaped acrylic window surfaces created weird refractions and reflections that made the accuracy poor as well.
I had a couple weeks to figure something out, with a bunch of PIC18F4680 MCUs on hand, and access to parts from Dick Smith's. I ended up buying a small PAL security camera to experiment with. I went to a nearby photo center and got some overexposed film negative to use as an IR-passing filter. With enough layers of film, the camera image was completely black other than a white dot when pointed at the sun. Looking at the analog video signal on a scope, I was able to rig up a couple fine-tuneable voltage dividers and then use the MCU's dual comparator peripheral to generate interrupts on sync pulses and on white pixels. I could then count scan lines, and detect scan lines with the sun in them, giving me a Y coordinate precise to a fraction of a degree. I also got an X coordinate by timing between syncs and white pixels, but I didn't need it for control. I then mounted the camera on the concentrator mechanism, and wrote a basic PID controller.
It worked pretty well, and we were able to happily concentrate sunlight while driving at highway speeds.
It turned out the linear servo mechanism had a design flaw, where it would lose too much mechanical advantage at the extremes, and in the presence of road vibrations it would jam. We discovered this fairly early on in the race. Luckily I had wireless control of the motor over our telemetry system, allowing me to keep the motor from burning out. We drove a couple hours with the system jammed, taking the power hit over losing race time. Someone realized we would be driving over a "cattle grid" soon, and we had the idea to try running the motor at full torque to see if the shock would be enough to unjam the mirrors. It worked, and we suddenly started getting several hundred watts of additional power! After that day, we tied a string to the mechanism and routed it up to the driver cockpit. Whenever the mirrors got stuck after that, we simply radioed the driver and they gave the string a yank.
This is the first I've heard of it. The physics of it seems fine. The engineering is going to be very hard for them to get right.
Just skimming their web site, range is qualified as "WLTP drive cycle". I assume that means software enforced torque and speed limits, and no one is going to want to drive with that enabled.
Using 4 independent motors implies they're using in-hub motors. They don't seem to mention a top speed anywhere. Unless their motors have dynamically adjustable air gaps (which I am guessing they don't), the motors will have a fixed KV constant. That means the top speed of the vehicle will be limited by battery voltage at some point. Maybe that limit is high enough not to matter, but it's funny they don't specify it.
Using in-hub motors also means there's a lot of mass on the wheels themselves. I'm not a mechanical engineer, but my understanding is that adding wheel mass makes suspension design a lot more difficult. On top of that, the rest of the car is presumably a couple hundred lb of batteries, and then a bunch of lightweight composites. The batteries will likely be in the floor, which is good for vehicle dynamics. The overall car will be significantly lighter than what people are used to. I think it's possible that the car will have a relatively rough ride, and could possibly have more noise than usual in the passenger compartment.
Crash-safety wise, I wouldn't want to drive that car on a busy highway. In the US, it would likely struggle to pass crash safety testing. I wonder if they are certifying it as a low speed vehicle, which would put it in the same regulatory class as a golf cart. It would have to have a 40kph speed limit on level ground.
It will probably cost more than a Tesla. At the end of the day, slapping a solar roof on a Tesla would probably get a lot of folk to the grocery store and back every few days. Mounting the same panels on the ground and plugging in the Tesla would be even more effective.
Thanks. I would assume you could make the front and rear electric motors optimized for different speeds. That would mean power loss towards top end speeds.
Maybe, but I would be surprised if they did that. From a manufacturing point of view, having two different motor types is more expensive.
More importantly, the motor drive electronics would have to be designed specifically to support that. A motor's top speed is limited by the supply voltage because a spinning motor generates its own voltage called back-EMF. When back-EMF matches the supply voltage, the motor drive can't push any current through the motor because there's not enough of a voltage difference. When the motor spins even faster than that, its back-EMF exceeds the supply voltage. This does something funny to the motor drive. The voltage across the FETs in its H-Bridge circuit swaps polarity and the FETs become "reverse biased". A reverse biased FET acts like a diode, and allows current to flow through it. As a result, the motor generates a braking torque. The only way for a motor drive to avoid that would be to have an additional semiconductor in series with the H-Bridge circuit. That would make the drive more expensive, and lower efficiency. It could be done, but I doubt it.
It's also hard to optimize a motor to work well at low speeds, but then have it be efficient at high speeds, even if not using it. One of the ways motors lose efficiency are in what are called "eddy current losses". Just the fact that the motor is spinning means that metal is moving through alternating magnetic fields, and that induces eddy currents in the metal. Those Eddy currents generate a braking torque, and heat up the metal.
This all assumes the motors are permanent magnet brushless motors. I think that's a safe assumption since they are in-hub. There are other types of motors, such as induction, that work differently and don't have the same "speed limit" or Eddy current losses when freewheeling. Most electric vehicles use one or two induction motors. They aren't in-hub, though, because they don't scale down in size well.
This is very cool, and a neat application / exploration of RL. Am I correct in understanding that the results are only from the results of simulation? If so, it'd be cool to see this work (both a time-lapse video and a chart) in the real world, over a day or over a week!
Are panels cheaper than the servo motor & Arduino used in this tool? Not to mention that at mass produced scales chip costs are usually 1/5 the price of an Arduino. What if every panel used these tools, we could increase the amount of power we gather from solar.
Yes. A 400w watt panel is 250$. When you account for labor and cost of all. The parts for a moving panel that can withstand high wind speeds…Placing more fixed panels is cheaper.
I don't know what, where, or how many you want, but these are by the pallet (~30ea) and about $6500. You can get much cheaper with used ones.
You get about 3hr of effective peak power per day (so a 400W panel will give you 1200Wh). You also need a DC converter for these to charge a battery or go to AC. Of course you'll want to mount them at the correct angle with a good view of the sun.
You generally have to buy them by the pallet-load, FOB ("free on board") at the source, which means you arrange shipping. Home Depot marks up 4x for local purchase.
But used panels, at 80% rated output, are even cheaper, as low as $150. A few of those will fail, so you keep spares. They are often repairable in a few minutes if you are not afraid of a soldering iron.
Any roofer will put in mounting brackets, and almost any electrician is happy to put in the panel.
Decent vendors such as sunelec.con will easily quote you a price for a one pallet load LTL freight shipment of a pallet of, say, 22 panels. Not like you have to arrange your own freight and often they get a better price because of they great monthly spend with the major us48 state LTL freight carriers.
Be sure to specify if you need liftgate service at the destination or not, because that will affect cost, otherwise by default a pallet by LTL freight will need a loading dock to receive.
That includes labor, mounting hardware, inverters and grid tie in. It also assumes high efficiency panels.
Watch out before buying older technology (lower efficiency) panels. Some have significant efficiency losses per degree Celsius increase in temperature.
I bought 42 Sunpower X21-335 panels used for about $4700 shipped to my door a few years ago and couldn't be happier. As far as I could tell they weren't actually ever unboxed.
> $3 per watt, installed on your roof is typical these days
Which is why I was so fascinated by someone saying that you could get panels at 250USD/400W=0.625USD/W; I suppose it's possible that all the other stuff (electrician, mounting, inverters) is the difference, but a factor of 5? That feels like an chance to do something hacky and come out way ahead (like, say, DIYing a panel to run your computers, thus cutting out rewiring the house and needing an inverter).
Yes, a factor of 5 is fairly close. The prices of panels and inverters has dropped, while the price of labor and wiring has stayed constant or increased. The end result is the factor of 5 that you mention, and DIY systems are becoming more popular.
One of the factors in cheaper solar is that the panels have gotten bigger. Panels grew from 250w to 300, 350, and now to 400w and 450w. The 450w panels are 82 inches by 42 inches, so taller than the average person. Larger panels require less mounting and less labor, so even if they cost more they might be slightly cheaper to install.
I think that utility scale solar will eventually beat residential solar on price because of less labor per watt to install. I sometimes think about a solar system that could be set on top of a house in a few hours and would contain the inverters and interlocks and be wired into a single breaker in the house electrical panel. A truck operator/installer and electrician could do two installs a day and the labor price would be significantly less. We are so far from this currently, with site surveys, permitting processes, individual panels in custom configurations and so on that result in several days of work spread out over months. I don't know if it could ever happen, but it is fun to think about at times.
> I think that utility scale solar will eventually beat residential solar on price because of less labor per watt to install.
Utility scale solar beats residential solar by a huge margin already. New utility scale solar projects on the California grid are priced around $20/MWh [1] compared to feed in tariff rates of $0.08923/kWh = $89.23/MWh [2].
As a renter who can't have solar installed on my home I find it pretty objectionable that my electricity rates subsidise expensive toy systems of homeowners.
> $3 per watt, installed on your roof is typical these days
In Germany, around 2019/2020 depending on the size of the installation it was around 1€ to 1,30€ fully and professionally installed on your roof. Right now with the increasing demand and very bad availability we are back at about 1,50€ to 1,80€ per Watt.
In Europe they are even cheaper as we don't have high tariffs on Chinese panels. I can buy a 350W panel for €150 including taxes, or €100 if I go to the equivalent of Craigslist and don't care about warranties.
Our solar panels are rated to 115mph winds. What are these servo mounts rated to?
Also, installation of the panels currently costs more than panels. They don't say (or I didn't find) how efficient the optimal fixed mount is, but the agent starts at 80%, so assume some fixed position is 80% of optimal. They increase that to 96%, so they reduce the number of solar panels by about 13%. If the installation labor cost increases more than ~26% because of the servo mount, then the servo mount hardware and frame would need to be cheaper than fixed mounts for it to break even. Similarly, the amount of aluminum being consumed by solar panel installions is non-trivial, and the movable frame is likely to increase that.
However, this is still a cool hobby project with a nice writeup.
Exactly. Any mechanism that moves the panel to follow the sun, must be sturdy enough to also withstand high winds, which is inevitably costlier than proof of concept ideas.
Better to add more panels.
A better way to ensure more power output is to have a set of panels with a small battery back to automate cooling of the panels and cleaning of the panels.
Interesting that the limiting factor is energy and wind. I wonder if you could devise a locking mechanism or something purely mechanical to solve it. Something closer to gears in a watch.
The intverter should be able to be configured with export limits. In a lot of jurisdictions that's mandatory anyway. Still better to over-specify so that in Winter you get the full amount while in Summer you just throw some away. Or take up a power-intensive hobby like Bitcoin mining in Summer.
Nope, at least for ours it limits on export produced (on a technical basis, not a planning permission one). It will deliberately downgrade the energy produced to ensure it doesn’t exceed export limits. You get used to optimising for sunshine for when you run power heavy equipment eg car charging
tl;dr - sun rises, temperature rises, wax material expands, motor actuates, solar panel tracks the sky as if it was a sun flower. Maybe the gloop is sentient or something.
Only if the additional cost and complexity of such a setup is worth said cost. It can also be used to move the panels into approximately the correct location then allow the other techniques to fine tune it. If the more complex systems have a fault it may be possible to fall back to the more simple method which, while probably not as good, would be more efficient then just a static panel.
When they say "may vary", they mean by a fraction of a degree! Astronomers care a lot about precisely accounting for atmospheric refraction because if you want to look at a specific star or whatever, it kind of matters that you point your telescope in exactly the right direction. But for just pointing a solar panel roughly towards the sun, the simplified model from that site should be more than sufficient.
Comments are speaking to it like it wasn't even addressed. Do you need data to understand that moving a cell out of a shadow would increase its capacity?
If that's the core proposition, yes, I want data on it. If that data is obvious and boring, then so is the proposition.
Besides, the panel can't move, only rotate and tilt. Have you actually read the article?
(With rotate and tilt only, pointing directly at the sun should maximize the power output from a single panel even with shade from other objects. You can get a very small amount of movement from the fact that the point of rotation is outside of the plane of the panel. But that's not mentioned at all, is it?)
In any case, if you solve a problem with machine learning that already has a non-machine learning solution, you will get this kind of comment. If on top of that you don't compare the existing solution and yours and show that yours significantly improves performance, it just looks like doing ML for the kicks.
You're forgetting about orientation (and altitude)? The observed position also varies a bit with air pressure and temperature, but iirc that's less than a few degrees.
Anyway, you grab yourself a nice ephemeris library like skyfield, do a little bit of your_location.at(now).observe(sun).apparent().altaz('standard'), and there's your angles.
The trickiest part might be getting an accurate time on whatever cheap controller your using.
Radio time signal stations broadcast time and I would hope it would be cheap enough as we have radio controlled clocks, that do not cost a fortune. Otherwise a small battery and a RTC would be enough and probably a better solution.
A GPS receiver can be had for like $15 these days. That will give you time and position, and require less maintenance than an RTC.
Of course, a $15 receiver might not have the best sensitivity, so reception might actually be a practical concern. On the other hand, you only need 4 satellites for a coarse fix, and you could seemingly tolerate a many minute cold fix time.
Sorry for the off-topic. I think if just 10% of the efforts were put on improving mini/micro/pico hydro electric generator rather than on solar systems, most of the rural areas will probably better off now with reliable power supply than relying solely on the intermittent solar power[1], [2].
In the developed world we're moving away from small hydro (bigger than this though) because of the effects it has on the environment. But in the developing world it would be good to see some advances!
The issue I always see on those is the pipe from the upstream reservoir to the generator gets knocked around and dislodged a lot when there's high water. It's less reliable and more maintenance intensive compared to solar it seems. Though it is more mechanical maintenance which is more friendly to more rural areas.
If you know the lat/lon of the panel (they don’t move right?) and the current clock time, just point it at the current Sun position if it’s daytime? Wouldn’t that be as optimal as possible?
Yes, but that's a different topic.
And isn't this just an exploratory project rather than an engineer effort or scientific research?
But even then, I got panels on my roof last year. A nearby tree that was trimmed when we installed them and now the shade partially covers panels in the morning. If we had gimbles perhaps they'd be more efficient. Maybe not, and maybe we should trim the tree, but the point is that perhaps there is a use case that hasn't been thought of yet. Say, dropping cells on mars in a relatively unknown environment.
One thing I don't understand on articles extolling the magical benefits of machine learning, is that I often feel like things aren't played fair. For example, looking at the diagrams comparing the machine learning process to a traditional process of dynamical modeling, I feel the machine learning process diagram is basically putting "magic" in the center square, leaving out a ton of stuff, as is very quickly shown in the very next diagram. In addition, the discussion completely leaves out other control techniques, such as the various gradient descent techniques. Those are techniques that also do not require a full dynamic system model or external knowledge outside of the parameters it can adjust and the control signal. If I am not mistaken, something like those could take advantage of the single control signal (power generated by the panel and depending upon the response time of this) and the n number of motor axis and go from there. And it would scale to several panel systems.
Additionally, the machine learning crowd seems to like to reinvent nomenclature that really muddles things up. I see something like this:
> The agent in this optimization problem employs a softmax actor-critic algorithm with tabular representations of the actor and critic networks. I chose this implementation after initially experimenting with a Q-learning agent using an epsilon-greedy policy, which had difficulty efficiently exploring the large action space
and have a hard time parsing it. Despite having experience in mathematics, software, physics, and electrical engineering, I'm trying to finally pick up some machine learning techniques but am having trouble getting the vocabulary as things seem to be arbitrarily renamed. I once remember being in a room where a machine learning practitioner actually got quite angry at the word "measurement" being used and was adamant it was replaced with something like "estimate" or "sensitivity" or something else. I just remember thinking that, yes, those are also words that describe what a measurement is.
I am also pretty confused on how the optimal power is being calculated which is then being used to generate the comparison percentage. Is it from the simulated model that is generated by the motor axis scan? That doesn't seem very realistic because they aren't taking into account several factors in their simulation, factors that will change on a daily, weekly, monthly, and yearly basis, so the gap could be larger. As far as I can see, the only way you'd know what the "optimal" power generation could be would be to dynamically model as much as the system as possible.
The title is also slightly misleading in the context of solar panels, where percentage efficiencies are usually talked about, so percentage optimal kind of gets confused with that.
> While primary factors driving optimal panel positioning are readily modeled (i.e., the sun’s position at each time of day), site-specific and panel-specific factors are less so. Elements like dynamic shading from nearby trees or structures, localized panel defects, drift in axis positions as systems degrade, etc. can have significant impacts on energy production.
Will changing the orientation of the panel really have some effect on these (besides the drift in axis position) ?
As a small p.v. user (domestic, wired by myself): tracking Sun gives around 10-20% annual electricity production (witch is meaningless for self-consumption scenario) and a bit earlier and late electricity during the day (you produce earlier in the morning and later in the evening, witch is interesting for self-consumption) but beside the mere cost you need more space: fixed installs need just to count fixed shadows, not static of course, but easy to handle. Rotating means you need more space for one-axis rotation and far more space for two, clustering panels in small groups.
In my personal case I have 12+9 classic chains modules, I need more than 2x physical space to transform them with a dual-axis tracking setup. That means it's cheaper just add some fixed panels eastward and westward to catch extra power earlier and later.
Also in those terms: lithium storage is very expensive BUT for self-consumption is still the cheaper option to have electricity for more time, just arriving to a meaningful production 1/1.5h earlier and later in the day does not help much given it's added cost.
In costs terms: these days it's even cheaper (in TCO terms) having hot water heated by p.v. than the more efficient thermal because that cost more, have more moving parts and regular maintenance that just making an a bit bigger p.v.
The real issue in all cases is that to have enough power to really pay back the investment "quickly" we need much non-shadowed southward space witch can be found somewhere but far from everywhere. A similar issue is for EVs: I like the idea of charging them "for free" from solar, BUT since I normally use a vehicle during the day or I use it only sometimes or I have two or more in a round-robin scheme. Also lithium storage lifetime is an issue, on scale the production capacity and recycling are issues. Until we solve them just produce some more Wh it's meaningless...
I used to be like OP. (have a similar background and have similar interests in tech for the planet)
Then I realised couple of things, an humbling experience:
1) given any position on earth, you can compute exactly what's the optimal inclination at any given point in time for a PV to maximize the energy production. Sure, there are reflection and secondary irradiation conditions (eg.: there is a lake close to it), but again, assuming the environment is static, it's way faster to just compute it statically rather than dynamically. Also, in most scenarios Beam irradiance from diffusion (the beam hitting the object) is order of magnitude higher than from reflective one (the same beam bouncing on a 3rd object first).
2) In mechanics movable part are the things to avoid. They have lower MTBF (mean time before failure) and as such they introduce complexity and increase cost
3) Economics is a key component of engineering. There is a cost to everything, the computational power, the energy needed by the servo, etc, etc. Given 1 and 2, a dynamic solution simply has a lower ROI than a static one.
I really appreciate the OP exploration here: there is a good overview of basic control theory and a good foundation of ML (although don't be deceived, this is a very simple modelling task that OP is overkilling with a way more complex model). That said, for everyone reading, this is not something you want to do in a real world situation.
On a large scale motorized sun tracking photovoltaics is very costly. The systems that can take the wind loading of six 1.65 x 1.00 meter size 60-cell panels on a pole cost more than the panels. Needs like a 6" OD sch80 pipe set into a concrete foundation.
Commodity fixed angle ground mount photovoltaics arrays are low cost.
If you do the dollar and kWh produced in year calculation for spending $40,000 on fixed mount ground pv, and $40k on a combination of pv panels on trackers, and compare the kWh proxied by both... The fixed ground mount comes out far ahead.
A tracking mount can make sense only if you have a VERY small amount of space to work with and want the absolute most kWh per month per square meter of area occupied on the ground. And don't care about money much.
Reminds me of a bug in a certain laptop docking firmware that considers TjMax of 95F (instead of 95C) and running the CPU at full throttle mode (like 400MHz) at all times.
Yeah, exactly. Tracking photovoltaics used to be worth it back when photovoltaics were $5 per watt. The price has dropped so much that even for fixed angle ground mounts, the mounting frames and labour to install them costs far more than the panels themselves. Sun-tracking is just not economical compared with cheap static panels.
(I spent quite a lot of time on an idea for rooftop solar thermal power and was trying to build a prototype when the solar panel prices started crashing. It pretty soon became inescapable fact that small scale solar thermal with all its moving parts just wasn't viable any more. I'd be surprised if even the large mirror-farm CSP is competitive these days.)
Thermal for heating/hot shower or thermal for some adventure in driving a generator?
For heating, photovoltaics supplying a heat pump is starting to give direct thermal a run for the money (well, not actually for the money yet, direct thermal is still cheaper, but at least in terms of how much you could harvest from a given roof area)
If money isn't an objection at all, e.g. if you strive for that sense of achievement of a good setup, there are hybrid modules that pick up the 20% or so photovoltaics achieves and still funnel the remaining energy into heating a liquid medium.
This idea was a flat "panel" of parabolic reflectors focused on heat exchanger tubes, driving a heat engine. The new bit was that the heat engine was going to be open loop (basically Brayton cycle with the compression stroke pushing air into the heat exchanger and expansion stroke driven by heated air from the exchanger) so its power density would have been much higher and cost lower than the usual Stirling cycle engines which cost a ridiculous amount for what they are.
I had all the thermodynamics worked out and it would have been something like 5x as cost effective as photovoltaic. Then the cost of photovoltaic panels dropped 10x in a year. C'est la vie, at least my roof is covered in PV now. I've thought of running some tubing under the panels to pre-heat water for our solar hot water system but these days it's scarcely worth the bother (at least where I live which is pretty much perfect for solar power.)
There are hybrid solution of panels that are basically layering a PV over a Thermal Panel (pipes with fluid inside). I never saw them getting massive traction. I wondered why.
Heliostat installations used to be common when panels were so expensive that spacing them out wide enough to keep them from shadowing each other when angled for dusk/dawn made sense. Now heliostats are only done in exceptional situations, for example I've seen some that didn't seem to be particularly old on rugged terrain were the quantity approach would be costly as well and easy foundation opportunities are sufficiently far from each other to prevent peer shadowing anyways.
But I do wonder if heliostats might see quite a revival in agrovoltaics: there, you want a certain distance between panels anyways, and perhaps the plants won't mind if you steal a little more light off-noon in exchange for less shadowing at noon. Electricity supply/demand would certainly applaud this bias, in a market with lots of photovoltaics a Wh at noon is certainly worth less than those closer the the periphery of the daily sun cycle.
And if you do agrovoltaics right, the structure will be expensive anyways (making the markup for heliostat insignificant) because imho it's still just an unfinished prototype if the structures for holding the panels aren't designed to double as an overhead rail system for farming powertools that could become a considerable efficiency gain over the century-old game of tractor vs mud.
I went on a solar power course at the Centre for Alternative Technology in Wales (~10 years ago, so perhaps this into may be somewhat out of date) and they'd calculated that the extra energy generated by moving a panel to the optimal direction during the day was less than the energy it took to move the panel, let along the extra costs of the mechanics. Bear in mind that in Wales there's plenty of cloudy weather during the year, so light is often diffuse, and it's quite a long way north (the gulf stream tricks people into thinking the UK is further south than it is, but the CfAT is 600 miles further north than Toronto). If you're somewhere with lots of direct sunlight that calculation might be different, but as others have pointed out, that doesn't take into account the cost of the mechanics and controllers.
I can imagine that this kind of research is done once 15 years ago, as I remember that too, but perhaps with the recent advance of technology might actually not be true anymore. I don’t have sources but in general some types of things get reiterated so long that time overtook it
As GP argued, the movement can be entirely precomputed, and even if it isn't "move the panel to face the brightest spot" is trivial to solve with simple components. The only real advances I can imagine to make this profitable would be better motors that are cheaper and use less energy, or new types of solar panels that are more directional (e.g. a solar concentrator setup where you move mirrors to hit a smaller collector).
The latter exist but don't seem more profitable (in the case of PV panels because of lifetime problems due to more heat), and while we have gotten a bit better with motors I don't think there even is a lot of headroom to gain much efficiency.
Y'all have plenty of water in Wales: it feels like a simple water clock could do a trivial repositioning of the panels to do precomputed solar tracking.
Can you make a purely mechanical solution to the tracking problem, at least along a single axis? If you put a water container on the left and the right side of the solar panels, the one in the sun will get hotter, expand, and that can move the solar panel. It's moving parts and extra cost, but no energy consumption.
I'm no engineer, so I can't determine whether it would work, but on the surface it looks like it should?
I remember a guy in a community that I frequent that build a small trailer that had fixed solar panels, he had build one for a reason I don't remember and afterward some companies were interested and that post was showing his second one he had just sold. It was a bunch of panels on an A frame so essentially one side had awful sun visibility but not the other. People wondered why he did it in such ways, as that meant that half the time they would be useless, why not a moving platform on a central pivot that you can adjust? Well turns out it was cheaper to add panels on the other sides than building a more complex frame that allowed pivot. I'm sure it could have been cheaper to build a system sure (maybe not in his case considering it was on a trailer), but I have an hard time believing the maintenance cost and failure potential is worth it. You can just add more panels... they are starting to become that cheap.
Yes, I agree this is vastly over-engineered. There are commercial solar farms that actively point toward the sun, but many (most?) do not, because they may fail. Before medicine, I studied and worked in renewable energy: in medicine, simplicity in critical engineering problems is more obviously important.
In this solar project, the metric should be a comparison to the yield from pointing at the sun based on lat,long, and (earth) time.
Really, the analysis would have to include anticipated costs of installation and maintenance in comparison to a dumb array.
Perhaps the ingenious author could consider xy or xyz movement in an intermittently shadowed environment instead of 2-axis rotation. This might be be a better job for machine learning, or just a well-known control system problem.
> 3) Economics is a key component of engineering. There is a cost to everything, the computational power, the energy needed by the servo, etc, etc. Given 1 and 2, a dynamic solution simply has a lower ROI than a static one.
A 'joke' we had in engineering school: Anyone can design something to do X for $5, but it's an engineer's job to get the same results for $3.
Optimisation has moved beyond the individual panel onto things like vertical bifacial panels in West/east orientation aiming to complement the fixed south facing output of other panels (and so get paid/avoid higher costs) or roof tile integrated to skip an install step and reduce transmission peaks.
I manage a solar micro grid that powers 9 Holmes, a small farm, and several other structures and utilities.
We use intelligent power management over the fiber network to tell certain loads when to turn on or off or to change their operating parameters based on power conditions.
I’ve been daydreaming about building a ml based forecaster that just gives the next few hours weather outlook based on pressure, temperature, humidity, and a wide Nigel image of the sky.
I know it is doable because I can do it myself, and probably without any intuition about the pressure. It would automatically calibrate the model wights by feedback from the actual events vs the forecast. This would be really useful for me at least, in managing battery usage and otherwise managing the various systems that store energy like air compressors and large mass refrigeration.
reply