Category Archives: Blog

Disclaimer: information contained herein represents solely the opinion of the author

Energy storage landscape in UK and Germany: 2010-2016

Annual revenues for time-shifting energy with a 1000 MWh, 125 MW storage device with 75% round-trip efficiency. Monetary unit scale is millions of EUR for Germany or GBP for Britain.

I am pleased to write this post highlighting our recent paper, “An analysis of storage revenues from the time-shifting of electrical energy in Germany and Great Britain from 2010 to 2016”. This was published in the Journal of Energy Storage in June and the work was led by my colleague Dr Grant Wilson at the University of Birmingham.

The paper investigates the changing electricity market conditions for bulk electricity storage in the UK and German markets during the period 2010-2016, during which time the price profiles of the two countries have changed significantly. This is due to several reasons as discussed in the paper, but it is especially due to increasing deployments of renewable energy generation, with the UK having deployed nearly 10 GW of solar and 14 GW of wind during this period. Germany has deployed 30 GW of solar and nearly 25 GW of wind during this period.

We examine the potential revenue that a large scale pumped hydroelectric storage plant could have generated during these years as well as analysing the electricity prices on the day ahead market. We quantify the impact of both average price and price volatility on the hypothetical storage plant revenue in both markets. We find that volatility is the primary driver of storage revenue and despite large deployments of renewable generation, the potential revenue has not increased particularly in either market over this period. The notable exception to this was the last quarter of 2016 when unplanned outages in the French Nuclear fleet led to exceptionally high electricity prices at peak times in the GB electrical system. These unplanned outages meant that France, which normally exports electricity into the UK through the Thanet interconnector, became a net importer of power which tightened capacity margins at peak times.

The analysis draws some interesting conclusions for bulk electricity storage, highlighting the challenges faced by bulk storage developers. In particular, the large deployments of renewable energy in both countries have so far failed to produce any reliable increase in market favourability for storage. One possible explanation is that electricity from renewable sources has predominantly displaced baseload generation (like coal) and the marginal plants have stayed the same or increased their production, keeping price levels similar at peak times but decreasing daytime prices. Rationally, we would expect this relationship to breakdown eventually as the current fleet of marginal plant can no longer meet all the demand at peak times, and new capacity must be constructed which will increase costs and reliably push up peak electricity prices. Indeed, several organisations have suggested that Britain capacity margin is tightening significantly.

The falling domestic demands in both GB and DE have also likely dampened markets for storage. This falling demand is thought to be largely a consequence of the financial crisis of the late 2000’s, therefore any sustained period of economic activity could significantly impact peak electricity prices and create a more favourable environment for storage. Finally, this work has examined the day-ahead market, which is just one in a number of electricity markets.

New paper in Applied Energy

I am pleased that our paper “Projecting battery adoption in the prosumer era” has been published in Applied Energy!
The website Science Trends also featured an article highlighting our work.

The paper investigates two aspects of battery adoption for residential consumers with PV generation. We use a data driven methodology based on 15-minute electricity consumption and PV generation data from the Pecan Street Project, from which electricity consumption data for several hundred households is available. The households are primarily located in Austin, TX, however other locations in the US are also included.

Firstly, the paper investigates what size of batteries are required for consumers to reach certain thresholds of self-sufficiency, and secondly, it investigates how the economic case for residential batteries looks under a range of electricity tariffs, including both current tariffs and future projections.

Figure 1: Illustrating self-sufficiency with PV and the effect of a battery. (A) The demand and generation from an example household – we see a significant amount must be exported. (B) The mean increase in household self-sufficiency for a given battery capacity. (C) The mean marginal increase in self-sufficiency as a function of battery size.

In the first part, we find that for the studied 369 consumers, the mean self-sufficiency from PV-only (without storage) is 36%, meaning that when a household installs a PV system that produces the equivalent of their yearly consumption, we expect them to export 64% of the total PV generation to the grid as it does not align with their consumption (see Fig 1A). Using batteries, we find that modest sizes can increase consumer self-sufficiency significantly, for example, a 10 kWh battery leads to a mean self-sufficiency increase of 25% across all households (see Fig. 1B). However, we find that (in terms of self-sufficiency), there is very little benefit of installing a battery with more than 3 kWh of storage per MWh of yearly PV generation (Fig. 1C).

In the second part of the analysis, we utilise consumer batteries explicitly for electricity bill minimisation. We find that while the economics of PV installations were very favourable in several regions (i.e. we find that PV has a Net Present Value (NPV) greater than $5000/kW for the average consumer under various CA tariffs), no current tariffs were favourable for battery storage and batteries produced negative NPVs (we studied both NEM and FIT based tariffs in our household regions – Austin, Boulder and San Diego). Therefore, projecting into the future, we considered a range of retail electricity price levels and FIT remuneration rates. Our results suggest that combined PV-battery systems generally become profitable with electricity prices above $0.40/kWh and solar rewards as low as $0.05/kWh or below. Interestingly, we find that decreasing PV installation costs has a strong negative effect on battery NPV, while decreasing battery costs and increasing efficiency had small positive effects (see Fig 2).

Fig 2: Comparing the difference in payback between PV-only and PV-battery systems for the median consumer. Payback difference = (Payback PV-only)-(Payback PV-battery). Average retail electricity prices and subsidy levels in different regions for the year 2015 are marked for context.

The main contributions of the work are to further understanding regarding the size of energy storage for households and to define the electricity prices and subsidy levels at which a large deployment of household batteries may be expected, which is important for grid operators and utilities to understand.

For anyone interested the battery scheduling optimisation code is available on my github and is described in this post.

Scheduling battery operation dependent on prices, demand and PV

Tags: Mixed Integer Linear Programming (MILP), Python, Pyomo, CPLEX, optimizing battery operation, residential batteries

In this blog post I will look at how to optimize a battery schedule using a Mixed Integer Linear Programming (MILP) formulation of the problem. I use data from a recent data-driven competition, which investigated using batteries to minimize a site’s electricity bill. I used this in a paper recently (Projecting battery adoption in the prosumer era in Applied Energy) and this post documents the problem formulation.

All the code and data is available in this repository containing a jupyter notebook which goes along with this post. This post is written to go along with the code in the repo.

The repository also contains an alternative formulation (also used in the paper) as well as notebook which applies the formulation to all test data set of the data driven competition to generate a score.

Getting set up

First of all, one needs to get set up with pyomo and CPLEX, which are the modelling language and the solver we will use. I wrote myself a rough set of notes for what I did to get set up and running with CPLEX/pyomo in jupyter notebooks on a linux server, and they might help… but I also imagine you can find some better resources for this – here is a link to the actual pyomo documentation.

Scheduling a battery using pyomo and MILP

Secondly, we need some data about electricity prices and battery properties. Additionally, since the problem is a little bit boring with only a single set of prices and more often than not people are interested in batteries deployed at a specific consumer site, we will consider batteries located at consumer sites with demand and generation, where there is a buy price and a sell price for electricity. Conveniently, the data can then come from the competition!

Our aim is to use the battery to reduce or increase the site load at different times, in order to minimize the site electricity bill. Since there are buy prices and sell prices for electricity, there is a difference between reducing the load to zero (i.e. reducing the amount the site must buy) and reducing the load below zero (i.e. increasing the amount that the site is selling). Each site has a PV installation, so there is both demand and generation, but the price at each period only depends on the net load. The demand (d(t)), PV generation (PV(t)), buy price (buyPrice(t)) and sell price (sellPrice(t)) are timeseries with a fixed value at each time period.

To calculate the net load, we sum the demand, the generation and the battery action at each period, as shown.

netLoad(t) = d(t)+PV(t)+ES(t) (1)

The price at each period is given either by the buy price, if the site is importing electricity, or the sell price, if the site is exporting electricity. This is summarised conditionally as follows:

IF  Net(t) > 0, price(t)=buyPrice(t) , ELSEIF  Net(t) < 0, price(t)=sellPrice(t) (2)

We can see from the first equation that the unknown is the input/output of the battery, ES(t) (if there is no battery then this term is equal to zero at all times)

The battery is then defined by its characteristics. We model a battery with the following parameters:

1. Maximum and Minimum states of charge, SOC_{max} and SOC_{min}
2. Charging and discharging power limits (which could be different, but in the example we assume they are equal)
3. Charging and discharging efficiency (\eta_{Char} and \eta_{Dis}, which again can be different, but we assume that they are equal and so the roundtrip efficiency is the product of the charging and discharging efficiency).

It must be noted here that this is a very simple battery model that does not take into account potentially important factors such as temperature, depth of discharge, applied current and voltage.

Now that we are done with the definitions we can go into the modelling procedure. As I mentioned earlier, we will use data from the recent data driven competition. The competition provided two datasets, a training set and a testing set. We will just use the testing set, since we are developing a data-independent model. In the competition, the demand was unknown and a forecast was given, and additionally the prices were only specified 1 day into the future. Hence, the training set was introduced so that competitors could learn specific features about each site and improve their algorithms.

Since we are interested in obtaining the optimum battery schedule, we will just take all the information we need from the testing dataset (in a folder called submit – you may need to sign up to get access – it is also all available in my repository). Therefore, for our chosen site (siteId=1) we will take values for the actual demand, actual PV generation and electricity prices for both buying and selling from the data provided (we won’t use the price, demand and generation forecasts at each period).

We will get the battery parameters from the metadata file from the competition repo:

The dataframe header showing battery properties

So we will initially model a battery with a capacity of 300 kWh, a charging power of 75 kW and a roundtrip efficiency of \eta_{Char} \eta_{Dis} = (0.95)^2.
Now let’s look at the data from site 1.

The data is in a csv file and it is easiest to access using pandas. It is split into different 10 day periods so we will just look at the first one.

Here is a plot of the demand and generation as well as the buy and sell prices for electricity.

Demand and generation from site 1 as well as the electricity prices

We can see that at certain times PV(t)>demand(t) and therefore the site will be exporting electricity at the sell price. Looking the price profile, we can also see that the export price is always a constant level which is less than the highest buy price. Therefore, we get the intuition that the optimum battery operation is likely to store electricity that would be sold and use it to replace electricity that would have to be bought at expensive times.

To set up the optimisation problem, we first define the variables and an objective function, which corresponds to the total price of the consumer electricity. Pyomo requires us to set up the problem algebraically. What this means is that we define a set of variables, the objective function and constraints on the variables. The majority of the difficulty of setting the problem up in this way is writing down the variables, their relationships to each other and constraints such that the problem makes sense and does exactly what we intend.

First off, we need to initialize the model, for which we will use the concrete model framework in pyomo (concrete model means that we will define all the parameters at the time of the model definition, as opposed to an abstract model where we formulate the problem algebraically and parameter values are specified at a later stage). We also set up the model index which will be our times.

# now set up the pyomo model
m = en.ConcreteModel()

# we use rangeset to make a sequence of integers
# time is what we will use as the index
m.Time = en.RangeSet(0, len(net)-1)

Now let’s write down the objective function taking in mind the Equations 1 and 2 above:

ObjFn = SUM [buyPrice(t)*posNetLoad(t) + sellPrice(t)*negNetLoad(t)] (3)

posNetLoad is the net load at every period if the net load is greater than zero and negNetLoad is minus the net load at every period if the net load is less than zero. posNetLoad and negNetLoad are the decision variables.

Let’s also write down rules governing the state of charge of the battery. This consists of two components; the rule governing the minimum and maximum states of charge and the rule governing how much the state of charge changes at each period. The rule governing the max and min states of charge is a simple constraint, and can be included using bounds on the variable SOC. In pyomo we can do this when initializing the variable:

 0 \leq SOC(t) \leq batteryCapacity (4)

# variables (all indexed by time)
m.SOC = en.Var(m.Time, bounds=(0,batt.capacity), initialize=0)

Integer constraints

Here is where the “mixed integer” part of the formulation comes in. We specify constraints ensuring that the battery can only either charge or discharge at a particular period. To do this we specify Boolean variables for charging and discharging, boolChar(t) and boolDis(t). We then bound the problem using the bigM method. Therefore, we formulate the following constraints:

posDeltaSOC(t) \leq M(1-boolDis(t)) (5)
posDeltaSOC(t) \geq -M*boolChar(t) (6)
negDeltaSOC(t) \geq -M*boolDis(t) (7)
negDeltaSOC(t) \leq M(1-boolChar(t)) (8)

The first constraint means that if the battery is discharging, the charging must be less than or equal to zero and the second forces it equal to zero. If the battery is charging then the first constraint imposes an upper limit that is at least two orders of magnitude greater than any feasible value and the second constraint imposes a redundant lower limit.

If discharging, the third constraint means gives a lower limit to the discharging (remember discharging is –ve) and the fourth gives a redundant upper bound. If charging, the third and fourth constraints force negDeltaSOC equal to zero.

To ensure that the battery cannot simultaneously charge and discharge at the same time we constrain that boolChar(t)+boolDis(t) = 1.

Other constraints and variable relationships

We then define posDeltaSOC and negDeltaSOC in terms of energy coming from the grid to the battery and energy coming from the PV, at this point ensuring that the charging efficiency decreases the amount of energy that actually makes it into the battery with the opposite for the discharging energy.

posEInGrid(t)+posEInPV(t) = posDeltaSOC(t)/ \eta_{Char} (9)

Similarly,

negEOutLocal(t)+negEOutExport(t) = negDeltaSOC(t)*\eta_{Dis} (10)

For the charging and discharging power limits, we must also use constraints rather than bounds, since we have split charging into the component which comes from the PV and that which comes from the grid. Therefore we have:

posEInGrid(t)+posEInPV(t) \leq ChargingLimit (11)
negEOutLocal(t)+negEOutExport(t) \geq DischargingLimit (12)

Then, to make sure that energy available for charging from PV is always less than or equal to the excess PV and the energy used locally must be less than or equal to the demand that is not met by PV, we specify the following constraints:

posEInPV(t) \leq \mbox{ Initial negative }netLoad(t) (13)
negEOutLocal(t) \geq \mbox{ -Initial positive }netLoad(t) (14)

Finally, we then define the decision variables in terms of the other variables:

posNetLoad(t) = \mbox{ Initial positive } netLoad(t)+posEInGrid(t)+negEOutLocal(t) (15)
negNetLoad(t) = \mbox{ Initial negative } netLoad(t)+posEInPV(t)+negEOutExport(t) (16)

Optimisation

Now that the problem is defined we can run the optimisation. We need to specify what tool we will use for the optimisation, which in our case will be cplex. Therefore we set:

opt = SolverFactory("cplex", executable="**insert you path here**")

And finally we can run!

# time it for good measure
t = time.time()
results = opt.solve(m)
elapsed = time.time() - t

Outputs

To get the model outputs, we cycle through the variables that we defined in the model component objects, and I prefer assigning each to a numpy array. Therefore, we loop through the model variable objects and assign the variables we want to arrays.

# now let's read in the value for each of the variables 
outputVars = np.zeros((9,len(sellPrice)))
j = 0
for v in m.component_objects(Var, active=True):
    print v.getname()
    #print varobject.get_values()
    varobject = getattr(m, str(v))
    for index in varobject:
        outputVars[j,index] = varobject[index].value
    j+=1
    if j>=9:
        break

Finally, we can then compare the consumer’s electricity bill with and without the battery, and look at what the optimal battery schedule and the consumer’s net demand are! We can also calculate the consumer’s electricity cost with the battery and compare this to their cost without the battery.

# get the total cost
cost_without_batt = np.sum([(buyPrice[i]*posLoad[i]/1000 + sellPrice[i]*negLoad[i]/1000) for i in range(len(buyPrice))])
cost_with_batt = np.sum([(buyPrice[i]*outputVars[7,i]/1000 + sellPrice[i]*outputVars[8,i]/1000) for i in range(len(buyPrice))])

The optimum battery operation for the site

Looking at the results we can see that the battery is doing the things we would expect – charging at the times when the price is low and discharging when the price is high. Finally, it is worth noting that in this example, there are a range of different optimum solutions – there are lots of different time options when the battery could charge and discharge at the same price (and the cost to the battery of PV-that-would-otherwise-be-exported is the same as the grid import price).

Concluding notes

The nice thing about using this setup is you can just keep adding more and more constraints and different forms of the cost function. For example, one could stipulate that a portion of the consumer’s electricity bill was related to their peak power usage. This allows one to model more and more complex electricity bill scenarios. However, as the problem gets more complicated, you need to be careful about what methods you are using to solve the problem, which is beyond the scope of this post (see the CPLEX manual for details regarding the type of problems it can solve). Additionally, as the number of constraints increases the computational time required will increase.

I wrote this post primarily because when I was first looking into MILP formulations, I didn’t find many easily accessible examples of code that had been used for this type of exercise in academic papers. Therefore, I wanted to document my process.

Finally, thanks to my colleague, Alejandro Pena Bello at the University of Geneva first suggested that I try pyomo for MILP formulations in python and shared some code with me, which was super helpful. Additionally, I found these notes from MIT opencourseware useful when trying to understand how to set the problem up. The tutorials were also helpful.

OSES 2018

I recently heard that the OSES (Offshore Energy and Storage Summit) conference series may still be considering abstracts for their conference on 4-6 July 2018 in Ningbo, China.

This is a great conference and a great place for ideas and discussion surrounding all aspects of offshore energy. Thermo-mechanical energy storage technologies in particular are a major theme due to their potential for use with large scale offshore renewable technologies, and the conference committee certainly contains some of leaders in this field; Seamus Garvey, Rupp Carriveau, Tonio Saint, Daniel Friedrich, Jonathan Shek and Andrew Pimm.

I was lucky enough to go to the inaugural OSES conference in 2014, where I presented a concept for isobaric CAES using carbon dioxide to maintain high pressure storage.  It was thoroughly enjoyable as well as introducing me to a lot of interesting people and ideas.

Unfortunately I won’t be able to attend this year, but I plan to return to the thermo-mechanical scene soon so perhaps in 2019…

Also – for anyone wondering – I never got around to publishing the “isobaric CAES using CO2” concept in a journal article, but last year another team from China had the same idea. I’m definitely annoyed that I didn’t get around to submitting it as I have had a very similar article ready to submit (the extension of my conference paper), but it will teach me to be more proactive and I guess it is good that other people are thinking hard about CAES too!

 

Is community energy storage a smart choice for a smart grid?

Our paper entitled Community Energy Storage: A smart choice for the smart grid? has recently been accepted in Applied Energy. The paper looks at the economic drivers for energy storage when considered from the perspective of an individual household compared with a community perspective.

The main motivation behind the paper was the engineering intuition that a battery in every household does not seem likely to be an optimal solution for the future smart grid, given high battery costs and the rare and expensive nature of the constituent raw materials for battery manufacture. In the paper, we study the economic arguments for both individual households and communities, which illustrated a number of key points as outlined below:

  • Most importantly, aggregating household demand profiles reduces the required battery capacity, with each kWh (kilo-Watt hour) of installed storage being significantly more effective at integrating PV generation when deployed at the community level.
  • It is possible to realize significant cost benefits at a community level from the economy of scale of both batteries and inverters.
  • However, the rate of economic return for individual household batteries is more robust to the changes in the solar resource, despite being less effective for PV integration. This is due to the fact that it doesn’t make economic sense for an individual household to purchase a battery which can always store all the surplus generation, rather an economically optimum size is likely to be a size which can be fully utilized most days.

Due to the points above, our work highlights the concern that residential storage may gain a significant market foothold despite community-level batteries being much more effective from both an economic and a costs perspective. Accordingly, it is crucial for energy policy to consider new market mechanisms to encourage community storage projects in areas where they are more economic and environmental.

Forming communities of neighbours by joining households along the road network in Cambridge, MA

Important modelling assumptions

The paper considers a future scenario wherein the electricity price ($0.35/kWh) is high enough that batteries do provide a return for some users, and we model demand, PV generation and network structure based on empirical data. We also assume a FIT rate which is much lower than current rates ($0.05/kWh), which is based on average wholesale prices for electricity production. Hence in a regime wherein solar PV is widespread, we may expect PV to be rewarded at near-wholesale price levels.

The location data comes from an electric utility in Cambridge and the raw data cannot be shared, however to model the demand and PV generation data we use the Pecan Street project, which supports free academic access to data. Using the location data, we generate hypothetical communities of households, which are grown outwards along the road network, using a multi-source breadth-first search method based on the Dijkstra shortest path algorithm. This could also be useful for estimating the topology of the real distribution network, however, validation in this regard is difficult as the exact topology is deemed sensitive for security reasons.

For each community, and for each individual household, we find the level of storage that maximizes the Net Present Value (NPV) of the battery system. We simulate approximately 4500 consumption patterns for a month using 484 actual demand patterns in the Pecan Street data. Furthermore, we generalize our results to a high degree by considering many different PV sizes, based on empirical distributions of the sizes that households tend to install.

All the code available for running the simulations is available on github.

 

Lightsail – another setback for thermo-mechanical energy storage

Recently there has been another blow for thermo-mechanical energy storage, and in particular adiabatic CAES, as Lightsail – the much hyped company bankrolled by Bill Gates – has entered “hibernation”. This appears to be due to running out of cash, with the company ceasing operations. This is can be added to list of failures in CAES, including SustainX and ApexCAES.

Lightsail’s concept – the $70 million idea

Many have watched the company with interest given its hype, larger than life founder and large amounts of investor funding – sources indicate this exceeded $70 million. Their various claims of high thermodynamic efficiencies which had already been achieved were also interesting – although it is hard to tell from the little available information whether this was simply a result of including the compression heat in their calculations (if you are interested in compressor performance you typically only consider the difference in air enthalpy between compressor inlet and outlet). As such, a little skepticism regarding this is probably healthy, which is emphasized by the company’s switch from CAES system to high pressure storage container manufacturer. Their water-spray injected compressor was an interesting idea, but was probably difficult to manufacture in practice. Moisture in compressed air can cause many problems including corrosion, aiding the build up of sludge deposits by mixing with oil/dust and as a result increasing pipe pressure losses, and causing faults in sensor and control equipment to name just a few issues off the top of my head. Crucially, it must also have been very difficult to avoid freezing during their expansion process, and water droplets/icicles in fast moving machinery can also be incredibly damaging. I should also note that their patent does also refer to oil spray injection.

Lightsail’s move to high pressure container storage manufacturer smacks of desperation and suggests to me that the management realised that they had completely underestimated the scale of the ACAES challenge – indeed a review from a former employee on glassdoor quotes the Cons of working at Lightsail as “Large scale energy storage is NOT an easy problem to solve”! The high pressure storage tank is probably the only part of the ACAES system that actually exists off-the-shelf, which throws up questions of why this would be the component of choice to manufacture. I have discussed in previous posts my opinion that it is most certainly a misconception to think that a ACAES system can be constructed with “off-the-shelf” components. The primary reason would seem to be that they thought they could implement a marked reduction in container costs compared to leading manufacturers, and a recognition that building high pressure vessels was certainly possible, as these type of containers do exist and have a market. This would have potentially allowed them to make incremental scientific progress and make a better or cheaper version of a product that already exists rather than the more ambitious aim of a game-changing energy storage technology. The problem with this turned out to be that they couldn’t make a product that was enough of an improvement to ship many orders.

It is very difficult to work out exactly what went on at Lightsail – I would love to talk to someone who worked there as I’m sure they were full of talented employees. There were also various reports about questionable spending habits on behalf of the senior management – again a look on glassdoor also suggests this. At least they were probably good to their employees, even offering pet insurance as a benefit…

In any case, it’s a huge shame for thermo-mechanical energy storage, and what’s most frustrating is that there is almost no ability for anyone to learn from the technical failures that undoubtedly sank the company. Picking through the Lightsail patents (now owned by the Silicon Valley Bank) is difficult, and they just seem to be full of quite generic ideas, with no indication of any ingenuity in the designs. This reinforces why these early stage companies are so frustrating, and can ultimately be bad for the technology development, as they simply put-off would be investors while no new scientific knowledge can be gained. Maybe I will email Bill Gates and see if he can push for any knowledge to be made public…

CAES: A simple idea but a difficult practice

Download available here.

In the mainstream there are two main branches of Compressed Air Energy Storage (CAES) – conventional and adiabatic.

  1. Conventional CAES

Conventional (also known as diabatic) CAES plants are essentially gas turbines in which air is pre-compressed using off-peak electricity, rather than running a turbine and compressor simultaneously. In these plants, off-peak grid electricity is used to compress air which is stored, and then mixed with natural gas and combusted during expansion. Compression is staged and the majority of the compression heat wasted (although some may be stored in a recuperator to pre-heat the air before combustion). Currently there are two commercial CAES plants worldwide; the Huntorf plant in Germany and the McIntosh plant in Alabama.

  • Huntorf CAES plant: Data from [1]. 310,000m3 cavern at a depth of 600m, pressure tolerance between 50 – 70 bar, converted from a solution mined salt dome. Daily charging cycle of 8h, output of 290MW for 2 hours. 0.8kWh of electricity and 1.6kWh of gas required to produce 1kWh of electricity. Notably, built when the price of gas turbines was historically high.
  • McIntosh CAES plant: Data from [2]. 538,000m3 salt cavern at a depth of 450m, pressure tolerance between 45-76 bar. Originally it provided an output of 110MW for 26 hours but in 1998 two extra generators were added and its total output capacity is now 226MW. 0.69kWh of electricity and 1.17kWh of gas to produce 1kWh of electricity.

Both plants are commercially viable and still running in their respective markets!

CAES

Figure 1: Schematic of diabatic CAES system.

As with Pumped Hydro Storage (PHS), CAES also requires favourable geography to provide the underground air storage caverns. However there are many more suitable sites worldwide than for PHS, although the costs are highly site specific. The costs of mining a suitable underground cavern where suitable geology doesn’t exist or creating an above-ground equivalent storage container are potentially prohibitive, whereas alternatively a naturally occurring cavern or somewhere easily minable may offer a very attractive price of storage in terms of $/kWh (or dollars per metre cubed of air storage).

Caverns can be created in salt geology (typically using salt solution mining techniques) or existing caverns can be exploited provided that they are capable of housing the desired pressure. Geological formations such as aquifers and salt formations (bedded salt and domal salt) offer potential locations. Costs can also be reduced if existing well infrastructure is in place from previous underground drilling operations. While specific geology is required, this geology is relatively widespread. For example, the EPRI suggests that up to 80% of the US could have favourable geology [3] (see Figure 2).

US CAES map with wind resources marked

Figure 2: US geology for compressed air caverns. Regions with high wind resources are also indicated with the idea that CAES sites and wind turbines could be co-located [4].

Estimates for the costs of cavern mining can be as low as $1/kWh of storage capacity if solution mining techniques can be used [5]. In solution mining, fresh water is pumped in a salt deposit, becomes saturated with salt and is then removed. One problem however is that disposal of this brine can cause environmental issues.

1.1 CAES Performance Characteristics and Applications

CAES systems have traditionally been designed as centralised storage facilities which are intended to cycle on a daily basis and to operate efficiently during partial load conditions. This design approach allows CAES units to swing quickly from generation to compression modes and means that they are well suited to ancillary services markets, providing frequency regulation. Their ability to operate on a (intra) daily cycles means that they are also useful for load-following/peak shaving. The air storage caverns can also be very large, allowing for multiple days worth of electricity storage.

It should be noted that the inlet pressure (45-76 bar) for the CAES high pressure turbine is much higher than the equivalent for a typical gas turbine (about 11 bar) so a typical gas turbine can only be used as the low pressure expander. The high pressure turbine at Huntorf is based on a small-intermediate steam turbine design.

1.2 Table of Cost Estimates

Typical Capacity Typical Power Efficiency Storage Duration $/kWh $/kW Lifespan Cycling capacity
500MWh – 2.5GWh 50 – 300MW n/a Hours – days 4-7 [6], 2-50 [7], 60  – 120 [8] 300-600 [6], 400-800 [7], 1000-1250 [8] 20-40 years High

Table 1: CAES cost characteristics

 

  1. Adiabatic CAES

Adiabatic CAES is an energy storage concept that removes the natural gas combustion from conventional diabatic CAES. In adiabatic CAES the heat generated by the compression of air (the charging process) is stored in a Thermal Energy Store (TES) which is separate from the ambient temperature high pressure air store. When the system is discharged the high pressure air is reheated using this stored heat and then expanded. Without the stored heat, the process has an unacceptably low efficiency – this is because significant exergy is stored in the heat as well as the cool high pressure air. When the heat is recovered, the expected practical efficiency of these systems is debated – though the second law of thermodynamics does not pose a ceiling on the efficiency as for  heat engine – it just means that the real process has to be less than 100% efficient. Pragmatic estimates of the real efficiencies of this type of system are debated; most of the academic literature estimates practical efficiencies in the range of 60-75% [9,10]. If a plant could be constructed with no inefficiencies in any process – the theoretical efficiency would approach 100%.

2.1 Status

As no demonstration plant has ever been successfully constructed, Adiabatic CAES must be considered as an unproven technology. It does however have significant promise for use with renewables integration, energy management, peak shaving and grid reserves. The largest planned demonstration ACAES facility is a 290 MW adiabatic CAES project based in Germany called project ADELE [11]. It is a consortium between German utilities RWE and GE, the German Aerospace Center DLR, construction company Zublin, the Fraunhofer IOSB and the Unversity of Magdeburg.

Adiabatic CAES

Figure 3: A simple schematic of an ACAES configuration. There is a thermal store for each compression stage.

A schematic diagram of an ACAES system is shown Figure 3. In this configuration, air is compressed and then cooled using counter-current heat exchangers that transfer the heat from the air into a thermal fluid. This thermal fluid could then be stored in an insulated tank and used to reheat the air prior to each expansion stage. Several people have also suggested the use of Packed Bed regenerators to store the compression heat in the air.

2.2 Underwater CAES

Underwater CAES is a sub-type of ACAES which exploits an underwater Compressed Air Store at a depth of typically around 400m. The ambient pressure at this depth is approximately 40 times the atmospheric pressure, and the air store is either a flexible bag or a dome structure open at the bottom. As air is pumped into the storage container it displaces water and thus the store can operate at a constant pressure. This idea was pioneered by Prof Seamus Garvey and Dr Andrew Pimm at the University of Nottingham, as well as by researchers at the University of Windsor Ontario and Canadian startup Hydrostor (whose work is ongoing at the time of writing).

2.3 Fuelless CAES

The usage of the term “adiabatic CAES” is also somewhat ambiguous, as the term “adiabatic” is sometimes used to refer to the compressions and sometimes to refer to the overall process – i.e. the energy storage process aims to be adiabatic in the sense that ideally, it would exchange negligible heat with the surroundings. Therefore some authors therefore prefer the use of the umbrella term Fuelless CAES. This then clearly encompasses all compressed air processes which aim to store and return energy without the use of fossil fuels. This includes systems which have typically been labelled as isothermal CAES.

2.4 Isothermal CAES

In isothermal CAES the compressions aim to be isothermal and reversible. This is theoretically achieved by minimising the temperature differences which drive heat flow from the compressors to the environment (which is at a lower temperature). A huge challenge here is to make an isothermal compression process which operates sufficiently quickly to be of practical industrial importance but which is still slow enough to maintain the small temperature differences required for high reversibility. One idea for near-isothermal compression which has been suggested by LightSail (a start-up company in California) involves a water spray into the compression chamber of a specially designed reciprocating compressor/expander unit (see Figure 4). The water droplets absorb the heat of compression and their high specific heat capacity causes the temperature increase in the compression chamber to be much smaller. This warm water is then stored and on discharge is re-injected as a mist into the reciprocating machine which now acts as an expander.

Figure 4: Illustrating a near-isothermal CAES concept [12]

Isothermal CAES was also being pioneered by SustainX, however this company has ceased operations citing spiralling system costs. Lightsail Energy and SustainX had a similar goal of an efficiency above 60% for their first generation of machines and believe that 75% is achievable in the long term. The SustainX prototype was a 1.5 MW machine.

2.5 ACAES Challenges

There are several challenges which must be overcome before adiabatic CAES can become a viable energy storage technology option.

  • Specialised compressor equipment must be developed, in which the heat generated during the compression procedure is stored in a highly reversible manner. This process seems most likely to consist of a series of adiabatic compressions in which heat losses from the compressor to the surroundings are minimised. The compressors must also operate with much higher compression ratios than current compressors which do not involve cooling during the compression. Each of the compressions is then followed by a cooling stage which aims to reversibly extract the compression heat. Possible options for heat extraction include packed bed regenerators or counter-current indirect contact air-to-fluid heat exchangers. This type of compression equipment is fundamentally different to industrial many industrial compressors. Why? Because the vast majority of compressors are designed to minimise the work required to achieve air at a given pressure. Most industrial compressions then typically involve trying to shed as much heat as possible from the compression process – as hot air takes more work to compress. The ACAES process is fundamentally different as reversibility should be maximised rather than work minimised. In fact, the greater the reversible work is per cubic metre of compressed air the higher the energy density of the storage system.
  • Specialised expansion equipment must also be developed. Air turbines which provide highly isentropic expansions and operate within the desired pressure ratios are required. The expansion process of an Adiabatic CAES system should aim to mirror as closely as possible the reverse compression process. Therefore it should include the same number of expansion stages and heating stages, and expansion stages must aim to minimise heat gain and return all heat reversibly during the heating stages. While these turbines do not currently exist on the industrial market, it is anticipated that their design can learn much from the current generation of gas turbines for power generation. The pressure ratios will likely be smaller than most current gas turbines. One specific advantage is that the material demands will be much less (in terms of temperature tolerance) than current gas turbines which operate with inlet temperatures up to 2200K.
  • Sliding pressures. Unless the system can be operated between constant operational pressures, both the compression and expansion machinery must operate at maximum efficiency over a range of pressure ratios. A single constant high pressure air storage is a primary advantage of UnderWater CAES.
  • High pressure air storage. Depending on the chosen method of storage high pressure, air storage tanks must be developed which have minimum cost. This has apparently been a problem area both for SustainX and LightSail, however LightSail have released statements which hint that they may have found a method of lowering the costs.
  • highly reversible heat exchangers will also be required which can minimise the temperature difference between the working fluid and the thermal storage medium while introducing minimal pressure drops.

2.6 Notable experimental ACAES development

Lightsail (California) – startup. http://www.lightsail.com/

Hydrostor (Ontario) – startup. https://hydrostor.ca/

SustainX (Massachusetts) – startup (liquidated)

Project Adele (Ongoing utility/academic collaboration – big unexplained delays??)

University of Windsor – Prof. Rupp Carriveau and Dr. David Ting

University of Nottingham – Prof Seamus Garvey and Dr Andrew Pimm

 

 

 

 

References

[1] BBC Brown Boveri. Huntorf Air Storage Gas turbine Power Plant. https://www.eon.com/content/dam/eon-content-pool/eon/company-asset-finder/asset-profiles/shared-ekk/BBC_Huntorf_engl.pdf

[2] M. Nakhamkin, L. Andersson, E. Swensen, J. Howard, R. Meyer, R. Schainker, R. Pollak, and B. Mehta, J. Eng. Gas Turbines Power 114, 695 (1992). https://doi.org/10.1115/1.2906644

[3] Compressed Air Energy Storage: Renewable Energy (2010, March 17) retrieved 22 April 2017 from https://phys.org/news/2010-03-compressed-air-energy-storage-renewable.html

[4] Succar, S & Williams, R.H.. Compressed Air Energy Storage: Theory, Resources, and Applications for Wind Power, Princeton University (published April 8, 2008)

[5] De Samaniego Steta, F. Modeling of an Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) Unit and an Optimal Model-based Operation Strategy for its Integration into Power Markets. EEH – Power Systems Laboratory. Swiss Federal Institute of Technology (ETH) Zurich

[6] Kaldellis, J. K. & Zafirakis, D., 2007. Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency.. Energy, Volume 32, p. 2295–2305.

[7] Chen, H. et al., 2009. Progress in electrical energy storage system: A critical review. Progress in Natural Science, Volume 19, pp. 291-312.

[8] EPRI, 2010. Electricity Energy Storage Technology Options. http://large.stanford.edu/courses/2012/ph240/doshay1/docs/EPRI.pdf

[9] G. Grazzini, A. Milazzo. A Thermodynamic Analysis of Multistage Adiabatic CAES. Proc IEEE, 100 (2) (2012), pp. 461–472

[10] Barbour, E, Mignard, D, Ding, Y,  Li, Y. Adiabatic Compressed Air Energy Storage with packed bed thermal energy storage, Applied Energy, Volume 155, 1 October 2015

[11] RWE Power. ADELE – Adiabatic Compressed Air Energy Storage for Electricity Supply. https://www.rwe.com/web/cms/mediablob/en/391748/data/364260/1/rwe-power-ag/innovations/Brochure-ADELE.pdf

[12] Fong, D. Insights by Danielle Fong. https://daniellefong.com/

 

 

 

A new high in renewable electricity for Germany – and a low in electricity prices!

On Sunday 8th May Germany hit a new high for electricity generation from renewables, with renewable plants supplying 87% of the demand. The result was several hours of negative electricity prices – to such an extent that commercial electricity consumers operating through the wholesale market were paid to consume electricity. This has been reported by articles in Quartz and The Independent.

German consumption, renewable energy supply, conventional supply and prices for 7/8 May 2016

German consumption, renewable energy supply, conventional supply and prices for 7/8 May 2016. Figure from Quartz article.

As I reported in an earlier blog post, negative electricity prices are generally a reflection of insufficient flexibility in the power network. In the case of Germany on the 8th of May 2016, negative prices occurred due to a combination of high wind and solar production, and low energy consumption (due to it being a Sunday). Accordingly, there was significantly more electrical supply than demand and the electricity prices were negative from 7am – 5pm. It is interesting to note from the Figure (taken from the Quartz post) that there doesn’t seem to be a huge difference between the conventional production on Saturday compared to that on Sunday, but this small increase in the ratio of renewable to conventional power resulted in a hugely negative price spike. Additionally we also see that on both days the production was higher than consumption – I assume that this is a reflection of German exports to other electricity markets. It only seems likely that as Germany strives for 100% renewable electricity this type of situation will occur more and more frequently and will make the situation for storage more favourable. It would be ironic if the low daytime electricity prices that have eroded the market for energy storage in Germany could become so low that they actually began to favour storage again.

Also, for interest, an article I wrote about negative electricity prices and energy storage.

 

 

 

Storage and the duck

The California duck curve is now infamous and is very often features in discussions around storage. The duck phenomenon is a result of several factors coming together at once to create a scenario in which there is significant strain on the electricity generation system.

The infamous California duck

The now infamous California duck.

Typically the output from solar panels is well-aligned with times of high electrical demand, especially in systems which have large cooling dominated loads. This is because it often gets hot when the sun is shining and people tend to be most active during the daylight hours.

Solar generation typically occurs when demand for electricity is high - during the middle of the day.

Solar generation typically occurs when demand for electricity is high – during the middle of the day.

However when there is a cool sunny day in systems which have a lot of solar panels that are typically used to meet cooling-driven loads, then the situation can arise in which the net demand for electricity which must be generated by conventional powerplants (i.e. coal, nuclear, gas) becomes depressed, as most of the demand can be met by the solar. This is a problem for utilities in itself as turning down the output on some of these plants (especially nuclear, to a lesser extent coal) is difficult and costly, so instead they sometimes opt to sell their electricity very cheaply (or even pay for it to be used when prices go negative). For utility-scale renewables this is also a problem, as they can end up in the situation where they simply have to stop producing electricity. On top of this, the power output from all the solar panels in a local region is very well correlated. Therefore they all start and stop producing power at close to the same time (there is some spread due to orientation and location). This leads to a sharp increase in the net demand leading up to the evening peak which typically occurs after the sun goes down. There are only certain types of plant which can react to changes in demand quickly (they have high ramp rates), for example gas and hydro and only hydro can do it cheaply, as conventional gas plants must already be running for some time at their Minimum Stable Generation levels before ‘ramping up’, which is often less economic and more polluting per unit of electrical output.

Solar Panel outputs from the Pecan Street project (https://dataport.pecanstreet.org/)

Solar Panel outputs from the Pecan Street project (https://dataport.pecanstreet.org/) all producing electricity at the same time. Red line is the average

The concern about the duck is a prime driver for energy storage development. This storage can come in several forms – i.e. not just batteries coupled with the solar panels. Some of these are highlighted in this NPR discussion which includes fuelless Compressed Air Energy Storage, Concentrated Solar Power with thermal storage in Molten Salts and Ice Storage for cooling.

Ultimately it is all down to the economics. If the costs of storage are less than the increased costs of utilities as a result of having to provide the additional flexibility the duck requires, or if storage can increase the value of renewable energy sufficiently then it will become a viable option. At present the costs of curtailment are likely to be less than storage, but as the amount of curtailment increases and storage costs fall then this could rapidly change.

 

 

Capacity markets and energy storage

I’ve been meaning to get my thoughts together about capacity markets and energy storage ever since the inaugural UK capacity market last year. So here it is, I start by talking about what a capacity market is and aims to do and then think about how it can affect energy storage economics.

The purpose of a capacity market is to ensure that there will be enough powerplants installed and available to generate (sufficient generating margin) for the future operations of an electricity system. The capacity market aims to do this by providing stable and regular payments to market participants who agree to guarantee capacity which can be used to meet peak demand at some point in the future, over and above the payment they receive for the energy that they sell.

Historically the electricity industries in most countries were developed as government owned monopolies. One of the legacies of this is that the wholesale price paid for electrical energy in today’s restructured markets doesn’t usually include the cost of building the powerplants themselves. Although in today’s restructured, re-regulated (liberalised) markets the costs of electricity are nearly always much higher than the marginal production cost (due to a number of factors including utilities’ market power), often these costs are not high enough to justify investments in new powerplants. This is known as the ‘missing money’ problem in electricity markets.  Because electricity demand is so variable, and the highest peak demand only covers a short time span and occurs infrequently, electricity systems often have more than sufficient capacity for normal demand levels but insufficient capacity to reliably cover the highest peak demand spikes. If electricity were a more normal commodity, at times of high demand, high electricity prices would cause some consumers to decide they didn’t want to buy electricity, which in turn would cause the demand to fall. The ‘market price’ reached in this situation may then persuade other potential generator operators to enter the market (by building new powerplants). However as the majority of consumers do not see and thus cannot respond to real-time electricity prices, during times of power scarcity, administrative controls often limit the market price to stop it becoming unreasonably high – the demand for electricity is very inelastic. The missing money problem then occurs when the market prices are limited by administrative actions such as price caps. This means that large potential rewards to generators are forgone, and this can in turn result in little investment in new plants which would provide electricity during these times of scarcity.

In an attempt to get around this issue, the capacity market then provides supplementary income to powerplants, in addition to what they earn in the energy market, to cover the costs of ensuring that they are available to generate in future. Capacity (in kW per year) is traded in the capacity market while wholesale electricity (in kWh) is traded in the energy market. The capacity payments are received by the capacity providers in the relevant delivery year and are typically paid for by a charge levied on all electricity suppliers (which in turn is passed on to consumers). There are also penalties for failure to meet a provider’s agreed capacity.

Capacity markets usually include a large primary auction at some specified interval (4 years in the UK) before real time followed by smaller incremental auctions at closer time(s). The price paid for all the accepted bids is the market clearing price – representing the price of the most expensive unit of capacity required. This is known as pay-as-clear. It aims to encourage plants to bid in at their actual marginal cost, rather than speculating what the market price may be. The plant with the lowest marginal cost should then make the largest profit as all the capacity providers receive the same payment. Figure 1 illustrates how the capacity market bids are evaluated.

Illustrating how the "pay-as-clear" capacity market auction works. The red line illustrates the target capacity. All bids below this are accepted and paid the price of the last kW of capacity.

Illustrating how the “pay-as-clear” capacity market auction works. The red line illustrates the target capacity. All bids below this are accepted and paid the price of the last kW of capacity. Source the energy collective.

One big risk with capacity markets is that they rely on accurately forecasting future demand levels, to determine the optimal level of capacity in future years. If future demand levels are over-predicted, this can result in a large oversupply of capacity and uneconomic plant retirements, burdening consumers with very high costs for reliability. This has happened in Western Australia.

The inaugural UK capacity market was run in 2014 to secure capacity for the winter peak season 2018-19. This is a T-4 auction (four years ahead of delivery) and there will also be a T-1 auction. Capacity providers are free to adjust their positions in private markets from one year ahead of the delivery year and throughout the delivery year, subject to some restrictions. The capacity providers must then be prepared to provide capacity when the system operator issues a capacity market warning. The market rules state that the “System Operator will issue a Capacity Market Warning (CMW) when the anticipated system margin in four hours’ time is less than 500MW. In the event of a System Stress Event starting which was not forecast, a CMW will also be issued. The CMW remains inforce until the forecast available margin is greater than the trigger level of 500MW.” Once the CMW is issued, providers must deliver their capacity obligation in four hours’ time to avoid CM penalties, should a System Stress Event be active at that time.

Where does energy storage fit into capacity markets?

Capacity markets are often touted as a good potential way for energy storage operators to gain revenue, which when combined with revenues from the energy market, could be sufficient to encourage investment. It has often been observed that the rewards open to energy storage devices in energy-only markets are generally not sufficient (certainly not at present) to justify investment in storage technologies, and the extra income from capacity markets is designed to justify investment in new infrastructure (i.e. generation or storage). Importantly, not only for storage but for all capital-intensive electrical infrastructure, the payments for providing capacity also have the advantage that they represent a reliable source of future income.

However in order to justify investment in new technologies like energy storage, the price reached in the capacity auction must be high enough, and if the price is to reach these high levels then either the level of required capacity must be set high by the system operator (the auctioneer), or some existing resources should not be allowed to enter into the market. Last year, the UK held its inaugural capacity market auction. The auction aimed to be “technology neutral” and nearly all existing generating resources were able to compete, including storage and demand response. The providers were only evaluated at their bid price rather than any other factors, for instance, emissions. The result of this was a price of £19/kW which was too low to for any new energy storage projects.

The real winners of the auction were the incumbent large utilities, whose existing plants got most of the capacity. These plants will be paid the result of the capacity auction on top of what they already earn in the energy market, provided they are still available to operate in the delivery year. This has frustrated many people as it is highly unlikely that many of these plants wouldn’t have generated anyway, so the net outcome of last year’s UK capacity market seems to have just been a significant increase in consumers’ electricity prices for the services they were already getting. What many people see as a problem is that the capacity market did not take into account any environmental factors that would remove, say, coal generation from the auction. DECC had originally stated that nuclear and coal would not be allowed to participate in the capacity auction, but after lobbying from the industry, changed this position.

It seems likely that if capacity markets are to be truly relevant to energy storage, then these markets will have to take into environmental factors like emissions into account. It is fairly obvious that in the short term the costs of meeting our capacity needs are lowest with fossil fuels, however it is the long term cost on society and the environment that these markets should aim to minimise. If we believe in decarbonisation then existing coal plants in particular should not be competing for new capacity payments. In order to encourage storage or other new low-carbon technologies, capacity markets should be further in advance and more ambitious – they should only be open to technologies whose emission levels comply with our decarbonisation targets.

The results of the inaugural UK market imply that the current UK government is much more concerned with security of supply than decarbonisation (although many sceptics say that security of supply has decreased due to our extended dependence on fossil fuels and the result of the auction will be an increase in electricity bills of £1 billion for the same service). Undoubtedly, security of supply is important, but it is, at best, overly-pessimistic to think that security of supply cannot be achieved simultaneously with decarbonisation. An ambitious capacity market would seek to address both of these challenges. Smaller capacity auctions with ambitious environmental targets and higher rewards that look further into the future may be one way to simultaneously achieve both of these aims. In this way these markets could drive up innovation in renewable-storage projects.