The energy system today is significantly impacted by two emerging energy transitions. On the one hand, we have the fast-accelerating use of Electric Vehicles (E.Vs). On the other hand, we have adopted solar installations for homes and businesses, unifying a fragmented energy system. Gradually, society is moving away from fossil-based products to renewable energy sources; from petrol and diesel for transportation and backup power, renewable energy is becoming an increasingly viable replacement for fossil fuels. Battery technology is also developing rapidly, allowing for more efficient storage of electricity in larger capacities and thereby incorporating more intermittent renewable energy sources into energy systems.
These factors have provoked the re-evaluation of today’s energy systems capabilities and the exploration of redesigns needed to meet new technology demands.
The incorporation of intermittent renewable sources of energy into the new age of energy systems is a non-negotiable demand that must be met. At the heart of this innovation, energy provision must be de-centralised and mobile to allow consumers to become energy suppliers at any given moment. It should be easily deployable and scalable to fit changing needs, with reliability and cost-effectiveness as significant pillars for customer satisfaction.
A swappable battery network could address these requirements, transforming how renewable electricity is generated, distributed and consumed. To validate this, we will look at how a swappable battery network would perform in terms of ease, speed and cost of rollout as well as reliability and cost to the customer compared to current alternatives. This analysis is based on information and market dynamics in Sub-Saharan Africa. The electricity grid infrastructure is still relatively nascent and, therefore, easier to adapt and change along with this new energy transition. The report will also examine how this could impact carbon emissions and other sustainability factors.
Today we almost all access electricity through plugs in our walls. This electricity comes from the network of cables and wires that originate their journey from large power stations. This requires significant investment into infrastructure and the required cables and wires to transport electrons that eventually power your TV, lights and everything else. In the UK alone that uses roughly 300 TWh of electricity per year there are an estimated 25,000km of transmission lines and 800,000km of distribution lines (estimated 2,750km of cable for every 1TWh of energy); enough cables to wrap around the entire planet 20 times. As applications electrify further, this requirement to build out such infrastructure will continue even though it is cost prohibitive, inflexible and takes a long time. A single connection to the grid in Sub-Saharan Africa urban environments is between $400 and $1,200 to the consumer and even then to maintain a reliable supply, backup diesel or petrol generation is still required [source].
The need for electricity in Sub-Saharan Africa is expected to grow by over 800 TWh or 9x by 2040. In West and Central Africa, only three countries are on track to give every one of their people access to electricity by 2030. At this slow pace, 263 million people in the region will be left without electricity in ten years [source]. To get to a point where everyone has reliable power based on current approach, it would cost about $120 billion per year until 2040. It would also require an estimated 2.2 million kilometres of cables (using UK as a baseline), enough to go 55 times around the planet. The West African Power Pool alone is estimated to require 16,000 km of new transmission power lines. As African governments struggle to fund and balance investments across roads, schools, waste management and more, any opportunity to reduce, eliminate or delay investment in power grids is welcomed. Anything new that could make even a small impact on this extreme need for infrastructure, could have a major benefit to end customers, both in terms of efficiency and cost. In addition to the large capital investment needed, transmission and distribution of electricity in Sub-Saharan Africa account for about $5 billion in annual losses on average equivalent to up to 20% of energy generated at source.
Sub-Saharan Africa currently has one of the lowest per capita km of electricity transmission lines, with an average of 68% of African citizens having access to an electric grid [source]. It's not too late to engineer this differently and prevent over-investment in the short term.
Another problem that exists in urban cities in Sub-Saharan Africa is the cable and wiring at the distribution level and the wiring within homes and businesses. Often this infrastructure is dated and even though there might be sufficient system generation capacity, homes and businesses can experience blackouts due to this. It can be very difficult to improve the grid and therefore reliability in urban settings, the current solution for most people is backup diesel or small petrol generators. Increasingly solar with stationary batteries is also being used, however the upfront costs to buy these systems outright remain prohibitive to most.
Urban electricity reliability across Africa where there is a connection is 65% compared to 22% in rural areas. Urban population already represents 41% of the populus and this is expected to rise to 75% by 2050. The above access, reliability and urban population growth also varies by country as shown in chart below:
This requires a significant focus on reliable electricity access in urban settings (which the battery network can support). It is generally harder to raise electricity access in urban areas than in rural areas [source].
Batteries are well understood as storage devices for chemical energy which can then be extracted for use as electricity, but what does “swappable” mean, and what value does a network of such batteries provide?
EV batteries can either be fixed (physically attached to the vehicle chassis and non-removable), or swappable (plug and play devices that are removable).
For EV’s swappable batteries create an alternative to charging with less downtime for the user. A depleted battery can easily be changed out for a fully charged one without significant mechanical intervention or disruption to the user’s activities. This offers us the key opportunity to shift business models away from the supply of physical batteries towards more flexible Battery-as-a-Service (BAAS) models.
A multi-use battery is an application-agnostic store of energy designed to empower users to decide how, where and when to use them. While they are similar in concept to ubiquitous plug-and-play AA batteries, they are far more powerful, and yet more portable and user-centric by design than industrial-size batteries. Such a battery can be used to power an EV one moment and then retasked as a backup “generator” for a home the next.
In April of 2022, the Indian government released a draft battery swapping policy in order to aid its transition to electric mobility nationwide by ensuring “lower upfront costs, minimal downtime, and lower space requirements”.
Some of its other stated objectives include:
The swappable nature of these batteries lends themselves to another possibility: sharing. Customer needs can now be met by a pool of available batteries rather than one or two single batteries that the customer owns or is assigned. Networked batteries therefore give us the same classical efficiency gains of a product-service system such as those you would find in a ridesharing service such as Uber (where the mobility needs of many customers are met by a relatively small number of drivers), for example, governed by the forces of demand and supply. Thanks to advances in Internet of Things (IoT) technology, batteries can now also be connected to the cloud and centrally monitored and maintained.
From an energy perspective, a network of batteries begins to exhibit some of the same values as the networked cables, wires and transformers of traditional grid systems, allowing for more efficient deployment of energy compared to batteries as isolated assets. It also uses the assets more efficiently compared to standalone systems or non swappable batteries in cars or for backup that might only be used 5-10% of the time.
How do we compare the apples of traditional grid systems against the oranges of the battery network? Here we examine 5 criteria:
The reliability of an energy system is measured by the proportion of energy distributed or served against the total amount of energy demanded. In Sub-Saharan Africa, energy reliability is a major issue, symptomised by blackouts, brownouts and load shedding in many countries. Aside from the non-availability of and poor planning around sufficient power generation, this can also be due to a number of factors, including poor maintenance of equipment, particularly at the last mile. This gap between supply and demand is often addressed with the use of petrol and diesel generators.
For a battery network on the other hand, we can define reliability as the risk of a customer not getting a battery once they have requested for one. The battery network therefore can play a dual role: firstly in supplementing or replacing the grid in order to make it more reliable; and secondly in replacing fuelled generators as a source of backup power when the reliability of the grid falls short.
As countries and regions make commitments towards achieving carbon neutrality or even net zero emissions, what advantages does a swappable battery network offer?
Firstly, batteries are the foundational means by which renewable sources of energy such as solar, wind and hydroelectricity – intermittent by nature, literally – can be harnessed and deployed in order to meet the man-made forces of energy demand and supply. Solar energy captured during the day can be stored in a battery and released at night when the sun is down, for example. Especially as wind and solar power reduce in cost on the one hand, and increase in feasibility and attractiveness on the other, the parallel need for energy storage therefore comes into sharper and sharper focus.
Despite these advantages, lithium-ion batteries do pose a significant danger to the environment if improperly disposed of; a challenge which most consumers are either ill-equipped or unmotivated to afford the necessary attention to. Individual battery ownership also means consumers make more selfish (but collectively less efficient) decisions about when and how much to charge and use their batteries. However, the shared battery network can prevent the responsibility of battery disposal from passing to customers in the first place, while simultaneously managing scarce energy and battery resources more efficiently. The sustainability impact of maintaining responsibility centrally (with the network supplier or operator) instead of distributing it to customers can therefore not be understated. While recycling lithium-ion batteries is not widespread today, they have the potential to be highly recyclable assets; with “up to 95%” of their constituent materials being recoverable, according to battery manufacturer Northvolt [source]. The market for global lithium-ion battery recycling is expected to grow from $4.9 billion in 2021 to about $40.6 billion by 2030 [source].
This is where the circular economy comes into play. The cost of mining the minerals that go into lithium-ion cells is significant, as is the cost of production of the batteries themselves, solar panels, wind turbines and other infrastructure required to support the kind of swappable, multi-use battery network that we’ve described in this paper.
With power generation becoming more modular and decentralised with smaller scale infrastructure such as rooftop solar, power generation can now sit much closer to the point of consumption, greatly reducing the losses in transmission associated with the traditional energy grid. In Ghana for example, 25% of the electricity generated is lost due to poor infrastructure, electricity theft or commercial losses, more than four-fold the EU average of 6%. [source]. Further, most thermal generators such as gas and coal are about 50% efficient, meaning that only about 35% of the input fossil fuel energy actually reaches the end consumer in a traditional grid system using thermal generation. Solar systems might be less efficient – only about 15% of generated capacity reaches the end consumer – but are situated close enough to the end consumer that that transmission losses are extremely low. This makes them more efficient in the final analysis, and cheaper for systems installed today, even on a per kW basis. The value of a solar-integrated battery network then becomes apparent, and efficiencies can be gained by locating swap / charge stations closer to users.
The battery network offers a number of other cost benefits. Swappable battery EV’s are more affordable because the batteries can be excluded from the upfront purchase cost and instead amortised over time. This is a significant benefit in regions where credit is often unavailable or prohibitively expensive. Customers now pay for battery access (Battery-as-a-Service) instead of the battery itself, which is cheaper because that access is shared amongst many paying customers. All told, the battery network can be up to 30% cheaper than petrol at the pump for mobility applications.
Even further along the product-service continuum, the physical battery itself is completely transparent to the user, and all they pay for is energy access. In this model, the battery forms part of the infrastructure that delivers energy to the customer in the same way that wires, cables, transformers and power plants do now. The energy in the battery is simply and literally available to them at the flick of a switch. This flexibility of deployment between battery as product and energy as service – and various points in between – is possible because the batteries are networked.
The swappable battery network then, is far more adaptable to modern customer-centric energy solutions than the traditional grid system. The battery itself is a flexible ‘energy in a box’ akin to a personal device like a power bank, but for higher end applications. For EV’s, particularly in fleet applications, energy theft is significantly reduced when compared to fuel which can be syphoned. Batteries are also cheaper, lighter and easier to handle than the direct petrol and diesel generator alternatives.
Another aspect of customer-centricity is ease of access for the customer. Just like traditional fuel stations, battery swap stations must be located at points of convenience to the customer without forcing lengthy or frequent detours – if any at all – in order to prevent the range anxiety that often accompanies the usage of Electric Vehicles. Likewise for battery-as-backup or remote power applications, how easily can the customer access a swap station and transport the battery to their point of need?
Finally, batteries offer greater insights to manage usage through data, which is not easily available in fuel or grid electricity.
Now that we have appreciated the theoretical bases of the battery network and how it compares to traditional grid systems, we delve deeper into understanding the dynamics of implementing such a battery network.
A simulation allows us to visualize the supply and demand forces at play, network reliability as measured by the number of unfulfilled battery requests, revenue per battery and more, across multiple scenarios.
Each of our simulations, which are only illustrative in nature, will employ some combination of the below assumptions and variables.
In the same way the laws of physics and our socio-economic and economic conditions rule our physical world, our fixed assumptions dictate the world that our battery network simulation operates in. These assumptions include:
Number of batteries per motorcycle. For the purposes of this simulation, we have assumed that each motorcycle takes two batteries. This falls in line with certain assumptions about the kWh-to-weight ratio of batteries, which impacts both the portability of the batteries, as well as the amount of energy that heavy users would typically need. Lower kWh capacity of the EV means more customer swaps per day.
Battery capacity. Amount of energy stored per battery, measured in kWh. This impacts the average distance that each EV can travel and therefore the frequency that they will swap and the distribution of swap stations. The higher the battery capacity, the lower the swap frequency and the number of swap stations required to meet demand
Battery cost. Primarily dictated by the energy density of the cells; The amount of energy contained in a battery per unit weight. This has a direct impact on the capacity of the battery. More energy dense cells tend to cost more. It must be noted however, that battery cell technology is evolving at a rapid pace both for existing and new technologies. Costs have declined significantly (~85% in last 10 years, with a further 70% decline predicted by 2030 , and more energy dense cells).
Battery lifetime (in cycles). The useful lifetime of the battery expressed in the number of times it can be charged and discharged. The longer the life cycle of the battery, the longer it can remain in use before it must be replaced, thereby reducing the cost of maintaining the network. Batteries that last longer generate more revenue before they must be replaced or shifted to non-mobility applications only.
Period of time. The number of weeks and period(s) of the year over which the model is run.
Agents are how we represent groups of customers in the simulation. They are based on research into a number of real-world user groups, each of whose behavior and energy consumption patterns vary in a number of ways, including the seasons, days of the week and times of day that they are most likely to demand energy from the network.
We then populate our simulation with any number of agent combinations to understand how the battery network would cope with the demand generated over a certain period of time, and what configuration of the battery network would be optimal for each of the said combinations.
Agents are broadly categorized into four groups: light and heavy mobility and; light and heavy non-mobility.
Heavy mobility users consume about 3x as much energy for mobility needs as the closest mobility and non-mobility users. This observation constitutes the rationale behind the most fundamental assumption of our battery network; cater to heavy electric two-wheeler usage in order to ensure the highest possible demand for energy being generated in the system.
These variables are the levers by which we can change how the simulated battery network behaves in response to a given set of inputs (specific combinations of agents and fixed assumptions).
There are four primary network variables that we will review here:
In a battery swapping model, we must first and foremost ensure that there are enough batteries available to satisfy users at every point they need to swap. By default, this means that the total number of batteries in the network must exceed the number of batteries being used at any given time, with the difference being kept in reserve at swap stations, ready to be swapped. This ratio (total number of batteries available to number of batteries being used in vehicles) is known as the Battery Reserve Ratio, and is a critical component in creating an efficient battery network. The lower the ratio, the lower the cost of operating the network.
Assuming a sufficiently efficient network where users are able to access batteries without difficulty (accounting for battery charging time, proximity to swap stations, etc.), this ratio should be a maximum of 2. This means that there is always a spare battery in a swap station for every battery in use. In the case of our model where we have assumed that each EV will utilize two batteries, a reserve ratio of 2 means two batteries in swap stations for every vehicle in operation.
Users however, will not all be swapping batteries simultaneously. Therefore, the battery reserve ratio can be further optimized to balance the cost of deploying and replacing batteries on one hand, and the reliability of the network on the other.
The scale of a battery network simply refers to the number of users being served by it. We can illustrate this by maintaining a fixed reserve ratio while we vary the number of users.
As illustrated above, the relationship between the reliability of the network and the number of users is not linear at a fixed reserve ratio.
As the number of batteries and users in the network increases, the reliability of the network approaches 100%. Therefore, the chances that a user will not have access to a battery at any given point in time reduces, simply because there are more available batteries.
For the battery network to make a significant impact on any urban energy network, the critical factor is the scale of motorcycles that then allows the network to open itself up to other less heavy use cases.
A perfectly optimized reserve ratio means nothing if the reserve batteries are not where they need to be when customers need them. Swap Station density refers to the number of swap stations present per unit area of the environment that the battery network is being deployed across. For a given number of batteries (determined by the scale and the reserve ratio, as described above), we can vary the swap station density by increasing or decreasing the number of batteries available per swap station.
Another factor is how long the batteries take to charge within the swap stations. This is a factor of the capacity of the batteries themselves and the amperage of the chargers or charging system that they are connected to. If we assume that a non-fully charged battery is unavailable to customers (until it has been charged), then a Swap Station that can charge batteries faster will be able to return batteries to the available network at a faster rate, and will therefore be more reliable.
Reliability has a major impact on Customer Experience; the less reliable the network the more attempts to swap will end in disappointment when a customer is unable to get a charged battery. Especially when compared to the analogous scenario of going to a fuel station for fuel or turning on a generator for power, it is clear that a battery network must first and foremost be reliable in order to be viable.
In the above scenario, we have a battery network that is just over 78% reliable, resulting in over 11,000 successful swap attempts on the first attempt. However, the inverse of this is that over 7,000 swap attempts would take more than one attempt, including 4,000 or so swap attempts that would require 4 or more. Considering that users are unlikely to try more than twice in the real world, this is significant, and points to an unhealthy and non-viable battery network.
By comparison, a battery network with over 99% reliability has far fewer occurrences of customers having to repeatedly ask for batteries. This means that customers are happier.
For any battery network, the single biggest cost factor will be the cost of the batteries. There is therefore a direct relationship between the return on investment of the battery network and that of the batteries themselves. We can measure this by looking at the Internal Rate of Return (IRR).
To see how this plays out in our simulation, we again look at our two main levers: scale and reserve ratio. This time we maintain a constant number of heavy mobility users as we vary the reserve ratio.
At a reserve ratio of 1.1, the IRR of the network is about 63.7%, and peaks at 65.3% with a reserve ratio of 1.3. Beyond this reserve ratio of 1.3, marginal gains in reliability (due to there being more spare batteries in the network) are accompanied by relatively large drops in IRR. In these scenarios, the cost of each additional battery is not worth any potential gains in reliability.
Thus far, we’ve looked at simulations that model a battery network that caters only to mobility users. However, there are other agent types that we can consider in those simulations, either in addition to or in place of some of those heavy mobility users. As we have already argued, the potential to converge disparate applications of energy use using battery technology as a cornerstone is the path to the new 21st century energy system.
So, how does the battery network cope when both mobility and non-mobility use cases are added to the mix?
First and foremost, can we run a reliable battery network that caters to both mobility and non-mobility users? Let’s take a scenario where we have a network reliability of 99.99% as our baseline. We simulate how this reliability changes as we add the same number of either mobility users on one side, compared to the same number of non-mobility users on the other. We do this in three steps, adding 7%, 15% and 25% more users respectively, all while maintaining a fixed battery reserve ratio.
An increase of 7% and even 15% more users on either side results in only a marginal drop in reliability, from the baseline of 99.99% to about 98%, although the drop for non-mobility users is slightly smaller. This tells us that adding non-mobility users has no significant negative impact on network reliability.
However, at the extreme ends where we attempt to add a full 25% more users of each type, our changes have a much more telling effect. For heavy mobility users we find ourselves with a network that is over 20% less reliable, whereas the network with the additional non-mobility users results is still reliable over 95% of the time. At significantly higher scale, the network maintains its reliability better with the addition of non-mobility users as compared to the addition of mobility users.
This is where the application-agnostic nature of the batteries can now be fully leveraged. For example, motorcycle riders in Ghana often spend about 30% of their income across both mobility and energy needs. In our modeling we assume that for every 100 heavy use motorcycle riders (>90km per day) we can provide battery service to 20 non-rider customers.
Significantly, this ability to deploy batteries outside of mobility use cases also plays an important role in maximizing the utilization rate of the battery network.
During non-business hours (nights and weekends), more EV’s will tend to lie dormant, creating a temporary over-supply of batteries in the network. Batteries that can only be used for mobility applications would therefore likely be very much under-utilized by default. This can be counter-balanced by creating more demand for batteries in non-mobility use cases such as backup power during night time and early morning hours.
It will be clear that while an under-supply of batteries will have a negative effect on the reliability of the network and vice versa. Likewise, that same under-supply of batteries will have a positive effect on IRR and vice versa. These two opposing forces create an interesting dynamic, the effects of which we can measure using the Reliability-Scaled IRR of the network, as illustrated in the following simulation.
We again illustrate the under-supply of batteries by keeping the number of batteries in the network constant and monitoring the effect of increasing the number of users on the network, again by 7%, then 15% and finally by 25%.
The positive effects of doing more with less (serving more customers with the same number of batteries and therefore generating more revenue) initially outweighs any negative effects of having battery availability being strained between more users (thereby reducing reliability). For mobility users, there is a fairly significant improvement at 7% and 15% increases in scale, compared to a largely unchanging Reliability-Scaled IRR for non-mobility users for similar increases in scale.
This relationship soon becomes inverted. The Reliability-Scaled IRR of the network drops significantly as we increase the number of users by 25%; negative effects on the reliability of the network start to significantly outweigh the efficiency gains. This effect is however far less severe where the additional users are non-mobility users (0.298 for mobility users compared to 0.537 for non-mobility users). Here again, it becomes clear that a multi-use battery network is more robust than a battery network dedicated solely to mobility use cases for significant increases in scale. While the Reliability-Scaled IRR in that scenario is lower than our baseline, the impact of being able to reach more users cannot be ignored, especially when carbon savings are taken into consideration.
To understand the sustainability impact of the battery network, we can measure the equivalent kilograms of CO2 that have been displaced by using battery power from solar charging instead of petrol for our corresponding mobility and non-mobility use cases; mostly internal combustion engine (ICE) vehicles and petrol generators. Once again, our simulation indicates that adding non-mobility users has a positive impact.
Displacing petrol has a positive impact on sustainability for both mobility and non-mobility users, but adding non-mobility users increases estimated kg of CO2 displaced at a faster rate compared to adding mobility users.
This is an inverse relationship to the number of users compared to the above parameters.
In a city like Accra with roughly 4 million people there are an estimated 150,000 or more ICE motorcycles and growing at a rate of up to 10% per year. Assuming all these motorcycles were electric and using Kofa swappable batteries this would provide an additional 30,000 potential customers with access to our battery network providing a cleaner and cheaper alternative to a petrol generator. Thus providing direct support to grid reliability for almost 1% of the population. This delivers economic gains and significantly reduces urban pollution and noise. Keeping with this example it would deliver over $50 Million of savings and reduce emissions by 270,000 tonnes CO2 per year in Accra alone from motorcycle usage only. Since the battery network can transition itself to stationary home or small business storage connected with solar after the mobility lifecycle, the impact of the network is even greater, and further supports reliable, renewable and affordable energy access to more customers.
The most critical element across any electricity system is making sure that the lights always remain on and this requires a real time second by second balance between the amount of electricity being generated and the amount being consumed. This balance must be maintained within a very tight threshold. Operators must make sure that they keep the system balanced by either reducing or increasing generation or reducing or increasing consumption. Most electricity systems around the world focus their efforts on the first option.
With the traditional grid, it can be very difficult to alter the amount of energy supplied through the system at any given moment; gas plants for example must be left on standby in order to respond fast enough in the event of a shortfall in the supply of energy, resulting in higher costs and emissions. This difficulty is greatly mitigated where there is already energy stored in the system in the form of a battery. The inverse of this is also true; unused energy produced in the system can be stored in batteries to be deployed later when the need arises. Batteries can then be added to or moved across the network to service the greatest need.
The increase in renewable generation such as solar and wind and the increase in smaller and more distributed power generation is making it much harder to maintain and invest in physical infrastructure and even more difficult to keep the system in balance at all times. If a cloud covers a large solar plant or the wind suddenly stops blowing, there is an immediate need for extra power generation or someone to reduce their consumption. This response is often required in seconds, something that a gas power plant would find difficult to manage in isolation. A battery or a consumer (home or business) on the other hand could respond fast enough to support such immediate changes.
Batteries are still up to eight times more expensive than energy from the grid, but is a future of grid parity – where the cost of the battery network is comparable to the cost of the main grid – even possible?
Firstly, we must consider that we are benchmarking against grid tariffs, and therefore any increases in those tariffs makes a future of grid parity that much more achievable. We must also consider the cost of grid and transmission infrastructure repair or replacement in the event of natural disasters. The interconnected nature of the grid also means that faults are difficult to localize and more expensive to repair, leading to black outs and brown outs for a relatively large geographic area and user base. Comparatively, the decentralized architecture of the battery network allows the effects of a fault to be contained to a much smaller area, and can even allow affected hardware to be repaired in less time or even swapped out with less effort and money. The battery network therefore can play a key role in improving climate resiliency.
Similarly, since the main comparable to the cost of the battery network is the price of fuel (to power vehicle and backup power alternatives), fuel price increases likewise improve the viability of the battery network for like-for-like use cases, and allow for more flexible pricing strategies. For parity to internal combustion engines, this number is believed to be about $100 per kWh.
The cost of lithium ion battery technology has reduced by around 85% over the past 10 years, from $721 per kWh in 2012 to $137 per kWh today. This is projected to reduce further to $58 per kWh by 2030. This increased efficiency of energy storage also goes hand-in-hand with increased energy densities per battery pack, meaning lighter batteries and a better experience for customers. To reach grid parity, these current trends of rapidly reducing battery cell costs and increasing energy density would need to continue. Indeed, some forecasts indicate that the solar plus batteries Levelized Cost of Electricity (LCE) could reach $0.05 per kWh by 2025 at which time they would be cheaper than thermal generation.
Different pricing strategies depending on use case thereby allowing the batteries to mimic cost to their nearest comparable i.e. petrol, diesel or grid depending on use cases. It could also be possible to provide some time-of-use pricing (price surging) that would allow energy from the battery network during certain times of day to be closer to grid parity.
If we look into the future and assume motorcycle usage in Africa will reach similar levels to India and Thailand, this would be 47-87% respectively per household penetration. There are an estimated one million households in Accra. Hence this would equate to 470,000 - 870,000 motorcycles in Accra. In such a scenario it would mean 95,000 to 174,000 out of motorcycle use customers which would be up to 4% of the population having access to cheaper, cleaner and more reliable electricity.
In total there are an estimated 800,000 registered motorcycles in Ghana growing at a rate up to 10%. Kenya has even more motorcycles estimated at 1.4 million and in 2021 grew by 17% year on year.