The co-location of energy storage systems at renewable energy parks has become increasingly common in recent years. While they certainly bring many benefits, working out the ideal size for site is far from straightforward, says Dr Gruffudd Edwards, Senior Data Scientist at specialist energy consultancy TNEI.
Developers are showing increasing interest in co-locating energy storage systems (ESS) – often batteries, alongside some combination of wind turbines, solar PV and potentially local demand customers at existing or prospective energy parks. While there is no doubt that the benefits of co-locating ESS are substantial, they can be challenging to estimate – which makes it tricky to work out what size of storage represents the best investment decision over its lifetime, for a specific park. In this article we discuss the nature of the severe uncertainties that make such decisions so challenging, but also some principles that can be employed to provide vital insight.
Many tools exist to help developers calculate the optimal storage size for their site - most of them are undoubtedly helpful and provide valuable insight. However, we caution against placing too much faith in their conclusions, unless they are truly able to acknowledge and account for the very severe and inter-related uncertainties involved, and to produce solutions that are in line with the user’s risk appetite.
An ESS connected to the grid at any location is already able to provide revenue through multiple streams, such as price arbitrage and grid support services like frequency response and voltage support. Markets are also likely to grow considerably over the coming years, given current trends. By co-locating ESS with renewable generation, several additional benefits may be realised, including:
- Increasing the ‘load factor’ of a grid connection by allowing the generation capacity to be increased, without having to spill energy during times of peak generation.
- Decreasing the amount of energy imported from the grid for sites with both demand and generation.
- Shifting exports towards times of day when market prices are at their highest, and/or shifting imports away from such hours.
- Facilitating participation of the generators in markets where they must deliver a certain amount of energy in a specific time interval, by reducing penalties arising from the forecast errors.
Finding the optimal size of an ESS for a specific energy park presents a very significant challenge, yet one that shouldn’t be postponed too long. Clearly, a developer can make a start on ESS co-location by acquiring a small store and upgrading later, perhaps several times, as the development path of markets is revealed. However, without a robust estimate of the final size of the ESS, the overall design of the site – including the size of the grid connection – may later prove to be far from optimal.
For a site where all major design parameters other than the ESS size are already determined and fixed, the challenge for a developer is to establish the point of optimal trade-off between capital expenditure and operating costs for the ESS versus the total increase in revenue it generates. This requires an understanding of how the various revenues will vary with ESS size, both those associated with the co-location and those that could be obtained anywhere. What makes this truly challenging to achieve is that these revenue stream values will all have to be estimated conditional on a specific approach to stacking the services – with often contradictory demands – which is itself a very challenging optimisation problem under uncertainty.
Another situation that developers might find themselves in is an energy park where the site’s grid connection size is considered fixed, but where both the ESS size and renewable energy generation capacities are open to optimisation. A particular example of this might be an existing wind park, perhaps undergoing re-powering, where there is interest in adding solar PV capacity, along with an ESS to ensure that very little of the energy generated by the PV is spilled due to exceeding the export limit. In this case, the challenge is to find the optimal trade-off between the capital costs of the ESS plus the additional generation on one side, versus the additional revenue from exporting more units of energy plus the revenue from all of the (conflicting) stacked services provided by the ESS.
The most flexible situation that a developer may find themselves in is one where very few project parameters are assumed to be fixed, so there are the three components to the capital expenditure side of the trade-off (grid connection enhancement, additional generation capacity and the ESS). These must be compared with reliable initial assessments of the total revenue that the renewable generators and ESS can generate given the grid connection, conditional on the ESS’ operational strategy and subject to severe uncertainty.
In general, simulating the co-located ESS’ optimal operational strategy is a huge challenge – but one that cannot be avoided for an accurate estimate of future benefits. This is because the store’s finite energy capacity means that the power import/ export decision taken at any given point in time often limits the possible decisions that can be taken at future times. If we do not know how much potential future revenue is being sacrificed by taking a specific action at the current time, current benefits and a loss of future options are hard to balance. The chain of implications of both present and future actions on the actions available further in the uncertain future often makes finding optimal decisions either very difficult or theoretically impossible.
However, all is not lost! It is quite straightforward to simulate how a store would behave if we neglect the revenues from price arbitrage, firming-up forecasted generation and the provision of grid services. With these factors ignored, we can assign simple objectives for the store’s operations, such as ‘minimise curtailed MWh exports’ or ‘minimise the sum of curtailed MWh exports and MWh imported from the grid (to meet demand)’. The very useful property of such objectives is that optimal performances are guaranteed by adopting greedy strategies.
This means that, e.g., if the amount of power being generated (net of demand) is greater than the grid connection’s maximum export limit, and the store has some space, then there is no doubt that the store should absorb as much as it can of that excess. There is certainly no benefit in holding back, in case that space has more value later. Likewise, whenever the level of net generation first falls below the export limit, the store should immediately empty itself as quickly as possible, without violating the export limit.
TNEI has recently analysed the problem of simulating ESS actions under such simplifying assumptions and have developed and interactive tool to calculate the benefits that the co-located store can deliver. We have developed a process of placing each possible energy park into one of 16 categories, and on that basis will allocate one of 7 possible objectives to the store’s operations, each of which has an algorithm for implementing the appropriate greedy strategy. The conditions under which a hypothetical ESS must conduct its strategy are accurately simulated through the use of historical weather records and demand traces. While using this tool (or a similar one) does not truly solve the problem of calculating the optimal ESS size, it provides rich insights on the nature of the trade-offs between the total capital costs of grid connections, generation capacity and ESS, and the total revenues they can provide from selling MWh through the grid and reducing grid imports to supply demand.
TNEI's Gruffudd Edwards will be speaking at the All Energy Conference on the 11th May 2022, on the topic of 'Optimising the size of storage co-located with renewables’, followed by a panel discussion alongside other industry professionals. Come along to hear more on this topic, or come and chat to us on our stand (P40) at the exhibition.