Research paper presents a novel analytical method to size energy storage in local electricity systems

A research paper by Department of Engineering Science researchers presents a novel analytical method to optimally size energy storage in local electricity systems.

The need for sizing energy storage stems from rising greenhouse gas emissions, considered the main contributor to climate change. In response, many countries have signed the Paris Agreement to curb emissions by switching to renewable energy. However, renewable generation is weather-dependent, causing electricity production to be intermittent and non-dispatchable (where power generation cannot be adjusted to match electricity demand).

Energy storage can mitigate the intermittency and non-dispatchability issues by storing surplus energy during high renewable generation and releasing that energy to bolster low generation periods. Proper sizing ensures storage has enough capacity to charge and discharge energy when required and achieve this without unutilised or wasted storage.

The paper presents a novel analytical method to size energy storage. The method first constructs a temporal storage profile of stored energy, based on how storage charges and discharges in response to renewable generation and load demand. The storage is sized according to the largest cumulative charge or discharge observed in the storage profile.

Paper co-author Han Ren;

“We believe this is the first storage sizing algorithm built on the theory that energy storage should be sized according to the largest cumulative charge or discharge it can experience. The new algorithm is fast, calculates the exact optimal size, and handles non-linear storage models. The new theory can enhance our understanding in the field of storage sizing”

The method yields the optimal storage size that maximises storage utilisation while eliminating unutilised storage capacity. When the renewable system runs using a conventional operation strategy, maximising storage utilisation also maximises renewable consumption and minimises load-shedding (a power shutdown in parts of the electricity system due to energy supply shortage).

The method was applied to two solar battery systems, which are controlled using a conventional operation strategy and are physically based in the northern temperate climate zone. Major findings from the studies include:

• The optimally sized storage does not have wasted storage capacity due to over-sizing, nor cause energy deficits due to under-sizing.
• Energy leakage affects the sizing of long-term storage and can cause energy deficits in the system.
• High demand and generation require larger storage, while low demand or low generation requires smaller storage. For these reasons, peak summer and peak winter require smaller storage, while early summer and early autumn require larger storage.
• Increasing storage size has a diminishing return on the additional storage energy provided to the system. The diminishing return thresholds are defined by the largest daily design and the annual design.
• The largest daily design only requires 3% of the storage size of the annual design, but provides 80% of the energy provided by the annual design.

“Something interesting we have found is that for solar battery homes near Oxford, the daily storage capacity is only 3% of the size of the annual storage, but provides 80% of the energy provided by the annual storage”

The proposed method can be used as a design support tool for energy analysts when determining the required storage capacity. Moreover, energy storage installers can utilise this method to size the storage for installation.

Future works can build upon the current methodology by incorporating more accurate and sophisticated storage models, and expanding into optimal storage placement and operation strategies.

Find out more

• An analytical method for sizing energy storage in microgrid systems to maximize renewable consumption and minimize unused storage capacity Authors; Han Kun Ren, Masaō Ashtine, Malcolm McCulloch, David Wallom