Michael Lippert, marketing and business development manager for energy storage at Saft, explains how to find the optimum sized megawatt-scale Li-ion Energy Storage System (ESS) for a large wind or solar plant.
The unpredictability of nature means that the output of wind and solar plants is highly variable. In turn, this means that in spite of forecasting, operators cannot be certain about the level of plant output. As the penetration of renewables on the grid has grown, this unpredictability has led to issues related to grid stability and congestion at substations at peak times.
Energy storage in the form of lithium-ion (Li-ion) battery energy storage systems (ESSs) can mitigate the variability and help both plant and grid operators.
What is not commonly known is how to select the optimum size of ESS to deliver maximum operational and financial benefit. An ESS can have several different roles and it is only by understanding its role and the specifics of its site that engineers can specify the right ESS for the job.
An ESS can play a number of roles, including control of ramp rates, power smoothing, power shaping, peak shaving and frequency regulation. To understand the philosophy behind sizing an ESS, it’s helpful to understand the impact of these roles on the electrochemistry inside the batteries.
Ramp rate control and smoothing
Grid operators limit the rate of change at which power is injected into the grid. This rate of change is known as ramp rate. Whereas a sudden shift in wind or passing cloud will cause a step change in output, the ESS will absorb or release energy to ensure that the grid sees a smooth ramped output instead of the step change.
The ESS itself will experience many small charge and discharge cycles as it controls many small steps in photovoltaic (PV) or wind output, but over the day the cumulative energy charged and discharged in 24 hours (known as throughput) can amount to two to three multiples of the capacity of the ESS (2C to 3C).
Smoothing, or power smoothing, is similar and has the role of keeping production within a given forecast window. Used for this role, the ESS compensates for short-term power sags and similarly to ramp rate control, the ESS will experience many small to medium charge and discharge cycles over the course of the day.
Power shaping, on the other hand, uses an ESS to shape the power output of a plant to deliver steady and predictable power like baseline generation.
An ESS used for a typical PV farm in this mode will deliver a large discharge in the morning before the sun’s energy, before charging up during peak daylight hours in the middle of the day and discharging again later in the day.
The operation means that an ESS at a solar farm will experience a single large charge and discharge cycle per day (1C) with a depth of discharge (DOD) of 70 per cent or more.
Peak shaving reduces congestion on the grid at peak times. The ESS charges when the plant’s power exceeds a set limit and releases energy into the grid later in the day once the peak has subsided. It ensures that the output of a plant never goes beyond an agreed limit and avoids revenue loss through curtailment.
Frequency regulation is a service normally provided by bringing primary and secondary reserve generating assets online at short notice. It ensures stability of the grid by injecting or absorbing active power to keep the frequency inside its limits. In doing so, the ESS helps the grid to accommodate more renewable sources.
The operational profile of the ESS will depend on the number and amplitude of frequency deviations of the grid. Typically, deviations are of short duration and only infrequently at full amplitude.
This means that an ESS used for frequency regulation will have a similar operational profile to one used for ramp rate control. It requires many small charge and discharge operations. Typically this leads to many small cycles of 3-4 per cent of DOD throughout the course of the day, adding up to a energy throughput of around six times its capacity (6C).
Developing an energy management strategy
Having delivered large-scale Intensium® Max ESSs with a combined power capacity of more than 90 MW, Saft has developed significant experience and insight on its real-world operation and optimisation.
The most important consideration for any ESS is to view it as part of a whole system rather than as a standalone component. Different aspects of the environment can have a significant impact on the whole life cost of an ESS, which is built up from its capital cost, maintenance and operational costs and the cost of curtailments and outages.
Finding the optimum size for an ESS requires the development of an Energy Management Strategy (EMS), which itself needs a number of inputs.
The first set of inputs are site specific. They include the limitations of the grid code and local legislation as well as measured data on the wind or solar power output. It’s important to use survey results from the actual site over a period of days or months. This takes account of local geography and variability and to achieve accurate sizing of the ESS, which in turn achieves better financial performance.
The second set of inputs are the customer’s objectives for the plant’s power output – basically the mode of operation, which can include one or more of the roles explained above.
Lastly, Saft contributes its understanding of energy storage technology, including energy, charge and discharge power capacities and the effect of aging on the electrochemistry.
Combined with modelling, these determine the cost profile, made up of operating revenues and penalties to balance lifetime costs, asset lifetime, OPEX and CAPEX costs.
Modelling to find the sweet spot
Modelling is an iterative process that starts with a first estimate of battery specification that is combined with the other inputs to the EMS to deliver a cost profile.
It calculates the lifetime costs and operating revenue for a particular size of ESS. By repeating the process with a range of different sizes, it’s possible to identify the sweet spot, where the operator will find the optimum balance between revenues and costs during the whole life of the installation.
At the heart of modelling is the algorithm that is used by our battery management systems in the field. It mimics the performance of the ESS down to the level of individual cells, taking account of electrical and thermal performance and electrochemical aging.
Varying the size and specification of the battery changes the cost profile. A smaller ESS will have a lower capital cost but could lead to lower revenues, more penalties, lower compliance with the grid code, or more curtailment losses. It will also alter the system’s calendar life.
Plotting total cost of ownership (TCO) against specification, Saft can tailor the size of the ESS to meet the customer’s business objectives and operating environment.
The capability to select the optimum size of ESS is not the only benefit that Saft has developed from its field experience. Its installed base has also given Saft a strong understanding of the factors that lead to high performance and a long and predictable life for Li-ion ESSs.
Good thermal management is the most important factor, ensuring that the temperature is consistent across the entire ESS. By minimising temperature variation, the cells and modules experience a constant rate of aging. In turn this allows for precise prediction of battery performance over its lifetime.
Other important aspects are to ensure accurate measurement of state of charge (SOC), good SOC management, and ensuring high energy efficiency of the battery system itself as well as the power converter and auxiliary systems such as cooling plant.
Together these extend the lifetime of the ESS, enhance performance and optimise the total cost of ownership.
By taking account of the many variables experienced in real-world operation and integrating these into its EMS and modelling, Saft has developed the capability to engineer an ESS that is optimised for the climatic, business and operational environment of an individual PV or wind plant. We have delivered containerised ESSs for wind farms and solar plants around the world, including Europe’s first commercial ESS installation in 2015, which was for a wind farm in the Faroe Islands.
Optimum ESS for Puerto Rico
One example of optimising the ESS was for a 10 MW PV plant in Puerto Rico. The grid code required that ramp rates be limited to no more than ten per cent in output change per minute and the operator also required support to the grid frequency by up to five per cent.
Saft used modelling to identify the optimum ESS as having 1.3 MWh energy storage capacity and 5 MW power rating. This balanced the requirements of peak power and the customer’s requirements. It also anticipated a drop in power over the lifetime of the ESS due to electrochemical aging to ensure that the ESS will continue to meet the minimum technical requirements even at the end of its lifetime.
Nine Megawatt-hour ESS for La Réunion
Another example is Saft’s largest Li-ion ESS to date at a 9 MWp PV plant at Bardzour on La Reunion in the Indian Ocean. The role of the ESS is power shaping to inject power into the grid at a constant 40 per cent of the plant’s peak power capacity. The ESS must also provide primary reserve for 15 minutes and provide voltage support.
Modelling identified the optimum size of the ESS as 9 MWh energy storage capacity and Saft delivered a 9 MWh Intensium® Max, housed in nine individual shipping containers. Now in operation, thermal monitoring and control keep the temperature constant throughout 476 modules in each of Bardzour’s nine Intensium Max containers, ensuring consistent and predictable performance.