How can you get the best value out of your energy storage?

Tue, 20/02/2018 - 12:55

Authoring Information:

Michael Lippert, business development manager for Saft’s Transportation, Telecom and Grid (TTG) division.

A key challenge for renewable energy plant and utility operators is the selection of the optimum size of lithium-ion (Li-ion) energy storage system to deliver maximum operational and financial benefit. This is because an ESS can have several different roles: control of ramp rates; power smoothing; power shaping/shifting; peak shaving; frequency regulation. Only by understanding its required role and the specifics of each site can we can specify the right ESS for the job. 

Developing an Energy Management Strategy

The ESS must be viewed as part of a whole system rather than as a standalone component. Different aspects of the environment can have a significant impact on its whole life cost, 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 System (EMS), which itself needs a number of inputs (see figure below).

Developing an Energy Management System requires a number of inputs.

The first set of inputs is 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 high resolution survey results from the actual site over a period of several months - and ideally an entire year - to reflect seasonal changes.

The second set of inputs is 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. These inputs must also include precise technical parameters and limits such as desired ramp rate, maximum power at grid connection point, provisions for frequency support etc. Furthermore, economic variables like the plant’s remuneration scheme must be known, including cost of outages, penalties for deviations from the specification, etc.

The ESS manufacturer will also contribute its understanding of energy storage technology, including energy, charge and discharge power capacities and the effect of aging on the battery electrochemistry. 

Combined with modelling, these factors 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

Modeling is an iterative process that starts with a first estimate of the ESS specification. It calculates the lifetime costs and operating revenue. 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 modeling is the algorithm that is the same as that used by 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 ageing

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 operating life.

The value of field experience

Experience in the field has shown that there are a number of factors that lead to high performance and a long and predictable life for a Li-ion ESS. 

Good thermal management is the most important factor, ensuring that the temperature is consistent across the entire ESS. By minimizing 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 optimize the total cost of ownership. 

By taking into account the many variables experienced in real-world operation and integrating these into our own EMS and modelling, Saft has engineered ESS installations optimized for a number of renewable energy applications as shown in these two examples.

9 MWh ESS for La Réunion 

The ESS installed at a 9 MWp PV plant at Bardzour on La Reunion in the Indian Ocean (see figure 2) provides power shaping to inject power into the grid at a constant 40 per cent of the plant’s peak power capacity. It also provides primary reserve at 10 percent of peak power for up to 15 minutes as well as providing voltage support.

Bardzour PV plant on La Reunion.

Modelling identified the optimum size of the ESS as 9 MWh energy storage capacity in the form of 9 containerised systems.

Optimizing wind power for the Faroe Islands

SEV, the Faroe Islands utility, has commissioned Europe’s first fully commercial Li-ion ESS operating in combination with a wind farm.  The 2.3 MW, 700 kWh containerized solution is helping to maintain grid stability so that the islanders can capture the full potential of their new 12 MW Húsahagi wind farm.

SEV has deployed a 2.3 MW ESS to maintain grid stability for the Faroe Islands.

SEV’s key aim was to overcome the short-term variations, lasting from seconds to minutes, that result from the variable nature of wind generation. A simplified model was used to calculate the power and energy for smoothing at the wind farm level. The most critical requirement was to achieve a ramp control that ensures the combined power flow of the wind turbines and the battery at the point of connection (POC) must not vary by more than 1 MW.

Lessons learned and conclusions

Owners and operators of power plant need to have complete confidence that their ESS is both consistent and predictable.  To achieve this, it is essential to recognize a number of key considerations:

  • Operational profiles are complex and multi-functional
  • Sizing is an iterative optimization process
  • System performance relies on consistent, predictable ESS performance