Energy Management System

Developing an Energy Management System requires a number of inputs. (Source: Saft)

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).

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.

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