Energy Management System (EMS)

The Lynus Energy Management System (EMS) is one of the most comprehensive tools in our library.

This makes it possible to get different electrical consumers and generators in the building on one control level.

The EMS works with the following data:

  • Historical data of the measured values from the database
  • Real-time data
  • future data
  • Machine Learning
  • Algorithms

Thus, Lynus' EMS knows exactly how the consumption and generation of the devices in the building will behave in the future and, thanks to this knowledge, can incorporate various schedules and controls back into the real-time system on site.

These historical as well as future data, the so-called predictions, are visible to the customer in various ways and can be retrieved at any time.

These include:

  • 24 hour prediction PV system
  • 24 hour prediction consumption
  • Future activation of consumers
  • Current performance data
  • Device specific data
  • Own consumption
  • Degree of self-sufficiency

Thus, degrees of self-sufficiency of up to almost 100% can be achieved. Self-consumption in the building is increased and CO2 emissions are reduced.

Lynus EMS can be connected to various devices on the generation or consumption side and thus also controlled or regulated.

The most common ones include:

  • Generators
  • PV systems
  • Battery storage
  • Power connector
  • Home consumption
  • E-charging stations
  • E-cartridges (heating inserts)
  • Heat pumps
  • Other large consumers

When it comes to the topic of e-cartridges (heating inserts) in hot water storage tanks, the topic of Legionella also comes up. Most conventional legionella protection circuits simply switch on at some point during the day to heat the storage tank to the necessary temperature. This usually involves the use of expensive mains electricity.

With the control of the heating insert by Lynus and its EMS, this heating phase is shifted to the time when there is surplus energy in the building. This saves the operation with expensive mains electricity and at the same time the issue of legionella is taken care of with the specially generated energy. If nevertheless once not enough surplus energy is available, the Legionella circuit is executed 1 times weekly.

Each consumer can be linked to a prioritization in the EMS.

Thus, for example, devices with high prioritization are supplied with surplus energy first. Consumers with low prioritization are supplied later. In addition to prioritization, other criteria and setting options can be specified for the loads.

These include

  • Prioritization (order of power distribution)
  • Battery charge states (switch-on and switch-off thresholds of the devices)
  • Regulate operation of the consumers under emergency power mode (e.g. completely deactivate in isolated operation)

In order to control the consumers in general, the EMS offers various ways of working:

  • Optimization of self-consumption
  • Peak shaving
  • Load management

Properties in operation self-consumption optimization:

  • Feed as little as possible of the generated PV energy into the grid
  • Consume as much as possible and distribute to consumers
  • Caching in battery to cover later consumption (evening or morning hours)
  • Only feed into the grid in the last phase and when there is saturation in the building

Characteristics in peak load shaving operation:

  • Capping and reducing peak loads
  • Use the power supply optimally and do not exceed
  • Use the battery as a buffer

Properties in operation load management:

  • Mains connection too small or cannot be enlarged
  • Mains connection can be increased by software, e.g. with a battery (buffer)
  • Use the network connection optimally and do not exceed it
  • Especially suitable for load management with several e-charging stations
  • Individual security outlets of the charging station groups can be monitored separately so that these outlets in their Services are not overloaded. (LS protection of the cable must always be present)

The influence of machine learning can also be adapted and regulated on a project-specific basis. Thus, it is possible, for example, to allocate battery capacities differently, or to switch on heat pumps at more optimal times.

The machine learning part can be operated in the following modes:

  • Tarif-optimized (timetables and power distribution are adjusted to day and night tariffs)
  • Energy optimized (timetables and power distribution are increasingly based on energy distribution over 24 hours adjusted)
  • Balanced mode (mixing between tariff and energy optimized)
  • Mode off (the machine learning part is not included in the real-time part)

General Notice:
Certain system-relevant commands are generally executed on the real-time system.

Without machine learning, a classic EMS reacts only to the actual state of the plant.

The sequence on the graph at "Today" can be briefly described as follows:

  • During the night hours, the battery is discharged to compensate for the mains power
  • During the course of the day, the battery and heating are supplied with surplus energy from the PV
  • In the evening hours, the battery discharges and in turn compensates for the off-peak electricity supply
  • Weather forecast is NOT included in energy management

The sequence on the graph at "Tomorrow" can be briefly described as follows:

  • Battery discharged all night and is empty in the morning hours
  • Since the bad weather was not taken into account, there is no battery capacity left to compensate for the high tariff electricity from the grid during the day
  • Heating switches on uncontrollably when the tariff is high
  • Thus everything from the network must be compensated for by the next day

The sequence on the graph at "The day after tomorrow" can be briefly described as follows:

  • In the morning hours, power still has to be drawn from the grid
  • During the day we have overproduction again and can charge and operate the battery and heating with this
  • Towards the evening, the compensates battery recovers the power from the grid

Lynus energy management responds to both the actual state of the plant, as well as future data, using various machine learning algorithms internally.

The sequence on the graph at "Today" can be briefly described as follows:

  • In the night hours, the battery is discharged to compensate for the grid power
  • During the course of the day, the battery and heating are supplied with excess energy from the PV
  • In the evening hours, the battery discharges and compensates Part of the power from the grid at off-peak rates
  • Weather forecast flows into the MIT energy management
  • So we know that the weather will be bad tomorrow and not enough energy will be available to turn on the heating operate or to charge the battery
  • In this way we operate the heating at night when the tariff is low and precharge the buffer

The sequence on the graph at "Tomorrow" can be briefly described as follows:

  • Part of the battery capacity was retained in order to cover the energy from the grid during high tariffs for days
  • Heating system remains off during high tariffs
  • No or little energy consumption during high tariffs
  • Since the calculations for the next day are included again, we know that there will be enough energy available for the battery and heating the next day

The sequence on the graph at "The day after tomorrow" can be briefly described as follows:

  • In the morning hours, power is drawn from the grid at low tariffs
  • During the day we have overproduction again and can charge and operate the battery and heating with this
  • Towards the evening the battery compensates power from the grid again

This allows us to achieve

-20%

Less energy consumption

-25%

less CO2 emissions

+30%

more own power consumption