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Amsterdam’s innovative communal energy hub

One of the greatest problems of the efficient deployment of renewable energy systems is that they are prone to experiencing fluctuations. To counteract this problem scientists have designed energy management systems (EMS) that collect energy measurement data from the field and make it available to users through graphics, online monitoring tools, and energy quality analysers for the management of energy resources. These systems, however, exhibit certain flaws, which engineers are relentlessly trying to improve.

Now (2020), scientists at the Fraunhofer Institute for Industrial Mathematics ITWM have designed a system which helps to counteract the ebbs and flows of renewable energy systems. The innovative energy management system connects photovoltaic systems, batteries, heat pumps and electric cars to power the floating homes in Amsterdam’s waterways with locally produced renewable energy.

The structure of the energy management system is modular – each module may be installed individually. Collectively, they serve as a communal energy hub. This hub constantly analyses where the energy needs to go. The 30 photovoltaic systems, heat pumps and batteries installed in individual houses in a district of Amsterdam function as one large system. If one house uses very little electricity while consumption in another house is high, the system will automatically redirect the energy from the solar panels of this house to the other. The idea behind the concept is to make the most of local power and not tap remote power from the public grid.

Designing smart energy management systems has been one of the most important areas of scientific research for several years. In 2016, scientists developed a Smart Home Energy Management System (SHEMS) to operate home appliances. Its aim was to reduce energy consumption by detecting the residents’ activity and differentiate between three states: Active, Away, or Sleep. SHEMS was designed with an algorithm based on the Hidden Markov Model (HMM) in order to estimate which state the home is currently in. The proposed system used WiFi technology for data transmission inside the home and GSM technology for external communication. The proposed system and its algorithm was successfully tested and 18% of energy saving was obtained.

In 2018, scientists designed an intelligent home energy management system that was based on Least Square Regression (LSR) analysis. The system was trained based on the historical data of the interaction of the home’s occupants with their appliances over a period of time. It monitored and computed the power consumption of the home user over a period of time. This system made decisions and controlled the output using LSR, based on what it had learnt by alerting the home users by means of accept or reject responses through Android GUI Apps. The system performance evaluation was based on the frequency prediction, which was given as 0.77 RMSE, and the activation time prediction, which was given as 127.89 seconds RMSE.

The advantages of the local energy management system are numerous: the management system equips the single modules with discrete intelligence. This enables the photovoltaic systems to operate to their full capacity. As standard PV units have to be throttled on very sunny days according to the law, scientists have found a way to work around this problem by rerouting the surplus electricity that grid operators do not want to buy and storing it in the home battery for later use. The implemented forecasting model boosts the efficiency of the batteries. The model factors the weather forecast into its predictive equation. First, it determines the energy output of the photovoltaic systems in the hours ahead as well as the heat consumption. Then it applies the results of these calculations to regulate storage. If the sky is hazy, but the weather is expected to clear up by the afternoon, the energy management system will not start storing energy in the morning and hold off until later to charge the batteries. In other words, the system adapts itself to weather conditions so as not to waste any energy.

The modules are a big step towards efficient energy management as they can be deployed individually and tailored to any given application. There is already a permanently-installed base of 60 to 70 of such systems ranging from private households and cafeterias to entire businesses and one sewage treatment plant.