×

Optimising the energy demand of neighbourhoods under DR umbrella

Given that contemporary urban life is showing a clear trend of accelerating, everyday tasks are slowly but surely becoming a burden for all of us. From rising in the early morning, showering, teeth brushing, clothes selection, driving, coping with morning rush-hour, working eight, nine or more hours, driving again, coping with evening rush-hour on the way home, spending some time with the kids, and going back to bed in the evening, far too little time is left over for any additional activities. Having that in mind, it would be unreasonable to expect that most of the people with similar schedules would show much enthusiasm for any new tasks.

Now, on top of all of that, imagine having to deal with managing your energy on a daily basis. Constantly monitoring import and export prices, toggling when to use locally produced energy, when to store it in batteries, when to import from the grid… Seems cumbersome, right? Well, this is where the RESPOND optimisation engine comes into play. It takes into consideration day-ahead energy prices, the forecasted renewable production and the predicted loads from individual users aggregated into a neighbourhood profile. 

 

Energy Demand of Neighbourhood under DR umbrella

*See below copyrights.

Using the supposed demand flexibility, the optimiser shifts the loads in intensity and in time to generate a profile that is the most cost-effective for end users and most stable for the grid operator. However, given that the RESPOND project is all about maintaining grid stability and making use of the untapped potential of residential demand response (DR) capacities, the DR events hold a special place in the optimisation process. The system allows for load shifting to occur both in cases where the convenience is dictated by current pricing (low prices drive loads up, and soaring prices drive loads down) and by direct requests from the utility company or DR aggregator by means of predefined DR events.

When the optimisation engine has completed its run, an “optimal neighbourhood profile” is available for the next day. If this profile is upheld by the end users, lowest cost and maximum stability are obtained. However, as the day progresses, the aggregated demand may slightly or more significantly differ from what the optimiser has deemed best. That’s where the energy monitoring service comes in. It looks at the optimised profile and the aggregated profile of an entire neighbourhood that is continuously being measured. Whenever a deviation is noticed, for example when actual load levels are higher than optimal, this service looks at data from individual households, scans for currently active appliances that are considered to be large energy consumers, and issues notifications through the RESPOND dashboard and mobile app that suggest to the end user that the activation of that appliance should be deferred to a later time. By doing this, the user may obtain a monetary reward and maintain grid stability. Thus, the energy management process is semi-automated resulting in a minimal additional burden for the end user, but he or she still maintains full control since no actions are taken without authorization. 

This level of automation is considered to be sufficiently sophisticated because maintaining a non-intrusive communication with end users is considered key in the technology adoption process especially when working with such sensitive topics, such as home automation and IoT. Looking back at the opening remarks once again, it is clear that this process is constructed with ingenuity and enough foresight to provide the best possible user experience by adapting to everyday habits and striving not to pose as an additional obligation with hopes of facilitating a bright, energy efficient future lifestyle for its users.

Author:
Marko Jelić, BSc EE & CS
Institute Mihajlo Pupin
Volgina 15, Belgrade, Serbia
marko.jelic@pupin.rs 

 

* Thanks to the following sources for the illustrations used:

  • Business vector created by rawpixel.com – www.freepik.com (more at: https://www.freepik.com/free-photos-vectors/business)
  • City vector created by macrovector – www.freepik.com (more at: https://www.freepik.com/free-photos-vectors/city)
  • Coffee vector created by macrovector – www.freepik.com (more at: href=https://www.freepik.com/free-photos-vectors/coffee)
  • Design vector created by freepik – www.freepik.com (more at: https://www.freepik.com/free-photos-vectors/design)
  • People vector created by macrovector – www.freepik.com (more at: https://www.freepik.com/free-photos-vectors/people)
  • Energomonitor Website: https://www.energomonitor.com

Integrating Building and IoT data in Demand Response solutions

Last 19th to 21st of June, our Partner Tekniker, represented by Iker Esnaola-Gonzalez and Francisco Javier Diez, participated in the LDAC2019 – 7th Linked Data in Architecture and Construction Workshop.

They got the opportunity to present RESPOND project and published a paper about “Integrating Building and IoT data in Demand Response Solutions”.

Building & IoT data in Demand Response

You can access the full paper and get to know better RESPOND’s approach and proceedings at both building and district levels: 👉 READ PAPER.