Project Objectives

District heating and cooling (DHC) systems play a vital role in many cities and facilities, and their establishment gains popularity. Nevertheless, popular as they are, they have a number of critical deficiencies that severely limit their operational efficiency and cause unnecessary increase in the operational cost of heat generation and distribution. The following main deficiencies are identified:

  • Outdated control based on rule-of-thumb techniques and absence of ICT
  • Similar simple techniques used for in-house heat supply temperature control and inability to react to supply temperature deviations
  • Absence of the human factor and recurrent use patterns in the control of DHC systems
  • Absence of passive storage opportunities
  • Absence of systematic methods for protecting the system in cases of peak demand for heat

The vision of the OPTi project can be summarised as follows:

To create a long-lasting impact by rethinking the way DHC systems are architected, controlled and operated. The overarching goal is to create durable business benefit for the relevant industries, with ensured optimal end-consumer satisfaction and engagement, while operating these systems in an environment-friendly manner. To this end, we aim to use predictive control and automated heat Demand Response methods, while exploiting passive heat storage capabilities.

In particular the vision of OPTi includes the following main pillars

  • Rethink DHC systems through an architecture that can be adapted in real-time to exogenous and unpredictable factors and dynamics
  • Exploit the hidden potential of passive-heat storage, thus turning the household into a heat battery
  • Develop automated heat Demand Response mechanisms that make the most efficient distribution and use of heat while minimizing DHC system operational costs
  • Bring the user in the foreground by: (i) minimizing user discomfort (ii) placing emphasis on securing user engagement through appropriate viable incentive mechanisms
  • Validate the principles and techniques above with data-driven approaches as well as with two real-life pilot trials in a hospital and in a block of residential buildings.

Twitter Tweets