The 5G network-based IoT Can provide Demand Flexibility & Mitigating Climate Effects

It is the complementary part of “IoT is Proving to be a Flexible and Convenient Way to Combat Climate Change in Buildings” article.

Method

The rationale behind this section is assessing the new method presented in the recent study by Moazami and et al. It is based on setting specific adaptation measures to buildings through simple communications between buildings and energy systems using CI. CI-based problem solving has been used since the 18th century so has a well-established history of use.

The proposed CI-DSM method by Moazami and et al. have advantages including adaptivity, flexibility and resilient against environmental variations, and self-organization. The proposed CI-DSM system is developed to control and set adaptation measures among buildings as agents in the complex urban energy system. the CI-DSM is providing climate resilience and demand flexibility as objectives of this paper.

In this regard, suppose a hypothetical urban area with groups of residential buildings in which performances of buildings were modelled. Building performance modelling features the hourly energy demand of buildings and calculating the energy for heating, cooling, hot water, fans, and heat recovery (if they are equipped with heat recovery systems). Then based on this model, the energy demand of buildings for an entire year is derived for the typical weather condition, extremely cold weather condition, and extremely warm weather condition. The two latter weather conditions are important to set as the boundary condition of our CI system for making a decision and managing the demand side of the energy performance in the hypothetical urban area. The typical weather condition is used as the reference or criterion for the CI system to compare and self-organizing the building groups in the urban area.

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CI system has properties including adaptivity means changing to deal with the environment, interaction which means agents/individuals interact with each other; and rules mean describing the behavior of an individual. These properties of a CI-based system help the DSM of buildings to become resilient and pass the environmental shocks (extreme weather conditions) safely. Then, the CI-DSM  is interpreted as a method to improve the climate resilience of urban energy systems through increasing the flexibility on the demand side. For further information refer to  Moazami and et al. work. Figure 2 shows a graphical representation of implementing CI in buildings in an urban area.

Figure 2) graphical representation of implementing CI in buildings in an urban area

In order to take one step forward and improve the utilized CI-DSM method by Moazami and his control algorithm, we put forward the 5G network-based IoT and user/occupant’s smartphones as the distributed ICT-based platform and terminal controller devices for the collaborative distribution network of buildings which leads to lower cost of infrastructure installation and computational power respectively. This collaborative distribution network can reduce reliance on the main grid, increase energy utilization efficiency, and decrease energy costs for occupants. Moreover, we develop and use a heuristic control algorithm according to the utilized control algorithm in Moazami’s study. We have added a planned prioritizing based on the occupant’s preference by using their smartphones as a local controlling and monitoring agent. For example, as shown in Figure 3, we have outlined our control algorithm based on the occupant’s thermal prioritizing of appliances in its building which placed in a heating-dominated city, where the need for heating is much greater than cooling and many of the existing residential buildings do not have any cooling system installed. In Moazami’s control algorithm, no further action is taken if the energy demand is still higher than the typical conditions after applying the adaptation measure to all the building groups. But in our proposed control algorithm, energy demand higher than the typical condition such as extremely cold weather conditions is curtailed by the real-time thermal priorities of appliances which were done by occupants via their smartphones.

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Figure 3) The heuristic control algorithm based on the occupant’s thermal prioritizing of appliances

The 5G network-based IoT plus occupant’s smartphones can integrate human feedback into the control loop and prevent occupant’s discomfort. The proposed IoT-based system is more flexible, empowers demand flexibility, improves occupant resilience, and helps to bring back the urban energy system towards equilibrium or stability after a shocking event such as extreme weather conditions faster and surprisingly agile. Moreover, this method changes the demand response from cutting down the entire building block to the precise load control, monitoring the load operating state, transmitting real-time operating data, and taking action to adjust their load state in every place at much lower than a time step, e.g. 15 minutes.

The proposed method provides much faster and seamless integration of existing agents due to the 5G Internet network and maximize user comfort thanks to its occupant-centric control algorithm as well as reduce the cost of installation since the physical architecture of 5G networks for power systems uses the same equipment as other functions. As a last but not least point, the network security and consumer privacy in 5G networks should be further investigated.

Conclusion

  • Increasing the occupants’ satisfaction and comfort level thanks to the occupant-centric control algorithms.
  • Empowering demand flexibility thanks to implementing adaptation measures in the building sector.
  • Improving occupant resilience and enhancing smartness due to the use of occupant’s smartphones.
  • Allowing easy, fast, and seamless integration of buildings, thanks to the IoT-based platform.
  • Reducing the cost of installation and becoming more cost-effective as compared to others thanks to using the same equipment and existing infrastructure.
  • Increasing energy utilization efficiency, due to the smart demand-side management.
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Source:@Solar_Edition (Lead Author: Hesam-Edin Hayati Soloot)
Photo:@Solar_Edition & @Amin Moazami
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