Internet-of-Things-Enabled Smart Local Energy Management System

: (Alternative Format Thesis)

Abstract

The “three D’s” requirement of energy sectors, as decarbonisation, decentralisation and digitalisation, have raised more requirements and challenges in local energy systems (LESs). With the implementation of the “three D’s”, distributed energy resources (DERs) have been widely applied to LESs to deliver clean energy in a decentralised mode. These DERs, on the other hand, have increased the uncertainties in LESs because of the uncertainty and intermittence of clean energy generation. This requires more flexibility on the demand side to match with the time-varying clean energy generations. And the digitalisation trend leads to various edge-side intelligent devices. These newly appeared devices bring intelligence to the local energy systems but also create massive edge data which causes huge computation and communication pressures on the traditional centralised system.

As a solution for edge data explosion, Edge-Cloud computing systems and Edge-Cloud-based Internet of Things (IoT) systems have largely enhanced the intelligence and flexibility of energy systems. However, there are limited works that have reviewed the Edge-Cloud computing systems utilised in energy systems. Thus, an extensive review of the Edge-Cloud computing system and its application to the smart grid are conducted. It first describes the relationship between cloud computing (CC), fog computing (FC) and edge computing (EC) to provide a theoretical basis for the differentiation. It then introduces the architecture of the Edge-Cloud computing system in the smart grid, where the architecture consists of both hardware structure and software platforms and key technologies are introduced to support functionalities. Thereafter, the application of the smart grid is discussed across the whole supply chain, including energy generation, transportation (transmission and distribution networks), and consumption. Finally, future research opportunities and challenges of Edge-Cloud computing while being applied to the smart grid are outlined.

Then, based on a developed Edge-Cloud IoT system, this thesis proposed a dark-grey-box thermal modelling method to accurately learn the thermal parameters of existing buildings for flexible energy operations on the demand side. This dark-grey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated in a real room, enabled by an IoT platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights into selecting appropriate feature-learning methods in practice.

Thereafter, based on the thermal modelling method, a DaaB model is developed to evaluate the energy flexibility of a building’s SHS. To produce a generalised DaaB model, a thermal dynamic equilibrium (TDE) state is defined to avoid impact from different SHS control modes and weather conditions. Then, the charging/discharging (C/D) capacity, rate, and efficiency of the DaaB are formulated by mathematical transformations based on the thermal model. The developed DaaB model is demonstrated in the case study facilitated by an IoT platform. This DaaB model delivers high accuracy, thus it can rationally reveal a dwelling’s energy flexibility.

Finally, simulations and applications of DaaB in LES are discussed in this thesis. The DaaB model is utilised in an hourly day-ahead local EMS to enable the self-consuming of clean energy resources. The DaaB operation is able to reduce 43.5% of redundant clean energy and 21.2% of electricity demand from the grid in the case study.

This work can help existing buildings to build an Edge-Cloud IoT system to measure its physical state and utilise it to develop an accurate thermal model for EMS operations. Based on the accurate thermal model, a DaaB model can be formulated to evaluate the energy flexibility of buildings and help the local EMS to realise decarbonisation and some other flexible operations.

Date of Award	23 Jun 2023
Original language	English
Awarding Institution	University of Bath
Supervisor	Chenghong Gu (Supervisor) & Philip Shields (Supervisor)