Lasantha Meegahapola

Electric spring (ES) has been introduced as a fast demand response (DR) technology that can vary the power consumption of its associated noncritical load (NCL) for bus voltage support and frequency stabilization. The combined use of ES and NCL creates a new generation of smart load (SL) technology. Furthermore, the connection of many SLs forms a smart load network (SLN), which can perform coordinated grid support functions. To further understand the usefulness of SLN, this paper provides a comprehensive investigation of the modeling and analysis of SLN in distribution grids. The detailed state-space model of the SL unit is investigated, which covers the average model of the inverter circuit, outer bus voltage loop, and inner voltage loop. Based on this state-space model, a new method of calculating the power flow of an arbitrary SLN is elaborated. The method relies on the establishment of three coefficient matrices to form a system of linear equations, which is done by simply observing the physical topology of an SLN. By solving the equations, the real-time bus voltage and SL power can be readily obtained. The integration of the state-space model of SL agents and the SLN model creates a simple and powerful tool to study the performance of the SLN in providing voltage regulation, assisting further research in the control of the active distribution network (ADN). Case studies demonstrate the usefulness of the proposed SLN model in providing voltage regulation and low-voltage ride-through (LVRT) support.

Increasing penetrations of variable renewable energy sources like wind and solar photovoltaic (PV) systems are challenging power system stability worldwide. Leveraging demand‐side behavior is becoming more popular to help overcome contemporary issues concerning balancing electricity generation and demand. As significant energy users with the potential to act as electricity producers through renewable energy sources, buildings are attractive assets for contributing to power system control from energy efficiency and demand response perspectives. Meanwhile, the proliferation of “smarter” buildings equipped with network‐connected sensors and devices using Internet of Things (IoT) platforms produces significant data volumes that lend themselves to novel artificial intelligence (AI) and machine learning (ML) methods that we can apply across the suite of demand response design steps. This paper reviews the application of AI and ML methods across these steps, which include building energy analysis and auditing, modeling and predicting building load demand, detecting and classifying building energy and power flexibility, implementing flexible building load control, and participating in demand response and other ancillary service markets. Throughout the paper, we comprehensively analyze the application of various AI and ML methods, highlighting their effectiveness and limitations. We also identify emerging pertinent challenges of interest to practitioners and researchers examining the implementation of such approaches for building demand response provision. This article is categorized under: Cities and Transportation > Buildings Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management