Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Abstract

Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

A review on the state of the art of dynamic voltage restorer: topologies, operational modes, compensation methods, and control algorithms

“…It is linked between the supply and sensitive load to compensate for line voltage harmonics, transient reduction, voltage sags and swells. The fundamental working idea is to inject a voltage of the proper amplitude and frequency to restore the load voltage when there is voltage sag, as shown in figure 1 [47].…”

“…The active power need of DVR during 'compensation' is met by the DC connection and energy storage devices. For load voltage correction during dips, the required vitality can be drawn from an auxiliary supply or the network grid itself [47]. When the DVR grid is weak, the auxiliary supply system is employed to boost performance.…”

“…The transformer's principal duties are to enhance the voltage generated by the VSI and to isolate and connect the DVR to the distribution system. Only by carefully selecting the electrical characteristics of the injection transformer can optimum efficacy and dependability be achieved When the ground fault situation is at its most severe [47]. The importance of subsequent positive chain recovery and voltage compensation cannot be emphasized.…”

“…When the DVR grid is unstable, the backup generator boosts performance. In most DVR applications, a combination of energy sources, including SMES (Superconducting magnetic energy storage), source battery, fly wheel, hybrid energy storage systems, super capacitors, Fuel cell, auxiliary power supply, and lead-acid are used [47,[49][50][51]. Energy storage may be expensive, but it might improve the DVR's functionality by mitigating voltage disturbances like voltage imbalance.…”

“…The DVR's primary function is to inject voltages through a booster transformer to the load voltage to mitigate the power quality faults [47]. Protection mode, standby mode, and injection/boost mode are the three operating modes of the DVR.…”