Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Abstract

In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

Recent Trends and Issues of Energy Management Systems Using Machine Learning

“…In [92], a novel DSMS based on hybrid bacterial foraging and particle swarm optimization is developed to optimize electricity costs, peak-to-average ratio (PAR), carbon dioxide emissions, and user comfort. ML-based DR programs have been reviewed for optimal dynamic pricing, scheduling, and control strategies using deep learning, supervised, unsupervised, and RL algorithms [93,94]. The traditional model-based EMS struggles to adapt to the varying operational conditions in the DER, leading to inaccurate energy consumption schedules.…”

Enhancement of System Resilience for Islanded Microgrid with Clean Energy

“…Since the optimization of energy management in this paper is focused on the minimal operational cost of energy system, the hydrogen fuel cell would be activated only when the system is in the emergent condition. The solver for the optimization of energy management is PSO (particle swarm optimization) algorithm [8,9]. Based on the information of residual energy in the BESS, PV generation, and load demand, the charging or discharging behaviour would be determined at each time slot by PSO algorithm.…”

A Single Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting under Malaysia Electricity Supply Industry

“…There was proposed methodology that combines the advantages of clustering and LSSVM to improve the precision of electricity price forecasts. These advancements are crucial for consumers to manage energy consumption and operation cost-efficiently [24], [25].…”