Artificial Intelligence Application to Flexibility Provision in Energy Management System: A Survey

Adewuyi, Oludamilare Bode; Folly, Komla A.; Oyedokun, D.T.O.; Sun, Yanxia

doi:10.1007/978-3-031-26496-2_4

Cited by 2 publications

(1 citation statement)

References 136 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The integration of intelligent DSM practices has led to numerous techno-economic benefits, such as reducing the cost of electricity for consumers, maximization of the operation of VREs generation units, minimization of the overall system's peak demand, etc. [8,9]. Advancements in cyber-communication have further revolutionized DSM operations by enabling real-time monitoring and efficient control of consumer devices, aligning with the priorities and needs of power systems, thus contributing to remarkable progress in the overall VRE-based electricity market design.…”

Section: Introductionmentioning

confidence: 99%

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Kiptoo,

Adewuyi,

Furukakoi

et al. 2023

Energies

View full text Add to dashboard Cite

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability.

show abstract

Section: Introductionmentioning

confidence: 99%

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Kiptoo,

Adewuyi,

Furukakoi

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

View full text Add to dashboard Cite

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

show abstract

Artificial Intelligence Application to Flexibility Provision in Energy Management System: A Survey

Cited by 2 publications

References 136 publications

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Contact Info

Product

Resources

About