Multilayer Iterative Stochastic Dynamic Programming for Optimal Energy Management of Residential Loads with Electric Vehicles

This work introduces a multilayer iterative stochastic dynamic programming (MISDP) framework for optimizing energy management in smart residential settings, incorporating electric vehicles to reduce energy costs while enhancing operational efficiency. The study investigates the complexities of manag...

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Bibliographic Details
Published in:International journal of energy research 2024-01, Vol.2024 (1)
Main Author: Aljohani, Tawfiq M.
Format: Article
Language:English
Subjects:
Accuracy
Adaptability
Adaptation
Batteries
Case studies
Comparative analysis
Control theory
Cost analysis
Cost control
Decision making
Discharge
Dynamic programming
Effectiveness
Efficiency
Electric vehicle charging
Electric vehicles
Electrical loads
Electricity
Energy
Energy conservation
Energy consumption
Energy costs
Energy management
Energy storage
Linear programming
Management decisions
Multilayers
Optimization
Optimization techniques
Performance indices
Real time
Real variables
Rechargeable batteries
Residential energy
Smart grid technology
Stochasticity
Vehicles
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Summary:This work introduces a multilayer iterative stochastic dynamic programming (MISDP) framework for optimizing energy management in smart residential settings, incorporating electric vehicles to reduce energy costs while enhancing operational efficiency. The study investigates the complexities of managing residential loads with integrated EV batteries, set against the backdrop of unpredictable charging demands and fluctuating energy prices. The proposed method is designed to optimize charging and discharging schedules, ensuring cost‐effective energy consumption without compromising the longevity of EV’s battery operations. The proposed MISDP strategy encompasses multi‐iteration processes, both at internal and external levels, that not only highlight the method’s capacity for precise, real‐time decision‐making but also underscore its adaptability to the dynamic nature of energy systems. The external iteration primarily focuses on adapting to broader operational variables, such as fluctuating prices and demand patterns, setting a framework for optimization. Concurrently, the internal iteration updates the details of EV battery operation, fine‐tuning charging and discharging strategies to refine the control law sequence for each operational period, ensuring optimal energy management. Throughout the iteration process, the framework ensures the performance index function remains bounded, adhering strictly to the evolving control law sequence. Through comparative analysis, the MISDP framework is evaluated against different optimization techniques, demonstrating its superior capability in achieving significant energy cost savings and operational effectiveness while ensuring convergence under stochastic conditions.
ISSN:0363-907X
1099-114X
DOI:10.1155/2024/6842580