首页|A machine learning-based stochastic optimal energy management framework for a renewable energy-assisted isolated microgrid system
A machine learning-based stochastic optimal energy management framework for a renewable energy-assisted isolated microgrid system
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NSTL
Taylor & Francis
This paper proposes a cost-based stochastic optimal energy managementframework for a renewable energy-assisted isolated microgrid system. Thesemicrogrids encourage the integration of multiple distributed energy sources,including the penetration of renewable energy. For this purpose, theoptimal day-ahead dispatch of the connected energy resources is obtainedfor an economically viable system by solving a nonlinear constrained optimizationproblem. The renewable energy and the load demand data forecasting areaccomplished using the Gaussian process regression learning model in theMATLAB/SIMULINK® environment for obtaining the day-ahead dispatch. Theoptimal problem is solved through sequential quadratic programming anda hybrid function approach incorporating particle swarm optimization fora comprehensive techno-economical analysis. A comparative assessment ofthe results is accomplished to obtain a more feasible and economical systemoperation corresponding to different time horizons and other critical factorssuch as fast iterations, computational accuracy, solution feasibility, convergencerate, etc.
Microgrid economicsmachine learningoptimizationrenewable energystochastic system
Maneesh Kumar、Barjeev Tyagi
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Department of Hydro and Renewable Energy, IIT Roorkee, Roorkee, India
Department of Electrical Engineering, IITRoorkee, Roorkee, India
NETL
NSTL
Taylor & Francis
