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An efficient microgrid model based on Markov fuzzy demand-side management

Jabash Samuel G. K., Sivagama Sundari M. S., Bhavani R., Jasmine Gnanamalar A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 3

art. no. e145569

EN

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.

2

Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization

Diwan Supriya P., Deshpande Shraddha S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 4

art. no. e140696

EN

As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm

3

Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions

Ławryńczuk Maciej

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 1

35--46

EN

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.

4

Suboptymalny algorytm regulacji predykcyjnej z modelami neuronowymi typu FIR

Ławryńczuk M.

Pomiary Automatyka Robotyka

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2008

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R. 12, nr 2

548--557

PL

W pracy omówiono nieliniowy algorytm regulacji predykcyjnej wykorzystujący modele neuronowe typu FIR (ang. Finite Impulse Response). W algorytmie zastosowano modele bazujące na sieci perceptronowej. Przedstawiony algorytm jest efektywny obliczeniowo, ponieważ wymaga cyklicznego rozwiązywania zadania optymalizacji kwadratowej, co może być wykonane w czasie rzeczywistym. Algorytm cechuje się dużą dokładnością regulacji, porównywalną z algorytmami wymagającymi bieżącej nieliniowej optymalizacji.

EN

This paper describes a suboptimal nonlinear Model Predictive Control (MPC) algorithm based on FIR (Finite Impulse Response) neural models. Multilayer Perceptron (MLP) neural network is used. The algorithm is computationally efficient because it results in a quadratic programming problem, which can be easily solved on-line by means of a numerically reliable software subroutine. The algorithm gives good closed-loop control performance, comparable to that obtained in the nonlinear MPC technique, which hinges on nonlinear optimisation.

5

A computationally efficient stable dual-mode type nonlinear predictive control algorithm

Ławryńczuk M., Tadej W.

Control and Cybernetics

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2008

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Vol. 37, no 1

99-132

EN

This paper describes a computationally efficient (sub-optimal) nonlinear predictive control algorithm. The algorithm uses a modified dual-mode approach which guarantees closed-loop stability. In order to reduce the computational burden, instead of online nonlinear optimisation used in the classical dual-mode control scheme, a nonlinear model of the plant is linearised on-line and a quadratic programming problem is solved. Calculation of the terminal set and implementation steps of the algorithm are detailed, especially for input-output models, which are widely used in practice.