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您的检索式：作者名="Geoffrey R.Walker"
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| 1 | Model Predictive Control Strategy for Residential Battery Energy Storage System in Volatile Electricity Market with Uncertain Daily Cycling Load显示文摘This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations. | Dejan P.Jovanović Gerard F.Ledwich Geoffrey R.Walker | 2023 | Journal of Modern Power Systems and Clean Energy2023，11，2： | 0 |
| 2 | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load显示文摘To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs,a novel modification of a conventional model predictive control is proposed.The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty.By modelling a probabilistic dependence among flow,load,and electricity tariffs,the expected cost function is obtained and used in the constrained optimization.The proposed control strategy explicitly incorporates the cycling nature of customer load.Furthermore,for daily cycling load,a fixed-end time and a fixed-end output problem are addressed.It is demonstrated that the proposed control strategy is a convex optimization problem.While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations.Also,the expected cost across the flow variations is considered.The density function of load probability improves load prediction over a progressive prediction horizon,and a nonlinear battery model is utilized. | Dejan P.Jovanović Gerard F.Ledwich Geoffrey R.Walker | 2022 | Journal of Modern Power Systems and Clean Energy2022，10，1： | 0 |
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