Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

IEEE Access

Muhammad Usman Shahid Khan¹  Muhammad B. Qureshi²  Chaudhry Arshad Arshad Mehmood²  Sahibzada Muhammad Ali²  Waqar Ahmed²  Bilal Khan²  Muhammad Jawad³  Amjad Ullah⁴  Iqrar Hussain⁵  Zahid Ullah⁵  Hammad Ansari⁶  Raheel Nawaz⁷ 

[1] Department of Computer Science, CUI Abbottabad Campus, Khyber Pakhtunkhwa, Pakistan;Department of Electrical Engineering, CUI Abbottabad Campus, Abbottabad, Pakistan;Department of Electrical Engineering, CUI Lahore Campus, Lahore, Pakistan;Department of Electrical Engineering, UET Peshawar, Peshawar, Pakistan;Department of Electrical Engineering, UMT Lahore Sialkot Campus, Lahore, Pakistan;Department of Mathematics and Natural Sciences, Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait;Manchester Metropolitan University, Manchester, U.K.;

关键词: Prosumer;    smart grid;    machine learning;    energy districts;    service level agreement;    smart contract;   

DOI  :  10.1109/ACCESS.2020.3029943

来源: DOAJ

【 摘 要 】

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

【 授权许可】

Unknown