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

Abstract

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.

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

“…Machine Learning (ML) models have been introduced later on to reduce the time complexity of SVD in the STLF problem. ML algorithms have enhanced the performance of STLF by showing profound accuracy in dealing with non-linearities of the electrical load data and accurate forecasting of the peaks of electrical load as compared to the statistical regression and dimension reduction models [23][24][25]. ML methods mainly comprise Artificial Neural Networks (ANNs) which can handle the stochastic nature of the weather-sensitive loads during the prediction of electrical load forecasting [26][27][28].…”

A Comprehensive Review on Smart Grids: Challenges and Opportunities

“…Ahmed et al [54] presented an Energy Management Model (EMM), which integrates both Gaussian Process Regression (GPR) and Machine Learning (ML). It consists of different stages: in the first one they integrate different models such as PES (Prosumer Energy Surplus), GR (Grid Revenue), and PEC (Prosumer Energy Cost) for training a ML model in order to obtain different base-performance parameters.…”

Deep Reinforcement Learning Based Pricing Strategy of Aggregators Considering Renewable Energy

“…Integrating the use of renewable energy and an energy storage system (ESS) into the formulation of competitive pricing strategies for an aggregator within the deep reinforcement learning framework becomes increasingly important for at least two reasons. First, renewable energy has been deeply integrated into smart grid infrastructure and considered as an environmentally and economically beneficial alternative to conventional fossil fuel, but high penetration of renewables can induce unavoidable grid system uncertainty and variability [27], [28]. Deep reinforcement learning methods can be suitable for addressing the uncertainty and variability [19], [29].…”