Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

Abstract

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

“…In this study, we compared the performance and applicability of two RL algorithms-ANN-PSO and PPO-by exploring their methods and applying them on stochastic steady-state economic optimization of a CSTR with FP-NLP as a benchmark algorithm [4][5][6]. We evaluate the RL algorithms' performance with their profitability and online computational times, and their applicability with their data requirements and training efficiencies.…”

“…Many chemical processes, like coal combustion and bioreactors, are complex, and thus deriving appropriate models and optimizing outputs is difficult [1][2][3]. Machine learning has shown success in optimizing complex systems such as scheduling electricity prices to manage demand and maximize power grid performance [4][5][6]. This motivates exploration of other machine learning techniques like reinforcement learning (RL) on model-free optimization [7].…”

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

“…Knowledge of these quantities is often limited to historic values, and for this reason, ML based forecasting techniques are often proposed in the literature. In [26] authors propose a proactive prediction of the energy demand of an entire city to be included in an intelligent management system for energy storage and flexible loads. Forecasting through deep learning techniques are also promising with good results for long-short-term-memory (LSTM) networks [27][28][29] and recurrent LSTM [27].…”

Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems

“…Although there may be a substantial benefit for homeowners to change their energy behavior, the use of market signals at the grid level can also be a way to turn residential buildings into a grid asset. On a similar topic, Sheha et al have demonstrated that a realtime market can be an effective means for coordinating demand-side behavior, allowing the grid to leverage distributed energy resources such as battery storage and home energy management systems [20,21].…”