INTELLIGENT ENERGY MANAGEMENT SYSTEM FOR SMART GRIDS USING MACHINE LEARNING ALGORITHMS

. Smart grid technology is rapidly advancing and providing various opportunities for efficient energy management. To achieve the full potential of smart grids, intelligent energy management systems (IEMS) are required that can optimally manage and control the distributed energy resources (DERs). In this paper, proposed an IEMS using the Deep Reinforcement Learning (DRL) algorithm to manage the energy consumption and production in a smart grid. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS is evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost.


Introduction
Smart grids are the future of electricity distribution systems that integrate various advanced technologies to improve the efficiency, reliability, and sustainability of power grids [1][2][3]. With the increasing penetration of renewable energy sources, electric vehicles, and smart appliances, the demand for intelligent energy management systems (IEMS) has also risen significantly [4] [12]. An EMS is a software system that utilizes real-time data from sensors, smart meters, and other IoT devices to optimize the operation of the smart grid. Machine learning (ML) algorithms play a crucial role in developing intelligent EMSs that can learn from historical data, predict future energy demands, and make optimal decisions based on the current grid status [5] [20]. This paper provides an overview of the intelligent EMS for smart grids using ML algorithms and discusses its benefits, challenges, and future directions.
A smart grid is a modern electricity distribution network that integrates various advanced technologies such as IoT, sensors, communication networks, and ML algorithms to optimize the operation and management of the grid [6] [18]. Unlike traditional grids, smart grids can automatically adjust their energy supply and demand based on the real-time data from the smart meters, sensors, and other IoT devices [7] [14]. This real-time data helps the grid operators to predict the energy demand and supply, monitor the grid status, and take necessary actions to avoid power outages, reduce energy waste, and improve the overall efficiency of the grid.

Figure 1.Intelligent Energy Management Systems (IEMS)
An IEMS is a software system that helps the grid operators to manage and optimize the energy distribution in real-time [7] [11]. The IEMS collects data from various sources such as smart meters, sensors, and other IoT devices, analyzes the data using ML algorithms, and makes optimal decisions to balance the energy demand and supply. The IEMS can perform various functions such as load forecasting, demand response, energy trading, and grid optimization to improve the efficiency and reliability of the grid [8][9][10].
ML algorithms are the core components of intelligent EMSs that can learn from the historical data, predict the future energy demand, and make optimal decisions based on the current grid status. ML algorithms can be classified into two categories: supervised and unsupervised learning [12] [17]. Supervised learning algorithms require labeled data to learn from historical patterns and predict future outcomes. On the other hand, unsupervised learning algorithms do not require labeled data and can learn from the patterns in the data. ML algorithms can be used for various tasks such as load forecasting, anomaly detection, energy trading, and demand response [13][19].

Measuriing devices
Smart Appliances Communication and networking system

Sensing devices
Intelligent Energy Management System for Smart Grids uses Decision Trees proposed. One approach to developing IEMS is through the use of decision trees, a machine learning technique that can be used to model complex decision-making processes. Decision trees have been used in various applications, including energy management, load forecasting, and fault detection [3] [15]. The work by Kamaljit Kaur and Gurpal Singh on an intelligent energy management system for smart grids using decision trees is a promising approach to optimizing the use of DERs.
Energy Management in Smart Grids uses Support Vector Machines was proposed. SVM is another machine learning algorithm used for intelligent energy management systems. SVM can be used to predict energy consumption based on historical data and various other factors [16].
Artificial Neural Network Based Energy Management System for Smart Grids used. ANN is a complex network of interconnected nodes that can be used to simulate the behavior of the human brain. ANN is used to predict energy consumption patterns based on various factors such as time of day, weather conditions, and historical data [6].
Fuzzy Logic-Based Intelligent Energy Management System for Smart Grids is a technique used for intelligent energy management systems that allows for more complex and nuanced decision-making [9]. It is used to represent uncertainty and imprecision in decision-making, which can be useful in situations where there are many factors to consider.
A Genetic Algorithm-Based Approach for Optimal Energy Management in Smart Grids uses genetic algorithms which are a type of machine learning algorithm that can be used to optimize solutions to complex problems [2]. They are used in intelligent energy management systems to find the most efficient ways to distribute energy based on various factors such as cost, availability, and demand.

Proposed Methodology
The proposed methodology uses deep reinforcement learning (DRL) algorithms to develop an intelligent energy management system for smart grids. DRL combines the power of deep neural networks with reinforcement learning, allowing the system to learn from experience and make decisions based on a reward system.

Proposed Deep Reinforcement Learning for Intelligent Energy Management in Smart Grids
The proposed DRL-based energy management system consists of three main components: state representation, action selection, and reward function.
State Representation: The state representation is used to capture the current state of the system, which includes energy demand, supply, prices, weather conditions, and other relevant factors. The state representation is encoded as a vector and fed into a deep neural network to predict the optimal actions. Action Selection: The action selection component determines the optimal actions to take based on the current state of the system. The DRL algorithm selects actions that maximize a cumulative reward over a specified time horizon. Actions could include adjusting energy supply or demand, optimizing energy storage, or purchasing energy from the grid.
Reward Function: The reward function is used to provide feedback to the DRL algorithm, indicating whether the selected actions were beneficial or not. The reward function is designed to encourage the system to make decisions that result in energy efficiency, cost savings, and grid stability. The reward function is based on a combination of factors such as energy cost, carbon emissions, and grid reliability.
The DRL algorithm used for this proposed methodology is the Deep Q-Network (DQN) algorithm. DQN is a popular DRL algorithm that combines deep neural networks with Q-learning. Q-learning is a type of reinforcement learning algorithm that learns an optimal policy by iteratively updating a state-action value function.
The DQN algorithm works by training a deep neural network to estimate the Q-values of state-action pairs. The Q-values represent the expected cumulative reward of taking a particular action in a given state. The DRL agent selects actions based on the highest Qvalue. The DQN algorithm is updated using a variant of stochastic gradient descent called the Bellman equation, which ensures that the Q-values converge to the optimal values over time.
The equations for the DQN algorithm are as follows: Q(s, a) is the Q-value of taking action a in state s, r is the reward received for taking action a in state s, γ is the discount factor, s' is the next state, and a' is the action taken in the next state. L (θ) is the loss function; θ is the set of weights in the neural network, and α is the learning rate. g) Every C steps, update the target Q-network weights to the current Q-network weights. 5. Repeat steps 3 for a fixed number of episodes or until convergence. Note that this is a simplified version of the DQN algorithm and there are several variations and modifications that can be made to improve its performance. Overall, the proposed DRL-based energy management system has the potential to optimize energy usage in smart grids, leading to improved efficiency, cost savings, and grid stability.

Figure 2.Comparison chart of Accuracy
The Figure 2 Shows the comparison chart of Accuracy demonstrates the existing SVM, ANN and proposed DRL. X axis denote the Dataset and y axis denotes the Accuracy ratio. The proposed DRL values are better than the existing algorithm. The existing algorithm values start from 60 to 72, 64 to 73 and proposed DRL values starts from 85 to 94. The proposed method provides the great results.

Conclusion
In this paper, proposed an Intelligent Energy Management System (IEMS) for smart grids using the Deep Reinforcement Learning (DRL) algorithm. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS was evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost. The DRL algorithm provides an efficient and effective approach for optimizing the energy management in smart grids, and the proposed IEMS can be used to facilitate the integration of renewable energy sources into the grid.