Acta Energetica

Application of Reinforcement Learning in Energy Storage Management

Nitin N. Sakhare, Muhamad Angriawan — Wed, 17 Jul 2024 00:00:00 +0000

Adding renewable energy sources to the power grid has made it necessary to have effective energy storage management systems to deal with problems like power outages and changes in the amount of energy available. Reinforcement learning (RL) has become a potential way to improve how energy storage works in this situation. This essay looks at how RL methods can be used in managing energy storage, with a focus on how they might improve the cost-effectiveness and efficiency of energy storage systems (ESS). RL algorithms, like Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO), can figure out the best ways to handle things by dealing with their surroundings and getting input on how well they did. RL agents can change how they act in changing and unclear situations by learning from their mistakes. This lets real-time decisions be made about how to send and schedule energy storage. RL-based ESS managers can find the best charging and dumping plans by looking at things like power prices, demand patterns, predictions for renewable energy production, and system limits. This helps them make the most money, keep the grid stable, and reduce running costs. RL methods are also flexible enough to meet a wide range of goals, such as lowering frequencies, moving loads, and shaving off peak power, all while taking long-term performance measures and practical limits into account. This essay talks about the latest improvements in RL-based energy storage management systems, the problems and benefits of using them, and possible directions for future study. Overall, using RL for managing energy storage has a lot of potential to make adding green energy sources to the power grid more efficient and long-lasting.

Predictive Analytics for Demand Response Management with AI

S. A. Sivakumar — Wed, 17 Jul 2024 00:00:00 +0000

In recent years, the integration of Artificial Intelligence (AI) into demand response management has garnered significant attention due to its potential to enhance energy efficiency, reduce costs, and mitigate environmental impact. This paper presents a comprehensive overview of predictive analytics for demand response management leveraging AI techniques. The primary objective is to forecast electricity demand accurately, enabling proactive decision-making and efficient resource allocation in response to fluctuating energy needs. he proposed framework integrates various AI methodologies, including machine learning algorithms, deep learning models, and predictive analytics techniques, to analyze historical consumption patterns, weather data, market dynamics, and other relevant factors influencing electricity demand. By leveraging advanced data processing capabilities, the system can identify complex patterns and correlations that traditional forecasting methods might overlook, thereby improving the accuracy of demand predictions. One of the key contributions of this research lies in its ability to adapt and learn from real-time data streams, enabling dynamic adjustments to demand response strategies. By continuously updating predictive models based on incoming information, the system can respond swiftly to sudden changes in demand patterns, market conditions, or external factors, optimizing resource utilization and minimizing operational costs. Additionally, the paper discusses the implementation challenges and considerations associated with deploying AI-based predictive analytics for demand response management, including data privacy concerns, model interpretability, scalability, and integration with existing infrastructure.

Optimizing Grid Reliability with AI-Enhanced Maintenance Strategies

Vivek Deshpande, Anasica S — Wed, 17 Jul 2024 00:00:00 +0000

Electricity systems are very important to modern life because they provide power to homes, companies, and factories. Making sure that these grids work properly is very important, because problems with them can have big effects on the economy and society. Regular checks and repair plans are a big part of traditional maintenance methods, but they can be expensive and not work very well. In the past few years, there has been a rising interest in using artificial intelligence (AI) to improve grid dependability and repair methods. This essay gives an in-depth look at how AI can be used to improve upkeep methods in order to make the grid more reliable. The suggested method uses advanced machine learning algorithms and real-time data analysis to guess when equipment will break down and arrange repair tasks in order of importance. AI models can find patterns and trends in old maintenance records and data on how well equipment is working, which can help them figure out when and where problems are most likely to happen. Predictive maintenance methods are an important part of maintenance tactics that use AI. AI programs can find early signs of wear and tear or failure on equipment by constantly checking its health with sensors and Internet of Things (IoT) devices. This lets repair be done on time. This preventative method can cut down on the chance of failures and downtime by a large amount. AI can also be used to make repair plans and the use of resources more efficient. AI programs can figure out the best way to maintain the grid while keeping costs low by looking at things like how important the technology is, how much it costs, and how it can be used.

Deep Learning Applications for Power Quality Monitoring

Ankur Gupta — Wed, 17 Jul 2024 00:00:00 +0000

Power quality tracking is very important for making sure that electrical systems work reliably and efficiently. A branch of AI called "deep learning" has become a powerful way to look at complicated and unpredictable data trends in many fields. This paper gives an outline of how deep learning can be used to measure power supply. We talk about new developments in deep learning methods like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep autoencoders, and how they might be used in power quality research. CNNs have been used a lot for power quality tracking jobs like feature extraction and classification, because they can find spatial relationships in multivariate time-series data. RNNs, especially long short-term memory (LSTM) networks, are good at figuring out how things depend on time and guessing what will happen with power quality in the future. Deep autoencoders are a way to learn without being watched that can be used to find problems and weird patterns in power systems. This lets you do preventative maintenance and find problems early. Additionally, we talk about the problems and benefits of using deep learning to check power quality. These include getting the data ready, training the models, being able to understand the results, and being able to scale the system. Deep learning has a lot of promise, but it also has some problems and unanswered research questions. For example, we need named training data, the ability for models to work in a variety of settings, and the ability to draw conclusions in real time.

Optimization of Renewable Energy Integration Using AI Techniques

A. Kingsly Jabakumar, Gaurav Pathak — Wed, 17 Jul 2024 00:00:00 +0000

The green energy sources aren't always available, adding them to current power systems is very hard. To solve this problem, many AI methods have been suggested as the best way to add green energy sources like wind and sun to the power grid. An in-depth look at how AI can be used to make the best use of green energy sources in power systems is given in this study. One of the most important AI methods used in this case is machine learning, which can be used to predict how much renewable energy will be produced and how much will be needed. This lets renewable energy supplies be better scheduled and managed. Optimization algorithms, such as genetic algorithms and particle swarm optimization, are another important type of AI. They can be used to find the best places for and sizes of green energy sources in the power grid. In addition, AI methods can be used to make power systems that use a lot of green energy sources more stable and reliable. AI-based control methods can be used to lessen the effect of changes in green energy output on the power grid, for example, making sure that there is a steady supply of electricity. This paper talks about how AI methods could be used to make the best use of green energy sources in power systems. By using AI, we can get around the problems that come with combining green energy sources and speed up the move to a future with sustainable and low-carbon energy.

AI-Enabled Predictive Maintenance for Distribution Transformers

Nouby M. Ghazaly — Wed, 17 Jul 2024 00:00:00 +0000

Power distribution networks depend on distribution transformers to work well, which makes sure that there is a steady flow of energy. But these transformers can fail in a number of ways, which can cause expensive downtime and service interruptions. Traditional methods of maintenance, like regular checks and preventative maintenance, aren't always effective and can cost more than they need to. In recent years, there has been a rise in interest in using machine learning (ML) and artificial intelligence (AI) to plan ahead for repair on power transformers. AI-powered predictive maintenance systems can look at both old and new data from transformers to find patterns and trends that could mean they are about to break down or malfunction. It is possible to improve upkeep tasks and lower the risk of unexpected downtime by predicting these problems before they happen. This paper gives a full picture of predicted maintenance for distribution transformers that use AI. It talks about the main problems with standard care methods and shows why using AI-driven methods is better. The study also talks about current AI-based forecast maintenance methods, such as preparing data, choosing features, and training models. In addition, it looks into the possibility of combining IoT devices to collect data and watch things in real time. In addition, the study talks about the problems and restrictions of using AI-powered predictive maintenance systems, like the need to constantly update models and worries about data privacy. The report also looks at the financial and environmental effects of putting these systems in place, focusing on the chances of saving money and making things last longer.

Smart Grids Optimization for Energy Trading with AI Solutions

Dharmesh Dhabliya — Wed, 17 Jul 2024 00:00:00 +0000

Smart grids are changing the energy industry by making it easier to use green energy and handle energy more efficiently. Energy trading is an important part of smart grids because it lets buyers and sellers trade energy based on current supply and demand. But because smart grids are so complicated and changeable, it's hard to find the best way to trade energy in them. This paper gives an in-depth look at how to make trading energy in smart grids more efficient using AI tools. We look at the research that has already been done on smart grid planning and stress how important AI is for solving the problems that come up with trading energy. Next, we suggest a new approach that uses AI tools like machine learning, deep learning, and optimization methods to make trading energy in smart grids more efficient. The suggested system has several important parts, such as gathering and editing data, predicting demand, planning output, and bidding on the market. For demand predictions, machine learning models are used to guess how much energy will be used in the future. For generation schedule, optimization methods are used to find the best mix of generators based on the predicted demand. For market bids, deep learning models are used to find the best trade plan and make the most money. We test the suggested framework's performance with real-world data and show that it can help smart grids trade energy more efficiently. The results we got show that the suggested framework can make sharing energy a lot more profitable and efficient. This can help build energy systems that are both long-lasting and reliable.

AI-Driven Dynamic Pricing Mechanisms for Demand-Side Management

Waleed F. Faris, Sweta Batra — Wed, 17 Jul 2024 00:00:00 +0000

More and more people are realizing that dynamic price systems are good for controlling demand-side energy use. In this situation, artificial intelligence (AI) is very important for finding the best price methods to get results like managing loads, shaving off busy hours, and lowering costs. This essay looks at how AI-driven dynamic price systems could be used for demand-side control in the energy industry. AI programs, especially machine learning and optimization methods, are used to correctly predict future demand patterns by looking at past usage data, market conditions, weather patterns, and customer behavior. Based on these predictions, changeable price models are made to give people a reason to use less energy during busy times or switch their usage to off-peak times. There are different kinds of these pricing systems, such as important peak pricing, time-of-use pricing, and real-time pricing. AI also makes it possible to use customizable price strategies that are based on the needs and interests of each customer. AI algorithms can constantly change prices for each customer group by looking at things like readiness to pay, comfort preferences, and device usage patterns. This makes the system more engaging and satisfied while also making it more efficient overall. Also, demand response programs are made easier by AI-driven dynamic price systems that give customers real-time feedback and rewards.

Optimizing Solar Power Generation with AI-Enhanced Tracking Systems

Yadu Prasad Gyawali, Anishkumar Dhablia — Wed, 17 Jul 2024 00:00:00 +0000

Solar power is a clean and green source of energy that is an important part of sustainable energy options. Making solar cells as efficient as possible is important for making them more profitable. This paper discussed about a study that looks at how artificial intelligence (AI) can be used with solar panel tracking systems to make them more efficient at making solar power. Traditional methods for watching solar panels use set formulas to change the angles of the panels based on where the sun is. But because weather and shade can change, these devices might not always make the best use of the energy they produce. AI-based tracking systems are flexible and dynamic because they constantly look at the surroundings and change the panel positions in real time. AI techniques, like machine learning and computer vision, are combined with sensors and motors in the suggested system to track where the sun is and change the directions of the panels accordingly. The AI model learns from both past data and real-time inputs to figure out where the sun will be and what the best panel angles are for making the most energy. Using AI to improve tracking systems can lead to more efficient energy production, lower upkeep costs, and more reliable systems. The system's ability to adapt to changing weather conditions means that it works at its best all day and all year. It is possible that adding AI to solar panel tracking systems could make solar power creation much more efficient and effective. For a more safe energy future, future study could focus on making AI programs even better, making the system more scalable, and looking into how it can work with other green energy technologies.