A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets

• URL: http://arxiv.org/abs/2203.06053v1
• Date: Fri, 11 Mar 2022 16:29:02 GMT
• Title: A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets
• Authors: Saeed Mohammadi and Mohammad Reza Hesamzadeh
• Abstract summary: Prosumer operators are dealing with challenges to participate in short-term electricity markets. These challenges include variation in demand, solar energy, wind power, and electricity prices. Machine learning approaches could resolve these challenges due to their ability to continuous learning of complex relations.
• Score: 0.7614628596146599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prosumer operators are dealing with extensive challenges to participate in short-term electricity markets while taking uncertainties into account. Challenges such as variation in demand, solar energy, wind power, and electricity prices as well as faster response time in intraday electricity markets. Machine learning approaches could resolve these challenges due to their ability to continuous learning of complex relations and providing a real-time response. Such approaches are applicable with presence of the high performance computing and big data. To tackle these challenges, a Markov decision process is proposed and solved with a reinforcement learning algorithm with proper observations and actions employing tabular Q-learning. Trained agent converges to a policy which is similar to the global optimal solution. It increases the prosumer's profit by 13.39% compared to the well-known stochastic optimization approach.

Related papers

• Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management [32.01109026974077]
  SimStock is a temporal self-supervised learning framework to learn robust and informative representations of financial time series data. We conduct experiments on four real-world datasets with thousands of stocks and demonstrate the effectiveness of SimStock in finding similar stocks, outperforming existing methods.
  arXiv  Detail & Related papers  (2024-07-18T17:54:13Z)
• Optimizing Quantile-based Trading Strategies in Electricity Arbitrage [0.0]
  This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets. Despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits.
  arXiv  Detail & Related papers  (2024-06-19T21:27:12Z)
• Learning Iterative Reasoning through Energy Diffusion [90.24765095498392]
  We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks. IRED learns energy functions to represent the constraints between input conditions and desired outputs. We show IRED outperforms existing methods in continuous-space reasoning, discrete-space reasoning, and planning tasks.
  arXiv  Detail & Related papers  (2024-06-17T03:36:47Z)
• Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning [0.0]
  Multi-agent Reinforcement learning (MARL) is a promising new approach to predicting the expected profit-maximizing behavior of energy market participants in simulation. We provide a model of the energy market to a basic MARL algorithm in the form of a learned OPF approximation and explicit market rules. Our experiments demonstrate that the model reduces training time by about one order of magnitude but at the cost of a slightly worse performance.
  arXiv  Detail & Related papers  (2023-03-03T08:26:22Z)
• Online Search with Predictions: Pareto-optimal Algorithm and its Applications in Energy Markets [32.50099216716867]
  This paper develops learning-augmented algorithms for energy trading in volatile electricity markets. We incorporate machine-learned predictions to design competitive algorithms for online search problems.
  arXiv  Detail & Related papers  (2022-11-12T04:12:10Z)
• Machine learning applications for electricity market agent-based models: A systematic literature review [68.8204255655161]
  Agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence. We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
  arXiv  Detail & Related papers  (2022-06-05T14:52:26Z)
• Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review [7.2090237123481575]
  Reinforcement Learning is an emerging machine learning technique with advantages compared with conventional optimization tools. This paper presents a review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization. Some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.
  arXiv  Detail & Related papers  (2022-05-07T08:02:25Z)
• ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions [91.3755431537592]
  We present the ApeRI-miodic (ARISE) process for investigating efficient markets. The ARISE process is formulated as an infinite-sum of some known processes and employs the aperiodic spectrum estimation. In practice, we apply the ARISE function to identify the efficiency of real-world markets.
  arXiv  Detail & Related papers  (2021-11-08T03:36:06Z)
• The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets [69.68068088508505]
  We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
  arXiv  Detail & Related papers  (2021-03-07T11:28:54Z)
• Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids [56.35173057183362]
  We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. We propose the formulation of a smart grid environment as a Markov game.
  arXiv  Detail & Related papers  (2020-05-05T07:32:40Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.