An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

• URL: http://arxiv.org/abs/2006.05784v3
• Date: Wed, 2 Dec 2020 12:10:34 GMT
• Title: An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
• Authors: Thibaut Th\'eate, S\'ebastien Mathieu and Damien Ernst
• Abstract summary: This article focuses on a yearly base load product from the Belgian forward market, named calendar (CAL) It introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65%.
• Score: 3.828689444527739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.

Related papers

• 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)
• Quality versus speed in energy demand prediction for district heating systems [3.4057682528839237]
  Energy demand prediction is essential in combined heat power systems when offering electrical energy in competitive electricity markets. We propose two sets of algorithms: (1) a novel extension to the algorithm proposed by E. Dotzauer and (2) an autoregressive predictor based on hour-of-week adjusted linear regression on moving averages of energy consumption. These two methods are compared against state-of-the-art artificial neural networks.
  arXiv  Detail & Related papers  (2022-05-10T15:47:48Z)
• Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting [0.0]
  This study investigates the predictive performance of a new hybrid model based on the Generalized long memory autoregressive model (k-factor GARMA) The performance of the proposed model is evaluated using data from Nord Pool Electricity markets.
  arXiv  Detail & Related papers  (2022-04-18T12:21:25Z)
• 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)
• Exploring market power using deep reinforcement learning for intelligent bidding strategies [69.3939291118954]
  We find that capacity has an impact on the average electricity price in a single year. The value of $sim$25% and $sim$11% may vary between market structures and countries. We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market.
  arXiv  Detail & Related papers  (2020-11-08T21:07:42Z)
• 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)
• Deep Convolutional Neural Network Model for Short-Term Electricity Price Forecasting [0.0]
  A novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results.
  arXiv  Detail & Related papers  (2020-03-12T06:06:18Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.