Prescribing net demand for two-stage electricity generation scheduling

• URL: http://arxiv.org/abs/2108.01003v3
• Date: Mon, 17 Apr 2023 17:57:44 GMT
• Title: Prescribing net demand for two-stage electricity generation scheduling
• Authors: Juan M. Morales, Miguel \'A. Mu\~noz and Salvador Pineda
• Abstract summary: We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation. We propose a bilevel program to construct a prescription of the net demand that does account for the power system's cost asymmetry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. The former must be conducted facing an uncertain net demand that includes non-dispatchable electricity consumption and renewable power generation. The latter copes with the plausible deviations with respect to the forward schedule by making use of balancing power during the actual operation of the system. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation (usually referred to as a point forecast), so as to minimize the need for balancing power in real time. However, it is well known that the cost structure of a power system is highly asymmetric and dependent on its operating point, with the result that minimizing the amount of power imbalances is not necessarily aligned with minimizing operating costs. In this paper, we propose a bilevel program to construct, from the available historical data, a prescription of the net demand that does account for the power system's cost asymmetry. Furthermore, to accommodate the strong dependence of this cost on the power system's operating point, we use clustering to tailor the proposed prescription to the foreseen net-demand regime. By way of an illustrative example and a more realistic case study based on the European power system, we show that our approach leads to substantial cost savings compared to the customary way of doing.

Related papers

• Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search [4.950434218152639]
  We propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents.
  arXiv  Detail & Related papers  (2024-11-06T15:49:28Z)
• Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM [0.471858286267785]
  This paper proposes a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. The proposed approach produced the best prediction results in comparison to the other benchmark models used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092.
  arXiv  Detail & Related papers  (2024-06-10T09:02:07Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Entangled Pair Resource Allocation under Uncertain Fidelity Requirements [59.83361663430336]
  In quantum networks, effective entanglement routing facilitates communication between quantum source and quantum destination nodes. We propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee. Our proposed model can reduce the total cost by at least 20% compared to the baseline model.
  arXiv  Detail & Related papers  (2023-04-10T07:16:51Z)
• Real-time scheduling of renewable power systems through planning-based reinforcement learning [13.65517429683729]
  renewable energy sources have posed significant challenges to traditional power scheduling. New developments in reinforcement learning have demonstrated the potential to solve this problem. We are the first to propose a systematic solution based on the state-of-the-art reinforcement learning algorithm and the real power grid environment.
  arXiv  Detail & Related papers  (2023-03-09T12:19:20Z)
• Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plants [3.8113588584597187]
  In power systems, one wishes to regulate the aggregate demand of an ensemble of distributed energy resources. We suggest a notion of predictability and fairness, which suggests that the long-term averages of prices or incentives offered should be independent of the initial states of the operators of the DER.
  arXiv  Detail & Related papers  (2021-10-06T18:20:07Z)
• 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)
• N-BEATS neural network for mid-term electricity load forecasting [8.430502131775722]
  We show that our proposed deep neural network modeling approach is effective at solving the mid-term electricity load forecasting problem. It is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias.
  arXiv  Detail & Related papers  (2020-09-24T21:48:08Z)
• 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)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

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.