Embedding Reliability Verification Constraints into Generation Expansion Planning
- URL: http://arxiv.org/abs/2504.07131v1
- Date: Sun, 06 Apr 2025 04:58:45 GMT
- Title: Embedding Reliability Verification Constraints into Generation Expansion Planning
- Authors: Peng Liu, Lian Cheng, Benjamin P. Omell, Anthony P. Burgard,
- Abstract summary: This study proposes an approach to embedding reliability verification constraints into generation expansion planning.<n>The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region.
- Score: 1.9632700283749582
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
Related papers
- Certified Guidance for Planning with Deep Generative Models [1.391198481393699]
Various guidance strategies have been introduced to steer the generative process toward outputs that are more likely to satisfy the planning objectives.<n>We introduce certified guidance, an approach that modifies a generative model, without retraining it, into a new model guaranteed to satisfy a given specification with probability one.<n>Our results confirm that certified guidance produces generative models that are always correct, unlike existing guidance methods that are not certified.
arXiv Detail & Related papers (2025-01-22T11:46:28Z) - Distilling Calibration via Conformalized Credal Inference [36.01369881486141]
One way to enhance reliability is through uncertainty quantification via Bayesian inference.<n>This paper introduces a low-complexity methodology to address this challenge by distilling calibration information from a more complex model.<n> Experiments on visual and language tasks demonstrate that the proposed approach, termed Conformalized Distillation for Credal Inference (CD-CI), significantly improves calibration performance.
arXiv Detail & Related papers (2025-01-10T15:57:23Z) - Adaptive Planning with Generative Models under Uncertainty [20.922248169620783]
Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains.
While continuous replanning at each timestep might seem intuitive because it allows decisions to be made based on the most recent environmental observations, it results in substantial computational challenges.
Our work addresses this challenge by introducing a simple adaptive planning policy that leverages the generative model's ability to predict long-horizon state trajectories.
arXiv Detail & Related papers (2024-08-02T18:07:53Z) - Constrained Synthesis with Projected Diffusion Models [47.56192362295252]
This paper introduces an approach to generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles.
The proposed method recast the traditional process of generative diffusion as a constrained distribution problem to ensure adherence to constraints.
arXiv Detail & Related papers (2024-02-05T22:18:16Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability [58.75803543245372]
Task and Motion Planning (TAMP) approaches are suited for planning multi-step autonomous robot manipulation.
We propose to overcome these limitations by composing diffusion models using a TAMP system.
We show how the combination of classical TAMP, generative modeling, and latent embedding enables multi-step constraint-based reasoning.
arXiv Detail & Related papers (2023-06-22T20:40:24Z) - When Demonstrations Meet Generative World Models: A Maximum Likelihood
Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.
Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
arXiv Detail & Related papers (2023-02-15T04:14:20Z) - Calibrating Over-Parametrized Simulation Models: A Framework via
Eligibility Set [3.862247454265944]
We develop a framework to develop calibration schemes that satisfy rigorous frequentist statistical guarantees.
We demonstrate our methodology on several numerical examples, including an application to calibration of a limit order book market simulator.
arXiv Detail & Related papers (2021-05-27T00:59:29Z) - Safe Continuous Control with Constrained Model-Based Policy Optimization [0.0]
We introduce a model-based safe exploration algorithm for constrained high-dimensional control.
We also introduce a practical algorithm that accelerates policy search with model-generated data.
arXiv Detail & Related papers (2021-04-14T15:20:55Z) - Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
We argue for the use of neural generative models to characterize the worst-case distribution.
This approach poses a number of implementation and optimization challenges.
We find that the proposed approach yields models that are more robust than comparable baselines.
arXiv Detail & Related papers (2021-03-18T14:26:26Z) - COMBO: Conservative Offline Model-Based Policy Optimization [120.55713363569845]
Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable.
We develop a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-actions.
We find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods.
arXiv Detail & Related papers (2021-02-16T18:50:32Z) - Control as Hybrid Inference [62.997667081978825]
We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference.
We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
arXiv Detail & Related papers (2020-07-11T19:44:09Z) - Efficient Ensemble Model Generation for Uncertainty Estimation with
Bayesian Approximation in Segmentation [74.06904875527556]
We propose a generic and efficient segmentation framework to construct ensemble segmentation models.
In the proposed method, ensemble models can be efficiently generated by using the layer selection method.
We also devise a new pixel-wise uncertainty loss, which improves the predictive performance.
arXiv Detail & Related papers (2020-05-21T16:08:38Z)
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.