Cost-Oriented Load Forecasting

Zhang, Jialun; Yi, Wang; Hug, Gabriela

doi:10.48550/arxiv.2107.01861

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…A closed-loop forecast-optimize module is described in [29] to solve the dayahead unit commitment problem, employing the loss function introduced in [11]. Additionally, [30] considers wind forecasting for short-term trading applications and [31] examines load forecasting for dispatch scheduling, both relying on two-step approaches that involve first inferring a convex loss function, then training the forecasting model. However, this approach does not directly leverage the optimization component.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Reduced modeling effort [28], [30], [31], [37] * --[29] * , † † - [33], [34] † -This work * , † † case studies of increasing complexity related to renewable trading. First, we examine trading in a DA market under different pricing mechanisms and propose strategies that balance trading cost and predictive accuracy.…”

Section: Multiple Types Of Uncertaintymentioning

confidence: 99%

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Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Stratigakos

Camal

Michiorri

et al. 2022

IEEE Trans. Power Syst.

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Deriving decisions from data typically involves a sequential process with two components, forecasting and optimization. Forecasting models learn by minimizing a loss function that stands as a proxy for task-specific costs (e.g., trading, scheduling) without considering the downstream optimization, which in practice creates a performance bottleneck and obscures the impact of data on decisions. This work proposes a single data-driven module that leverages the structure of the optimization component and directly learns a policy conditioned on explanatory data. For this purpose, we describe an algorithm to train ensembles of decision trees by directly minimizing taskspecific costs, and prescribe decisions via a weighted Sample Average Approximation of the original problem. We then develop a generic framework to assess the impact of explanatory data on prescriptive performance. To illustrate the efficacy of the proposed modeling approach, we consider two case studies related to trading renewable energy. First, we examine trading in a day-ahead market and propose strategies that balance optimal trading decisions and predictive accuracy. Next, we append a storage device and co-optimize the day-ahead offers and the operational policy, based on a tractable approximation using the linear decision rule approach. The empirical results demonstrate improved prescriptive performance compared to solutions derived under the standard stochastic optimization framework. Further, we provide valuable insights on how explanatory data impact optimization performance and how this impact evolves under different market designs.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: Multiple Types Of Uncertaintymentioning

confidence: 99%

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Stratigakos

Camal

Michiorri

et al. 2022

IEEE Trans. Power Syst.

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“…For inertia forecasting, to ensure the frequency stability of power systems, system operators need to maintain an adequate level of system inertia, whereas inaccurate inertia forecasts will lead to extra or improper scheduling and increase the operation cost. Inertia forecasting errors will drive the cost increment, but the exact economic consequence may vary in different system conditions, time periods, and even the signs of error [20]. In other words, higher inertia forecasting errors do not necessarily mean higher operation cost.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

Chen

Sun

Chu

et al. 2023

IEEE Trans. Smart Grid

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With the increasing penetration of renewables, the power system is facing unprecedented challenges of low-inertia levels. The inherent ability of the system to defense disturbance and power imbalance through inertia response is degraded, and thus, system operators need to make faster and more efficient scheduling operations. As one of the most promising solutions, machine learning (ML) methods have been investigated and employed to realize effective inertia forecasting with considerable accuracy. Nevertheless, it is yet to understand its vulnerability with the growing threat of cyberattacks. To this end, this paper proposes a methodological framework to explore the vulnerability of ML-based inertia forecasting models, with a special focus on data integrity attacks. In particular, a cost-oriented false data injection attack is proposed, for the first time, with the primary objective to significantly increase the system operation cost while retaining the stealthiness of the attack via minimizing the differences between the pre-perturbed and after-perturbed inertia forecasts. Moreover, we propose four vulnerability assessment metrics for the ML-based inertia forecasting models. Case studies on the GB power system demonstrate the vulnerability and impact of the ML-based inertia forecasting models, as well as the stealthiness and transferability of the proposed cost-oriented data integrity attacks.

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Cost-Oriented Load Forecasting

Cited by 2 publications

References 31 publications

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

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