Stochastic optimization of multi-energy system operation considering hydrogen-based vehicle applications

Mei, Jie; Zuo, Yuefei; Lee, Christopher; Wang, Xuntuo; Kirtley, James L.

doi:10.1016/j.adapen.2021.100031

Cited by 32 publications

(11 citation statements)

References 35 publications

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“…The source code and the free data are made available. 4 The metrics computed on the test period are displayed in Table II. The p-values of the Diebold & Mariano test are displayed in Figure 6.…”

Section: Results A) Configurationsmentioning

confidence: 99%

“…Electricity Price Forecasting (EPF) is a problem that has interested specialists in the field for many years [1], [2] in order to better adjust electricity production to the needs of consumption for this resource that is difficult to store. This also allows stock market activities, such as making it possible for owners of renewable energy production facilities to make their investments profitable by anticipating price movements and promoting intelligent applications such as self-consumption [3] or battery optimization [4]. These issues have become increasingly crucial in recent months with high price volatility.…”

Section: Introductionmentioning

confidence: 99%

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Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Tschora,

Guns,

Pierre

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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We consider day-ahead electricity price forecasting on the European market. In this market, participants can offer electricity for sale or purchase for a specific price by submitting overnight orders. Market operators determine the market clearing price -the price at which the amount of electricity supplied equals the amount of electricity demanded -using the Euphemia balancing algorithm. EUPHEMIA is a quadratic optimization problem that maximizes the social welfare defined as the sum of the supplier surplus and consumer surplus while ensuring a null energy balance. This mechanism deeply influences the price calculation, but has so far been little considered in electricity price forecasting algorithms. Existing models are generally based on identifying relationships between exogenous characteristics (consumption and production forecasts) and the market clearing price to be predicted. A few studies have examined the EUPHEMIA mechanism during prediction, by doing costly manual transformations on order books. In this article, we overcome this limitation by considering the pricing mechanism during model training. For this, we use a predict-and-optimize strategy with differentiable optimization. We design a fully differentiable and scalable solving method for the EUPHEMIA optimization problem and apply it on real-life data from the European Power Exchange (EPEX). We design different model architectures using our differentiable solver and empirically study the impact of taking into account the optimal calculation of prices within the training of the neural network.

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Section: Results A) Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Tschora,

Guns,

Pierre

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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show abstract

“…[10] proposed a multi-stage SO for the design of a hydrogen supply chain network under uncertain demand. Jie Mei et al studied a multienergy system operation using SO to handle the uncertainties from solar power, and demand [11]. Conditional value at risk (CVaR) is frequently used along with SO to improve the robustness.…”

Section: Introductionmentioning

confidence: 99%

Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties

et al. 2023

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“…The authors in [14][15][16] proposed power flow management methods for MESs with integrated natural gas and electricity. A two-stage scheduling method for MES considering solar power forecasting based on random forest [17] is proposed in [18]. Refs.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [14]- [16] proposed power flow management methods for MESs with integrated natural gas and electricity. A two-stage scheduling method for MES considering solar power forecasting based on random forest [17] is proposed in [18]. References [19], [20] adopted an integrated power and thermal energy system, including a detailed cogeneration model to efficiently accommodate more wind power.…”

Section: Introductionmentioning

confidence: 99%

Low‐carbon operation of a multi‐energy system with hydrogen‐based vehicle applications

Mei

Lee

Kirtley

2022

IET Renewable Power Gen

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In order to cope with the challenges of improving energy efficiency, increasing the integration of renewable energy, and achieving carbon emission reduction, multi‐energy systems have received more and more attention in recent years and have been developing rapidly. Traditionally, different energy infrastructures are usually scheduled and operated independently, which leads to inefficient use of energy and waste of resources. By integrating into a multi‐energy system, different energy infrastructures can be coupled and optimized into one unit. In this article, from a low‐carbon point of view, the optimal scheduling of a real multi‐energy system with hydrogen‐based vehicle applications is proposed. The simulation results show that the proposed optimal scheduling can help quantify the daily operation cost and carbon emissions and achieve considerably operating cost saving and carbon reduction by reasonably arranging and utilizing all the devices in the system.

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Stochastic optimization of multi-energy system operation considering hydrogen-based vehicle applications

Cited by 32 publications

References 35 publications

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties

Low‐carbon operation of a multi‐energy system with hydrogen‐based vehicle applications

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