Large fluctuations in locational marginal prices

Nesti, Tommaso; Moriarty, John; Zocca, Alessandro; Zwart, Bert

doi:10.1098/rsta.2019.0438

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…From an economic perspective, the LMPs represent the value of electricity in each location, and it is used as remuneration/payment of electricity for buyers and sellers [3]. From an operational perspective, the LMPs reflect the restrictions in the transmission system according to the availability of supply resources and demand.…”

Section: Introductionmentioning

confidence: 99%

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

et al. 2022

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Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

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Section: Introductionmentioning

confidence: 99%

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

et al. 2022

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“…However, the high variability of the underlying driving processes means that these prices may fluctuate rapidly and unpredictably Price spikes in such networks are of concern and the problem of their prediction is extremely difficult. The paper by Nesti et al [10] combines the use of multiparametric programming techniques to combine seamlessly the analysis of network flows-the optimal power flow problem-with large deviations theory to study the problem of predicting extreme price fluctuations in LMP-based energy markets. It is assumed that the modeller has a complete stochastic description of the generation and demand processes and of the physical characteristics of the network.…”

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confidence: 99%

Introduction: the mathematics of energy systems

Mancarella

Moriarty

Philpott

et al. 2021

Phil. Trans. R. Soc. A.

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The urgent need to decarbonize energy systems gives rise to many challenging areas of interdisciplinary research, bringing together mathematicians, physicists, engineers and economists. Renewable generation, especially wind and solar, is inherently highly variable and difficult to predict. The need to keep power and energy systems balanced on a second-by-second basis gives rise to problems of control and optimization, together with those of the management of liberalized energy markets. On the longer time scales of planning and investment, there are problems of physical and economic design. The papers in the present issue are written by some of the participants in a programme on the mathematics of energy systems which took place at the Isaac Newton Institute for Mathematical Sciences in Cambridge from January to May 2019—see http://www.newton.ac.uk/event/mes . This article is part of the theme issue ‘The mathematics of energy systems’.

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