Long-term wind and solar energy generation forecasts, and optimisation of Power Purchase Agreements

被引：19

作者：

Mesa-Jimenez, J. J. ^{[1
,2
]}

Tzianoumis, A. L. ^{[3
]}

Stokes, L. ^{[4
]}

Yang, Q. ^{[1
]}

Livina, V. N. ^{[2
]}

机构：

[1] Brunel Univ London, Kingston Lane, Uxbridge, Middx, England

[2] Natl Phys Lab, Hampton Rd, Teddington, Middx, England

[3] City Univ London, Northampton Sq, London, England

[4] Mace Grp, 155 Moorgate, London, England

来源：

ENERGY REPORTS | 2023年 / 9卷

基金：

英国工程与自然科学研究理事会;

关键词：

Power purchase agreements; Markov chain Monte Carlo; Stochastic forecast; Renewable energy optimisation; PROBABILISTIC FORECASTS; SPEED; RADIATION; SYSTEM; MODEL;

D O I：

10.1016/j.egyr.2022.11.175

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Due to more affordable solar and wind power, and the European Union regulations for decarbonisation of the economy, more than 40% of the Fortune 500 companies have targets related to green energy. This is one of the main reasons why multi-technology Power-Purchase Agreements (PPAs) are becoming increasingly important. However, there are risks associated with the uncertainty and variable generation patterns in wind speed and solar radiation. Moreover, there are challenges to predict intermittent wind and solar generation for the forecasting horizon required by PPAs, which is usually of several years. We propose a long-term wind and solar energy generation forecasts suitable for PPAs with cost optimisation in energy generation scenarios. We use Markov Chain Monte Carlo simulations with suitable models of wind and solar generation and optimise long-term energy contracts with purchase of renewable energy.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

引用

页码：292 / 302

页数：11

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