alternativeАльтернативная энергетика и экология (ISJAEE)Alternative Energy and Ecology (ISJAEE)1608-8298Международный издательский дом научной периодики "Спейс10.15518/isjaee.2020.07-18.24-43alternative-1918Research ArticleI. ВОЗОБНОВЛЯЕМАЯ ЭНЕРГЕТИКА 1. Солнечная энергетикаI. RENEWABLE ENERGY 1. Solar EnergyПрогнозирование выработки солнечных станций и фотоэлектрических установок: основные подходы и результативностьPhotovoltaic Power Forecasting: Basic Approaches and Featureshttps://orcid.org/0000-0001-5836-8615КиселеваС. В.KiselevaS. V.

Софья Валентиновна Киселева, канд. физ.-мат. наук, ведущий научный сотрудник научно-исследовательской лаборатории возобновляемых источников энергии 

ID E-3324-2014Scopus Author ID 57201352245

д. 1, Ленинские горы, Москва, 119991, Россия 

 

 

 

 

 

Sofia Kiseleva, Ph. D. in Physics and Mathematics, Senior Researcher at Renewable Energy Sources Laboratory 

ID E-3324-2014Scopus Author ID 57201352245

Leninskie Gori, Moscow, 119991, Russia

 

k_sophia_v@mail.ruЛисицкаяН. В.LisitskayaN. V.

Наталья Владимировна Лисицкая, ведущий инженер ФГБУН Объединенный институт высоких температур Российской академии наук

Scopus: ID 57194546812

д. 13, Ижорская ул., Москва, 125412, Россия 

ID 57194546812

Bd.2, 13 Izhorskaya Str. Moscow, 125412, Russia

 

https://orcid.org/0000-0001-8089-8225ФридС. Е.FridS. E.

Семен Ефимович Фрид, канд. техн. наук, заведующий лабораторией Федерального государственного бюджетного учреждения науки

SPIN-код: 2420-5962

ResearcherID: C-3046-2014 

ScopusID: 6602192623 д. 13, Ижорская ул., Москва, 125412, Россия 

Semen Frid, Ph.D. in Engineering, Head of Laboratory, Joint Institute for High Temperatures of the Russian Academy of Sciences

SPIN-код: 2420-5962ResearcherID: C-3046-2014ScopusID: 6602192623

Bd.2, 13 Izhorskaya Str. Moscow, 125412, Russia

 

s_frid@oivtran.ruНИЛ ВИЭ географического факультета МГУ имени М.В.ЛомоносоваРоссияLomonosov Moscow State University, Faculty of GeographyRussian FederationОбъединенный институт высоких температур РАНРоссияJoint Institute for High Temperatures of the Russian Academy of SciencesRussian Federation20201908202007-182442Copyright © Международный издательский дом научной периодики "Спейс, 20202020Международный издательский дом научной периодики "СпейсМеждународный издательский дом научной периодики "Спейсhttps://www.isjaee.com/jour/about/submissions#copyrightNoticehttps://www.isjaee.com/jour/article/view/1918

Рассмотрена  задача  прогноза  производительности  солнечных электростанций  (СЭС) и  фотоэлектрических установок  (ФЭУ). Сделан  обзор  современных  методов  прогноза  и  фактической  основы  для  его  проведения. Представлены принятые классификации методов, основу которых образует подход «прямые прогнозы – косвенные прогнозы». В первом случае прогнозируется производительность СЭС или ФЭУ, во втором случае первым этапом является прогноз прихода солнечной радиации с последующим пересчетом в выработку станций. Соответственно, в первом  случае большое  значение  имеют  ряды  данных о  производительности  станции  в  течение длительных периодов в прошлом и применяются в основном статистические методы и методы машинного обучения. Второй подход базируется на численном прогнозе погоды, который обеспечивает, в том числе, прогноз приходящей солнечной радиации. Значительное влияние на выбор методов прогноза оказывает требуемое пространственное  и  временное  разрешение. Последнее определяется  принятыми  в  стране или регионе  правилами рынка  электроэнергии. Проблема пространственного разрешения прогноза  является важной для протяженных по занимаемой площади СЭС, а также при прогнозе производительности сети солнечных станций. Приведены принятые в настоящее время метрики прогнозов, которые позволяют оценить погрешности, а также сравнить результативность различных методов прогнозирования. Показана перспективность разработки  вероятностных прогнозов как  альтернативы  детерминистским подходам, в частности, для более полного удовлетворения требований сетей. Рассмотрены принятые в некоторых странах требования к прогнозу производительности СЭС, штрафные санкции при высокой погрешности прогноза. Рассмотрены примеры финансовых потерь от неточности прогноза на примере СЭС в США и Китае. Обсуждаются требования оптового рынка  энергии  и мощности  РФ, который  устанавливает  предельно  допустимые  отклонения  от  заявленного производства энергии станциями (в том числе СЭС) и штрафы за эти отклонения. Приведены результаты сценарных оценок финансовых потерь генераторов на солнечной энергии в РФ от ошибочных прогнозов производительности.

The article is devoted to the tasks of forecasting the productivity of solar power plants (SPP) and PV-unit. A review of modern forecasting methods and the actual basis for its implementation is made. Accepted classifications of methods are presented, the basis of which is the “direct forecasts - indirect forecasts” approach. The basis of the second approach is the forecast of solar radiation with subsequent conversion to the productivity of solar power plants. Accordingly, in the first case, a series of data on plants productivity over long periods in the past are of great importance and mainly statistical and machine learning methods are used. The second approach is based on a numerical weather forecast, which provides, among other things, a solar radiation forecast. A significant influence on the choice of forecasting methods is provided by the required spatial and temporal horizon. The latter is determined by the electricity market rules in a particular country or region. The problem of spatial resolution is important for power plants occupying large areas, as well as in forecasting the productivity of a network of solar plants. The article presents the currently adopted forecast metrics which allow estimating errors, as well as comparing the effectiveness of various forecasting methods. The prospects of developing probabilistic forecasts as an alternative to deterministic approaches are shown. Probabilistic forecasts are more likely to meet network requirements. The requirements of grid operators in some countries for forecasting the productivity of SPP and the penalties in case of a high forecast error are considered in the article. Examples of financial losses from forecast errors are shown on the example of solar power plants in the USA and China. The requirements of the Russian Federation wholesale energy and capacity market are discussed. The market sets the maximum permissible deviations from the declared energy production by stations (including SPP) and fines for these deviations. The article presents the financial  losses estimates from errors in productivity forecasts for solar energy plants in the Russian Federation.

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The authors declare that there are no conflicts of interest present.