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Вероятностное потокораспределение как реакция на стохастичность нагрузки в энергосистеме

Вероятностное потокораспределение как реакция на стохастичность нагрузки в энергосистеме

Abstract

В существующей практике для анализа и управления режимами энергосистем в основном используются детерминистические подходы, которые реализуются в виде известных методов и моделей расчета установившихся и переходных режимов. С помощью этих методов можно получить решения только при фиксированных параметрах схемы системы и при допущении, что задаваемые в узлах активные и реактивные мощности нагрузки и генерации сохраняются неизменными. В условиях реальной эксплуатации стохастический характер изменчивости потребления создают случайные флуктуации напряжений в узлах и перетоков мощности в линиях электрической сети энергосистемы. Такие случайные флуктуации режима могут быть оценены с помощью моделирования вероятностного потокораспределения. В статье приводятся результаты исследования влияния глубины случайных флуктуаций мощности нагрузки системы на вероятности распределения напряжений в узлах и потоки активной и реактивной мощности в линиях. Моделирование вероятностного потокораспределения в условиях стохастического изменения нагрузки выполняется для разных уровней флуктуаций и при утяжелении режима системы, вплоть до пиковой мощности нагрузки. Тестовые исследования для количественной оценки влияния стохастической изменчивости нагрузки на вероятностное распределение параметров режимов проводили на примере электрической сети реальной энергосистемы. Сравнивали результаты моделирования вероятностного потокораспределения для данных флуктуаций нагрузки, представляемых в виде дискретных выборок величин активной мощности, получаемых аналитическим путем методом Монте-Карло и данных реальных замеров их значений в исследуемой сети.For the analysis and control of power systems deterministic approaches that are implemented in the form of well-known methods and models of calculation of steady-state and transient modes are mostly use in current practice. With the use of these methods it is possible to obtain solutions only for fixed circuit parameters of the system scheme and assuming that active and reactive powers as well as generation in nodal points of the network remain the same. In reality the stochastic character of power consumption cause the casual fluctuations of voltages at the nodes and power flows in electric power lines of the power system. Such casual fluctuations of operation can be estimated with the use of probabilistic simulation of the power flows. In the article the results of research of the influence of depth of casual fluctuations of the load power of the system on the probability distribution of voltage at nodes as well as on the flows of active and reactive power in the lines are presented. Probabilistic modeling of flow under stochastic load change is performed for different levels of fluctuations and under loading of the mode of the system up to peak load power. Test study to quantify the effect of stochastic variability of loads on the probabilistic distribution parameters of the modes was carried out on behalf of the electrical network of the real power system. The results of the simulation of the probability flow distribution for these fluctuations of the load, represented in the form of discrete sample values of the active power obtained with the use of the analytical Monte-Carlo method, and real data measurements of their values in the network under examination were compared.

Keywords

ВЕРОЯТНОСТНОЕ ПОТОКОРАСПРЕДЕЛЕНИЕ,ПЛОТНОСТЬ РАСПРЕДЕЛЕНИЯ,ФЛУКТУАЦИЯ НАГРУЗКИ,РАСПРЕДЕЛЕНИЕ ПОТЕРЬ,РАСПРЕДЕЛЕНИЕ НАПРЯЖЕНИЙ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold