Open AccessChallenge from Transpower: Determining the effect of the aggregated behaviour of solar photovoltaic power generation and battery energy storage systems on grid exit point load in order to maintain an accurate load forecast
Author(s) -
Catherine Zoe Wollaston Hassell Sweatman,
Nuttanan Wichitaksorn,
Anwen Jiang,
Troy Farrell,
Niall Bootland,
Georgia Miskell,
Geoffrey Pritchard,
C. Chrystall,
Graham Robinson
Publication year - 2020
Publication title -
australian and new zealand industrial and applied mathematics journal. electronic supplement
Language(s) - English
Resource type - Journals
ISSN - 1445-8810
DOI - 10.21914/anziamj.v60i0.14619
Subject(s) - photovoltaic system , load profile , computer science , grid , solar irradiance , grid connected photovoltaic power system , battery (electricity) , irradiance , load balancing (electrical power) , power (physics) , automotive engineering , energy storage , simulation , environmental science , maximum power point tracking , electrical engineering , meteorology , engineering , electricity , mathematics , voltage , physics , geometry , inverter , quantum mechanics
With limited data beyond the grid exit point (GXP) or substation level, how can Transpower determine the effect of the aggregated behaviour of solar photovoltaic power generation and battery energy storage systems on GXP load in order to maintain an accurate load forecast? In this initial study it is assumed that the GXP services a residential region. An algorithm based on non-linear programming, which minimises the financial cost to the consumer, is developed to model consumer behaviour. Input data comprises forecast energy requirements (load), solar irradiance, and pricing. Output includes both the load drawn from the grid and power returned to the grid. The algorithm presented is at the household level. The next step would be to combine the load drawn from the grid and the power returned to the grid from all the households serviced by a GXP, enabling Transpower to make load predictions. Various means of load forecasting are considered including the Holt--Winters methods which perform well for out-of-sample forecasts. Linear regression, which takes into account comparable days, solar radiation, and air temperature, yields even better performance.