Open AccessKullback–Leibler divergence‐based distributionally robust optimisation model for heat pump day‐ahead operational schedule to improve PV integration
Author(s) -
Li Zihao,
Wu Wenchuan,
Zhang Boming,
Tai Xue
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.2062
Subject(s) - schedule , mathematical optimization , divergence (linguistics) , computer science , robust optimization , monte carlo method , photovoltaic system , kullback–leibler divergence , mathematics , engineering , statistics , philosophy , linguistics , artificial intelligence , operating system , electrical engineering
For its high coefficient of performance and zero local emissions, the heat pump (HP) has recently become popular in North Europe and China, which shows potential in absorbing local photovoltaic (PV) generation. In this study, the authors describe a distributionally robust optimisation (DRO)‐based HP day‐ahead operational schedule model (HP‐DOSM) to match the PV power generation, which can well capture the uncertainties of weather, PV, and load prediction errors. Moreover, this DRO‐based HP‐DOSM can be transformed into a tractable deterministic model. The DRO method they proposed is suitable for linear expectation constrained optimisation whose ambiguity set is constructed using Kullback–Leibler divergence, which could be further transformed into deterministic conic/linear constraints. Compared with robust optimisation (RO) models, the authors’ model is less conservative since more statistical information on the uncertainties is utilised. Numerical tests were conducted to demonstrate its performance, compared with the RO model via Monte Carlo simulations.