
Investigation and mathematical modelling of the impact of incentive signals to consumers on their consumption, load forecast and network operation
Author(s) -
Konotop Irina,
Waczowicz Simon,
Klaiber Stefan,
Bretschneider Peter,
Mikut Ralf,
Westermann Dirk
Publication year - 2016
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.2015.1175
Subject(s) - computer science , incentive , mathematical optimization , renewable energy , demand response , electric power system , consumption (sociology) , energy consumption , wind power , computation , power (physics) , electricity , economics , microeconomics , engineering , mathematics , social science , physics , electrical engineering , algorithm , quantum mechanics , sociology
Due to increasing contribution of renewable energy sources to the overall energy production, future control of energy consumption should achieve the principle ‘generation following demand’. Within this approach, also known as demand side management, consumers are prompted to alter demands through an incentive signal. This work addresses the use of variable price as such incentive signal to influence power consumption of private households in a controllable manner. The mathematical modelling is based on simulations of optimal power flow and includes several optimisation constraints and criteria, according to which both direct and inverse models are constructed. Therefore, the problem of power signals and price signals is considered as fully‐coupled. The analysis is performed for a model, representing typical conditions and scenarios of a residential network with household customers, including solar and wind power plants. The optimisation criteria are constructed with the emphasis on distribution system operators. The results of computations, performed with simplified and extended optimisation models, show a pronounced effect on the overall power consumption, reduction of the extreme values and minimisation of the peaks. Finally, further steps are proposed to extend the model to target various optimisation constraints.