Abstract
Load management of electrical devices in residential buildings can be applied with different goals in the power grid, such as the cost optimization regarding variable electricity prices, peak load reduction or the minimization of behavioral efforts for users due to load shifting. A cooperative multi-objective optimization of consumers and generators of power has the potential to solve the simultaneity problem of power consumption and optimize the power supply from the superposed grid regarding different goals. In this paper, we present a multi-criteria extension of a distributed cooperative load management technique in smart grids based on a multi-agent framework. As a data basis, we use feasible power consumption and production schedules of buildings, which have been derived from simulations of a building model and have already been optimized with regard to self-consumption. We show that the flexibilities of smart buildings can be used to pursue different targets and display the advantage of integrating various goals into one optimization process.
Highlights
The use of cost-efficient flexibilities in production and consumption of electrical power is a key factor in the realization of an energy supply concept based on solar and wind energy (Elsner et al 2015)
Single day simulations Simulation setup In order to execute simulations with well-funded input data, preliminary work has been conducted regarding the behavioral adaptation of users as well regarding the composition and aggregation of numerous smart devices into one smart building model
The findings derived from the research about users behavior and its adaptational efforts served as input for the parameterization of the smart building model
Summary
The use of cost-efficient flexibilities in production and consumption of electrical power is a key factor in the realization of an energy supply concept based on solar and wind energy (Elsner et al 2015). Load management of electrical devices in residential buildings can be applied with different goals in the power grid: In the approach presented in our contribution, an abstract interface between the smart building model and the smart grid optimization is provided, based on a set of admissible schedules with associated behavioral adaption costs.
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