Authors:
Sandford Bessler
1
;
Domagoj Drenjanac
1
;
Eduard Hasenleithner
1
;
Suhail Ahmed-Khan
1
and
Nuno Silva
2
Affiliations:
1
FTW Telecommunications Research Center, Austria
;
2
EFACEC Energia M´aquinas e Equipamentos El´ectricos and S.A, Portugal
Keyword(s):
Flexibility Models, Load Predictive Models, Optimization Models, Energy Scheduling, EV Charging, HVAC, PV Generation, Aggregated Energy Controller, Day-Ahead Pricing, Setpoint Following.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy Management Systems (EMS)
;
Energy-Aware Systems and Technologies
;
Integration of Smart Appliances
;
Load Balancing in Smart Grids
;
Optimization Techniques for Efficient Energy Consumption
;
Smart Grids
;
Smart Homes (Domotics)
Abstract:
Flexibility information that characterizes the energy consumption of certain loads with electric or thermal
storage has been recently proposed as a means for energy management in the electric grid. In this paper we
propose an energy management architecture that allows the grid operator to learn and use the consumption
flexibility of its users. Starting on the home asset level, we describe flexibility models for EV charging and
HVAC and their aggregation at the household and low voltage grid level. Here, the aggregated energy controller
determines power references (set points) for each household controller. Since voltage limits might be
violated by the energy balancing actions, we include a power flow calculation in the optimization model to
keep the voltages and currents within the limits. In simulation experiments with a 42 bus radial grid, we are
able to support higher household loads by individual scheduling, without falling below voltage limits.