A determining factor for a successful implementation of a demand-based pricing model or control strategy in electricity markets is not only the effects of peak load management, but also the economical consequences for the utility operator and the end customer. In this economical modeling a subset of 460 residential customers has been implemented in a software tool analyzing the economical outcome of three different tariffs. Two demand-based tariffs were investigated and compared with a traditional energy-based tariff. The demand-based tariffs transform the flat income curve into a more complex, due to a stronger economical dependency to the system peak loads. The demand-based tariffs move the revenues to the high-peak period, November-March, and the utility operator gains a good matching between system peaks and distribution of incomes
[1]
Hiroshi Asano,et al.
Household Response to Incentive Payments for Load Shifting: A Japanese Time-of-Day Electricity Pricing Experiment
,
2000
.
[2]
Raimo P. Hämäläinen,et al.
Customer level analysis of dynamic pricing experiments using consumption-pattern models
,
1995
.
[3]
Kerstin Sernhed,et al.
Pay for Load Demand. Electricity Pricing with a Load Demand Component
,
2003
.
[4]
Ali Feliachi,et al.
Residential load control through real-time pricing signals
,
2003,
Proceedings of the 35th Southeastern Symposium on System Theory, 2003..
[5]
D. Brandt,et al.
A linear programming model for reducing system peak through customer load control programs
,
1996
.