A genetic algorithm approach to the smart grid tariff design problem
- Resource Type
- Authors
- James McDermott; Paula Carroll; William Rogers
- Source
- Soft Computing. 23:1393-1405
- Subject
- 0209 industrial biotechnology
Fitness function
Operations research
business.industry
Computer science
Tariff
ComputerApplications_COMPUTERSINOTHERSYSTEMS
Computational intelligence
02 engineering and technology
Theoretical Computer Science
020901 industrial engineering & automation
Smart grid
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
Electricity market
020201 artificial intelligence & image processing
Geometry and Topology
Electricity
business
Software
- Language
- ISSN
- 1433-7479
1432-7643
Smart metering in electricity markets offers an opportunity to explore more diverse tariff structures. In this article residential electricity demand and the System Marginal Price of Ireland’s Single Electricity Market are simulated to estimate the wholesale risk associated with possible tariffs. A genetic algorithm (GA) with a stochastic fitness function is proposed to search for time-of-use tariffs that minimise wholesale risk to the supplier in residential markets. Alternative search algorithms and fitness functions are investigated in detail, as well as trade-offs in GA and simulation parameter settings.