Selective and optimal dynamic pricing strategy for residential electricity consumers based on genetic algorithms

Area of Science:

• Energy Economics
• Computational Intelligence
• Smart Grids

Background:

• Rising residential populations increase electricity demand, straining supply and leading to grid overload.
• Current demand prediction by utility companies (UC) is often inefficient, with actual demand exceeding supply.
• Dynamic pricing offers a method to influence consumer behavior and balance energy demand and supply.

Purpose of the Study:

• To develop a novel, data-driven, dynamic Time-of-Use (ToU) electricity pricing strategy for residential consumers.
• To optimize energy production infrastructure usage and reduce grid system overload.
• To align electricity demand with supply through intelligent pricing mechanisms.

Main Methods:

• Clustering real consumption data using k-means to categorize consumers based on usage patterns.
• Heuristically determining ToU tariff periods and parameters for different consumer categories.
• Employing genetic algorithms to minimize a cost function based on price elasticity and consumer behavior models.
• Utilizing three years of real consumption data from two Hungarian towns for validation.

Main Results:

• The proposed ToU pricing strategy effectively reduces demand during peak hours by targeting high-consumption categories.
• Implementation leads to a profit margin shared between the utility company and consumers.
• Optimal pricing ensures positive financial gains for both parties despite parameter uncertainties.
• The strategy relies solely on consumption data, preserving consumer privacy.

Conclusions:

• The consumption data-driven dynamic ToU pricing strategy is efficient in managing residential electricity demand and supply.
• This approach enhances grid stability, optimizes resource allocation, and offers economic benefits to stakeholders.
• The privacy-preserving nature of the method makes it a viable solution for modern smart grids.