Dynamic Energy Pricing Engine for Retail Markets
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Solution Overview
Problem
Current energy pricing systems in retail markets lack efficiency in dynamically adjusting prices based on forecasted energy usage and changing market conditions, leading to inefficiencies and resource misallocation for retailers and sales agents.
Innovation Solution
A system and method that utilize a pricing engine to determine and update energy prices for consumers based on forecasted energy usage, incorporating factors like raw cost of energy, cost to provide energy, and margin, allowing for automatic generation and output of tailored transactable prices, including fixed and indexed pricing models, and enabling quick adaptation to changes in market conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual pricing methods are used for energy retail, then sales agents can negotiate prices with customers, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service pricing where the pricing engine automatically generates customized energy prices without requiring manual intervention from sales agents. The engine uses customer usage data, forecasted energy costs, and market conditions to autonomously determine optimal prices, freeing sales agents from time-consuming calculations while maintaining operational control.
Solution Approach 2:
The patent replaces manual mechanical pricing processes with an automated computational pricing engine. Instead of sales agents manually calculating prices based on spreadsheets or manual formulas, the system uses automated software to perform real-time pricing calculations, substituting human effort with algorithmic processing that is faster and more consistent.
2Productivity
If customized pricing is generated for each customer, then customer yields increase, but resource dedication to pricing increases
Solution Approach 1:
The pricing engine serves multiple functions simultaneously: it generates customized prices for individual customers, performs market analysis, forecasts energy costs, and optimizes pricing strategies all through a single automated system. This multi-functionality allows the system to deliver personalized pricing to many customers without proportionally increasing resource requirements, as the same engine handles diverse pricing tasks efficiently.
Solution Approach 2:
The system dynamically adjusts pricing parameters such as energy costs, usage patterns, and market conditions to generate optimized prices for each customer. By automatically changing these parameters based on real-time data rather than manual adjustment, the system achieves high customer yields without requiring proportional increases in human resources for pricing management.
3Adaptability or versatility
If pricing is updated frequently to reflect market changes, then adaptability improves, but system complexity increases
Solution Approach 1:
The pricing system is designed to be dynamic, automatically adjusting prices in response to changing market conditions, energy costs, and customer usage patterns. The pricing engine continuously monitors relevant parameters and updates pricing recommendations without manual intervention, enabling the system to adapt to market changes while maintaining manageable complexity through automated processes rather than manual system reconfiguration.
Data Source
AI summary
A method of pricing energy includes determining a price for supplying energy to a consumer over a future term based on at least a forecasted cost of energy and a forecasted energy usage of the consumer. The method further includes automatically determining an updated price for supplying energy based on at least the forecasted energy usage of the consumer used in initially determining the price. The method further includes outputting the updated price for supplying energy.