Energy Provider Demand Optimization Using Incentive-Based Setpoints
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Solution Overview
Problem
Energy providers face high operational costs due to excessive energy consumption by customers, leading to high loads during peak demand times, necessitating an efficient system for energy distribution and setpoint control to minimize costs and improve customer efficiency.
Innovation Solution
A method and system that predict energy demand based on incentives offered by energy providers, optimizing production and setpoints to determine the optimal amount of refined resources to produce, considering incentive costs, raw resource costs, and revenue, while ensuring demand is met or exceeded, using processing circuits to manage energy assets and dispatch resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy providers increase production to meet customer demand, then customer energy supply is ensured, but operational costs and peak loads increase
Solution Approach 1:
The system performs demand prediction and production optimization in advance before peak demand occurs. By predicting refined resource demand and determining optimal production amounts ahead of time, the energy provider can prepare production schedules that avoid costly peak-load operations while ensuring supply reliability when needed.
Solution Approach 2:
The optimization system dynamically adjusts production levels and incentive values based on predicted demand, time-varying prices, and cost conditions. This dynamic approach allows the system to respond flexibly to changing conditions, maintaining reliability while minimizing operational costs through adaptive production scheduling.
2Loss of energy
If energy providers offer incentives to reduce customer demand, then peak loads and operational costs decrease, but incentive costs increase
Solution Approach 1:
The system optimizes the value of incentives as a decision variable in the mathematical model, adjusting incentive parameters to achieve the optimal balance between demand reduction and incentive cost. By changing the incentive value parameter, the system can minimize total operational cost while achieving sufficient demand response from customers.
Solution Approach 2:
The system uses predicted demand as feedback to determine optimal incentive values. The optimization model incorporates the relationship between incentive values and predicted demand, allowing the system to adjust incentive offerings based on how they will affect customer behavior and overall system costs.
3Reliability
If energy providers produce more refined resources than minimum demand, then supply reliability improves, but production costs and energy waste increase
Solution Approach 1:
The system determines the optimal amount of refined resources to produce in advance by predicting future demand and considering storage capabilities. This preliminary optimization allows the system to produce exactly the right amount needed to meet demand while utilizing storage devices to buffer against demand fluctuations, avoiding both shortages and waste.
Solution Approach 2:
Storage devices act as intermediaries between production and consumption. The optimization model incorporates storage capacity to decouple production timing from consumption timing, allowing the system to produce refined resources at optimal times and store them for later use, thereby minimizing waste while ensuring demand satisfaction.
Data Source
AI summary
A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.