Enterprise Power Source Scheduling Optimization
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Solution Overview
Problem
The uncertainty of green energy availability and fluctuating market costs hinders consumers from participating in competitive power markets or procuring power from green energy resources, leading to increased risk, cost, and carbon emissions in power procurement.
Innovation Solution
A method and system for optimal scheduling of power sources that utilize historical data to calculate variances in market prices, demand, and solar generation, and then model constraints and objective functions to minimize cost, carbon emission, and market price risk through quadratic programming, while also performing real-time adjustments to battery charging and discharging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If consumers procure power from competitive market or green energy resources, then cost reduction and carbon emission reduction are achieved, but uncertainty in availability and risk in cost increase
Solution Approach 1:
The system continuously monitors real-time power data, forecasted parameters, and actual performance, then adjusts scheduling decisions dynamically. This feedback loop allows consumers to respond to changing conditions in green energy availability and market prices, reducing uncertainty while maintaining carbon emission reductions
Solution Approach 2:
The system performs day-ahead scheduling that forecasts green energy availability and market prices in advance, allowing consumers to pre-commit to optimal power source selections. This preliminary action reduces uncertainty by planning ahead while still capturing the benefits of green energy and competitive pricing
2Quantity of substance
If consumers procure power from competitive market or green energy resources, then cost reduction is achieved, but risk in cost increases
Solution Approach 1:
The scheduling system dynamically adjusts power source selection based on real-time market conditions and green energy availability. This dynamic approach allows consumers to capture cost reductions when conditions are favorable while mitigating cost risks when conditions deteriorate, achieving both low cost and low risk simultaneously
Solution Approach 2:
The system changes operational parameters (power source allocation, charging/discharging schedules) based on varying market prices and green energy availability. By continuously optimizing these parameters, the system achieves cost reduction while managing risk through adaptive response to parameter changes
3Reliability
If consumers establish contracts with generators or avail electricity from retailers, then risk avoidance is achieved, but cost and carbon emission increase
Solution Approach 1:
The day-ahead scheduling system acts as an intermediary between consumers and multiple power sources (green energy, competitive market, contracts). It selectively intermediates transactions to achieve risk avoidance through diversified sourcing while capturing cost and carbon emission benefits through optimized allocation
4Reliability
If consumers establish contracts with generators or avail electricity from retailers, then risk avoidance is achieved, but carbon emission increases
Solution Approach 1:
The system segments power procurement into multiple independent sources (green energy, competitive market, contracts) and allocates demand across them. This segmentation allows consumers to maintain risk avoidance through contract coverage while reducing carbon emissions by prioritizing green energy sources for segments where available
Data Source
Figure 1~2
Figure 3
Figure 4A
AI summary
The uncertainty of availability and risk associated with the cost of power procurement hinders the consumer from using new sources of supply/storage devices or taking part in the competitive power markets or procuring power from green energy resources. Present disclosure provides a method and a system for optimal scheduling of power sources for meeting electricity demand of enterprise. In particular, the system performs portfolio optimization formulation for cost, risk and carbon emission minimization for the enterprise. The portfolio optimization formulation simulates market scenario and guides in providing effective power purchase strategy for enterprise along with real-time adjustments. In particular, the level of risk to be considered along with cost of power procurement and carbon footprint determine portfolio allocation for each time block in portfolio optimization formulation. Thus, enterprise may choose most appropriate terms of contract, best installation size of renewable resources or batteries, and supply sources.