Multi-Stage Pricing for Park Integrated Energy Systems
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Solution Overview
Problem
The coal mining industry faces challenges with low energy utilization efficiency and high pollutant emissions, necessitating an efficient, low-carbon, and clean operation strategy to manage energy supply while coal remains a significant energy source.
Innovation Solution
A multi-stage multi-level cooperative operation method is implemented for park integrated energy systems, utilizing a short-time scale dynamic pricing model with Karush-Kuhn-Tucker conditions and Benders decomposition, combined with an improved regret degree matching mechanism to optimize energy scheduling and pricing, incorporating energy supply, recovery, and storage subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional coal-based energy supply is used to ensure reliable energy supply, then energy supply reliability is maintained, but energy utilization efficiency is low and pollutant emissions are high
Solution Approach 1:
The patent merges multiple energy supply sources (coal-fired units, renewable energy, demand response) into an integrated energy system that operates cooperatively. This allows the system to maintain reliable supply from traditional coal sources while incorporating efficient renewable energy and flexible demand response to improve overall energy utilization efficiency.
Solution Approach 2:
The integrated energy system performs multiple functions simultaneously: base load supply from coal-fired units, peak load management through demand response, and renewable energy integration. This multi-functionality allows the system to maintain reliability while improving efficiency through diversified energy sources and operations.
2Reliability
If traditional coal-based energy supply is used to ensure reliable energy supply, then energy supply reliability is maintained, but pollutant emissions are high
Solution Approach 1:
The patent converts the harmful effect of coal-based energy generation into a benefit by using it as a stable base load source while offsetting its emissions through renewable energy integration and carbon emission cost internalization. The coal-fired units provide reliable supply while their environmental externalities are managed through market mechanisms and renewable supplementation.
Solution Approach 2:
The system changes the operational parameters of coal-fired units by introducing dynamic pricing signals and carbon emission costs that modify their operation patterns. This leads to optimized coal utilization and reduced emissions while maintaining the essential reliable supply function.
3Loss of energy
If dynamic pricing and multi-stage optimization are implemented to improve energy utilization efficiency, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the optimization process into distinct stages (day-ahead and real-time) and layers (system operator and user levels), each with specific decision variables and constraints. This segmentation makes the complex optimization problem more manageable while achieving improved energy utilization efficiency through coordinated multi-stage decisions.
4Productivity
If multi-stage multi-level cooperative operation is implemented to reduce operation costs, then operation costs are reduced, but computational difficulty increases
Solution Approach 1:
The patent divides the complex multi-stage multi-level optimization problem into separable subproblems that can be solved using Benders decomposition. This segmentation allows the system to reduce operation costs through coordinated optimization while managing computational difficulty by solving smaller subproblems iteratively rather than one monolithic complex problem.
Data Source
AI summary
The present disclosure discloses a method for performing multi-stage multi-level cooperative operation on a park integrated energy system containing a mineral industry user. The method includes: constructing a short-time scale dynamic pricing model, including an upper-layer CMIES operation optimization model and a lower-layer user demand response model; solving the short-time scale dynamic pricing model to obtain day-ahead and intraday scheduling plans and a next-day electricity price; and based on the day-ahead and intraday scheduling plans and the next-day electricity price, computing a strategy transfer probability distribution of each subject within a preset deadline, and simulating a decision-making process by a Monte Carlo method until each subject reaches a correlated equilibrium state to obtain a pricing solution of CMIES within the preset deadline. According to the present disclosure, the energy utilization efficiency of the system can be improved, and the operation cost can be reduced.