Collaborative optimization method, system, equipment and storage medium for electric and heating networks and building user
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in minimizing the economic cost of Electrical and Heating Networks (EHN) and reducing heating energy costs for consumers in a park while ensuring comfort temperature, considering the diverse energy supply options and thermal inertia of buildings.
Innovation Solution
A collaborative optimization method is established for EHN and building users, involving a coordinated scheduling framework, optimal thermal power flow scheduling, and building models to minimize operation and heat consumption costs, incorporating models for hydraulic, thermal, and energy conversion equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If thermal power flow scheduling is optimized to reduce operation costs, then economic cost is reduced, but system complexity increases due to multiple models and constraints
Solution Approach 1:
The patent segments the EHN optimization problem into distinct functional modules: hydraulic model for flow constraints, thermal model for temperature constraints, heat exchanger model for heat transfer, radiator model for heat release, and capacity/energy conversion equipment model for operational constraints. This segmentation allows each aspect to be modeled and optimized independently while maintaining overall system coordination, reducing the complexity of managing the entire system as a single monolithic model.
Solution Approach 2:
The patent introduces a coordinated optimization scheduling framework that acts as an intermediary layer between the various models and constraints. This framework integrates the hydraulic model, thermal model, heat exchanger model, radiator model, and equipment model, coordinating their interactions to achieve optimal thermal power flow scheduling. The framework mediates between conflicting constraints and objectives, enabling cost reduction without requiring direct management of all system complexities simultaneously.
2Loss of energy
If building thermal inertia is utilized for cost reduction, then heat consumption cost is reduced, but control difficulty increases
Solution Approach 1:
The patent applies preliminary action by pre-charging or pre-discharging the building's thermal inertia before peak heating demand periods. The building model simulates thermal characteristics and incorporates the thermal inertia effect in advance, allowing the system to store thermal energy during low-cost periods and release it during high-cost periods. This preliminary utilization of thermal inertia reduces heat consumption costs while the optimization framework handles the control complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the building model continuously monitors thermal characteristics and adjusts the heating strategy based on actual thermal inertia effects. The coordinated optimization framework receives feedback from the building's thermal response and adjusts the thermal power flow scheduling accordingly, enabling cost reduction through thermal inertia while maintaining manageable control through closed-loop adjustment.
3Adaptability or versatility
If multiple energy supply options are provided, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing the EHN to support multiple energy supply options including CHP units, heat pumps, and traditional heating sources within a single integrated framework. The capacity and energy conversion equipment model accommodates different types of heating equipment with varying operational characteristics, allowing the system to adapt to different energy supply scenarios without requiring separate optimization models for each option.
Solution Approach 2:
The patent applies dynamics by enabling the system to dynamically switch between different energy supply options based on real-time conditions. The coordinated optimization framework allows flexible adjustment of the mix of heating sources, enabling the system to adapt to changing energy prices, demand patterns, and equipment availability. This dynamic capability provides adaptability while the unified modeling framework prevents excessive complexity by treating all options within a single optimization structure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach optimizes thermal power flow scheduling to reduce EHN operation and building user heat costs while meeting heat load demands, utilizing thermal inertia and autonomous radiator adjustments.
Implementation Method 1
the thermodynamic characteristics of the building lead to its thermal inertia, which can be used in the park's capacity and energy conversion equipment
Implementation Method 2
constraints of the building model include a heat balance constraint of wall in a heating zone of the building and a heat balance constraint of indoor air in the heating zone of the building
Implementation Method 3
Constraints of the thermal model include a node flow conservation constraint, a pipeline heat dissipation constraint, and an energy conservation constraint of heat source and load
Implementation Method 4
Constraints of the heat exchanger model include a heat balance constraint of the heat exchanger
Implementation Method 5
Constraints of the radiator model include a heat constraint released by the radiator
Data Source
AI summary
A collaborative optimization method for an Electric and Heating Networks (EHN) and a building user includes: establishing a coordinated optimization scheduling framework of the EHN and a building in a park; based on the coordinated optimization scheduling framework, constructing an optimal thermal power flow scheduling model of the EHN, and constructing a building model to simulate thermal characteristics and energy consumption of the building; based on the optimal thermal power flow scheduling model and the building model, taking the lowest operation cost of EHN and the lowest heat consumption of building user as a goal, constructing a collaborative optimization model of the EHN and the building user; outputting a collaborative optimization scheme of the optimal thermal power flow scheduling of the EHN and the heat consumption of the building user through the collaborative optimization model of the EHN and the building user.