Bi-level optimization scheduling method for air conditioning system based on demand response
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Solution Overview
Problem
The existing single-level optimization structures struggle to achieve optimal scheduling for air conditioning systems in building energy management, particularly in balancing demand response and water tank energy storage capacity, leading to inefficiencies in energy usage and stability within the power grid.
Innovation Solution
A bi-level optimization scheduling method is introduced, which constructs a lumped heat capacity model, calculates power consumption, and optimizes objective functions to match day-ahead water tank energy storage capacity with daily running strategies, utilizing Grey Wolf algorithms and flexibility indexes to enhance energy flexibility and savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-level optimization structure is used for air conditioning system scheduling, then the optimization process is simpler, but it cannot achieve desired optimization effect due to interrelated optimization variables and time sequencing
Solution Approach 1:
The patent divides the optimization problem into two distinct levels: upper-level optimization for water tank energy storage capacity settings, and lower-level optimization for demand response strategies. This segmentation allows each level to focus on specific variables and timeframes, resolving the contradiction between structural simplicity and optimization effectiveness by creating a modular two-tier architecture that handles variable interrelations and time sequencing appropriately at each level.
Solution Approach 2:
The patent transitions from a single-level (one-dimensional) optimization structure to a bi-level (two-dimensional) optimization structure. This dimensional change enables the system to simultaneously optimize storage capacity settings and demand response strategies across different time horizons and variable types, achieving desired optimization effects that were unattainable with a single-level approach.
2Ease of operation
If day-ahead water tank energy storage capacity is set without considering daily running strategy, then storage capacity can be determined independently, but the matching between storage capacity and daily operation is poor
Solution Approach 1:
The patent implements a feedback mechanism where the lower-level demand response optimization results inform and adjust the upper-level water tank capacity settings. The bi-level structure allows the daily running strategy outcomes to feed back into the capacity determination process, ensuring that storage capacity is optimized based on actual operational requirements and performance, thereby achieving precise matching between capacity and strategy.
Solution Approach 2:
The patent performs preliminary optimization of demand response strategies at the lower level before finalizing water tank capacity settings at the upper level. This preliminary action ensures that capacity decisions are made with knowledge of optimal operational strategies, enabling precise matching between storage capacity and daily running requirements while maintaining systematic independence in the optimization process.
3Ease of manufacture
If various demand response strategies are not combined reasonably in demand response stage, then individual strategies can be implemented simply, but the overall demand response effect is suboptimal
Solution Approach 1:
The patent merges multiple demand response strategies (pre-cooling, pre-heating, load shifting, etc.) into a unified lower-level optimization framework. This combining approach allows the system to simultaneously consider and coordinate various strategies, achieving superior overall demand response effects while maintaining implementation simplicity through automated optimization that handles the complexity of strategy coordination.
Data Source
AI summary
A bi-level optimization scheduling method for an air conditioning system based on demand response includes: constructing a lumped heat capacity model to describe a heat storage capacity of a building to thereby obtain a building heat storage model; obtaining a function relational expression of describing an indoor dry bulb temperature and a cooling and heating load of the building based on the building heat storage model; constructing a power consumption calculation model under a working condition of demand response based on the function relational expression; constructing optimization objective functions based on the power consumption calculation model; and substituting the optimization objective functions into a bi-level optimization process, and optimizing the bi-level optimization process to obtain an optimal scheduling strategy for the air conditioning system participating in demand response. The method can achieve a global optimization of demand response scheduling strategy, and improve economy and energy saving of system operation.