Energy Hub Heat Pump Capacity and Power Allocation Optimization
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Solution Overview
Problem
The optimization of heat pump capacity and power distribution among various energy source equipment in energy hubs is challenging due to nonlinear stochastic characteristics, requiring complex mathematical processing and commercial optimizers, with limited studies addressing this issue effectively.
Innovation Solution
A double-layer optimization method using a quadratic function-based binary search algorithm for the upper layer and a multi-objective evolutionary algorithm NSGA-II for the lower layer, integrating to directly optimize the capacity and power of heat pumps and energy source equipment without extensive model processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical analysis is performed to linearize the nonlinear problem and commercial optimizers (cplex, gurobi) are used for solving, then the optimization accuracy is improved, but the computational complexity and time consumption increase significantly
Solution Approach 1:
The patent transforms the original nonlinear stochastic optimization problem into a deterministic equivalent model by changing the parameter representation of random variables. Specifically, it uses the relationship between moment generating functions and cumulant generating functions to convert stochastic parameters into deterministic equivalents, thereby eliminating the need for complex commercial optimizers while maintaining optimization accuracy.
Solution Approach 2:
The patent replaces the mechanical application of commercial optimization software (cplex, gurobi) with a customized optimization algorithm based on deterministic equivalent transformation. This substitution eliminates the need for linearization processing and direct solution of complex nonlinear stochastic models, reducing computational complexity while preserving solution accuracy.
2Device complexity
If the original nonlinear stochastic model is optimized directly without linearization and commercial optimizers, then the computational complexity is reduced, but the optimization accuracy and reliability may deteriorate
Solution Approach 1:
The patent ensures optimization reliability by transforming stochastic parameters into deterministic equivalents through moment generating function theory. This parameter transformation maintains the mathematical equivalence of the original problem while enabling the use of simpler optimization algorithms, thus preserving solution reliability without requiring commercial optimizers.
Solution Approach 2:
The patent introduces deterministic equivalent models as an intermediary between the original nonlinear stochastic model and the optimization algorithm. This intermediary transformation layer converts the complex stochastic problem into a form that can be solved by standard optimization methods, ensuring both computational simplicity and solution reliability.
3Loss of energy
If waste heat recovery technology and heat pump interconnection mechanism are introduced, then the operational cost and greenhouse gas emissions are reduced, but the system complexity and difficulty of optimization increase
Solution Approach 1:
The patent merges the waste heat recovery system, heat pump interconnection mechanism, and multiple energy source equipment into a unified energy hub model. By integrating these subsystems into a single deterministic equivalent optimization framework, the patent achieves energy efficiency improvements while managing system complexity through unified mathematical transformation rather than separate complex optimizations.
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 simplifies the optimization process, achieving efficient, economic, and green operation of energy hubs by directly optimizing the original model, reducing operational costs and emissions, and avoiding the need for commercial optimizers.
Implementation Method 1
Introducing a cold and hot interconnection mechanism of a heat pump into the energy hub can recover heat from a cooling loop and provide the heat to a heating loop
Data Source
AI summary
The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.