Spatiotemporal Network Flow Model for Multi-Energy System Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for energy flow optimization in multi-energy systems are inefficient due to their complexity, reliance on algebraic models that require numerous matrix inversion operations, and limitations in handling nonlinear problems, making them unsuitable for large-scale or strongly nonlinear systems.
Innovation Solution
The method employs a spatiotemporal network flow model using a shortest-path and max-flow algorithm to optimize energy flow in multi-energy systems with micro gas turbines and energy storage devices, avoiding matrix inversion operations and providing a more intuitive and efficient solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing mathematical methods with algebraic equations are used to solve energy flow optimization problems, then the problem can be formulated systematically, but the computational complexity increases significantly due to numerous matrix inversion operations
Solution Approach 1:
The patent replaces the traditional algebraic equation-based mathematical methods with a network flow model approach. This substitution transforms the problem from solving complex algebraic equations requiring matrix inversions to finding optimal flows in a network structure, thereby reducing computational complexity while maintaining systematic formulation capabilities
Solution Approach 2:
The patent changes the fundamental parameters and variables of the optimization problem by transforming energy flow variables into network flow variables. This parameter transformation allows the use of efficient network flow algorithms instead of computationally intensive algebraic methods, improving productivity while reducing algorithmic complexity
2Adaptability or versatility
If algebraic models are used for energy flow optimization, then the model can handle linear problems, but it becomes unsuitable for large-scale or strongly nonlinear systems
Solution Approach 1:
The patent substitutes the algebraic modeling approach with a network flow modeling approach that is inherently more suitable for nonlinear systems. The network flow model can naturally represent nonlinear relationships through arc cost functions and capacity constraints, improving adaptability to nonlinear systems while maintaining solution reliability through proven network flow algorithms
3Productivity
If traditional optimization methods are applied to multi-energy systems, then the system can be analyzed, but the computational time increases due to the complex multi-constraint nonlinear nature of the problem
Solution Approach 1:
The patent transforms the complex multi-constraint nonlinear optimization problem into a network flow problem by changing the mathematical representation of constraints and variables. This transformation enables the use of efficient network flow algorithms that solve the problem much faster, improving optimization speed while reducing computational time loss
Solution Approach 2:
The patent segments the complex multi-energy system into a network structure with nodes representing system components and arcs representing energy flows. This segmentation allows the application of network flow algorithms that can efficiently handle the multi-constraint nonlinear nature of the problem, thereby improving computational efficiency and reducing optimization time
Data Source
AI summary
A method for energy flow optimization in a multi-energy system based on spatiotemporal network flows is disclosed. The multi-energy system includes M energy storage devices, photovoltaic power generators and N micro gas turbines (MGTs). The method includes: obtaining an objective function of the network flows in the multi-energy system; determining constraints of the network flows, where the constraints include a power balance constraint, an energy storage period constraint, an energy storage capacity constraint, an energy storage charging and discharging constraint, a generator power constraint, and an energy storage charge quantity constraint; establishing a network flow model with the objective function and constraints; solving the network flow model with a shortest-path and max-flow algorithm, to obtain an optimal power flow (OPF) in the multi-energy system; operating the multi-energy system by adjusting parameters of the energy storage devices and the MGTs based on the OPF.