UAM Network Optimization via Genetic Algorithm and Solar Microgrids

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Solution Overview

Problem

The development and operation of Urban Air Mobility (UAM) networks face challenges in managing grid electricity usage efficiently, particularly due to the strain of rapid charging requirements for electric VTOL aircraft, which can lead to infrastructure and sustainability issues.

Innovation Solution

A data-driven simulation framework that uses a genetic algorithm to optimize the composition of UAM networks, including the number of vehicles, chargers, and solar microgrid sizing, to minimize total acquisition costs while adhering to operational constraints such as maximum average passenger delay and grid usage, incorporating solar energy and battery storage systems to reduce dependence on the local electric grid.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If rapid charging infrastructure is deployed to support electric VTOL aircraft, then charging speed and operational efficiency are improved, but grid overload and infrastructure strain occur

Engineering Contradiction:

Improvecharging speedVSAvoidgrid overload

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary actions by deploying energy storage systems and solar microgrids at vertiports before aircraft arrive. These infrastructure elements are pre-configured to buffer and manage charging loads, preventing grid overload before it occurs during rapid charging operations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Energy storage systems and solar microgrids act as intermediary components between the grid and rapid charging infrastructure. These intermediaries buffer the direct connection, absorbing peak charging demands and preventing them from overloading the main grid while still enabling fast charging capability

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If more vertiports are deployed to expand UAM network capacity, then network coverage and passenger demand fulfillment are improved, but total acquisition costs and infrastructure complexity increase

Engineering Contradiction:

Improvenetwork coverageVSAvoidinfrastructure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The UAM network is segmented into independent vertiport units, each with its own energy storage system and solar microgrid. This modular segmentation allows individual vertiports to be deployed and optimized independently, reducing overall system complexity while expanding network coverage through incremental deployment

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Each vertiport is designed as a universal module that can be deployed in various locations and configurations. The standardized design with integrated energy management systems allows the same infrastructure template to serve multiple functions across different vertiports, reducing complexity through reuse rather than customization

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of energy

If solar microgrids and battery storage systems are integrated at vertiports, then carbon footprint reduction and grid independence are improved, but acquisition costs and system complexity increase

Engineering Contradiction:

Improvecarbon footprintVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

Solar microgrids and battery storage systems are merged into integrated energy management units at each vertiport. This combination creates a unified system that simultaneously provides renewable energy generation, energy storage, and load management functions, reducing overall system complexity compared to separate deployments while maximizing carbon footprint reduction benefits

Inventive Principle:

Principle #5Merging (Combining)

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 UAM network infrastructure to ensure sustainable and efficient operations, reducing carbon footprint and preventing grid overload, while providing informed decision-making for stakeholders in planning and deploying UAM networks.

Implementation Method 1

The vertiport energy system includes a solar microgrid, an on-site battery storage system, and a charger

Methodology Applied

Scientific EffectPhotovoltaic effect: Photovoltaic Effect

Implementation Method 2

The vertiport energy system includes a solar microgrid, an on-site battery storage system, and a charger

Methodology Applied

Scientific EffectElectrical energy storage: Battery (electricity)

Data Source

PatentUS20240346202A1Methods and systems for power grid optimization for charging systems of electric aircraft

Publication Date: 2024.10.17 PURDUE RES FOUND

AI summary

A method of optimizing an Urban air mobility (UAM) network is disclosed which includes receiving a predetermined vertiport network, wherein each vertiport in the predetermined network is represented by a plurality of parameters, receiving a customer demand schedule representing customer demand for each said vertiport, using a model simulating the UAM network thus outputting a solution for the plurality of parameters, inputting the output of the simulation to a genetic algorithm (GA), optimizing the GA to thereby generate an optimized solution based on minimizing a mathematical function associated with the plurality of parameters until a predetermined termination criteria associated with network size is reached, and outputting the optimized solution.