Software-Defined Modular Energy System with Liquid Piston Heat Engine
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Solution Overview
Problem
Traditional energy systems face challenges in scalability, adaptability, and efficiency due to labor-intensive industrial designs and programmed control systems that struggle to manage dynamic loads and mixed generation/storage components, leading to energy shortages and increased environmental impact.
Innovation Solution
A software-defined modular energy system using a liquid piston heat engine (LPHE) with a processor-based energy management system that optimizes energy system plant design, simulates reliability, and generates operational controls to manage energy storage and generation across various components, enabling flexible and efficient energy distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional programmed control systems are used to manage energy systems, then the control logic is fixed and easy to implement, but the system cannot adapt to dynamic loads and mixed generation/storage components
Solution Approach 1:
The patent replaces traditional programmed control systems with a reinforcement learning-based intelligent control system. The RL agent learns optimal control strategies through interaction with the environment, enabling adaptive management of dynamic loads and mixed generation/storage components without requiring complex predefined control logic.
Solution Approach 2:
The control approach transitions from fixed programmed parameters to dynamically adjustable parameters learned through reinforcement learning. The system continuously adapts control parameters based on real-time system state and environmental conditions, enabling flexibility while maintaining manageable complexity through the RL framework.
2Object-affected harmful factors
If renewable energy sources are used to reduce environmental impact, then carbon emissions are reduced, but energy generation is intermittent and unreliable
Solution Approach 1:
The patent combines multiple energy generation sources (renewable and non-renewable) and storage components into a unified hybrid energy system. This merging allows the system to leverage the environmental benefits of renewables while compensating for their intermittency through complementary sources and storage, thereby improving overall reliability without sacrificing emission reduction goals.
Solution Approach 2:
The reinforcement learning control system continuously monitors system state, generation output, and load demands, using this feedback to dynamically adjust control strategies. This enables the system to respond to intermittent renewable generation by coordinating storage charging/discharging and supplemental generation activation, maintaining reliability while preserving environmental benefits.
3Productivity
If industrial scale energy systems are designed to meet high energy demand, then energy production capacity increases, but the design and operation become highly labor intensive
Solution Approach 1:
The reinforcement learning-based energy management system operates autonomously, making control decisions and optimizing system performance without requiring continuous human intervention. The RL agent learns and executes control strategies independently, significantly reducing labor intensity while maintaining or enhancing energy production capacity at industrial scale.
4Adaptability or versatility
If software-defined modular design is implemented to improve scalability, then system flexibility increases, but system complexity in integration increases
Solution Approach 1:
The patent implements a universal software-defined control architecture that manages diverse energy components (generation, storage, loads) through a common reinforcement learning framework. This universal approach enables scalable integration of modular components while reducing integration complexity by applying the same control methodology across different component types and scales.
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
The solution enhances energy system resilience, scalability, and adaptability by minimizing energy losses and optimizing energy storage and generation, addressing intermittent renewable energy sources and increasing demand while reducing environmental impact.
Implementation Method 1
A software-defined modular machine can include machines for energy storage, generation, and management. The machines can be based on a liquid piston heat engine
Data Source
AI summary
Disclosed techniques include software-defined modular energy system design and operation. A set of energy system plant requirements for a mechanical system is obtained. The mechanical system comprises a plurality of components. The plurality of components includes a liquid piston heat engine. One or more processors are used to optimize a plant description. The plant description is based on the set of energy system plant requirements and a library of components. Processors are used to design a specification for an energy system plant. The specification includes components from the library of components and couplings among the components. An energy system plant design is output based on the specification. The design enables energy system plant construction. The energy system plant design is simulated to enable a reliability analysis and to provide feedback about the plant description. The simulating generates operational controls to enable energy system plant functionality.