Multi-Source Energy Provisioning for Low-Carbon Peak Demand
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Solution Overview
Problem
Existing systems fail to efficiently manage and optimize energy consumption across multiple sources, including electric providers, on-premises storage, and electric vehicle batteries, based on environmental factors and demand, leading to inefficiencies and increased reliance on non-renewable energy during peak times.
Innovation Solution
A system that determines future energy needs and selects the most environmentally friendly energy source based on historical data, real-time analytics, and predictive models, optimizing energy usage to minimize environmental impact and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy is received from multiple sources without optimization, then energy supply reliability is improved, but energy management complexity increases
Solution Approach 1:
The system dynamically changes operational parameters by selecting different energy sources based on environmental factors such as carbon intensity, availability, and cost. The energy management system adjusts which energy source (electricity provider, EV battery, or on-premises storage) is active at any given time, transforming a static multi-source configuration into a dynamic optimized system that maintains reliability while managing complexity through automated parameter adjustment
Solution Approach 2:
The energy management system performs self-service by automatically determining future energy needs, evaluating environmental factors, and selecting appropriate energy sources without requiring manual intervention. The system uses historical consumption data and predictive analytics to autonomously manage the complexity of coordinating multiple energy sources, thereby maintaining supply reliability while eliminating the burden of complex manual energy management
2Quantity of substance
If energy consumption is increased during peak times, then energy availability is improved, but environmental impact worsens
Solution Approach 1:
The system performs preliminary action by determining future energy needs in advance and proactively selecting low-carbon energy sources before peak consumption periods occur. By using historical data and predictive models, the system prepares energy storage solutions ahead of time and schedules energy reception from environmentally friendly sources, thereby ensuring energy availability during peak times while avoiding the harmful environmental impact of reactive high-carbon energy consumption
Solution Approach 2:
The energy management system implements continuous feedback by monitoring environmental factors such as carbon intensity and energy source availability in real-time. This feedback loop enables the system to adjust energy consumption patterns dynamically, reducing reliance on high-carbon sources during peak times by switching to pre-charged storage or low-carbon grid energy, thereby maintaining energy availability while minimizing environmental harm through data-driven decision making
Data Source
AI summary
An example operation may include one or more of determining a future time to receive energy at a location based on historical energy consumed at the location over time, determining respective environmental factors of receiving energy from a plurality of energy sources at the location at the future time, wherein the plurality of energy sources include an electricity provider, an electric vehicle (EV) battery, and an on-premises energy storage system of the location, selecting an energy source from among the plurality of energy sources at the location based on the respective environment factors at the future time, and receiving energy from the selected energy source at the future time.