Energy Usage Guidance for Renewable-Aligned Grid Load Shifting
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Solution Overview
Problem
The mismatch between peak user electrical energy consumption and optimal renewable energy generation times leads to over-reliance on nonrenewable energy sources, resulting in environmental pollution and inefficiency, with consumers unaware of the energy source composition of their electricity usage.
Innovation Solution
A computerized system generates forecasted electrical energy usage guidance using historical and forecasted data, including renewable generation, curtailment, emissions rates, and grid alerts, to encourage consumption of renewable energy and shape load on the power grid, utilizing machine learning models to prioritize energy usage based on predefined goals and rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical energy is supplied to meet peak user consumption, then user demand is satisfied, but reliance on nonrenewable energy sources increases and renewable energy generation efficiency decreases
Solution Approach 1:
The system implements feedback by providing consumers with real-time information about the carbon intensity and renewable content of their electricity consumption. This feedback mechanism enables consumers to adjust their behavior to prefer times when renewable energy is abundant, creating a loop that aligns consumption patterns with generation capacity and reduces reliance on nonrenewable sources.
Solution Approach 2:
The system performs preliminary action by forecasting future renewable energy generation and carbon intensity levels, then providing advance guidance to consumers about optimal times for energy consumption. This allows consumers to plan their energy usage before the actual consumption occurs, shifting load to times when renewable energy is available and avoiding nonrenewable generation.
2Quantity of substance
If electrical energy consumption is increased to meet peak demand, then user needs are met, but the alignment with optimal renewable energy generation times deteriorates
Solution Approach 1:
The system applies dynamics by providing time-varying guidance that changes based on forecasted renewable generation conditions. Instead of static energy consumption advice, the system dynamically adjusts recommendations to match the temporal patterns of renewable energy availability, enabling flexible load shifting that maintains total consumption while optimizing timing alignment.
3Loss of information
If consumers are provided with detailed energy usage information, then awareness of energy source composition improves, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing a centralized information service that processes complex grid data and transforms it into simplified, actionable guidance for consumers. This intermediary layer handles the complexity of integrating forecasts, carbon intensity data, and generation patterns, while presenting only essential information to consumers through user-friendly interfaces.
Data Source
AI summary
Techniques are described for providing forecasted electrical energy usage guidance to consumers of electrical energy supplied by a power grid in a given region. The forecasted electrical energy usage guidance is designed to encourage the consumption of electrical energy generated by renewable energy sources and to discourage the consumption of electrical energy generated by nonrenewable energy sources. The techniques can utilize historical and forecasted electrical energy generation data and historical and forecasted electrical energy demand data for the power grid to generate the energy usage guidance. In some examples, historical marginal operating emissions rate data, renewable electrical energy generation curtailment data, demand data, grid alert data, and location marginal pricing data may also be used.