Microgrid Energy Optimization Using Individualized Demand Forecasting
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Solution Overview
Problem
Traditional demand reduction methods in microgrid systems fail to optimize energy use and distribution efficiently, as they do not consider individual appliance loads, localized costs, and emerging resources like wind, solar, and storage, leading to suboptimal peak demand management and increased energy costs.
Innovation Solution
A near-real-time energy optimization system that uses a server and databases to collect and analyze individualized energy usage data, customer preferences, and location characteristics, optimizing energy distribution and use by forecasting demand and prices at a micro level, incorporating distributed generation and storage, and adjusting appliance control to minimize costs and peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional one-way signaling methods are used to control appliance cycling, then utility peak demand can be reduced, but energy optimization is insufficient because individualized customer needs, localized costs, and emerging resources are not considered
Solution Approach 1:
The system segments the utility system into multiple microgrids, each with its own optimization. This allows individualized control of appliances and resources at the microgrid level, considering local costs, customer needs, and emerging resources like wind and solar, thereby improving energy optimization without requiring complete system-wide complexity
Solution Approach 2:
The system applies local quality by optimizing each microgrid independently with consideration of localized factors such as customer-specific cost to serve, local appliance loads, and site-specific emerging resources. This enables tailored energy optimization for each microgrid while maintaining overall utility peak demand reduction
2Loss of energy
If uniform dispatching strategies are applied to all customers, then implementation is simple, but energy costs increase because individualized optimization opportunities are missed
Solution Approach 1:
The system transitions from static uniform dispatching strategies to dynamic optimization that adapts to real-time conditions in each microgrid. The system continuously adjusts appliance control based on emerging resources availability, localized costs, and customer-specific factors, reducing energy costs while managing complexity through automated real-time decision-making
3Loss of energy
If appliance cycling is controlled without considering other appliance loads on the circuit, then control implementation is straightforward, but peak demand management becomes suboptimal
Solution Approach 1:
The system merges the control of multiple appliances and resources within each microgrid, coordinating them collectively rather than independently. This holistic approach considers the interaction between appliance loads and emerging resources on the circuit, achieving optimal peak demand management through unified microgrid-level optimization
Data Source
AI summary
An energy distribution may include a server and one or more databases. The system may communicate with an energy provider to receive energy provider data, at least one information collector to receive information collector data such as individualized energy usage data, customer preferences, and customer or location characteristics, and the one or more databases for receiving data for optimization. The system may calculate a cost of service or avoided cost using at least one of the individualized energy usage data and a system generation cost at a nearest bus. The system may also forecast individualized demand by end-use, individualized demand by location, energy prices, or energy costs. The system may optimize energy distribution, energy use, cost of service, or avoided cost using the forecasted individualized demand by end-use, the forecasted individualized demand by location, the forecasted energy prices, and the forecasted energy costs.