Microgrid Energy Management System with Interval-Based Economic Dispatch
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Solution Overview
Problem
Conventional microgrid control systems face challenges in maintaining reliable operation due to imprecise forecasting and high variability in renewable generation and load demand, leading to suboptimal scheduling and increased computational complexity, particularly in real-time power balance management.
Innovation Solution
A microgrid energy management system (EMS) that periodically updates distributed energy resource schedules and determines power set points based on renewable energy generation and load forecasts, using mathematical optimization techniques to maximize renewable energy utilization and reduce fossil fuel dependency, while accounting for various operational constraints and modes of operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If day-ahead DER scheduling with online ED is implemented, then optimal operation plan is generated, but reliability deteriorates due to imprecise forecasting and high variability in renewable generation and load demand
Solution Approach 1:
The system performs day-ahead DER scheduling in advance to generate an optimal operation plan, then executes real-time adjustments through interval-based economic dispatch. This preliminary planning combined with real-time adaptation resolves the contradiction by preparing optimal schedules beforehand while maintaining reliability through continuous real-time monitoring and adjustment.
Solution Approach 2:
The system transitions from static day-ahead scheduling to dynamic real-time economic dispatch across multiple intervals. By continuously updating power setpoints based on actual renewable generation and load conditions, the system maintains reliability while achieving optimal operation, resolving the contradiction between predetermined planning and adaptive reliability.
2Reliability
If online ED over multiple intervals is implemented, then real-time control decisions are provided, but computational complexity increases significantly
Solution Approach 1:
The system segments the real-time control period into multiple intervals, performing economic dispatch calculations for each interval separately rather than optimizing the entire period simultaneously. This segmentation reduces computational complexity at each step while maintaining real-time control reliability through cumulative optimization across intervals.
Solution Approach 2:
The system performs partial optimization by focusing economic dispatch calculations on current and near-future intervals rather than the entire day-ahead period. This partial action approach reduces computational burden at each execution point while still providing reliable real-time control decisions for the most critical time periods.
3Device complexity
If simplified optimization considering only power balance is deployed, then computational burden is reduced, but manufacturing precision deteriorates due to lack of detailed operating constraints from power flow analysis
Solution Approach 1:
The system segments the optimization problem into two levels: day-ahead DER scheduling that incorporates detailed power flow constraints and operating limits, and interval-based economic dispatch that focuses on real-time power balance. This segmentation allows detailed precision constraints to be applied in advance while reducing real-time computational burden.
Solution Approach 2:
The system performs detailed power flow analysis and operating constraint verification in the day-ahead scheduling phase before real-time operation begins. By preliminarily establishing optimal schedules that satisfy all detailed constraints, the system reduces real-time computational requirements while maintaining manufacturing precision through pre-validated operating plans.
Data Source
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AI summary
A microgrid includes a plurality of distributed energy resources such as controllable distributed electric generators (104, 106, 108, 110, 112) and electrical energy storage devices (114). A method of controlling operation of the microgrid includes periodically updating a distributed energy resource schedule for the microgrid that includes on/off status of the controllable distributed electric generators (104, 106, 108, 110, 112) and charging/discharging status and rate of the electrical energy storage devices (114) and which satisfies a first control objective for a defined time window, based at least in part on a renewable energy generation and load forecast for the microgrid (200). The method further includes periodically determining power set points for the controllable distributed energy resources (104, 106, 108, 110, 112) which satisfy a second control objective for a present time interval within the defined time window, the second control objective being a function of at least the distributed energy resource schedule for the microgrid (210).