Non-Intrusive Load Management Under Forecast Uncertainty in Energy Constrained Microgrids

•Models to manage load in microgrids with uncertain forecasts•Formulation of controllers as mixed-integer quadratic programs are tractable for small systems•Simulate droop-based active power-sharing and utility-theoretic customer load model•Use of predictive controllers improves quality-of-service m...

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Bibliographic Details
Published in:Electric power systems research 2021-01, Vol.190, p.106632, Article 106632
Main Authors: Lee, Jonathan T., Anderson, Sean, Vergara, Claudio, Callaway, Duncan S.
Format: Article
Language:English
Subjects:
Customers
Demand-side management
Distributed generation
Dynamic programming
Electric power demand
Electrical loads
Electricity consumption
Electricity distribution
Electricity generation
Energy storage
Load limits
Load management
Mathematical models
Microgrids
Optimal control
Power consumption
Power supply
Predictive control
Schedules
Solar energy
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Summary:•Models to manage load in microgrids with uncertain forecasts•Formulation of controllers as mixed-integer quadratic programs are tractable for small systems•Simulate droop-based active power-sharing and utility-theoretic customer load model•Use of predictive controllers improves quality-of-service metrics This paper addresses the problem of managing load under energy scarcity in islanded microgrids with multiple customers and distributed solar generation and battery storage. We explore an understudied, practical approach of scheduling customer-specific load limits that does not require direct control of appliances or a market environment. We frame this as a stochastic, model-predictive control problem with forecasts of solar resource and electricity demand, and develop alternative solutions with two-stage stochastic programming and approximate dynamic programming. We test the efficacy of the alternative solutions against heuristic and deterministic controllers in an environment simulating the customers’ responses to load limits. We show that using forecasts to schedule limits can significantly improve power availability and the customers’ benefits from consumption, even without the controller having a full model of the customers’ responses.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106632