Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to co-optimize the HVAC-B...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 19; H. 12; S. 1 - 10
Hauptverfasser: Ma, Jie, Ma, Xiandong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Air conditioning
Algorithms
Alternative energy sources
Batteries
BESS
Consensus algorithm
Cost function
Costs
directed network
Distributed generation
Energy costs
Energy management
Energy management system
Energy storage
HVAC
HVAC equipment
Microgrids
multi-layer energy management
Multilayers
Optimization
Power management
Renewable energy sources
Robustness
Solar energy
surplus-based consensus algorithm
ISSN:1551-3203, 1941-0050
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Zusammenfassung:This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to co-optimize the HVAC-BESS by taking into account solar energy forecasts. A surplus-based consensus algorithm is proposed to solve the optimization problem, where the local power mismatch is introduced as a surplus term, and the HVAC-BESS can thus be co-scheduled to maximize renewable energy efficiency at the peak generation time. A set of the convex cost functions are formulated to minimize the HVAC's user dissatisfaction degree and alleviate power loss during the BESS operation. The goal is to collectively minimize the total energy cost in a distributed manner, subject to individual load constraints and power balance constraints. It is theoretically proved that a global convergence of the proposed algorithm is achieved provided that the directed network is strongly connected. The results from a number of case studies are promising, demonstrating the effectiveness and robustness of the algorithm under practical scenarios.
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2023.3248122