Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, genera...

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Vydáno v:International transactions on electrical energy systems Ročník 2023; s. 1 - 33
Hlavní autoři: Jasim, Ali M., Jasim, Basil H., Alhasnawi, Bilal Naji, Flah, Aymen, Kraiem, Habib
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Hindawi 2023
John Wiley & Sons, Inc
Wiley
Témata:
Adaptive systems
Algorithms
Alternative energy
Artificial intelligence
Control stability
Cost analysis
Data transmission
Demand side management
Design
Direct current
Distributed generation
Electric converters
Electric power demand
Electrical loads
Electricity
Energy consumption
Energy Internet
Energy resources
Energy storage
Flow stability
Fuzzy logic
Genetic algorithms
Heuristic methods
Incremental conductance
Industrial plant emissions
Internet of Things
Load shifting
Maximum power
Neural networks
Optimization techniques
Particle swarm optimization
Peak demand
Peak load
Photovoltaics
Real time
Renewable energy
Renewable resources
Search algorithms
Solar oscillations
Solar power
Systems stability
Turbines
Voltage converters (DC to DC)
Wind power
Wind turbines
ISSN:2050-7038, 2050-7038
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Shrnutí:High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.
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ISSN:2050-7038
2050-7038
DOI:10.1155/2023/6615150