Publication record · 18.cifr/2020.wang.sdm-cyber-physical-microgrid

Software-Defined Microgrid Control: The Genesis of Decoupled Cyber-Physical Microgrids

v1.0.0

Lizhi Wang (University of Connecticut), Yanyuan Qin (University of Connecticut), Zefan Tang (University of Connecticut), Peng Zhang (Stony Brook University)

RAI18.cifr/2020.wang.sdm-cyber-physical-microgrid

IEEE Open Access Journal of Power and Energy· 2020· doi:10.1109/OAJPE.2020.2997665

This paper presents a software-defined microgrid (SDM) control framework that decouples the cyber and physical layers of microgrids. By abstracting physical resources into software-defined virtual resources, the SDM enables flexible, programmable, and resilient microgrid control. The proposed architecture supports decoupled cyber-physical operation and demonstrates improved power sharing, frequency restoration, and voltage regulation compared to conventional droop control.

software-defined networkingmicrogrid controlcyber-physical systemsdroop controlsecondary controldistributed energy resources

✦ Research context

What this agent contributes to the literature.

Problem solved

Conventional droop control couples cyber and physical layers, limiting flexibility and resilience.
Power system operators lack programmable, reconfigurable control over distributed energy resources.
Frequency and voltage restoration in islanded microgrids is degraded by tight cyber-physical interdependence.
Microgrid designers need a framework that improves power sharing without hardware-layer changes.

Novelty

Introduces Software-Defined Microgrid (SDM) framework that decouples cyber and physical microgrid layers.

Related research

Computing related research...

Canvas contract1-in / 1-out · unpacked into microgrid, control_params legacy ports

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sha256:7480645d293b87b214da65bc58236ea67674b3e643f266c1b786e4ab2b945545

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python main.py

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input:application/json

Outputs

output:application/json

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Pre-filled with the paper's canonical scenario. Click Invoke agent to reproduce the original result, or edit the JSON below to run a counterfactual.

inputapplication/jsonoptional

Unified canvas input. The executor unpacks this JSON blob into legacy per-port files: microgrid, control_params

Leave empty to run the paper's canonical scenario.

{
  "microgrid": {
    "f_nom": 50,
    "V_nom": 1,
    "ders": [
      {
        "id": "DER1",
        "S_rated": 10000,
        "droop_m": 0.0002,
        "droop_n": 0.001,
        "Rv": 0.05,
        "Xv": 0.1
      },
      {
        "id": "DER2",
        "S_rated": 8000,
        "droop_m": 0.00025,
        "droop_n": 0.00125,
        "Rv": 0.05,
        "Xv": 0.1
      },
      {
        "id": "DER3",
        "S_rated": 6000,
        "droop_m": 0.000333,
        "droop_n": 0.00167,
        "Rv": 0.05,
        "Xv": 0.1
      },
      {
        "id": "DER4",
        "S_rated": 4000,
        "droop_m": 0.0005,
        "droop_n": 0.0025,
        "Rv": 0.05,
        "Xv": 0.1
      }
    ],
    "loads": [
      {
        "id": "Load1",
        "P": 15000,
        "Q": 5000
      },
      {
        "id": "Load2",
        "P": 5000,
        "Q": 2000
      }
    ],
    "adjacency": [
      [
        0,
        1,
        1,
        0
      ],
      [
        1,
        0,
        1,
        1
      ],
      [
        1,
        1,
        0,
        1
      ],
      [
        0,
        1,
        1,
        0
      ]
    ]
  },
  "control_params": {
    "c_f": 2,
    "c_V": 1.5,
    "k_f": 5,
    "k_V": 3,
    "T_sim": 2,
    "dt": 0.01
  }
}

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