# A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations

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## Abstract

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## 1. Introduction

- an unsupervised, volt–VAR (VV) function, defined by the IEEE 1547-2018 standard;
- the ProDROMOS particle swarm optimization optimal power flow (PSO OPF) method.

## 2. Voltage Regulation Methods

#### 2.1. Autonomous Volt–VAR Control

#### 2.2. Extremum Seeking Control

_{non}is the nominal voltage. This is a proposed new grid support function that can achieve the global optimum. We assume that each bus in the circuit feeder, can provide measurements in these experiments and more details will be provided in the RT simulation platform section.

#### Extremum Seeking Control Parameter Selection

- The ProDROMOS manager constructs the objective function, J.
- The ProDROMOS manager configures the DER with unique ωs to avoid controller conflict while producing 10 or more data points per cycle.
- The parameters l and h are set significantly less than ω, such that the perturbation is passed through the washout filter s/(s + h) but is removed by the lowpass filter l/(s + l).
- Parameter a is selected to produce the smallest reactive power oscillation that is observable in the objective function, J.
- Parameter k is set based on designer experience, the stability of ESC simulation, and desired time to reach local minimum of J.
- The ProDROMOS monitors the objective function and makes PF changes to each DER to improve the performance of the system.

#### 2.3. ProDROMOS

**V**| >

**V**

_{lim}**V**is a vector of bus voltages,

**V**

_{base}is a vector of the nominal voltages for each bus, and

**PF**is a vector of the DER PFs. The objective function is minimized when the bus voltages are at

**V**

_{base}and PF = 1.

**V**was selected to be the ANSI C84.1 Range A limits of ±0.05 per unit (pu), so any solutions outside the limits would be highly penalized. The third term was a simplified method to discourage solutions that moved away from unity power factor, because these solutions would curtail active power (and expense the PV owner through net metering, power purchase agreements, etc.) at high irradiance times. More sophisticated methods for determining the curtailment magnitude were considered, but the simple approach shown here was implemented. For the experiments conducted in this project, w

_{lim}_{0}= 1.0, w

_{1}= 2.0, and w

_{2}= 0.05. The optimization was configured so that if all the bus voltages were within an acceptance threshold (set to 0.2% of nominal voltage) the PSO would not run. If any of the voltages were outside ANSI Range A the PSO would run. Furthermore, if the new PF values did not change the objective function by an objective threshold (set to 1 × 10

^{−7}), the new PFs would not be sent to the DER devices to minimize communications and DER memory writes.

## 3. Evaluation Environments

#### 3.1. Distribution Systems of Study

#### 3.2. Real-Time Evaluation Platform

## 4. Results for PNM Model

#### 4.1. Simulations with the PNM Model

#### 4.2. Baseline Simulation

#### 4.3. Volt–VAR Simulation

#### 4.4. Extremum Seeking Control Simulation

#### 4.5. State-Estimation-Based Particle Swarm Optimization

## 5. Results for National Grid Model

#### 5.1. Simulations with the National Grid Model

#### 5.2. Baseline Simulation with National Grid Model

#### 5.3. Volt–VAR

#### 5.4. Extremum Seeking Control

#### 5.5. State-Estimation-Based Particle Swarm Optimization

## 6. PHIL Results

#### 6.1. PNM Baseline

#### 6.2. PNM Particle Swarm Optimixation PHIL

## 7. Discussion

_{bl}is the baseline voltage, v

_{nom}is the nominal voltage (1 pu), v

_{reg}is the voltage from the voltage regulation method, T is the time period of the simulation, b is the bus, and t is the simulation time. The scores representing the average voltage improvement for all buses averaged over a four-hour simulation period in units of pu. Table 3 summarizes the effectiveness for the PNM model of each approach per phase as well as the average of each phase, calculated with:

## 8. Conclusions

## 9. Patents

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Acronyms and Definitions

Abbreviation | Definition |

AC | Alternating Current |

ANSI | American National Standards Institute |

Dbus | Data Bus |

DER | Distributed Energy Resource(s) |

DERMS | Distributed Energy Resource Management System |

DETL | Distributed Energy Technology Laboratory |

DNP3 | Distributed Network Protocol 3 |

DSO | Distribution System Operator |

EPRI | Electric Power Research Institute |

ESC | Extremum Seeking Control |

GHI | Global Horizontal Irradiance |

IED | Intelligent Electronic Device |

IEEE | Institute of Electrical and Electronics Engineers |

ITM | Ideal Transformer Method |

LTC | Load Tap Changer |

MA | Maine |

NG | National Grid |

NM | New Mexico |

NREL | National Renewable Energy Laboratories |

O&M | Operations and Maintenance |

OLTC | On-Load Tap Changer |

OPF | Optimal Power Flow |

PCC | Point of Common Coupling |

PF | Power Factor |

PHIL | Power Hardware-in-the-Loop |

PNM | Public Service Company of New Mexico |

PRoDROMOS | Programmable Distribution Resource Open Management |

PSO | Particle Swarm Optimization |

PSO PF | Particle Swarm Optimization Optimal Power Factor |

pu | Per unit |

PV | Photovoltaic |

RT | Real-Time |

SANDIA | Sandia National Laboratories |

SIL | Software in-the Loop |

VV | Volt–VAR |

WinIGS | Integrated Grounding System Analysis program for Windows |

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**Figure 1.**Block diagram of extremum seeking control (ESC). Refer to [36] for description.

**Figure 2.**The particle swarm optimization optimal power flow (PSO OPF) optimization method and information flows.

**Figure 12.**PSO PNM target reactive power levels for each distributed energy resources (DER). The target reactive power is calculated from the PSO power factor (PF) set point and the PV forecast at that time.

**Figure 14.**Minimum, maximum, and average bus voltages vs time for the VV test compared to the baseline data controlling all PV inverters.

**Figure 16.**Minimum, maximum, and average bus voltages vs time for the ESC test, compared to baseline results controlling all PV inverters.

**Figure 17.**Minimum, maximum, and average bus voltages vs time for the PSO test compared to the baseline results controlling all PV inverters.

**Figure 18.**Comparison between simulated and power hardware-in-the-loop (PHIL) baseline minimum, maximum, and average bus voltages for the PNM model.

**Figure 19.**Comparison between simulated and PHIL baseline minimum, maximum, and average bus voltages for the PNM model.

**Figure 21.**Comparison of minimum, maximum and average voltage regulation approaches for the NG feeder controlling all PV inverters.

J Function | l | h | r_{comm} | Inverter 1 (258 kW) | Inverter 2 (10 MW) | Inverter 3 (1 MW) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

P | f | a | k | P | f | a | k | P | f | a | k | ||||

$\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{\left(\frac{{V}_{i}-{V}_{n}}{{V}_{n}}\right)}^{2}$ | $\frac{\sqrt{5}}{800}$ | $\frac{\sqrt{5}}{800}$ | 2 s | 258 kW | $\frac{\sqrt{2}}{40}$ | 51.6 kVar | −2.58 × 10^{7} | 10 MW | $\frac{\sqrt{3}}{40}$ | 50 kVar | −1 × 10^{9} | 1 MW | $\frac{\sqrt{5}}{40}$ | 50 kVar | −1 × 10^{8} |

J function | l | h | r_{comm} | Three-Phase Inverter | Inverters on Phase A | Inverters on Phase B | Inverters on Phase C | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

f | a | k | f | a | k | f | a | k | f | a | k | ||||

$\frac{2}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{\left(\frac{\overline{{V}_{i}}-{V}_{n}}{{V}_{n}}\right)}^{2}$ | $\frac{\sqrt{5}}{800}$ | $\frac{\sqrt{5}}{800}$ | 2 s | $\frac{1}{40}$ | $\frac{S}{15}$ | $\frac{-S}{100}$ | $\frac{\sqrt{2}}{40}$ | $\frac{S}{10}$ | $\frac{-S}{100}$ | $\frac{\sqrt{3}}{40}$ | $\frac{S}{10}$ | $\frac{-S}{100}$ | $\frac{\sqrt{5}}{40}$ | $\frac{S}{10}$ | $\frac{-S}{100}$ |

PNM Feeder Score | |||||
---|---|---|---|---|---|

Phase A (×1000) | Phase B (×1000) | Phase C (×1000) | Average (×1000) | Average Impact (%) | |

VV | 0.467 | 0.468 | 0.466 | 1.401 | 12.9% |

ESC | 2.745 | 2.748 | 2.591 | 8.084 | 74.5% |

PSO | 2.727 | 2.731 | 2.541 | 7.999 | 73.7% |

Best Score | 3.650 | 3.681 | 3.519 | 10.850 |

NG Feeder Score Controlling All PV | |||||
---|---|---|---|---|---|

Phase A (×1000) | Phase B (×1000) | Phase C (×1000) | Average (×1000) | Average Impact (%) | |

VV | −0.058 | 1.855 | 1.281 | 3.078 | 15.2% |

ESC | −0.345 | 4.971 | 3.068 | 7.694 | 38.0% |

PSO | −0.345 | 1.878 | 2.079 | 3.612 | 17.8% |

Best Score | 2.937 | 9.624 | 7.678 | 20.238 |

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## Share and Cite

**MDPI and ACS Style**

Summers, A.; Johnson, J.; Darbali-Zamora, R.; Hansen, C.; Anandan, J.; Showalter, C. A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations. *Energies* **2020**, *13*, 3562.
https://doi.org/10.3390/en13143562

**AMA Style**

Summers A, Johnson J, Darbali-Zamora R, Hansen C, Anandan J, Showalter C. A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations. *Energies*. 2020; 13(14):3562.
https://doi.org/10.3390/en13143562

**Chicago/Turabian Style**

Summers, Adam, Jay Johnson, Rachid Darbali-Zamora, Clifford Hansen, Jithendar Anandan, and Chad Showalter. 2020. "A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations" *Energies* 13, no. 14: 3562.
https://doi.org/10.3390/en13143562