# Management Optimization of Electricity System with Sustainability Enhancement

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Modeling of Uncertainties

#### 2.1. Modeling of Probable Load

#### 2.2. Probabilistic Wind Modeling

#### 2.3. Probabilistic Line Rating Modeling

## 3. Probabilistic Power Flow

## 4. Formulation of the Issue

#### 4.1. Objective Functions

#### 4.1.1. Overall Cost of Power Production

#### 4.1.2. Probable Reliability Objective

## 5. Solution Method

#### 5.1. Enhanced Particle Swarm Optimization

#### 5.2. Islanding Prevention

#### 5.3. Suggested Method

## 6. Test System and Scenarios

#### 6.1. First Case Study: OTS with SLR

#### 6.2. Case Study: OTS with DTR

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviation

ACOPF | AC optimal power flow |

CM | Congestion management |

EENS | Expected energy not supplied |

FACTs | Flexible AC-TS |

LD | Load demand |

MPSO | Modified particle swarm optimization |

MOMPSO | Multi-objective MPSO |

MCS | Monte Carlo simulation |

NTO | Network topology optimization |

OTS | Optimal transmission switching |

PEM | points estimation method |

PPF | Probabilistic power flow |

PDFs | probability distribution functions |

RO | Robust optimization |

SOTS | Stochastic optimal transmission switching |

SLRs | Static line ratings |

SLR | Static line ratings |

TL | Transmission lines |

TN | Transmission network |

WT | Wind turbine |

## Appendix A

Scale parameter c = 8.549 m/s; |

Shape parameter k = 1.98; |

V_i = 5 m/s; |

V_r = 15 m/s; |

V_o = 25 m/s; |

and P_r = 1.5 MW. |

## References

- Alnowibet, K.; Annuk, A.; Dampage, U.; Mohamed, M.A. Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework. Sustainability
**2021**, 13, 11836. [Google Scholar] [CrossRef] - Mohamed, M.A.; Mirjalili, S.; Dampage, U.; Salmen, S.H.; Al Obaid, S.; Annuk, A. A Cost-Efficient-Based Cooperative Allocation of Mining Devices and Renewable Resources Enhancing Blockchain Architecture. Sustainability
**2021**, 13, 10382. [Google Scholar] [CrossRef] - Abhinav, R.; Pindoriya, N.M. Grid integration of wind turbine and battery energy storage system: Review and key challenges. In Proceedings of the 2016 IEEE 6th International Conference on Power Systems (ICPS), New Delhi, India, 4–6 March 2016; pp. 1–6. [Google Scholar]
- Dabbaghjamanesh, M.; Wang, B.; Kavousi-Fard, A.; Hatziargyriou, N.D.; Zhang, J. Blockchain-Based Stochastic Energy Management of Interconnected Microgrids Considering Incentive Price. IEEE Trans. Control Netw. Syst.
**2021**, 8, 1201–1211. [Google Scholar] [CrossRef] - Aghajan-Eshkevari, S.; Sasan, A.; Morteza, N.; Mohammad, T.A.; Somayeh, A. “Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods”, Control Structures, Objectives, and Optimization Methodologies. Sustainability
**2022**, 14, 2137. [Google Scholar] [CrossRef] - Shojaei, F.; Rastegar, M.; Dabbaghjamanesh, M. Simultaneous placement of tie-lines and distributed generations to optimize distribution system post-outage operations and minimize energy losses. CSEE J. Power Energy Syst.
**2020**, 7, 318–328. [Google Scholar] - Dabbaghjamanesh, M.; Kavousi-Fard, A.; Dong, Z.Y. A Novel Distributed Cloud-Fog Based Framework for Energy Management of Networked Microgrids. IEEE Trans. Power Syst.
**2020**, 35, 2847–2862. [Google Scholar] [CrossRef] - Putranto, L.M.; Irnawan, R.; Priyanto, A.; Isnandar, S.; Savitri, I. Transmission Expansion Planning for the Optimization of Renewable Energy Integration in the Sulawesi Electricity System. Sustainability
**2021**, 13, 10477. [Google Scholar] [CrossRef] - Reusser, C.A.; Pérez, J.R. Evaluation of the emission impact of cold-ironing power systems, using a bi-directional power flow control strategy. Sustainability
**2020**, 13, 334. [Google Scholar] [CrossRef] - Abdulwahid, A.H.; Wang, S. A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid. Sustainability
**2018**, 10, 1059. [Google Scholar] [CrossRef] [Green Version] - Salimi, A.A.; Karimi, A.; Noorizadeh, Y. Simultaneous operation of wind and pumped storage hydropower plants in a linearized security-constrained unit commitment model for high wind energy penetration. J. Energy Storage
**2019**, 22, 318–330. [Google Scholar] [CrossRef] - Dehbozorgi, M.R.; Rastegar, M.; Dabbaghjamanesh, M. Decision tree-based classifiers for root-cause detection of equipment-related distribution power system outages. IET Gener. Transm. Distrib.
**2020**, 14, 5809–5815. [Google Scholar] [CrossRef] - Shepero, M.; van der Meer, D.; Munkhammar, J.; Widén, J. Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data. Appl. Energy
**2018**, 218, 159–172. [Google Scholar] [CrossRef] - Teh, J.; Lai, C.-M. Reliability impacts of the dynamic thermal rating and battery energy storage systems on wind-integrated power networks. Sustain. Energy Grids Netw.
**2019**, 20, 100268. [Google Scholar] [CrossRef] - Douglass, D.A.; Gentle, J.; Nguyen, H.; Chisholm, W.; Xu, C.; Goodwin, T.; Chen, H.; Nuthalapati, S.; Hurst, N.; Grant, I.; et al. A review of dynamic thermal line rating methods with forecasting. IEEE Trans. Power Deliv.
**2019**, 34, 2100–2109. [Google Scholar] [CrossRef] - Zeng, L.; Xia, T.; Elsayed, S.; Ahmed, M.; Rezaei, M.; Jermsittiparsert, K.; Dampage, U.; Mohamed, M. A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs. Sustainability
**2021**, 13, 5777. [Google Scholar] [CrossRef] - Lai, C.M.; Teh, J.; Cheng, Y.H. Fuzzy evaluation of transmission line end-of-life reliability model. In Proceedings of the 2019 International Automatic Control Conference (CACS), Keelung, Taiwan, 13–16 November 2019; pp. 1–4. [Google Scholar]
- Li, Y.; Hu, B.; Xie, K.; Wang, L.; Xiang, Y.; Xiao, R.; Kong, D. Day-Ahead Scheduling of Power System Incorporating Network Topology Optimization and Dynamic Thermal Rating. IEEE Access
**2019**, 7, 35287–35301. [Google Scholar] [CrossRef] - Mohamed, M.A.; Awwad, E.M.; El-Sherbeeny, A.M.; Nasr, E.A.; Ali, Z.M. Optimal scheduling of reconfigurable grids considering dynamic line rating constraint. IET Gener. Transm. Distrib.
**2020**, 14, 1862–1871. [Google Scholar] [CrossRef] - Kazemi, B.; Kavousi-Fard, A.; Dabbaghjamanesh, M.; Karimi, M. IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response. 2022. IEEE Transactions on Intelligent Transportation Systems. Available online: https://osuva.uwasa.fi/handle/10024/13474 (accessed on 24 April 2022).
- Karimi, S.; Musilek, P.; Knight, A.M. Dynamic thermal rating of transmission lines: A review. Renew. Sustain. Energy Rev.
**2018**, 91, 600–612. [Google Scholar] [CrossRef] - Rizwan, M.; Waseem, M.; Liaqat, R.; Sajjad, I.A.; Dampage, U.; Salmen, S.H.; Al Obaid, S.; Mohamed, M.A.; Annuk, A. SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities. Electronics
**2021**, 10, 2542. [Google Scholar] [CrossRef] - Luo, L.; Abdulkareem, S.S.; Rezvani, A.; Miveh, M.R.; Samad, S.; Aljojo, N.; Pazhoohesh, M. Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. J. Energy Storage
**2020**, 28, 101306. [Google Scholar] [CrossRef] - Razmjouei, P.; Kavousi-Fard, A.; Dabbaghjamanesh, M.; Jin, T.; Su, W. Ultra-Lightweight Mutual Authentication in the Vehicle Based on Smart Contract Blockchain: Case of MITM Attack. IEEE Sens. J.
**2020**, 21, 15839–15848. [Google Scholar] [CrossRef] - Zhang, L.; Shaby, B. Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution. arXiv
**2020**, arXiv:2008.06400. [Google Scholar] [CrossRef] - Tajalli, S.Z.; Mardaneh, M.; Taherian-Fard, E.; Izadian, A.; Kavousi-Fard, A.; Dabbaghjamanesh, M.; Niknam, T. DoS-resilient distributed optimal scheduling in a fog supporting IIoT-based smart microgrid. IEEE Trans. Ind. Appl.
**2020**, 56, 2968–2977. [Google Scholar] [CrossRef] - Ashkaboosi, M.; Ashkaboosi, F.; Nourani, S.M. The Interaction of Cybernetics and Contemporary Economic Graphic Art as. “Interactive Graphics”; University Library of Munich: Munich, Germany, 2016. [Google Scholar]
- Dabbaghjamanesh, M.; Zhang, J. Deep learning-based real-time switching of reconfigurable microgrids. In Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 17–20 February 2020; pp. 1–5. [Google Scholar]
- Ghaffari, S.; Ashkaboosi, M. Applying Hidden Markov Model Baby Cry Signal Recognition Based on Cybernetic Theory. IJEIR
**2016**, 5, 243–247. [Google Scholar] - Dabbaghjamanesh, M.; Wang, B.; Kavousi-Fard, A.; Mehraeen, S.; Hatziargyriou, N.D.; Trakas, D.N.; Ferdowsi, F. A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids. IEEE Trans. Power Deliv.
**2019**, 35, 560–570. [Google Scholar] [CrossRef]

**Figure 4.**The EENS Pareto optimal front and overall production cost objective applying MOMPSO at $\left(\varphi =4\right)$ for DTR.

Variable | P | ${\mathit{c}}_{1}$ | ${\mathit{c}}_{2}$ | ${\mathit{c}}_{3}$ | $\mathit{\psi}$ | $\mathit{\sigma}$ | $\mathit{\zeta}$ | ${\mathit{k}}_{\mathit{m}\mathit{a}\mathit{x}}$ |
---|---|---|---|---|---|---|---|---|

Value | 5 | 0.5 | 0.5 | 0.5 | 1 | 5 × 10^{−3} | 1 | 30 |

Generator Unit | ${\mathit{U}}_{12}$ | ${\mathit{U}}_{20}$ | ${\mathit{U}}_{50}$ | ${\mathit{U}}_{76}$ | ${\mathit{U}}_{100}$ | ${\mathit{U}}_{155}$ | ${\mathit{U}}_{197}$ | ${\mathit{U}}_{350}$ | ${\mathit{U}}_{400}$ |
---|---|---|---|---|---|---|---|---|---|

Size (MW) | 12.00 | 20.00 | 50.00 | 76.00 | 100.00 | 155.00 | 197.00 | 350.00 | 400.00 |

Fuel | Steam/Oil | CT/Oil | Hydro | Steam/Coal | Steam/Oil | Steam/Coal | Steam/Oil | Steam/Coal | Nuclear |

$Fuel(\$/Mbtu)$ | 8.4 | 15.17 | 0 | 1.78 | 8.4 | 1.78 | 8.4 | 1.78 | 0.6 |

$\mathrm{Cost}\left(\$/Mwh\right)$ | 85.5 | 149.56 | 0.1 | 17 | 67.95 | 14.71 | 74.75 | 14.96 | 22 |

$\mathit{\phi}$ | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Wind penetration (0%) Lines open | - | [111–114] | [210,211]; [111–114] | [118–121]; [209–111]; [210,211] | [310,311]; [106–110]; [118–121]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 189,685 | 186,439 | 185,285 | 184,633 | 184,125 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.77\times {10}^{3}$ | $5.831\times {10}^{3}$ | $6.93\times {10}^{3}$ | $7.78\times {10}^{3}$ | $10.273\times {10}^{3}$ |

Wind penetration (10%) Lines open | - | [111–114] | [210,211]; [109–111] | [110–112]; [109–111]; [215,216] | [312–314]; [106–110]; [118–121]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 164,860 | 161,340 | 160,215 | 159,730 | 159,310 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.49\times {10}^{3}$ | $5.31\times {10}^{3}$ | $6.33\times {10}^{3}$ | $7.08\times {10}^{3}$ | $9.273\times {10}^{3}$ |

$\phi $ | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Wind penetration (15%) Lines open | - | [109–111] | [210,211]; [114–111] | [118–121]; [109–108]; [210–205] | [310–305]; [106–110]; [118–117]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 189,685 | 186,439 | 185,285 | 184,633 | 184,125 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.77\times {10}^{3}$ | $5.831\times {10}^{3}$ | $6.93\times {10}^{3}$ | $7.78\times {10}^{3}$ | $10.273\times {10}^{3}$ |

Wind penetration (20%) Lines open | - | [119–114] | [210,211]; [109–103] | [118–121]; [109–108]; [210,211] | [311–314]; [106–110]; [218–222]; [109–104] |

$\mu \left[Gen.cost\right]$ ($/h) | 164,820 | 143,620 | 142,510 | 140,715 | 140,030 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.36\times {10}^{3}$ | $5.01\times {10}^{3}$ | $5.95\times {10}^{3}$ | $6.69\times {10}^{3}$ | $8.83\times {10}^{3}$ |

Generator Unit | ${\mathit{U}}_{12}$ | ${\mathit{U}}_{20}$ | ${\mathit{U}}_{50}$ | ${\mathit{U}}_{76}$ | ${\mathit{U}}_{100}$ | ${\mathit{U}}_{155}$ | ${\mathit{U}}_{197}$ | ${\mathit{U}}_{350}$ | ${\mathit{U}}_{400}$ |
---|---|---|---|---|---|---|---|---|---|

Size (MW) | 12.00 | 20.00 | 50.00 | 76.00 | 100.00 | 155.00 | 197.00 | 350.00 | 400.00 |

Fuel kind | Oil/Steam | Oil/CT | Hydro | Coal/Steam | Oil/Steam | Coal/Steam | Oil/Steam | Coal/Steam | Nuclear |

$Fuel(\$/MBtu)$ | 8.4 | 15.17 | 0 | 1.78 | 8.4 | 1.78 | 8.4 | 1.78 | 0.6 |

$\mathrm{Cost}\left(\$/Mwh\right)$ | 85.5 | 149.56 | 0.1 | 17 | 67.95 | 14.71 | 74.75 | 14.96 | 22 |

Change in output (Mw) | 4.5 | 0 | 0 | 29.74 | −17.37 | 0 | −98 | 81.13 | 0 |

$\phi $ | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Wind penetration (0%) Lines open | - | [109–111] | [210,211]; [109–111] | [118–121]; [109–111]; [210,211] | [310,311]; [106–110]; [118–121]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 189,625 | 186,412 | 185,235 | 184,587 | 184,098 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.56\times {10}^{3}$ | $5.42\times {10}^{3}$ | $6.44\times {10}^{3}$ | $7.23\times {10}^{3}$ | $9.53\times {10}^{3}$ |

Wind penetration (10%) Lines open | - | [119–111] | [210,211]; [109–111] | [118–121]; [109–111]; [210,211] | [310,311]; [106–110]; [118–121]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 164,710 | 161,240 | 160,215 | 159,510 | 159,205 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.31\times {10}^{3}$ | $4.91\times {10}^{3}$ | $5.85\times {10}^{3}$ | $6.54\times {10}^{3}$ | $8.57\times {10}^{3}$ |

$\mathit{\phi}$ | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Wind penetration (15%) Lines open | - | [109–111] | [210,211]; [114–111] | [118–121]; [109–108]; [210–205] | [310–305]; [106–110]; [118–117]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 153,101 | 150,05 | 149,520 | 148,490 | 147,850 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.41\times {10}^{3}$ | $5.01\times {10}^{3}$ | $6.12\times {10}^{3}$ | $6.77\times {10}^{3}$ | $8.93\times {10}^{3}$ |

Wind penetration (20%) Lines open | - | [119–114] | [210,211]; [109–103] | [118–121]; [109–108]; [210,211] | [311–314]; [106–110]; [218–222]; [109–104] |

$\mu \left[Gen.cost\right]$ ($/h) | 141,520 | 140,320 | 137,210 | 134,415 | 131,830 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.36\times {10}^{3}$ | $5.01\times {10}^{3}$ | $5.95\times {10}^{3}$ | $6.69\times {10}^{3}$ | $8.83\times {10}^{3}$ |

$\mathit{\phi}$ | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

SLR Lines open | - | [109–111] | [210,211]; [114–111] | [118–121]; [109–108]; [210–205] | [310–305]; [106–110]; [118–117]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 146,820 | 143,620 | 142,510 | 140,715 | 140,030 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.41\times {10}^{3}$ | $5.01\times {10}^{3}$ | $6.12\times {10}^{3}$ | $6.77\times {10}^{3}$ | $8.93\times {10}^{3}$ |

DTR Lines open | - | [109–111] | [210,211]; [109–111] | [118–121]; [109–111]; [210,211] | [310,311]; [106–110]; [118–121]; [109–111] |

$\mu \left[Gen.cost\right]$ ($/h) | 141,520 | 140,320 | 137,210 | 134,415 | 131,830 |

$\mu \left[EENS\right]$ (Mwh/y) | $2.10\times {10}^{3}$ | $4.41\times {10}^{3}$ | $5.23\times {10}^{3}$ | $5.64\times {10}^{3}$ | $7.53\times {10}^{3}$ |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hou, W.; Man Li, R.Y.; Sittihai, T.
Management Optimization of Electricity System with Sustainability Enhancement. *Sustainability* **2022**, *14*, 6650.
https://doi.org/10.3390/su14116650

**AMA Style**

Hou W, Man Li RY, Sittihai T.
Management Optimization of Electricity System with Sustainability Enhancement. *Sustainability*. 2022; 14(11):6650.
https://doi.org/10.3390/su14116650

**Chicago/Turabian Style**

Hou, Wei, Rita Yi Man Li, and Thanawan Sittihai.
2022. "Management Optimization of Electricity System with Sustainability Enhancement" *Sustainability* 14, no. 11: 6650.
https://doi.org/10.3390/su14116650