# Simultaneous Long-Term Planning of Flexible Electric Vehicle Photovoltaic Charging Stations in Terms of Load Response and Technical and Economic Indicators

^{1}

^{2}

^{3}

^{*}

*World Electric Vehicle Journal*in 2021)

## Abstract

**:**

## 1. Introduction

## 2. The General Framework of the Optimization Problem

- (1)
- First, the sample amount of the problem variables that should be examined and analyzed with the information about the problem are determined through the available real statistics.
- (2)
- Then, these amounts are normalized and mapped to zero and one.
- (3)
- The capillary function is then applied to the normalized information, and the correlation coefficients between the variables are obtained.
- (4)
- Then, random samples are produced in large numbers using real normalized information and the obtained correlation coefficients.
- (5)
- Finally, the samples are mapped to their true range from zero to one.

- (1)
- Combine the population of parents (${P}_{o}$ =${P}_{t}$) and children (${Q}_{o}$= ${Q}_{t}$) and form the population (${R}_{t}={P}_{t}{\displaystyle \cup}{Q}_{t}$) with size 2N.
- (2)
- Run the unsuccessful sort on ${R}_{t}$ to specify the different layers ${F}_{i}$. i = 1, 2,…, l.
- (3)
- Create a population of ${P}_{i+1}$ size N by selecting superior solutions from non-dominant layers (${F}_{1},{F}_{2},\dots ,{F}_{i}$) in order of priority.
- (4)
- Calculate the population ${Q}_{i+1}$ of children of size N using RKGA operators.
- (5)
- Continue steps 1 to 4 until the convergence criteria are reached.

## 3. Modeling Uncertainties

#### 3.1. Load Uncertainty

#### 3.2. Electricity Price Uncertainty

## 4. Charging Station Modeling

## 5. Type and Capacity of Vehicle Batteries

#### 5.1. The Initial Charge of Vehicle Batteries

#### 5.2. Vehicle Charging Schedule

## 6. Modeling the Location Problem

#### 6.1. Profits from Discharge Programs

#### 6.2. Profits from Recharge Programs

#### 6.3. Profits from Reduced Power Purchases from the Upstream Network

## 7. Numerical Simulation of the Proposed Method

## 8. Conclusions

## 9. Offers

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

Parameter | Corresponding Description |
---|---|

$DL{F}_{i,t,h}^{e}$ | Demand level factor (in Monte Carlo experiment, in bus i, year t, demand level h) |

$PL{F}_{t,h}^{e}$ DNO DGOs | Price level factor in year t, demand level h, and Monte Carlo experiment e Distribution network operators Distribution generation owners |

${\mu}_{i,t,h}^{D}$ | The forecasted mean value of demand level factor (in bus i, year t, demand level h) |

${\lambda}_{i,t,h}^{D,e}$ | Random demand (in Monte Carlo experiment, in bus i, year t, demand level h) |

${\sigma}_{i,t,h}^{D}$ | Standard deviations of demand level factors (in bus i, year t, demand level h) |

${P}_{i,t,h}^{e}$ | Base active power demand (in Monte Carlo experiment, in bus i, year t, demand level h) |

${P}_{i,base}$ | Base active power (in bus i) |

${Q}_{i,t,h}^{e}$ | Base reactive power demand (in Monte Carlo experiment, in bus i, first year, demand level h) |

${Q}_{i,base}$ | Base reactive power (in bus i) |

${S}_{i,t,h}^{e}$ | Base apparent power (in Monte Carlo experiment, in bus i, first year, demand level h) |

${S}_{i,base}$ | Base apparent power (in bus i) |

${\rho}_{t,h}$ | The base price of power purchased from the grid (year t, demand level h) |

${\mu}_{i,t,h}^{\rho}$ | The forecasted mean value of price level factor (in bus i, year t, demand level h) |

${\lambda}_{i,t,h}^{\rho ,e}$ | Random demand (in Monte Carlo experiment, in bus i, year t, demand level h) |

${\sigma}_{i,t,h}^{\rho}$ | Standard deviations of demand level factors (in bus i, year t, demand level h) |

SOC | Amount of charge of vehicle batteries |

${P}_{v}$ | Power of charging the batteries |

$Es$ | Battery capacity |

${t}_{charge}$ | Required time to charging in hours |

${t}_{discharge}$ | Required time to discharge in hours |

${C}_{total}^{discharge}$ | The cost of discharge service |

${R}_{total}^{discharge}$ | Total gained revenge from discharging vehicle battery |

${B}_{total}^{discharge}$ | Total profit from discharging vehicle battery |

${C}_{total}^{charge}$ | The cost of charge service |

${R}_{total}^{charge}$ | Total gained revenge from charging vehicle battery |

${B}_{total}^{charge}$ | Total profit from charging vehicle battery |

${P}_{t,h}^{D}$ | Demand power (year t, demand level h) |

${P}_{t,h}^{loss}$ | Power loss (year t, demand level h) |

${P}_{i,t,h}^{cs}$ | Transmitted power of charging station (in bus i, year t, demand level h) |

$InfR$ | Inflation rate (percent) |

$IntR$ | Interest rate (percent) |

Indices | Corresponding Description |
---|---|

$i$ | Base i |

$t$ | Year |

$h$ | Demand level |

$\rho $ | Power price |

$D$ | Demand |

$e$ | Monte Carlo experiment |

## References

- Miller, I.; Arbabzadeh, M.; Gençer, E. Hourly Power Grid Variations, Electric Vehicle Charging Patterns, and Operating Emissions. Environ. Sci. Technol.
**2020**, 54, 16071–16085. [Google Scholar] [CrossRef] - Wang, B.; Zhao, D.; Dehghanian, P.; Tian, Y.; Hong, T. Aggregated Electric Vehicle Load Modeling in Large-Scale Electric Power Systems. IEEE Trans. Ind. Appl.
**2020**, 56, 5796–5810. [Google Scholar] [CrossRef] - Li, M.-W.; Wang, Y.-T.; Geng, J.; Hong, W.-C. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dyn.
**2021**, 103, 1167–1193. [Google Scholar] [CrossRef] - Zhang, Z.; Hong, W.-C. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl. Based Syst.
**2021**, 228, 107297. [Google Scholar] [CrossRef] - Sanjeevikumar, P.; Zand, M.; Nasab, M.A.; Hanif, M.A.; Bhaskar, M.S. Using the Social Spider Optimization Algorithm to Determine UPFC Optimal Size and Location for Improve Dynamic Stability. In Proceedings of the ECCE-Asia, Singapore, 24–27 May 2021. [Google Scholar]
- Zand, M.; Nasab, M.A.; Hatami, A.; Kargar, M.; Chamorro, H.R. Using Adaptive Fuzzy Logic for Intelligent Energy Management in Hybrid Vehicles. In Proceedings of the 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, 4–6 August 2020; pp. 1–7. [Google Scholar]
- Nasri, S.; Nowdeh, S.A.; Davoudkhani, I.F.; Moghaddam, M.J.H.; Kalam, A.; Shahrokhi, S.; Zand, M. Maximum Power Point Tracking of Photovoltaic Renewable Energy System Using a New Method Based on Turbulent Flow of Water-Based Optimization (TFWO) Under Partial Shading Conditions. In Energy Systems in Electrical Engineering; Springer: Cham, Switzerland, 2021; pp. 285–310. ISBN 9789813364561. [Google Scholar]
- Tightiz, L.; Nasab, M.A.; Yang, H.; Addeh, A. An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis. ISA Trans.
**2020**, 103, 63–74. [Google Scholar] [CrossRef] [PubMed] - Ghasemi, M.; Akbari, E.; Zand, M.; Hadipour, M.; Ghavidel, S.; Li, L. An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units. Electr. Power Compon. Syst.
**2019**, 47, 1826–1840. [Google Scholar] [CrossRef] - Nasab, M.A.; Zand, M.; Eskandari, M.; Sanjeevikumar, P.; Siano, P. Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services. Smart Cities
**2021**, 4, 1173–1195. [Google Scholar] [CrossRef] - Zand, M.; Nasab, M.A.; Sanjeevikumar, P.; Maroti, P.K.; Holm-Nielsen, J.B. Energy management strategy for solid-state transformer-based solar charging station for electric vehicles in smart grids. IET Renew. Power Gener.
**2020**, 14, 3843–3852. [Google Scholar] [CrossRef] - Ngo, H.; Kumar, A.; Mishra, S. Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles. Transp. Res. Part D Transp. Environ.
**2020**, 85, 102385. [Google Scholar] [CrossRef] - AlHajri, I.; Ahmadian, A.; Elkamel, A. Stochastic day-ahead unit commitment scheduling of integrated electricity and gas networks with hydrogen energy storage (HES), plug-in electric vehicles (PEVs) and renewable energies. Sustain. Cities Soc.
**2021**, 67, 102736. [Google Scholar] [CrossRef] - Zhanhong, W.; Mingbiao, Z.; Zhenheng, L.; Xuejun, C.; Yonghua, H. Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis. Front. Energy Res.
**2021**, 9, 1–10. [Google Scholar] [CrossRef] - Wang, N.; Tang, L.; Pan, H. A global comparison and assessment of incentive policy on electric vehicle promotion. Sustain. Cities Soc.
**2019**, 44, 597–603. [Google Scholar] [CrossRef] - Olatunde, O.; Hassan, M.Y.; Abdullah, P.; Rahman, H.A. Hybrid photovoltaic/small-hydropower microgrid in smart distribution network with grid isolated electric vehicle charging system. J. Energy Storage
**2020**, 31, 101673. [Google Scholar] [CrossRef] - Zand, M.; Neghabi, O.; Nasab, M.A.; Eskandari, M.; Abedini, M. A Hybrid Scheme for Fault Locating in Transmission Lines Compensated by the Thyristor-Controlled Series Capacitors. In Proceedings of the 15th International Conference on Protection and Automation of Power Systems (IPAPS), Shiraz, Iran, 30–31 December 2020. [Google Scholar]
- Orsi, F. On the sustainability of electric vehicles: What about their impacts on land use? Sustain. Cities Soc.
**2021**, 66, 102680. [Google Scholar] [CrossRef] - Zou, Y.; Zhao, J.; Ding, D.; Miao, F.; Sobhani, B. Solving dynamic economic and emission dispatch in power system integrated electric vehicle and wind turbine using multi-objective virus colony search algorithm. Sustain. Cities Soc.
**2021**, 67, 102722. [Google Scholar] [CrossRef] - Li, H.; Rezvani, A.; Hu, J.; Ohshima, K. Optimal day-ahead scheduling of microgrid with hybrid electric vehicles using MSFLA algorithm considering control strategies. Sustain. Cities Soc.
**2021**, 66, 102681. [Google Scholar] [CrossRef] - Zhang, Y.; Liu, X.; Zhang, T.; Gu, Z. Review of the electric vehicle charging station location problem. Commun. Comput. Inf. Sci.
**2019**, 1123, 435–445. [Google Scholar] [CrossRef] - Behera, S.; Behera, S.; Barisal, A.K. Dynamic Combined Economic Emission Dispatch integrating Plug-in Electric Vehicles and Renewable Energy Sources. Int. J. Ambient Energy
**2021**. Accepted for publication. [Google Scholar] [CrossRef] - Muratori, M.; Elgqvist, E.; Cutler, D.; Eichman, J.; Salisbury, S.; Fuller, Z.; Smart, J. Technology solutions to mitigate electricity cost for electric vehicle DC fast charging. Appl. Energy
**2019**, 242, 415–423. [Google Scholar] [CrossRef] - Azimi, Z.; Hooshmand, R.-A.; Soleymani, S. Energy management considering simultaneous presence of demand responses and electric vehicles in smart industrial grids. Sustain. Energy Technol. Assess.
**2021**, 45, 101127. [Google Scholar] [CrossRef] - Khan, Z.; Iyer, G.; Patel, P.; Kim, S.; Hejazi, M.; Burleyson, C.; Wise, M. Impacts of long-term temperature change and variability on electricity investments. Nat. Commun.
**2021**, 12, 1–12. [Google Scholar] [CrossRef] - Wang, L.; Nian, V.; Li, H.; Yuan, J. Impacts of electric vehicle deployment on the electricity sector in a highly urbanised environment. J. Clean. Prod.
**2021**, 295, 126386. [Google Scholar] [CrossRef] - Heinisch, V.; Göransson, L.; Erlandsson, R.; Hodel, H.; Johnsson, F.; Odenberger, M. Smart electric vehicle charging strategies for sectoral coupling in a city energy system. Appl. Energy
**2021**, 288, 116640. [Google Scholar] [CrossRef] - Farias, H.O.; Rangel, C.S.; Stringini, L.W.; Canha, L.N.; Bertineti, D.P.; Brignol, W.D.S.; Nadal, Z.I. Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations. Energies
**2021**, 14, 1370. [Google Scholar] [CrossRef] - Mehrjerdi, H. Resilience-robustness improvement by adaptable operating pattern for electric vehicles in complementary solar-vehicle management. J. Energy Storage
**2021**, 37, 102454. [Google Scholar] [CrossRef] - Migliavacca, G.; Rossi, M.; Siface, D.; Marzoli, M.; Ergun, H.; Rodríguez-Sánchez, R.; Hanot, M.; Leclerq, G.; Amaro, N.; Egorov, A.; et al. The Innovative FlexPlan Grid-Planning Methodology: How Storage and Flexible Resources Could Help in De-Bottlenecking the European System. Energies
**2021**, 14, 1194. [Google Scholar] [CrossRef] - Zeng, B.; Liu, Y.; Xu, F.; Liu, Y.; Sun, X.; Ye, X. Optimal demand response resource exploitation for efficient accommodation of renewable energy sources in multi-energy systems considering correlated uncertainties. J. Clean. Prod.
**2021**, 288, 125666. [Google Scholar] [CrossRef] - Nezamabad, H.A.; Zand, M.; Alizadeh, A.; Vosoogh, M.; Nojavan, S. Multi-objective optimization based robust scheduling of electric vehicles aggregator. Sustain. Cities Soc.
**2019**, 47, 101494. [Google Scholar] [CrossRef] - Hamwi, M.; Lizarralde, I.; Legardeur, J. Demand response business model canvas: A tool for flexibility creation in the electricity markets. J. Clean. Prod.
**2021**, 282, 124539. [Google Scholar] [CrossRef] - Mowry, A.M.; Mallapragada, D.S. Grid impacts of highway electric vehicle charging and role for mitigation via energy storage. Energy Policy
**2021**, 157, 112508. [Google Scholar] [CrossRef] - Xiang, Y.; Cai, H.; Liu, J.; Zhang, X. Techno-economic design of energy systems for airport electrification: A hydrogen-solar-storage integrated microgrid solution. Appl. Energy
**2021**, 283, 116374. [Google Scholar] [CrossRef] - Zand, M.; Nasab, M.A.; Khoobani, M.; Jahangiri, A.; Hosseinian, S.H.; Kimiai, A.H. Robust Speed Control for Induction Motor Drives Using STSM Control. In Proceedings of the 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), Tabriz, Iran, 2–4 February 2021. [Google Scholar]
- Huang, P.; Sun, Y.; Lovati, M.; Zhang, X. Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements. Energy
**2021**, 222, 119931. [Google Scholar] [CrossRef] - Kühnbach, M.; Bekk, A.; Weidlich, A. Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany. Energy Strat. Rev.
**2021**, 34, 100609. [Google Scholar] [CrossRef] - Chondrogiannis, S.; Poncela-Blanco, M.; Marinopoulos, A.; Marneris, I.; Ntomaris, A.; Biskas, P.; Bakirtzis, A. Power system flexibility: A methodological analytical framework based on unit commitment and economic dispatch modelling. In Mathematical Modelling of Contemporary Electricity Markets; Academic Press: Cambridge, MA, USA, 2021; pp. 127–156. [Google Scholar]
- Basu, M. Heat and power generation augmentation planning of isolated microgrid. Energy
**2021**, 223, 120062. [Google Scholar] [CrossRef] - Alismail, F.; Abdulgalil, M.; Khalid, M. Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration. Sustainability
**2021**, 13, 2526. [Google Scholar] [CrossRef] - Parsa, N.; Bahmani-Firouzi, B.; Niknam, T. A social-economic-technical framework for reinforcing the automated distribution systems considering optimal switching and plug-in hybrid electric vehicles. Energy
**2021**, 220, 119703. [Google Scholar] [CrossRef] - Lugovoy, O.; Gao, S.; Gao, J.; Jiang, K. Feasibility study of China’s electric power sector transition to zero emissions by 2050. Energy Econ.
**2021**, 96, 105176. [Google Scholar] [CrossRef] - Alshaalan, A. Basic Concepts of Electric Power System Planning. In Advances in Business Information Systems and Analytics; IGI Global: Hershey, PA, USA, 2021; pp. 306–325. [Google Scholar]
- Zand, M.; Nasab, M.A.; Neghabi, O.; Khalili, M.; Goli, A. Fault locating transmission lines with thyristor-controlled series capacitors By fuzzy logic method. In Proceedings of the 14th International Conference on Protection and Automation of Power Systems (IPAPS), Tehran, Iran, 31 December 2019–1 January 2020; pp. 62–70. [Google Scholar] [CrossRef]
- Zand, Z.; Hayati, M.; Karimi, G. Short-Channel Effects Improvement of Carbon Nanotube Field Effect Transistors. In Proceedings of the 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, 4–6 August 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Canale, L.; Di Fazio, A.; Russo, M.; Frattolillo, A.; Dell’Isola, M. An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings. Energies
**2021**, 14, 1078. [Google Scholar] [CrossRef]

**Figure 12.**Load curve of the studied network in different scenarios and application of 10-year load growth.

**Figure 13.**Voltage curve of the studied network in different scenarios and 10-year load growth application considering two parking spaces of electric vehicles.

**Figure 14.**Voltage curve of the studied network in different scenarios and 10-year load growth application considering three electric vehicle parking lots.

**Table 1.**The main parameters of the proposed method and program execution time for the studied systems.

Parameters | Intended Value |
---|---|

Number of population | 150 |

Maximum repetition | 100 |

Solving time (seconds) | 159.7 |

Mutation rates | 0.02 |

Crossover rate | 0.03 |

Marriage rate | 0.10 |

Chromosomes | Representation |
---|---|

Chromosome 1 | 11011|00100110110 |

Chromosome 2 | 11011|11000011110 |

Offspring 1 | 11011|11000011110 |

Offspring 2 | 11011|00100110110 |

The Initial Charge | SOC_{1} | SOC_{2} | SOC_{3} |
---|---|---|---|

Number of vehicles | n_{1} | n_{2} | n_{3} |

Load Information | Without DRP | With DRP |
---|---|---|

Maximum load supply profit (USD) | - | - |

Charge program benefit (USD) | - | - |

Profit from purchasing energy from the overhead network (USD) | $2.9742\times $ 10^{7} | $2.9911\times $ 10^{7} |

Profit from loss reduction (USD) | - | $7.9246\times $ 10^{7} |

Investment cost (USD) | - | - |

Total profit (USD) | $2.9742\times $ 10^{7} | $3.0703\times $ 10^{7} |

Load Information | Without DRP | With DRP | ||
---|---|---|---|---|

Bass number Optimal capacity of the power plant (kW) | 8 741 | 9 895 | 8 649 | 9 784 |

Maximum load supply profit (USD) | $2.0357\times $ 10^{6} | $1.7831\times $ 10^{6} | ||

Charge program benefit (USD) | $0.8086\times $ 10^{5} | $0.7083\times $ 10^{5} | ||

Profit from purchasing energy from the overhead network (USD) | $3.8281\times $ 10^{7} | $3.7343\times $ 10^{7} | ||

Profit from loss reduction (USD) | $7.5998\times $ 10^{5} | $1.4047\times $ 10^{6} | ||

Investment cost (USD) | $1.1092\times $ 10^{7} | $0.9715\times $ 10^{7} | ||

Total profit (USD) | $3.0070\times $ 10^{7} | $3.0890\times $ 10^{7} |

Load Information | Without DRP | With DRP | ||||
---|---|---|---|---|---|---|

Bass number Optimal capacity of the power plant (kW) | 6 547 | 8 791 | 9 895 | 6 463 | 8 580 | 9 654 |

Maximum load supply profit (USD) | $2.7785\times $ 10^{6} | $2.1116\times $ 10^{6} | ||||

Charge program benefit (USD) | $1.1038\times $ 10^{5} | $0.8388\times $ 10^{5} | ||||

Profit from purchasing energy from the overhead network (USD) | $4.1348\times $ 10^{7} | $3.8693\times $ 10^{7} | ||||

Profit from loss reduction (USD) | $9.8129\times $ 10^{5} | $1.4950\times $ 10^{6} | ||||

Investment cost (USD) | $1.5139\times $ 10^{7} | $1.1505\times $ 10^{7} | ||||

Total profit (USD) | $3.0085\times $ 10^{7} | $3.0883\times $ 10^{7} |

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

© 2021 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**

Azimi Nasab, M.; Zand, M.; Padmanaban, S.; Khan, B.
Simultaneous Long-Term Planning of Flexible Electric Vehicle Photovoltaic Charging Stations in Terms of Load Response and Technical and Economic Indicators. *World Electr. Veh. J.* **2021**, *12*, 190.
https://doi.org/10.3390/wevj12040190

**AMA Style**

Azimi Nasab M, Zand M, Padmanaban S, Khan B.
Simultaneous Long-Term Planning of Flexible Electric Vehicle Photovoltaic Charging Stations in Terms of Load Response and Technical and Economic Indicators. *World Electric Vehicle Journal*. 2021; 12(4):190.
https://doi.org/10.3390/wevj12040190

**Chicago/Turabian Style**

Azimi Nasab, Morteza, Mohammad Zand, Sanjeevikumar Padmanaban, and Baseem Khan.
2021. "Simultaneous Long-Term Planning of Flexible Electric Vehicle Photovoltaic Charging Stations in Terms of Load Response and Technical and Economic Indicators" *World Electric Vehicle Journal* 12, no. 4: 190.
https://doi.org/10.3390/wevj12040190