# An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Background Study

_{f}can be compared between the standard SG and synchronverter [14]. However, a current-controlled synchronverter is the same as a current source, providing voltage and frequency support for the system. In [15], a voltage-controlled synchronverter technique is proposed to alleviate the shortcomings of the current-controlled synchronverter. The goal of voltage-controlled synchronverter techniques is to simulate the rotor inertia and system frequency modulation characteristics of SG in frequency control to improve the system’s frequency stability [16]. The reactive voltage relationship is primarily considered in voltage control to control the stable voltage output [17]. The synchronverter performs power management and frequency modulation functions thanks to the power controller and voltage frequency controller [18]. The synchronverter is a system that simulates the inertia of a traditional power system by combining control algorithms, renewable energy sources, energy storage devices, and power electronics [19]. The synchronverter is a system that connects various storage units, generation units, and the utility grid. In today’s grid systems, variable wind turbines are employed, and these turbines are connected with back-to-back inverters, allowing for total decoupling of inertia from the utility grid [18]. The AC to DC converters and an additional inverter at the front end connect the energy storage and wind systems. This system is unresponsive to changes in inertia [12]. According to the literature, the main model concepts for many topologies are identical; however, the implementation of each topology model differs. Only a few topologies use mathematical equations to fully simulate synchronous generator behavior, and only a few topologies use swing equations to copy the synchronous generator’s inconsistent performance. The simple structure of the synchronverter-based wind energy storage system is presented in Figure 1.

#### 2.1. Virtual Synchronous Generator

_{m}and T

_{e}represent the mechanical and electromagnetic torque of the prime mover, respectively; θ

_{1}represents the angle of work; ω and ω

_{ref}represent the rotor angular velocity and rated angular velocity, respectively; the J parameter is the rotational inertia coefficient of the rotor; and D is the damping coefficient of the rotor.

_{abc}is the stator side-induced electromotive force; e

_{abc}is the three-phase output voltage of the stator side; and L

_{s}and C

_{s}are armature inductors and capacitors, respectively.

#### 2.2. Frequency Modulation Coordination

#### 2.3. Optimal Energy Storage Capacity

## 3. Proposed Methods

#### 3.1. Virtual Synchronous Generator Model

#### 3.2. Optimized Control Strategy

_{bf}is the FM power of the energy storage system; P

_{f}is the FM power of the wind field; P

_{wf}is the FM power of the wind turbine; P

_{bref}is the given power value; T is the FM duration of the wind; ${P}_{b}$ is the energy storage capacity; ${N}_{f}$ is the rated frequency of the system; $\frac{d{f}_{pll}}{dt}$ is the collected system frequency; df

_{pll}/dt is the rate of change in system frequency; and ${T}_{j}$ and ${K}_{f}$ are the inertial time constant and active power FM coefficient of the synchronverter, respectively.

#### 3.3. Optimal Economic Capacity

_{bat}is the energy storage device cost; C

_{pcs}is the power conversion system cost; and C

_{bop}is the auxiliary equipment cost.

_{bat}expressed as

_{rated}is the rated power of the ESS (kW.h); η is the conversion efficiency of the ESS (%); C

_{E}is the unit power price ($/(kW.h)); P

_{rated}is the rated power of the ESS (KW); and t is the discharge time (h) of the energy storage system.

_{p}is the unit power price of P

_{CS}($/kW).

_{B}is the unit electricity price of auxiliary equipment ($/(kW.h)).

_{CS}and auxiliary equipment generally have a service life of ten years.

_{rep}is the average annual reduction ratio of the cost of energy storage devices; k is the number of battery replacements, k = N/n − 1, and n is the battery life (years); β is the βth replacement of the battery in the energy storage system.

_{f}is the operation and maintenance cost per unit of power (USD/(kW·year)). Variable operation and maintenance costs mainly consider electricity cost, C

_{e}(USD/year), whereas C

_{e_P}is the average annual electricity cost of the ESS per unit of power (USD/(kW·year)).

## 4. Results and Discussion

#### 4.1. Results

#### 4.2. Discussion

_{rated}and rated capacity E

_{rated}are shown in Table 3. According to the maximum frequency deviation of the grid and considering the operating performance of the energy storage system. The rated power P

_{rated}of the ESS is determined by optimizing the control parameters of the energy storage system and the rated capacity E

_{rated}of the ESS is determined according to the state of charge (SOC) of the ESS. Meanwhile, the average annual cost per unit power of the ESS C

_{LCC}is calculated according to the life cycle cost model. The capacity configuration is based on the minimum primary FM effect J as the optimization objective.

## 5. Conclusions

- One can improve the economics of energy storage by determining the design method of its rated power and the capacity according to the FM requirements.
- The energy storage capacity under high wind speeds is configured to be 5.9% of the installed capacity, which is a reduction of 26% compared with the 8% capacity required for independent support of energy storage.
- The comprehensive optimal energy storage capacity configuration of the coordinated FM control strategy is improved.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

FM | Frequency modulation |

ESS | Energy storage system |

SOC | State of charge |

${P}_{f}$ | FM power |

${N}_{f}$ | Rated frequency of the system |

P_{wf} | Power of the wind turbine |

${K}_{f}$ | Inertial time constant |

${P}_{b}$ | Energy storage capacity |

C_{bat} | Energy storage device cost |

C_{pcs} | Power conversion system cost |

C_{bop} | Auxiliary equipment cost |

E_{rated} | Rated power of the ESS |

C_{B} | Unit electricity price |

C_{f} | Operation and maintenance cost |

J | Optimization objective |

## References

- Ramírez, M.; Castellanos, R.; Calderón, G.; Malik, O. Placement and sizing of battery energy storage for primary frequency control in an isolated section of the Mexican power system. Electr. Power Syst. Res.
**2018**, 160, 142–150. [Google Scholar] - Bhutta, M.S.; Sarfraz, M.; Ivascu, L.; Li, H.; Rasool, G.; ul Abidin Jaffri, Z.; Farooq, U.; Ali Shaikh, J.; Nazir, M.S. Voltage Stability Index Using New Single-Port Equivalent Based on Component Peculiarity and Sensitivity Persistence. Processes
**2021**, 9, 1849. [Google Scholar] - Rahman, F.S.; Kerdphol, T.; Watanabe, M.; Mitani, Y. Optimization of virtual inertia considering system frequency protection scheme. Electr. Power Syst. Res.
**2019**, 170, 294–302. [Google Scholar] - El-Bidairi, K.S.; Nguyen, H.D.; Mahmoud, T.S.; Jayasinghe, S.; Guerrero, J.M. Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia. Energy
**2020**, 195, 117059. [Google Scholar] - Belila, A.; Amirat, Y.; Benbouzid, M.; Berkouk, E.M.; Yao, G. Virtual synchronous generators for voltage synchronisation of a hybrid PV-diesel power system. Int. J. Electr. Power Energy Syst.
**2020**, 117, 1056776. [Google Scholar] - Zhu, T.; Nazir, M.S.; Ali Mokhtarzadeh, A.; Abdalla, A.N.; Nazir, H.M.; Chen, W. Improve performance of induction motor drive using weighting factor approach-based gravitational search algorithm. Int. J. Electron.
**2022**, 109, 900–913. [Google Scholar] - Nazir, M.S.; Abdalla, A.N.; MMetwally, A.S.; Imran, M.; Bocchetta, P.; Javed, M.S. Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System. Sustainability
**2022**, 14, 5301. [Google Scholar] - Abdalla, A.N.; Nazir, M.S.; Tao, H.; Cao, S.; Ji, R.; Jiang, M.; Yao, L. Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview. J. Energy Storage
**2021**, 40, 102811. [Google Scholar] - Das, C.K.; Mahmoud, T.S.; Bass, O.; Muyeen, S.; Kothapalli, G.; Baniasadi, A.; Mousavi, N. Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response. J. Energy Storage
**2020**, 29, 101315. [Google Scholar] - Nazir, M.S.; Abdalla, A.N.; Sohail, H.M.; Tang, Y.; Rashed, G.I.; Chen, W. Optimal planning and investment of Multi-renewable power generation and energy storage system capacity. J. Electr. Syst.
**2021**, 17, 171–181. [Google Scholar] - Soni, N.; Doolla, S.; Chandorkar, M.C. Improvement of transient response in microgrids using virtual inertia. IEEE Trans. Power Deliv.
**2013**, 28, 1830–1838. [Google Scholar] - D’Arco, S.; Suul, J.A.; Fosso, O.B. A virtual synchronous machine implementation for distributed control of power converters in smartgrids. Electr. Power Syst. Res.
**2015**, 122, 180–197. [Google Scholar] - Sakimoto, K.; Miura, Y.; Ise, T. Stabilization of a power system including inverter type distributed generators by the virtual synchronous generator. IEEJ Trans. Power Energy
**2012**, 132, 341–349. [Google Scholar] - Alipoor, J.; Miura, Y.; Ise, T. Power system stabilization using virtual synchronous generator with alternating moment of inertia. IEEE J. Emerg. Sel. Top. Power Electron.
**2015**, 3, 451–458. [Google Scholar] - Jongudomkarn, J.; Liu, J.; Ise, T. Comparison of current-limiting strategies of virtual synchronous generator control during fault ride-through. IFAC-Pap. Line
**2018**, 51, 256–261. [Google Scholar] - Hirase, Y.; Abe, K.; Sugimoto, K.; Shindo, Y. A grid-connected inverter with virtual synchronous generator model of algebraic type. Electr. Eng. Jpn.
**2013**, 184, 10–21. [Google Scholar] - Guerrero, J.M.; Vasquez, J.C.; Matas, J.; de Vicuna, L.G.; Castilla, M. Hierarchical control of droop-controlled ac and dc microgrids-a general approach toward standardization. IEEE Trans. Ind. Electron.
**2011**, 58, 158–172. [Google Scholar] - Liu, J.; Miura, Y.; Bevrani, H.; Ise, T. Enhanced virtual synchronous generator control for parallel inverters in microgrids. IEEE Trans. Smart Grid.
**2015**, 8, 2268–2277. [Google Scholar] - Tamrakar, U.; Galipeau, D.; Tonkoski, R.; Tamrakar, I. Improving transient stability of photovoltaic-hydro microgrids using virtual synchronous machines. In Proceedings of the 2015 IEEE Eindhoven Power Tech, Eindhoven, The Netherlands, 29 June–2 July 2015; pp. 1–6. [Google Scholar]
- Onaolapo, A.K.; Carpanen, R.P.; Dorrell, D.G.; Ojo, E.E. Reliability Evaluation and Financial Viability of an Electricity Power Micro-Grid System with the Incorporation of Renewable Energy Sources and Energy Storage: Case Study of KwaZulu-Natal, South Africa. IEEE Access
**2021**, 9, 159908–159924. [Google Scholar] - Kerdphol, T.; Rahman, F.S.; Watanabe, M.; Mitani, Y. Optimization of Virtual Inertia Control Considering System Frequency Protection Scheme. In Virtual Inertia Synthesis and Control; Springer: Berlin/Heidelberg, Germany, 2021; pp. 227–247. [Google Scholar]
- Chen, J.; Jin, T.; Mohamed, M.A.; Annuk, A.; Dampage, U. Investigating the Impact of Wind Power Integration on Damping Characteristics of Low Frequency Oscillations in Power Systems. Sustainability
**2022**, 14, 3841. [Google Scholar] - Nazir, M.S.; Abdalla, A.N.; Zhao, H.; Chu, Z.; Nazir, H.M.J.; Bhutta, M.S.; Javed, M.S.; Sanjeevikumar, P. Optimized economic operation of energy storage integration using improved gravitational search algorithm and dual stage optimization. J. Energy Storage
**2022**, 50, 104591. [Google Scholar] - Al-Ghussain, L.; Ahmad, A.D.; Abubaker, A.M.; Mohamed, M.A. An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses. Sustain. Energy Technol. Assess.
**2021**, 46, 101273. [Google Scholar] - Meng, L.; Zafar, J.; Khadem, S.K.; Collinson, A.; Murchie, K.C.; Coffele, F.; Burt, G.M. Fast frequency response from energy storage systems—A review of grid standards, projects and technical issues. IEEE Trans. Smart Grid.
**2019**, 11, 1566–1581. [Google Scholar] - Judge, P.D.; Green, T.C. Modular multilevel converter with partially rated integrated energy storage suitable for frequency support and ancillary service provision. IEEE Trans. Power Deliv.
**2018**, 34, 208–219. [Google Scholar] - Arifin, Z.; Firmanto, A. Battery Energy Storage System as Frequency Control at Substation based on Defense Scheme Mechanism. In Proceedings of the 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 21–22 July 2021. [Google Scholar]
- Alsiraji, H.A.; El-Shatshat, R. Comprehensive assessment of virtual synchronous machine based voltage source converter controllers. IET Gener. Transm. Distrib.
**2017**, 11, 1762–1769. [Google Scholar] - Vargas, R.Z.; Lopes, J.C.; Colque, J.C.; Azcue, J.L.; Sousa, T. Energy Storage System Integration with Wind Generation for Primary Frequency Support in the Distribution Grid. Simpósio Bras. Sist. Elétricos-SBSE
**2020**. [Google Scholar] [CrossRef]

**Figure 8.**Comparison of the output of the original control strategy of the FM coordination energy storage and wind energy storage.

Type of Battery | Unit Capacity Price C_{E} (USD/kW·h) | Unit Power Price C_{p} (USD/kW) | O&M Cost C_{f} (USD)/(kW·h) | Charging Electricity Price C_{c} (USD/kW.h) | Conversion Efficiency (%) | Life Time Period (Year) |
---|---|---|---|---|---|---|

Lithium battery | 21,600 | 7270 | 1040 | 3.5 | 0.85 | 10 |

Wind Speed | 6.2 m/s | 8.6 m/s | 11.2 m/s |
---|---|---|---|

With energy storage supports | 8% | 8% | 8% |

Original strategy: secondary fall compensation | 6.7% | 5.6% | 13.9% |

Optimization strategy: energy storage and compensation | 5.5% | 5.2% | 5.9% |

**Table 3.**Capacity allocation is based on the robust economic model, comprehensive optimal capacity allocation and control variable.

Model | Parameter | Value |
---|---|---|

Robust economic model | Control variable (T_{j}) | 4 |

Control variable (K_{f}) | 5 | |

Economic evaluation index (J) | 0.131 | |

Economic evaluation index (C_{LCC}) (10^{3} USD) | 66.30 | |

Power (%) | 1.4 | |

Optimal capacity allocation | Control variable (T_{j}) | 8 |

Control variable (K_{f}) | 13 | |

Economic evaluation index (J) | 0.098 | |

Economic evaluation index (C_{LCC}) (10^{3}) | 150 | |

Power (%) | 3.7 | |

Control variable | Control variable (T_{j}) | 12 |

Control variable (K_{f}) | 20 | |

Economic evaluation index (J) | 0.096 | |

Economic evaluation index (C_{LCC}) (10^{3}) | 23,200 | |

Power (%) | 5.9 |

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**

Chen, W.; Liu, B.; Nazir, M.S.; Abdalla, A.N.; Mohamed, M.A.; Ding, Z.; Bhutta, M.S.; Gul, M.
An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination. *Sustainability* **2022**, *14*, 8510.
https://doi.org/10.3390/su14148510

**AMA Style**

Chen W, Liu B, Nazir MS, Abdalla AN, Mohamed MA, Ding Z, Bhutta MS, Gul M.
An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination. *Sustainability*. 2022; 14(14):8510.
https://doi.org/10.3390/su14148510

**Chicago/Turabian Style**

Chen, Wan, Baolian Liu, Muhammad Shahzad Nazir, Ahmed N. Abdalla, Mohamed A. Mohamed, Zujun Ding, Muhammad Shoaib Bhutta, and Mehr Gul.
2022. "An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination" *Sustainability* 14, no. 14: 8510.
https://doi.org/10.3390/su14148510