# Electrification of Oil and Gas Platforms by Wind Energy

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^{†}

## Abstract

**:**

## 1. Introduction

_{2}emissions comes from oil and gas installations in the North Sea. These emissions are mainly due to the usage of gas turbines, which provide electrical power to the processes needed for oil and gas production. These emissions need to be significantly reduced in order to meet the goals of the European green deal of reducing the net greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels [2].

_{2}are emitted even when the turbines are running idle. However, the capacities of the buffers are subject to space and weight constraints at or close to the wind turbines and the production facilities.

_{2}emissions) while maintaining a stable system with minimal power imbalance.

## 2. Computational Models

#### 2.1. Principles

#### 2.2. System Balance

#### 2.3. Gas Turbine Model

#### 2.4. Wind Power Model

#### 2.5. Battery Model

#### 2.6. Demand Model

#### 2.7. Wind Speed Sampling

#### 2.8. Cost Model

_{2}). The normalized CO

_{2}emission rate ${\widehat{E}}_{C{O}_{2}}$ is calculated using the emission rate ${E}_{C{O}_{2}}$ divided by the maximum CO

_{2}emission rate $MA{X}_{C{O}_{2}}$. In Figure 11, we used $MA{X}_{C{O}_{2}}=4.5$$\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, corresponding to 3 gas turbines delivering 10 MW each. Figure 11 also shows that the system is defined to be high quality when it is stable and has low greenhouse gas emissions, and it is of poor quality otherwise.

## 3. Estimation of Battery Degradation

## 4. Control Policies

_{2}emissions. The third objective is to optimize the lifetime of the system components. It is, at the same time, an advantage if a control policy is explainable and easily implementable in practice.

#### 4.1. Policy for When to Start the Gas Turbine System

#### 4.1.1. Dynamic Energy Level Policy

#### 4.2. Policy for When to Stop the Gas Turbine System

#### 4.2.1. Fixed Level Policy

#### 4.2.2. Available Wind Power Policy

#### 4.3. Battery Policy

#### 4.3.1. Maximal Power Output Policy

#### 4.3.2. Limited Power Output Policy

#### 4.4. Strategies

## 5. Simulations

#### 5.1. Inputs and Outputs

#### 5.2. Wind Speed Inputs

#### 5.3. Power Demand Inputs

#### 5.4. Simulation Case Example

_{2}emissions. However, we can notice important differences between the two strategies in the periods where the gas turbines are engaged, as well as in the state of charge of the batteries. Analysis of single simulation cases give insight into how the energy management strategies work in specific scenarios. However, to provide answers about which strategy is best, we have to look at ensembles of simulation results.

#### 5.5. Simulation with Stochastic Inputs

_{2}emission rate, but since the stability was constantly equal to 1, we considered it irrelevant for the remaining analysis.

_{2}emissions relative to the case where the whole power demand was supplied using gas turbines (today’s situation), which we refer to as the baseline case. This is presented in Section 5.5.1. The second one is the battery life time.

#### 5.5.1. CO_{2} Emissions Results

_{2}emissions were integrated for the whole simulation period to obtain the total amount of emissions. These total amounts were normalized with the baseline emissions (no wind turbines or batteries) and then averaged for the whole set of samples (wind speed and power demand). The results are shown in Figure 20, Figure 21, Figure 22 and Figure 23. In the figures, the values in the cells stand for the % emissions relative to baseline when we allow the gas turbines to be switched off completely (0 emissions), as well as the % emissions when a single gas turbine is kept idle (shown in parentheses) with a minimum CO

_{2}emission rate of 0.5 $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$ (today’s practice). As a first observation, we notice the significant increase in CO

_{2}emissions when keeping a gas turbine idle, and this is regardless of the strategy, the number of batteries, and the number of wind turbines. We notice that the applied strategies have a comparable effect with respect to CO

_{2}emissions, where Strategy 2 performs slightly better. We also notice that the impact of adding wind turbines appears to be significantly more important than the impact of adding batteries, although this effect is less significant for Strategy 2.

_{2}emissions can be reduced by up to around 80% depending on the configuration and up to 55% if one gas turbine is kept idle.

#### 5.5.2. Battery Degradation

## 6. Conclusions

_{2}emissions and the estimated battery degradation. If one gas turbine is kept idle all the time, the CO

_{2}emissions can be reduced by 30–55%, depending on the control strategy, the number of wind turbines, and the number of batteries. However, if all the gas turbines are shut completely down when they are not needed, the CO

_{2}emissions may be reduced by more than 80% given sufficient wind power capacity. The simulations show that with sufficient battery capacity, it is possible to shut down the gas turbines for extended periods of time and start them on demand, thus enabling significant reductions in CO

_{2}emissions compared to running the gas turbines in idle mode. The battery storage can be controlled in a way that does not severely limit its lifetime, and it is estimated not to have spent more than approximately 50% of its lifetime within the first 20 years for strategies 3 and 4 when using two or more wind turbines.

_{2}emissions.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

$AWP$ | Available wind power | $AWE$ | Available wind energy |

${\rho}_{air}$ | Air density | A | Wind turbine swept area |

$BIC$ | Bayesian Information Criterion | ${N}_{wt}$ | Number of wind turbines |

${C}_{lim}$ | Charging power limit | ${C}_{rate}$ | Battery Charging rate |

${C}_{p}$ | Performance power coefficient of wind turbine | ${D}_{lim}$ | Discharging power limit |

D | Total battery degradation | ${E}_{co2}$ | CO_{2} emissions |

${E}_{max}$ | Energy capacity of battery | ${E}_{nox}$ | NOx emissions |

$GHMM$ | Gaussian Hidden Markov Model | ${N}_{wt}$ | Number of wind turbines |

${P}_{base}$ | Base power | ${P}_{bat}$ | Battery power |

${P}_{d}$ | Demand power | ${P}_{gt}$ | Gas turbine’s power |

${P}_{imb}$ | Power imbalance | ${P}_{wind}$ | Wind farm power |

${S}_{qt}$ | System quality | ${S}_{st}$ | System stability |

$SoC$ | State-of-charge | ${\eta}_{c}$ | Battery charging efficiency |

${\eta}_{d}$ | Battery discharging efficiency | ${c}_{0}$ | Sigmoid’s midpoint on charge |

${d}_{0}$ | Sigmoid’s midpoint on discharge | ${k}_{c}$ | Steepness of charge |

${k}_{d}$ | Steepness of discharge | $pu$ | Per unit |

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**Figure 2.**Maximum ramp-up and ramp-down, CO

_{2}, and NO

_{x}emissions, all as functions of the gas turbine-generated power.

**Figure 3.**Fastest possible ramp-up of a gas turbine from zero to maximal power output and back to zero and its corresponding greenhouse gas emission rate.

**Figure 5.**Efficiency factors ${\eta}_{c}$ and ${\eta}_{d}$ modelled using logistic functions with parameters $\alpha =0.04$ and $\beta =0.95$.

**Figure 6.**Battery model calibrated with sample data SNL_18650_LFP_25C_0-100_0.5-1C_d_timeseries.csv from [12].

**Figure 8.**The histogram of the occurrence of each (log) demand level of the original time series (gray bars) together with the state distributions and the stationary joint probability distribution (solid black curve) for the GHMM.

**Figure 10.**ERA5 wind speed at 100 m at Hywind Tampen in September 2018. The cut-in and cut-out wind speeds of the DTU 10MW turbine are marked with red lines.

**Figure 11.**Plots of stability and quality from Equation (7).

**Figure 12.**Proposed life curve for the LFP batteries. The dots are at 0.01, 0.1, 0.2, 0.5, and 1.0 SoC swings.

**Figure 20.**CO

_{2}emissions relative to baseline when the gas turbines can be turned off completely (0 emissions) or are kept idle with an emission rate of 0.5 $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, shown in parentheses.

**Figure 21.**CO

_{2}emissions relative to baseline when the gas turbines can be turned off completely (0 emissions) or are kept idle with an emission rate of 0.5 $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, shown in parentheses.

**Figure 22.**CO

_{2}emissions relative to baseline when the gas turbines can be turned off completely (0 emissions) or are kept idle with an emission rate of 0.5 $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, shown in parentheses.

**Figure 23.**CO

_{2}emissions relative to baseline when the gas turbines can be turned off completely (0 emissions) or are kept idle with an emission rate of 0.5 $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, shown in parentheses.

Policy | |||
---|---|---|---|

Gas Turbines On | Gas Turbines Off | Battery Degradation | |

Strategy 1 | Dynamic-level Section 4.1.1 | Fixed-level Section 4.2.1 | Limited power output at 10% Section 4.3.2 |

Strategy 2 | Dynamic-level Section 4.1.1 | Fixed-level Section 4.2.1 | Maximum power output Section 4.3.1 |

Strategy 3 | Dynamic-level Section 4.1.1 | Available-wind-power Section 4.2.2 | Limited power output at 10% Section 4.3.2 |

Strategy 4 | Dynamic-level Section 4.1.1 | Available-wind-power Section 4.2.2 | Maximum power output Section 4.3.1 |

Input | Description |
---|---|

Wind speed samples | We used 50 samples of 7 days, sampled at 1 h intervals and with interpolation possibilities to 1 s intervals |

Power demand samples | We used 50 samples of 7 days, sampled at 1 h intervals and with interpolation possibilities to 1 s intervals |

Batteries | We used battery units of a capacity of 10 MWh, with ${C}_{rate}=0.5$ ${\mathrm{h}}^{-1}$ and ${D}_{rate}=1$ ${\mathrm{h}}^{-1}$. We varied the number of batteries between 1 and 7, which correspond to variation of energy storage capacity between 10 MWh and 70 MWh. Varying the number of batteries was modelled as one large battery and parameterized using ${E}_{max}\in 10,20,30,40,50,60,70$ MWh |

Wind turbines | We used wind turbine units of a capacity of 10 MWh and varied their number between 1 and 7, corresponding to variation of maximum wind power generation between 10 MWh and 70 MWh |

Gas turbines | We used 3 gas turbine units of a maximum power of 12 MW each |

Control strategy | We simulated the four control strategies that are defined in Table 1 |

Output | Description |
---|---|

Wind turbines | A time series of available wind power $AWP$ and generated wind power ${P}_{wind}$ from Equation (5) presented in MW |

Gas turbines | A time series of the emission rates ${E}_{co2}$ and ${E}_{nox}$ presented in $\mathrm{k}\mathrm{g}{\mathrm{s}}^{-1}$, and the generated power ${P}_{gt}$ from Equation (3) |

Battery | A time series of the battery state of charge $SoC$ presented in % and the power delivered or consumed ${P}_{bat}$ presented in MW |

Simulation metrics | A time series of the system stability ${S}_{st}$ and quality ${S}_{qt}$ from Equation (7) |

Stochastic metrics | Total CO_{2} emissions relative to a base case with gas turbines only and battery degradation from Equation (10) |

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**MDPI and ACS Style**

Saadallah, N.; Heggelund, Y. Electrification of Oil and Gas Platforms by Wind Energy. *Energies* **2023**, *16*, 3062.
https://doi.org/10.3390/en16073062

**AMA Style**

Saadallah N, Heggelund Y. Electrification of Oil and Gas Platforms by Wind Energy. *Energies*. 2023; 16(7):3062.
https://doi.org/10.3390/en16073062

**Chicago/Turabian Style**

Saadallah, Nejm, and Yngve Heggelund. 2023. "Electrification of Oil and Gas Platforms by Wind Energy" *Energies* 16, no. 7: 3062.
https://doi.org/10.3390/en16073062