# Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms

^{1}

^{2}

^{*}

## Abstract

**:**

_{2}emissions of the proposed method are reduced by 18.9% and 17.3%, respectively.

## 1. Introduction

_{2}emissions from offshore oil projects without increasing the capital expenditure significantly [1]. In Norway, about 62% of the carbon dioxide emissions in 2012 came from offshore oil extraction and processing tasks [2]. As a result, a lot of efforts have been put into developing energy efficient technologies to mitigate the CO

_{2}emissions in offshore oil facilities. In general, the most feasible current ways to realize CO

_{2}emission reduction in a cost effective way include: (i) improving the efficiency of energy generation and utilization, (ii) the use of offshore energy generation technologies [3].

_{2}emissions. In [19], the Exergy balance of two platform configurations, with and without CCS, were assessed and the potential opportunities were found for improving the efficiency of the CCS section. In [1], a CCS with the pre-treatment and post-combustion units was proposed, which reduces the CO

_{2}emissions of a platform in the North Sea by more than 15%. However, as a CCS itself is an energy-intensive unit, the introduction of a CCS reduces the CO

_{2}emissions, it also causes the deterioration of the energy efficiency of offshore platforms [20].

_{2}-capture systems has not be proven offshore except for gas processing with high CO

_{2}-contents. To explore the feasibility of these new technologies in marine engineering, this paper introduce the various abovementioned technologies into the Bohai oil and gas platform to modify the structure of the offshore platform energy system. Simultaneously, there are significant discrepancies between the available energy and the power demands. Hence, it is necessary to coordinate the use of various energies, i.e., electrical power, heat, associated gas, and imported fuel (diesel in most methods). In this paper, the energy system of the offshore oil extraction and processing platform is regarded as an Integrated Energy System (IES) for considering the coupling of multiple forms of energy [21].

## 2. IESs for Offshore Oil Extraction and Processing

_{2}emissions produced reach about 300–500 tons, and more than 80% corresponds to the operation of gas turbines.

_{2}capture unit proposed in [1] for an oil platform is used to mitigate the emissions. The first level is a Pre-CO

_{2}capture unit with the structure presented in [30], where natural gas is converted into hydrogen. The CO

_{2}generated in the conversion is absorbed by chemical absorption with triethanolamine and the hydrogen is fed to the turbines. The second level is a Post-CO

_{2}capture unit which is responsible for the carbon capture of the flue gas. A super-capacitor (SC) based energy storage is used to balance the source and loads. Figure 1 illustrates the proposed IESs and its relationship with the offshore oil extraction and processing system.

## 3. Generalized Energy and Material Flow Model

**Z**, is introduced to describe the relationship between production outputs and the consumption of energies and materials in the Process Section.

**B**is a highly sparse matrix. When all entries of a column in

**B**are zero, the corresponding production are not fed back to the input:

## 4. Multi-objective Optimal Planning of the IESs

#### 4.1. IESs Model

_{2}capture unit, a super-capacitor based energy storage, and pipeline system for gas and heat. According to the Section 3, an energy hub model is used to describe the IESs. The electricity output of Co-firing turbines is formulated as Equation (4):

_{2}section converts a part of associated gas into the hydrogen as a fuel for GT and the CO

_{2}to be absorbed, which consumes a proportion of power, i.e., E

_{Pre}:

_{2}amount captured, i.e., CDE

_{CCS}, as shown in Equations (14) and (15):

#### 4.2. Oil Extracting and Processing System Model (OEPS)

**Z**for OEPS from this model, so as to describe the relationship between OEPS production output and material & energy input.

#### 4.2.1. Theory of exergy analysis for energy-material coupling model

_{i−L}= 0. Equation (30) is the equation for calculating exergy destruction for EMCE i, where ε

_{i}is efficiency defect of EMCE i [33]:

#### 4.2.2. EMCE Matrix of Offshore Platform OEPS

#### Drilling and Mining System

_{ma}and electricity exergy EX

_{DM_U}in producing per unit output of well stream o

_{ws}. Its EMCE matrix is shown in Equation (33).

#### Crude Oil Process System

_{ws}into mixed oil o

_{mix-oil}, mixed associated gas o

_{mix-gas}and mixed water o

_{mix-wa}and delivers them respectively to crude oil processing unit, natural gas process system and water treatment system. Total amount of electricity and heat exergy consumed by this unit during separation is EX

_{CR-sep_U}. CR-oil processes mixed oil from CR-sep into oil product o

_{oil}for output; total amount of electricity and heat exergy consumed by this unit is EX

_{CR_oil_U}. EMCE matrices of CR-sep and CR-oil are shown in Equations (34) and (35):

#### Natural Gas System

_{mix-gas}from the CR-sep to output combustible associated gas. The NG-co is responsible for natural gas compression transportation. Electricity and heat exergy consumed by this two unit, and their EMCE matrixes are shown in Equations (36) and (37):

#### Water Treatment System

_{mix-wa}from CR-sep into wastewater o

_{wa_WT}that complies with discharge standard, electricity exergy consumed by this unit is EX

_{WT_U}. Its EMCE matrix is shown in Equation (38):

#### Living-Quarters System

_{fwa}from external source and discharge properly wastewater o

_{swa-LQ}; electricity and heat exergy consumed by these units is EX

_{LQ_U}; the EMCE matrix is shown in Equation (39):

#### EMCE Matrix of OEPS

#### 4.3. Multi-Objective Stochastic Optimization Model

#### 4.3.1. Uncertainty Analysis

#### 4.3.2. Objective Function

_{TTC}and f

_{TCDE}:

#### 4.3.3. Constraints

#### 4.3.4. Optimization Method and Solving Steps

_{TAC}and f

_{CDE}of the offshore platform during the maximum load are optimized, optimal capacity solution set of IESs component is obtained in feasible region. The target values are normalized separately and summed according to the 50% weighting factor, the solution with the smallest value is selected as the best one.

_{TTC}and f

_{TCDE}in the planning period for the offshore platform. The multi-objective genetic algorithm NSGAII and Monte Carlo simulation are used in this paper to solve the problem, and statistical sampling and probability distribution functions are used to simulate the effects of uncertain variables [35]. The flow chart of multi-objective stochastic optimization is shown in Figure 6.

## 5. Results

^{3}/h. According to forecast in Figure 7b, associated gas production is high in early production period but falls sharply afterward, suggesting associated gas may fall short of the demand of gas turbine in the middle and later periods of production, thus requiring diesel supply. Meanwhile, actual oil extraction may be different from the plan, which must be considered in planning. This study assumes that the mean of the random variables is ±10% of the predicted value, as shown in Figure 7a,b.

_{2}emissions. In addition, the electricity and thermal consumption of the offshore platform depends mainly on the real-time operating conditions of OEPS. Figure 9 shows data samples of correlations between associated gas production and electric load (Figure 9a), between associated gas production and thermal load (Figure 9b), between electric load and thermal load (Figure 9c). Distributed data closer to center line suggests a higher correlation between variables. It can be seen from the Figure 9 that the electric and thermal loads are highly correlated to each other, while they have roughly the same correlation with the associated gas.

_{2}emissions are not considered. Method 2 offers a stochastic planning method that takes account of random effects of electric and thermal loads and associated gas production, on the basis of Energy-Hub model as shown in Figure 4. Method 3 offers a stochastic planning method that takes account of correlation between IESs and OEPS, on the basis of Generalized Energy and Material Flow Model (GEMFM) as shown in Figure 2.

_{2}emissions in Method 1 than in methods 2 and 3. Figure 10 also suggests better results from Method 3 than from Method 2. This is because: when fluctuation in associated gas production affects electric and thermal loads of IESs, IESs can quickly respond to load fluctuation through SC charging/discharging to meet the electricity demand of OEPS; the cascaded utilization of waste heat and diesel coordination and compensation in IESs weakens the secondary impact of fluctuation in associated gas production on load. Therefore, it is helpful to consider the impact of IESs -OEPS correlation on IESs optimization.

_{2}and CCS processes consume about 5.8% of electricity; CCS process consumes about 6.8% of heat energy.

^{3}/h more associated gas are allocated to IESs by Method 2, which means 332.5 kW of exergy, but it consumes more 1844 kW.

_{2}emissions in a 20-year planning period using the three methods. Method 3 entails the minimum total capital cost, total maintenance cost, total operating cost and total CO

_{2}emissions over the planning period, because it significantly cuts acquisition and installation costs for gas turbine and requires minimum diesel consumption over the planning period. Compared with Method 1, Method 2 can reduce 11.21% and 12.4% the total cost and CO

_{2}emissions, respectively. And the reduction of total cost and CO

_{2}emissions are 18.9% and 17.3% by Method 3, respectively. Hence, Method 3 is the most economy and environmental friendly planning method.

## 6. Conclusions

_{2}emissions due to less use of primary energy and deployment of CCS.

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

**Sets and Indices**

L | the set of energy outputs |

P | the set of energy inputs |

C | the coupling matrix of the Energy Section |

S | the set of energy storages |

P_{m} | the mth input energy carrier of the Energy Section |

L_{n} | the nth output energy carrier of the Energy Section |

c_{nm} | the coupling factor which defines the coupling between P_{m} and L_{n} |

O | the set of the production outputs |

U | the set of non-energy inputs |

Z | the coupling matrix of the Process Section. |

E_{θ} | the sub-matrix which reflect the relationship between energy and energy |

E_{τ} | the sub-matrix which reflect the relationship between energy and non-energy |

Z_{π} | the sub-matrix which reflect the relationship between non-energy and energy |

Z_{ψ} | the sub-matrix which reflect the relationship between non-energy and non-energy |

U_{m} | the mth non-energy inputs to the Process Section |

H_{r} | the rth output energy carrier of the Process Section |

F_{n} | the nth output non-energy carrier of the Process Section |

e_{rn}/z_{nn} | the coupling factor which defines the coupling of an energy-material coupling element (EMCE) |

u_{kj} | the kth non-energy input of the jth EMCE |

l_{wj} | the wth energy input of the jth EMCE |

f_{jj} | the jth non-energy output of the jth EMCE |

h_{jj} | the jth energy output of the jth EMCE |

I | the sets of energies imported to the Energy Section |

B | the feedback matrix |

b_{nm} | the feedback factor between O_{m} and L_{n} |

**Parameters and Variables**

E_{x} | electricity generation or consumption of unit x |

G_{x} | associated gas input to unit x |

V_{AS} | calorific value of associated gas, 38 MJ/Sm^{3} |

V_{DO} | calorific value of diesel oil, 42 MJ/kg |

η^{E}_{x} | electricity conversion efficiency of unit x |

η^{Q}_{x} | electricity conversion efficiency of unit x, |

D_{x} | diesel oil input to unit x |

H_{2} | hydrogen inputs |

CDE_{x} | CO_{2} emission of unit x |

λ_{x}(.) | emission function of the input material x |

Q_{x} | heat generated/consumed by unit x |

δ_{x}(.) | heat function of the input material x |

α_{x} | waste heat utilization ratio by x |

σ_{pre}(Gpre) | power consuming function of Pre-CO_{2} |

Rh(Gpre) | hydrogen generation function of Pre-CO_{2} |

k^{E}_{CCS} | electrical consumption of CCS, 0.256 kW/kg |

k^{Q}_{CCS} | heat consumption of CCS, 0.38 kW/kg |

L_{E} | the electricity output of the energy section |

L_{H} | the heat output of the energy section |

L_{CDE} | the CO_{2} output of the energy section |

P_{1} | the total amount of associated gas input |

P_{2} | the total diesel input |

EX_{x_k} | kinetic exergy of material flow x |

EX_{x_p} | potential exergy of material flow x |

EX_{x_ph} | physical exergy of material flow x |

EX_{x_ch} | chemical exergy of material flow x |

EX _{x_P} | product exergy for EMCE i |

EX_{i_U} | utilized exergy for EMCE i |

EX _{i_L} | exergy loss for EMCE i |

EX _{i_D} | exergy destruction for EMCE i |

EX _{o_x} | output exergy of material flow x |

EX _{br_x} | input exergy of material flow x |

m_{x} | mass flow rate of fluid x |

v_{x} | flow speed of fluid x |

g | gravitational acceleration |

H | relative height of fluid x |

h | specific enthalpy |

s | specific entropy |

T_{0} | the ambient temperature |

R | molar gas constant on material flow x |

P_{0} | function of environmental pressure |

P_{00} | partial pressures of material flow x |

ω_{i} | coefficients of electricity consumed by EMCE i |

ξ_{i} | coefficients of heat consumed by EMCE i |

o_{x} | output material flow x of EMCE i |

θ_{x} | the coefficient of converting |

U_{y} | input material flow y of EMCE i |

O_{i} | output matrix of EMCE i |

Z_{i} | EMCE matrix of EMCE i |

U_{i} | input matrix of EMCE i |

QX_{o-x} | mass flow rate of fluid x |

GX_{o-x} | volume flow rate of fluid x |

VX_{o-x} | output speed of fluid x |

P_{a0} | ambient pressure |

P_{ax-1} | inlet pressure of fluid x |

P_{ax-2} | outlet pressure of fluid x |

T_{x-1} | inlet temperatures of fluid x |

T_{x-2} | outlet temperatures of fluid x |

Hex_{ows} | average liquid depth |

Cp_{x-1} | constant-pressure specific heat of fluid x at inlet |

Cp_{x-2} | constant-pressure specific heat of fluid x at outlet |

c(.) | probability density function of Copula |

u_{i} | the specific value of the i^{th} uniformly distributed random variable |

A_{cov} | covariance matrix |

I | unit matrix |

q_{i} | normal integral |

q | vector quantity comprising q_{i} |

Φ^{−1} | inverse cumulative distribution function |

f_{TTC} | total cost |

f_{TCDE} | total carbon dioxide emissions |

Y | project planning period |

f_{TAC}^{y} | annual total cost in the y^{th} year |

f_{CDE}^{y} | annual carbon dioxide emissions in the y^{th} year |

f_{CC}^{y} | capital cost in the y^{th} year |

f_{MC}^{y} | maintenance cost in the y^{th} year |

f_{OC}^{y} | operating cost in the y^{th} year |

Ieq | equation constraint set |

Ieq_{max} | upper limits of inequation constraint set |

Ieq_{min} | lower limits of inequation constraint set |

Pr{.} | probability of satisfied inequation in {.} |

ω_{i} | gas flow at node i |

ω_{i}^{max} | upper limits of gas flow at node i |

ω_{i}^{min} | lower limits of gas flow at node i |

P_{s,i} | injection power at node i |

P_{s,i}^{max} | upper limits of injection power at node i |

P_{s,i}^{min} | lower limits of injection power at node i |

M_{s,i} | supply mass flow rate at node i |

M_{s,i}^{max} | upper limits of supply mass flow rate at node i |

M_{s,i}^{min} | lower limits of supply mass flow rate at node i |

β | confidence level of chance constraint |

r | the rate load |

op | operating power |

dp | design power |

**Abbreviations**

GT | Co-firing gas turbines |

EFB | exhaust-fired-boilers |

ORC | organic Rankine cycle |

CCS | CO_{2} capture and storage system |

SC | super-capacitor |

AS | Associated gas |

DO | Diesel oil |

Pre | Pre-CO_{2} capture unit |

CDE | carbon dioxide emissions |

DM | the drilling and mining unit |

ws | well stream |

CR-sep | the three-phase separation unit |

CR-oil | the crude oil process unit |

NG-tr | the natural gas treatment unit |

NG-co | the natural gas compression unit |

WT | the water treatment unit |

LQ | the living-quarters unit |

## Appendix A

#### Appendix A.1. Exergy Formula for DM

#### Appendix A.2. Exergy Formula for Crude Oil Process System

#### Appendix A.2.1. Exergy Formula for CR-sep

#### Appendix A.2.2. Exergy Formula for CR-oil

#### Appendix A.3. Exergy Formula for Natural Gas System

#### Appendix A.3.1. Exergy Formula for NG-tr

#### Appendix A.3.2. Exergy Formula for NG-co

#### Appendix A.4. Exergy Formula for Water Treatment System

#### Appendix A.5. Exergy Formula for Living-quarters System

## Appendix B

#### Appendix B.1. Input/Output Material Conversion Relationship for Crude Oil Process System

#### Appendix B.2. Input/Output Material Conversion Relationship for Natural Gas System

#### Appendix B.3. Input/Output Material Conversion Relationship for Water Treatment System

#### Appendix B.4. Input/Output Material Conversion Relationship for Living-quarters System

## Appendix C

Type | SGT-A35 | SGT-A35 (GT62) | SGT-A35 (GT61) | SGT-A35 (GT30) | SGT-A35 (GT30) |
---|---|---|---|---|---|

Power output (MW) | 27.2 | 29.9 | 32.1 | 31.9 | 32.2 |

Gross efficiency (%) | 36.4 | 37.5 | 39.3 | 37.3 | 37.5 |

Heat rate (kJ/kWh) | 9904 | 9589 | 9159 | 9644 | 9611 |

Exhaust mass flow (kg/s) | 91.0 | 95.0 | 94.0 | 99.2 | 99.8 |

Exhaust temperature (°C) | 501 | 503 | 509 | 504 | 503 |

Type | 11 MW Version | 13 MW Version | 15 MW Version |
---|---|---|---|

Power output (MW) | 10.36 | 12.9 | 14.32 |

Gross efficiency (%) | 34.8 | 34.8 | 35.4 |

Heat rate (kJ/kWh) | 10342 | 10355 | 10178 |

Exhaust mass flow (kg/s) | 33.8 | 39.4 | 44.0 |

Exhaust temperature (°C) | 508 | 555 | 540 |

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**Figure 7.**Estimated curve of an oil platform in the Bohai Sea in the coming 20 years (

**a**) electric and thermal load demands; (

**b**) associated gas production.

**Figure 8.**Analysis of IESs sensitivity under maximum reference load in 2026 (

**a**) annual total cost; (

**b**) annual carbon dioxide emissions.

**Figure 9.**Data samples of correlation between random variables at maximum reference load in 2026: (

**a**) associated gas production and electric load; (

**b**) associated gas production and heat load; (

**c**) heat load and electric load.

Decision Variables | Type |
---|---|

E_{GT} (kW) | deterministic |

E_{ORC} (kW) | deterministic |

E_{CCS} (kW) | deterministic |

E_{SC} (kW) | deterministic |

Q_{oil} (kW) | deterministic |

Q_{ccs} (kW) | deterministic |

G_{GT} (Sm^{3}/h) | deterministic |

G_{EFB} (Sm^{3}/h) | deterministic |

G_{Pre} (Sm^{3}/h) | deterministic |

D_{GT} (kg) | deterministic |

D_{EFB} (kg) | deterministic |

P_{1} | uncertain |

L_{E} | uncertain |

L_{H} | uncertain |

**Table 2.**Power and heat generation/consumption of units using three methods at maximum reference load in 2026.

Unit | Method1 | Method2 | Method3 |
---|---|---|---|

GT-power generation (kW) | 44,157 | 41,311–41,351 | 41,090–41,106 |

GT-high temperature waste heat generation (kW) | 37,850 | 35,409–35,443 | 35,220–35,234 |

EFB-thermal generation (kW) | 14,100 | 15,951–16,040 | 15,813–15,869 |

EFB-low temperature waste heat generation (kW) | 25,328 | 23,694–24,022 | 23,367–23,373 |

ORC-power consumption (kW) | - | 5923.5–6005.5 | 5841.5–5843.2 |

Pre-CO_{2}-power consumption (kW) | - | 1883.8–1968.9 | 1772.3–1784.2 |

CCS-power consumption (kW) | - | 650.2–886.7 | 559.5–764.7 |

CCS-thermal consumption (kW) | 1050.5–1240.0 | 913.4–1169.3 | |

Carbon dioxide capture (kg) | - | 2539.8–3460.9 | 2185.5–2987.1 |

SC-power generation (kW) | - | 25–40 | 22–35 |

**Table 3.**Comparison of energy system performances using three methods at maximum reference load in 2026.

Criteria | Method1 | Method2 | Method3 |
---|---|---|---|

Total power generation (kW) | 44,157 | 47,034–47,356 | 45,932–46,149 |

Total thermal generation (kW) | 14,100 | 15,951–16,040 | 15,213–15,469 |

Associated gas consumption (Sm^{3}/h) | 8100 | 8530–8633 | 8450–8470 |

Diesel oil consumption (kg) | 3485 | 2463–2471 | 2256–2282 |

Unit | Method1 | Method2 | Method3 | Ref. |
---|---|---|---|---|

GT-size | 2 × 27,200 (kW) | 32,200 (kW) + 14,320 (kW) | 32,200 (kW) + 14,320 (kW) | |

GT-capital cost | 450 ($/kW) | 450 ($/kW) | 450 ($/kW) | [6] |

EFB-size | 3 × 5232 (kW) | 2 × 5232 (kW) + 6977 (kW) | 2 × 5232 (kW) + 6977 (kW) | [6] |

EFB-capital cost | 3 × 32 × 10^{4} $ | 2 × 32 × 10^{4} $ + 38 × 10^{4} $ | 2 × 32 × 10^{4} $ + 38 × 10^{4} $ | [6] |

ORC-size | - | 6000 (kW) | 6000 (kW) | |

ORC-capital cost | - | 1.33 × 10^{6} ($) | 1.33 × 10^{6} ($) | [36] |

SC-size | - | 30 (kW) | 25 (kW) | |

SC-capital cost | - | 143 ($/kW) | 143 ($/kW) | [37] |

CCS-size | - | 3.0 (tons/h) | 2.5 (tons/h) | |

CCS-capital cost | - | 1.35 × 10^{6} ($) | 1.24 × 10^{6} ($) | [38] |

Total capital cost | 2.51 × 10^{7}($) | 2.47 × 10^{7} ($) | 2.46 × 10^{7} ($) |

**Table 5.**Proportions of electricity and heat consumed by five subsystems of the offshore platform OEPS.

Systems | Power Percentage (+10%) | Thermal Percentage (+10%) |
---|---|---|

Drilling and mining | 46.5% | 28.6% |

Crude oil process | 31.4% | 64.3% |

Natural gas system | 6.4% | 3.1% |

Water treatment | 10.2% | 0 |

Living-quarters | 5.5% | 4.0% |

Total | 100% | 100% |

**Table 6.**Exergy consumption (kW) of OEPS five subsystems using three methods under maximum reference load in 2026.

Systems | Method1 | Method2 | Method3 | |||
---|---|---|---|---|---|---|

Power | Thermal | Power | Thermal | Thermal | Thermal | |

Drilling and mining | 20,533 | 4033 | 21,662 | 4254 | 21,010 | 4227 |

Crude oil process | 13,865 | 9066 | 14,623 | 9565 | 14,188 | 9278 |

Natural gas process | 2826 | 437 | 2982 | 461 | 2892 | 447 |

Water treatment | 4504 | 0 | 4753 | 0 | 4609 | 0 |

Living-quarters | 2429 | 564 | 2562 | 595 | 2485 | 577 |

Total | 44,157 | 14,100 | 46,582 | 14,875 | 45,184 | 14,429 |

**Table 7.**Total cost and total CO

_{2}emissions in a 20-year planning period using the three methods.

Parameters | Method1 | Method2 | Method3 |
---|---|---|---|

Total maintenance cost ($) | 6.93 × 10^{5} | 6.72 × 10^{5} | 6.69 × 10^{5} |

Total operating cost ($) | 2.57 × 10^{8} | 2.24 × 10^{8} | 2.05 × 10^{8} |

Total cost ($) | 2.82 × 10^{8} | 2.49 × 10^{8} | 2.30 × 10^{8} |

Total carbon emission (tons) | 4.69 × 10^{6} | 4.08 × 10^{6} | 3.88 × 10^{6} |

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, A.; Zhang, H.; Qadrdan, M.; Yang, W.; Jin, X.; Wu, J.
Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms. *Energies* **2019**, *12*, 756.
https://doi.org/10.3390/en12040756

**AMA Style**

Zhang A, Zhang H, Qadrdan M, Yang W, Jin X, Wu J.
Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms. *Energies*. 2019; 12(4):756.
https://doi.org/10.3390/en12040756

**Chicago/Turabian Style**

Zhang, Anan, Hong Zhang, Meysam Qadrdan, Wei Yang, Xiaolong Jin, and Jianzhong Wu.
2019. "Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms" *Energies* 12, no. 4: 756.
https://doi.org/10.3390/en12040756