# Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption

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## Abstract

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## 1. Introduction

_{2}emissions [5]. DG can be used as reserve power sources, while batteries are used for energy storage. Integrating WT into operating DPS allows the reduction in annual diesel fuel consumption by 50% [6].

## 2. WDHS Configuration, Capacity and Location

_{2}emissions, as well as other technical and economic parameters applied to choose the optimal equipment configuration and capacity of the system elements, can be the objective function when solving the task of decreasing fuel consumption. They primarily include levelized cost of energy (LCOE), annual energy production (AEP), capital expenses (CAPEX), net present value (NPV) or net present cost (NPC), and payback period. Moreover, electric reliability, environmental friendliness and social criteria can be the objective function [32]. The formulas to specify these criteria are detailed in [33]. The key or characteristic criterion selection depends on the research goals.

#### 2.1. WDHS Optimal Sizing and Location

_{2}emissions go up due to ESS reduction [8].

#### 2.2. Project Efficiency Calculations

_{2}emissions, wind energy operating reserve at the optimal level of WT energy penetration [72].

#### 2.3. Reliability and Stability Estimate

## 3. Main Equipment Improvement

#### 3.1. Variable Speed DG

#### 3.2. WT

#### 3.2.1. HAWT

- Turbine structure.

_{p}= 0.593. Real operating efficiency will always be lower than the theoretical one due to rotary strengths, such as wake rotation, turbulence caused by drag or vortex shedding (tip losses), which reduce the maximum efficiency.

- Blades.

- Turbine.

- Generator.

- Layout.

#### 3.2.2. VAWT

- Structure.

- Layout.

#### 3.3. Economy Mode Setting Device

_{ω}), ICE fuel consumption (g

_{e}) and ICE shaft speed (ω). These parameters are transferred to the learning controller where they are compared with similar parameters available in the data memory block. If the parameters of the current mode match one of the modes’ parameters stored in the data memory unit, the learning controller reads out the information about the position of the fuel pump rail (value h) and transmits it to the management controller. The management controller forms a control signal in the ICE fuel supply system. This results in the fuel pump rail moving in the right direction by the required number of steps. If the learning controller input data do not match any mode stored in the data memory block, the learning controller performs several learning cycles of the memory block. Following the learning process, a new optimal position of the fuel pump rail is formed, which is transmitted to the management controller. The new mode parameters are additionally formed by input (P

_{ω}, g

_{e}and ω) and output (h) parameters which are stored in the data memory block.

#### 3.4. Energy Storage System

_{2}). It has low power losses, a high energy density, a quick response time and a high charge–discharge rate. Thoker et al. (2021) suggested using a sliding mode controller based on neural network adaptive radial basis function in order to control the SMES converter. Disturbance simulation results in Matlab confirmed the proposed control circuit efficiency, lowering the values of frequency deviations and setting time [118].

#### 3.5. Hybrid Electric Power Plants Based on DG, WT and Other RES Types

^{2}/day average solar insolation and a wind speed of 3.18 m/s. The LCOE of WSDHS is nearly two times lower, while the RES fraction is 1000 higher than those of WDHS. Even despite bigger capital expenses (250% higher), the WSDHS project will still be feasible [122]. This explains the fact why SDG projects have become most widespread in hot climate countries (in Africa and Asia). However, if wind speed increases to 5.5 m/s, the LCOE disparity will reduce to 37%, while the RES fraction for WDHS will only be 27% lower.

_{2}emissions. In an area with high average wind speeds, integrating PV modules into WDHS reduces annual fuel consumption 7.7 times and the NPC by 26%. In an area with a high average clearness index and solar insolation, integrating PV provides even better results: annual fuel consumption decreases 9.3 times, and NPC by 32%. However, if WT unit output is increased twice, the WDHS (WT-DG-ESS) project becomes more feasible. It should be noted that building WSDHS (PV-WT-DG- ESS) requires a larger area, several times bigger than the land area for WDHS, which can be a limiting factor when realizing the projects [124].

## 4. Control System

#### 4.1. WDHS Control Systems: Dispatch Strategies or Optimal Scheduling

_{2}emissions, and, therefore, allows the maximum fuel saving to be achieved. CO

_{2}emissions go down by 8, 21 and 22% on average compared to LF, CC and CmD, respectively [21].

#### 4.1.1. Energy Management System (EMS)

#### 4.1.2. Frequency and Voltage Control

#### 4.2. WT Control System Improvement

#### 4.2.1. Wind Prediction

^{2}= 0.9539 [136].

_{2}emissions. The proposed algorithm allows to make an optimum compromise satisfying the conditions of both reducing operation costs and environmental impact [137].

#### 4.2.2. Maximum Power Point Tracking (MPPT)

#### 4.2.3. Pitch Control

#### 4.3. ESS Control Algorithms

## 5. Conclusions and Future Directions

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

ABC | artificial bee colony |

AC | alternating current |

ACP-WDCAS | wind–diesel hybrid system with adiabatic air compression and storage at constant pressure |

AEP | annual energy production |

ANN | artificial neutral network |

ARMA | auto-regressive and moving-average |

BEM | blade element momentum |

BESS | battery energy storage system |

BFA | bacterial foraging algorithm |

BC | bidirectional converter |

BO | bonobo optimizer |

BOACO | bi-objective ant colony optimization |

BP | back propagation |

CACS | annualized cost of system |

CAES | compressed air energy storage |

CAPEX | capital expenses |

CC | cycle charging |

CD | conventional dispatch |

COE | cost of energy |

CmD | combination dispatch |

CS | cuckoo search |

CSA | crow search algorithm |

CVT | continuously variable transmission |

DC | direct current |

DG | diesel generator |

DEA | differential evolution algorithm |

DFIG | doubly fed induction generator |

DP | dynamic programming |

DPS | diesel power station |

DPSP | deficiency of power supply probability |

DTC | direct torque control |

ED | economic dispatch |

EENS | expected energy not supplied |

EI | environmental impact |

EL | ensemble learning |

ELSS | effective load-carrying capability |

EMS | energy management system |

EMSD | economy mode setting device |

ENS | energy not served (supplied) |

ESS | energy storage system |

EXC | energy excess percentage |

FA | firefly algorithm |

FC | fuel cell |

FCAS | frequency control ancillary services |

FESS | flywheel energy storage system |

FPA | flower pollination algorithm |

FPK | Fokker–Planck–Kolmogorov |

FSWT | fixed speed wind turbines |

FWT | Ferris wheel turbine |

GA | genetic algorithm |

GBS | grid bridge system |

GFMI | grid forming inverter |

GO | generator order |

GOA | grasshopper optimization algorithm |

GPR | Gaussian process regression |

GSC | grid-side converter |

GSO | group search optimization |

HAWT | horizontal axis wind turbine |

HBB-BC | hybrid big bang–big crunch |

HCS | hill-climbing search |

HDI | human development index |

HOGA | Hybrid Optimization by Genetic Algorithms |

HOMER | Hybrid Optimization of Multiple Energy Resources |

HS | harmony search |

HSBCS | harmony search-based chaotic search |

ICE | internal combustion engine |

IMOGWO | improved multi-objective grey wolf optimizer |

INV | inverter |

IRR | internal-rate-of-return |

JC | job creation |

LA | lead-acid |

LCC | life cycle cost |

LCOE | levelized cost of energy |

LEP | loss of energy probability |

LI | lithium-ion |

LF | load following |

LLS | linear least-square |

LOLP | loss of load probability |

LPSP | loss of power sup-ply probability |

LSA | lightning search algorithm |

MAPE | mean absolute percentage error |

MCS | Monte Carlo simulation |

MDFA | multidimensional firefly algorithm |

MFO | moth-flame optimizer |

MOEA | multi-objective evolutionary algorithm |

MOPSO | multi-objective particle swarm optimization |

MPPT | maximum power point tracker |

MRFO | manta ray foraging optimizer |

NARX-BPNN | non-linear auto-regressive model with exogenous inputs—back-propagation neural network |

NPC | net present cost |

NPV | net present value |

NSPSO | natural selection particle swarm optimization |

OLC | operating life cycle |

OT | optimal torque |

PAFC | phosphoric acid fuel cell |

PD | proportional–differential |

probability density function | |

PEMFC | proton exchange membrane fuel cell |

PFND | Pareto-front non-dominated sorting |

PMSG | permanent magnet synchronous generator |

PSF | power signal feedback |

PSO | particle swarm optimization |

PSOGSA | particle swarm–gravitational search algorithm |

PV | photovoltaic |

P&O | perturb and observe |

R | rectifier |

RC | renewable contribution |

REF | renewable energy fraction |

RES | renewable energy sources |

RSA | renewable source availability |

RSDG | rotating-stator mode for diesel generator |

RSC | rotor side converter |

SA | simulated annealing |

SCDG | super-capacitor diesel generator |

SMES | superconductive magnetic energy storage |

SOC | state of charge |

SWARA | stepwise weight assessment ratio analysis |

TAC | total annual cost |

THD | total harmonic distortion |

TLBO | teaching–learning-based optimization algorithm |

TS | tabu search |

TSR | tip speed ratio |

UC | ultra-condenser |

UPFC | unified power flow controller |

VAWT | vertical axis wind turbine |

VOC | voltage-oriented control |

VRB | vanadium redox battery |

VSWT | variable-speed wind turbines |

WASPAS | weighted aggregated sum product assessment |

WCA | water cycle algorithm |

WDHS | wind–diesel hybrid system |

WOA | whale optimization algorithm |

WRIG | wound-rotor induction generator |

WT | wind turbine |

ZB | zinc-bromine |

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**Figure 2.**WDHS structure sample. Adapted from Ref. [75].

**Figure 3.**WDHS block diagram: (

**a**) without ESS on the AC bus; (

**b**) with ESS on the AC bus; (

**c**) with ESS on the DC bus. DG—diesel generator; R—rectifier; WT—wind turbine; INV—inverter; L—load; ESS—energy storage system; BC—bidirectional converter.

**Figure 4.**Specific fuel consumption change for a 400 kW DG depending on the load. Adapted from Ref. [83].

**Figure 5.**Fuel map of a 25 kW (3000 rpm) diesel engine. Adapted from Ref. [85].

**Figure 6.**Variable and fixed speed DG efficiency at the load change. Adapted from Ref. [12].

**Figure 7.**Variable speed WDHS. Adapted from Ref. [13].

**Figure 8.**Modified hybrid DG as part of the solar diesel hybrid system. Adapted from Ref. [14].

**Figure 9.**HAWT optimal blade shape. Adapted from Ref. [87].

**Figure 10.**Invelox wind turbine. Adapted from Ref. [96].

**Figure 11.**Flowchart of the economy mode setting device (EMSD). Reprinted with permission from Ref. [15]: learning controller (1); management controller (2); content-addressable memory block (3); data memory block (4); data bus (5). DG power (P

_{ω}), ICE fuel consumption (g

_{e}) and shaft speed (ω).

**Figure 12.**Specific fuel consumption at load power change for a variable speed DG (red line) and a fixed speed DG (blue line). Reprinted with permission from Ref. [15].

**Figure 13.**WDHS with a storage system based on compressed air. Adapted from Ref. [17].

**Figure 14.**Flowchart of the WDHS with the high penetration operation algorithm: W

_{WT}—wind turbine energy generation; W

_{L}—load energy consumption; SOC—battery state of charge; SOC

_{min}—minimum allowable value of SOC. Adapted from Ref. [125].

Technology/Method | Maximal Decrease, % | Year | Ref. | |
---|---|---|---|---|

Fuel Consumption | COE or LCOE | |||

Optimization methods | ||||

Size optimization with load shifting control | N/A | 40 | 2021 | [8] |

Size optimization with ESS | N/A | 58 | 2020 | [9] |

ESS location optimization | 3.2 | N/A | 2017 | [10] |

Main equipment | ||||

Size increase: | 2013 | [11] | ||

– WT number | 51 | N/A | ||

– ESS capacity | 12 | + 28 | ||

Variable speed DG | 40 | N/A | 2022 | [12] |

1992 | [13] | |||

Dual speed mode DG | 2022 | [14] | ||

– with ESS | 10.9 | N/A | ||

– without ESS | 42 | N/A | ||

Economy mode setting device | 30 | N/A | 2020 | [15] |

Additional photovoltaic (PV) source | ||||

– with ESS | 53 | 21 | 2021 | [8] |

– without ESS | N/A | 60 | 2020 | [9] |

ESS | ||||

Hydrogen | 2.5 | N/A | 2007 | [16] |

Compressed air energy storage | 27 | N/A | 2019 | [17] |

Lithium-ion | 72 | 20 | 2020 | [18] |

ZB flow battery | 67 | 56 | ||

Pumped hydro storage | 40 | N/A | 2014 | [19] |

Vanadium redox battery | 77 | N/A | 2012 | [20] |

Control strategies and systems | ||||

Generator order | 22 | + 20 | 2021 | [21] |

Cycle charging with short-term forecasting | 34 | N/A | 2021 | [22] |

Energy management system on deep Q network | 28 | N/A | 2022 | [23] |

ESS charge algorithm | 2.5 | N/A | 2013 | [24] |

Optimization Algorithm | Objective Function | System Configuration | Constraints | Performance Comparison | Year | [Ref.] |
---|---|---|---|---|---|---|

Lightning search algorithm (LSA) | Minimize annual cost | PV-WT-DG-ESS | Energy not served (ENS) and renewable energy fraction (REF) | N/A | 2020 | [9] |

Improved multi-objective grey wolf optimizer (IMOGWO) | Minimize annualized cost of system (CACS) and deficiency of power supply probability (DPSP) | PV-WT-DG-tidal-ESS | Number of DG, PV, WT, tidal, ESS, generation unit power output, supply–demand balance, SOC | Better convergence than MOGWO and multi-objective particle swarm optimization (MOPSO) | 2020 | [25] |

Hybrid teaching–learning-based optimization algorithm (TLBO) and clone selection | Minimize annual total cost (TAC), loss of power supply probability (LPSP), and fuel cost | PV-WT-DG-ESS | Number of PV, WT, DG, ESS and charge quantity of battery | Better quality results than GA and PSO | 2016 | [36] |

Multi-objective evolutionary algorithm (MOEA) and GA | Minimize NPC and maximize human development index (HDI) and job creation (JC) | PV-WT-DG-ESS | Power balance, excess energy, SOC | N/A | 2016 | [37] |

Hybrid artificial neutral network (ANN), GA and Monte Carlo simulation (MCS) | Minimize NPC | PV-WT-DG-ESS | Probability of ENS limit | N/A | 2013 | [38] |

Markov-based GA | Minimize total cost | PV-WT-DG | Number of PV, WT and DG, loss of load probability (LOLP) and CO_{2} emissions | Better quality results and much smaller CPU time than chronology-based GA | 2012 | [39] |

Hybrid simulated annealing (SA)–tabu search (TS) | Minimize COE | PV-WT-DG- -Fuel Cell (FC)-biodiesel-ESS | Initial cost, unmet load, capacity shortage, fuel consumption, REF and components’ size | Better quality results and faster convergence than SA and TS | 2012 | [40] |

Flower pollination algorithm (FPA/SA) | Minimize LPSP and maximize cumulative savings | PV-WT-ESS | Number of PV and batteries, PV tilt angle | Better results quality than GA; more precise optimum values than PSO; less convergence time | 2015 | [41] |

Harmony search (HS)-based chaotic search (HSBCS) | Minimize life cycle cost (LCC) | PV-WT-ESS | LPSP, swept area of WT blades, total area of PV and number of ESS | Better average index, better standard deviation and mean simulation time than HS | 2016 | [42] |

Hybrid GA and exhaustive search technique | Minimize total cost | PV-WT-ESS | Number of PV, WT and ESS, PV tilt angle and wind generator installation height | Smaller number of iterations than GA | 2016 | [43] |

Natural selection particle swarm optimization (NSPSO) | Minimize LPSP, LCC, loss of energy probability (LEP) and energy fluctuation rate K_{l} | PV-WT-ESS | Number of type I/II PV, type I/II WT and ESS | Avoids a premature convergence effectively than GA; provides precise results with a lower fitness function value than GA | 2017 | [44] |

Wind Penetration Rate | Equipment Specific Performance Features | WT Usage, % | |
---|---|---|---|

By Installed Power | By Generated Energy | ||

Low | DG runs continuously. WT reduces the load on DG. WT participates in covering the main load. Automatic control system is not needed. | <50 | <20 |

Medium | DG runs continuously. At high WT, generation rate secondary loads are connected. An automatic control system is needed. | 50–100 | 20–50 |

High | At high WT, generation rate DG turns off [78]. Tools to maintain frequency and voltage are necessary. An intelligent control system is needed. | 100–400 | 50–150 |

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**MDPI and ACS Style**

Sosnina, E.; Dar’enkov, A.; Kurkin, A.; Lipuzhin, I.; Mamonov, A.
Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption. *Energies* **2023**, *16*, 184.
https://doi.org/10.3390/en16010184

**AMA Style**

Sosnina E, Dar’enkov A, Kurkin A, Lipuzhin I, Mamonov A.
Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption. *Energies*. 2023; 16(1):184.
https://doi.org/10.3390/en16010184

**Chicago/Turabian Style**

Sosnina, Elena, Andrey Dar’enkov, Andrey Kurkin, Ivan Lipuzhin, and Andrey Mamonov.
2023. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption" *Energies* 16, no. 1: 184.
https://doi.org/10.3390/en16010184