Integrated renewable energy system (IRES) is integration of different energy sources to provide uninterrupted and viable solution for electrification especially for areas not connected to main grid due to difficult terrain and economic reasons. IRES has many advantages like non-depleting, non-polluting nature, better load matching and better renewable energy utilization. In the present study, mathematical modelling, size optimization and techno-economic analysis of standalone IRES have been carried out. Hybrid system is modelled to have maximum contribution from wind and solar energy with minimum net present cost (NPC) of system to meet electric load demand of CRC building, IIT Madras, India (13.01°N and 80.24°E). The results show that most feasible system configuration consists of 12 kW Photovoltaics, 3 kW wind turbine and 15 kW biogas generator with NPC and cost of energy equal to $ 117,098 and $ 0.09/kWh respectively. The IRES generates 71,826 kWh of energy to meet AC load of 64,396 kWh per year. The capacity factor and percentage contribution of PV, wind turbine and biogas generator are 17.8%, 6.57%, 39.1% and 26%, 2.4%, 71.6% respectively. The paper also presents sensitivity analysis of hybrid system with variation in capital cost of different components.

Uninterruptible and viable power supply to every household is one of the main challenge Indian government has taken, especially in rural and remote places which due to difficult terrain or due to economic reasons not connected to the main power grid. In such locations, renewable energy sources like Photovoltaics (PV) and wind energy are gaining attention due to easy installation, higher energy utilization rate, lower power transmission loss and lower operational cost. However, unpredictable and inconsistent nature are major drawback of some of the renewable sources to provide viable power energy. To counter this different renewable and non-renewable sources can be integrated together for increased system efficiency, greater balance of energy and viable power supply (

Effective integration of hybrid energy sources has been gaining attention in the scientific community since past few decades to solve the technical and economic barriers for using renewable and distributed systems (

Ozden and Tari (

From the literature study, it has been observed that considerable work has been done on optimization of integrated system considering different configuration and optimization technique. However, most of the study did not optimize the system, considering seasonal variation in energy load demand. This is important for accurate designing of hybrid system to have maximum resource utilization with minimum system cost to meet load demand all time. Present study focus on size optimization and economic analysis of hybrid integrated renewable energy system with seasonal load demand. Mathematical modelling, feasibility study and control strategy of all the system component has been done. Also, sensitivity analysis of hybrid system with variation in system component cost has been carried out. The optimization of integrated system has been carried out using HOMER software.

The paper is organized as follows, Section 2 describes the proposed hybrid integrated renewable energy system. Section 3 explains specification and mathematical modeling of components of proposed IRES system and seasonal electric load demand. Section 4 explains methodology, constraints and control strategy implemented in present system. Section 5 discuss size, economic and energy analysis of optimized IRES configuration and section 6 discusses sensitivity analysis of system with variation in cost of system components.

The integrated energy system must be feasible and viable, i.e. at each time step some or the other energy system i.e. primary generation, backup system or storage system must be available to meet electric load demand. To achieve this, there are many possible configurations in which system components can be designed and integrated. Probabilistic approach of simulations of system components has been carried out for sizing based on wind speed, solar radiation, biogas specification, electric load demand and certain technical and physical parameter. The schematic of proposed IRES is shown in Figure

Schematic of integrated hybrid renewable energy system.

For validation, modelling has been carried out similar to done by Baghdadi et al. (

The power output of the wind turbine depends upon wind speed at the location and turbine specification and is calculated by Eq. (1) (

Where V(t) is speed of wind at time t, P_{r} is wind turbine rated power and V_{ci}, V_{r}, and V_{co} are respectively wind turbine cut in, rated and cut out speed (

By knowing wind profile at reference height, wind speed at any other height can be calculated by Eq. (2).

Where V_{h} is wind speed at height H, V_{ref} is wind speed at reference height H_{r}, and α is wind speed power law coefficient. The overall effective electrical power output of wind turbine (P_{Weff}) can be expressed as Eq. (3).

Where A_{w} is wind turbine swept area, η_{w} is wind turbine efficiency and η_{inv} is invertor’s efficiency. The wind speed for the complete year at height of 10 m above ground at IIT Madras, India is shown in Figure

Wind speed profile at IIT Madras, India.

Specification of wind turbine (

Parameter | Unit | Value |
---|---|---|

Rated Power | kW | 3 |

Rated wind speed | ms^{–1} |
12 |

Cut-in wind speed | ms^{–1} |
3.1 |

Cut-out wind speed | ms^{–1} |
24 |

Rated voltage | V | 240 |

Rotor diameter | m | 4.5 |

Swept area | m^{2} |
15.1 |

The basic element of solar PV module is a solar cell that converts incident solar radiation directly into electrical energy (DC current) (

Where N_{p} and N_{s} are respectively numbers of PV modules connected in parallel and series, I_{sc} is short circuit current (A) of PV module, V_{oc} is open-circuit voltage (V) of PV module, and FF is fill factor of panel.

The overall effective power from PV array is calculated by Eq. (5).

Where η_{mod} is PV module efficiency, η_{inv} is inventor efficiency.

The effects of solar irradiance at tilted PV (I_{T}), solar irradiance coefficient (γ_{c}), average temperature of PV (T_{PV,avg}), temperature coefficient (βc), efficiency of PV panel at standard test conditions (η_{STC}) and area of PV (A_{PV}) on the electrical output (E_{pv}) of the systems can be calculated by Eq. (6) (

The temperature of PV cell can be calculated by Eq. (7).

Where T_{amb} is ambient temperature, NOCT is nominal operating cell temperature (°C), S_{mod} is solar incident radiation (Wm^{–2}) on the PV module and S_{stc} is incident solar radiation at standard test condition (Wm^{–2}).

The power obtained from PV panel is directly related to the slope at which panel is installed. The maximum incident radiation on the solar panel for the given location is calculated by equation (8) (

Where declination angle δ is given by,

The yearly solar radiation at CRC building, IIT Madras is calculated and compared with the Indian Meteorological Department (IMD) solar radiation database (

Solar radiation profile at CRC building, IIT Madras, India (13.01°N, 80.24°E).

Specification of PV module (

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

Cell Configurations (Nos.) | 60 | Maximum System Voltage (DC) | 1000 |

P_{max} (W) (Tolerance: +3%) |
250 | Series Fuse Rating (A) | 15 |

V_{oc} (V) (Tolerance ±3%) |
37.00 | Nominal Operating Cell Temp. (°C) | 44.6 |

I_{sc} (A) (Tolerance ±3%) |
8.55 | Temp. Coefficient of P_{max} (%/°C) |
–0.45 |

V_{max} (V) (Tolerance ±3%) |
30.95 | Temp. Coefficient of V_{oc} (%/°C) |
–0.36 |

I_{max} (A) (Tolerance ±3%) |
8.08 | Temp. Coefficient of I_{sc} (%/°C) |
0.043 |

The charging of battery takes place when power produced by wind turbine and PV is more than electric load demand. Battery bank is used to serve load demand when the power output of wind turbine and PV system is less than threshold value and is insufficient to meet load demand. Battery bank capacity is selected based on total power needed and autonomy period of operation in a day. The battery bank cost for complete duration of the project has three components: capital cost, operation & maintenance (O&M) cost, and replacement cost. The capital cost of battery bank depends on its size and specification, O&M cost includes maintenance cost at regular interval whereas replacement cost is cost of replacing battery after particular duration (lifetime). Thus selection of right battery bank is important during integration of system. The tabular lead acid batteries are largely used for solar application in India because of their low cost and robust usage. Thus, authors have considered tabular lead acid batteries for present analysis. The characterization of battery to know its charge and discharge status is determined by its state of charge (SOC). The SOC is defined as the ratio of current capacity to nominal capacity of the battery. When battery is fully charged, SOC is one and when the battery is empty, SOC is zero. The instantaneous SOC of battery can be calculated by Eq. (10) (

Where SOC(t) is state of charge of battery at time t, SOC(t–1) is state of charge at (t–1) hours, σ is battery self-discharge rate, I_{bat} is battery current at time t (A), η_{bat} is battery charge efficiency and C_{bat} is capacity of battery bank.

The instantaneous battery current is given by Eq. (11) (

Where P_{pv}(t) and P_{w}(t) are respectively instantaneous power generated by PV and wind turbine system, P_{load}(t) is instantaneous load demand and V_{bat}(t) is terminal voltage of battery bank. The capacity of the battery bank C_{bat} is given by Eq. (12) (

Where E_{load} is total energy demand, AD is daily autonomy, DOD is depth of discharge of battery, η_{inv} is inventor efficiency, and η_{bat} is battery efficiency.

The biogas generator (Bio-Gen) is used as secondary power source for the proposed IRES system. The generator is used to meet peak load and when PV, wind turbine and battery power are insufficient to fulfill load demand. Biogas is used as fuel by biogas generator to produce electrical power. The biogas is produced by biodegradation of organic material fed into gasifier. The power output of biogas generator is given by Eq. (13) (

Where B is amount of biomass, C_{VBG} is calorific value of biogas (kJ-kg^{–1}), η_{BG} is efficiency of biogas generator and η_{gas} is efficiency of gasification of gasifier. The specification of generator considered for present study is listed in Table

Specification of generator (

Parameter | Specification |
---|---|

Model | ELEMAX SH5300EX Generator |

Engine type | 4 stroke, single cylinder, side valve, Spark Ignition engine |

Ignition system | Transistorized Coil Ignition (TCI) |

Rated Power | 6.3 kW @ 3600 rpm |

Generator AC output | 5.3 kVA @ 220 V, 60 Hz |

Cooling system | Forced Air Cooling |

Power converter is required to maintain flow of energy from different power sources of IRES to electric load by converting electric energy from one form to another (AC to DC and vice versa) (

The electric load profile is the main influencing factor for designing and optimizing of integrated hybrid energy system. So, it is very important to know how load vary from weekdays to weekends and from season to season for accurate designing of hybrid system to have maximum utilization of resources and minimum system cost. The electric loads considered for present study are computers, fans, lights, electronic devices and machinery. The load demand of the building varies with weather condition. Chennai has three major seasons’ summer, monsoon and winter. March to August is summer season, September to October is monsoon season and November to February is winter season. The seasonal average electric load profile and frequency histogram of load for CRC building, IIT Madras is shown in Figure

Seasonal load demand at CRC, IIT Madras.

The optimization of integrated hybrid system has been done by HOMER software. The software simulates all possible system configurations to find the optimum combination of hybrid system to match seasonal electric load demand (

The proposed optimisation process is based on lowest NPC of the system which is total of capital cost, O&M cost, replacement cost and salvage cost of the integrated system over the project life. The net present cost of the system calculated by HOMER is given by Eq. (14) (

Where C_{ann} is annualized cost, CRF is capital recovery factor, i is annual real discount rate, R_{proj} is project lifetime (25 years).

The capital recovery factor is ratio used to calculate present value of an annuity that is amount of cash flow annually over the lifetime of the project and is given by Eq. (15) (

Where i is nominal discount rate.

The levelized cost of energy (COE) is average cost to generate per kWh of useful electrical energy by integrated system. It is one of the important economic assessment factor considered while optimizing integrated energy system. It is the ratio of total annualized cost (C_{ann}) of system to useful energy served (E_{ser}) by the system and is calculated by Eq. (16) (

Salvage cost (C_{sal}) is value of the components of integrated system at the end of the project. The salvage cost is calculated by Eq. (17) (

Where C_{rep} is component’s replacement cost, R_{rem} is component’s remaining life, R_{comp} is the lifetime of the project.

The real discount rate is used to convert between one-time cost and annualized cost and is calculated by Eq. (18) (

Where i’ is nominal discount rate and f is expected inflation rate.

Present operational strategy aim at maximizing wind and solar PV energy utilization and reducing operational duration of biogas generator and thereby reducing pollutant emission. Economic data and operation life of different components of integrated hybrid energy system are listed in Table

Economic data and operational life of components of IRES.

Component | Capital cost (in $) | O & M cost (in $) | Replacement cost (in $) | Life |
---|---|---|---|---|

Wind turbine | 1667/kW | 50/kW/year | 1667/kW | 25 year |

Solar PV | 925/kW | 25/kW/year | 925/kW | 25 year |

Solar inverter and control panel | 198/kW | 8/kW/year | 198/kW | 10 year |

Battery (200 Ah, 12 V) | 153/batt | 7/batt/year | 125/batt | 5 year |

Biogas-generator | 470/kW | 0.01/kWh | 380/kW | 20,000 h |

The dispatch strategy for IRES is a control algorithm of interaction among various system components. The control operation is done on the basis of percentage of accurate load demand, wind turbine & PV power output and state of charge of battery. Figure

Flow chart of operational strategy of proposed HRES.

In the present section based on optimization strategy results of the proposed hybrid system are discussed based on optimal size, economic and energy output of the system.

The effectiveness of optimization strategy in the proposed system is demonstrated by finding the contribution of individual components involved namely wind turbine, PV, battery, biogas generator and converter system. Maximum wind, PV and battery usage is adopted to meet the electric load demand. The HOMER software simulates 2358 solutions based on technical and economic parameters to find the optimal configuration, out of which 783 were feasible and 1575 were not feasible due to capacity constraint. The optimisation process is based on net present cost. Table

Different configuration of some of the viable combination of integrated hybrid energy systems.

Combination | Solar PV (kW) | Windturbine (kW) | Bio-gen (kW) | Battery (no.) | Converter (kW) | COE ($/kWh) | Total NPC ($) | Operating cost ($/yr) | Initial capital ($) | Bio-gen (kWh/yr) | Solar PV (kWh/yr) | Wind turbine (kWh/yr) | Battery Autonomy (hr) | Battery Usable Nominal Cap (kWh) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 12 | 3 | 15 | 40 | 11 | 0.090 | 117098.1 | 6621.1 | 31503.9 | 51391.8 | 18708.2 | 1726.4 | 3.27 | 24.02 |

2 | 13 | 3 | 15 | 40 | 12 | 0.091 | 117288.7 | 6558.3 | 32506.5 | 50153.4 | 20267.2 | 1726.4 | 3.27 | 24.02 |

3 | 14 | 3 | 15 | 39 | 11 | 0.091 | 117305.4 | 6506.7 | 33190.1 | 49335.5 | 21826.3 | 1726.4 | 3.19 | 23.42 |

4 | 13 | 3 | 15 | 40 | 12 | 0.091 | 117432.7 | 6561.6 | 32608.0 | 50086.3 | 20267.2 | 1726.4 | 3.27 | 24.02 |

5 | 14 | 3 | 15 | 39 | 12 | 0.091 | 117592.4 | 6510.5 | 33427.3 | 49146.5 | 21826.3 | 1726.4 | 3.19 | 23.42 |

6 | 16 | 3 | 15 | 39 | 12 | 0.092 | 117988.0 | 6404.1 | 35199.0 | 47195.0 | 24944.3 | 1726.4 | 3.19 | 23.42 |

7 | 16 | 3 | 15 | 40 | 12 | 0.092 | 118195.4 | 6409.0 | 35342.8 | 47128.8 | 24944.3 | 1726.4 | 3.27 | 24.02 |

8 | 13 | 3 | 15 | 40 | 14 | 0.092 | 118331.7 | 6600.7 | 33001.3 | 50086.3 | 20267.2 | 1726.4 | 3.27 | 24.02 |

9 | 15 | 3 | 15 | 40 | 16 | 0.094 | 119761.6 | 6534.6 | 35284.9 | 48018.6 | 23385.3 | 1726.4 | 3.27 | 24.02 |

10 | 14 | 6 | 15 | 39 | 12 | 0.097 | 122569.8 | 6520.0 | 38282.0 | 47624.8 | 21826.3 | 3452.8 | 3.19 | 23.42 |

11 | 14 | 6 | 15 | 40 | 12 | 0.097 | 122596.5 | 6510.6 | 38430.6 | 47454.6 | 21826.3 | 3452.8 | 3.27 | 24.02 |

12 | 11 | 6 | 15 | 40 | 13 | 0.097 | 122617.5 | 6711.4 | 35856.3 | 50830.5 | 17149.2 | 3452.8 | 3.27 | 24.02 |

13 | 15 | 6 | 15 | 39 | 12 | 0.098 | 122980.3 | 6472.0 | 39313.6 | 46567.6 | 23385.3 | 3452.8 | 3.19 | 23.42 |

14 | 16 | 6 | 15 | 39 | 12 | 0.098 | 123314.7 | 6426.3 | 40238.6 | 45647.5 | 24944.3 | 3452.8 | 3.19 | 23.42 |

15 | 11 | 9 | 15 | 40 | 11 | 0.103 | 126981.2 | 6685.5 | 40553.6 | 49254.8 | 17149.2 | 5179.2 | 3.27 | 24.02 |

The optimisation process is based on net present cost which is total capital cost, O&M cost, replacement cost and salvage cost of all components of integrated system over the project life. The NPC and COE of the optimum IRES are $ 117,098 and $ 0.09 per kWh respectively. Table

Net present cost breakdown analysis of optimum hybrid integrated energy system.

Component | Principal cost (in $) | O & M cost (in $) | Replacement cost (in $) | Fuel cost (in $) | Salvage cost (in $) | Total ($) |
---|---|---|---|---|---|---|

Wind turbine | 5,000.00 | 1,939.13 | 0.00 | 0.00 | 0.00 | 6.939.13 |

Solar PV | 11,100.00 | 3,878.25 | 0.00 | 0.00 | 0.00 | 14,478.25 |

Solar inverter and control panel | 2,233.94 | 1,166.84 | 1,973.55 | 0.00 | 267.58 | 5,106.76 |

Battery | 6,120.00 | 3,619.70 | 19,495.61 | 0.00 | 1,156.56 | 28,078.75 |

Biogas-generator | 7,050.00 | 10,073.77 | 16,973.19 | 28,589.51 | 691.27 | 61,995.19 |

Complete IRES | 31,503.94 | 20,677.70 | 38,442.35 | 28,589.51 | 2,115.41 | 117,098.09 |

The total energy generated by integrated hybrid energy system in complete year is 71,826 kWh to supply AC primary load of 64,396 kWh per year. The energy output of 12 kW PV is 18,708 kWh/year with mean output of 51.3 kWh/day and has penetration factor of 29%. The energy output of wind turbine is 1,726 kwh/year and has penetration factor of 2.68%. The energy output of 15 kW biogas generator is 51,392 kWh/year with specific fuel consumption of 2.15 kg/kWh and mean electrical efficiency of 30.4%. The generator operate for 4,195 hours/year and has operational life of 4.85 years. The battery bank has autonomy of 3.27 hours with 11,486 kWh energy input, 9,198 kWh of energy output and 2,298 kWh of energy losses for a year. The expected life of battery is 4.11 years. The 11 kW inverter has capacity factor of 20.9% with 6,004 hours of operation in a year. The energy input, output and losses for inverter are 21,785 kWh, 20,695 kWh and 1,089 kWh for a year respectively. The 11 kW rectifier has capacity factor of 6.92% with 2,619 hours of operation in a year. The energy input, output and losses for rectifier are 7,204 kWh, 6,844 kWh and 360 kWh for a year respectively. The month wise percentage contribution of wind turbine, solar PV and biogas generator is shown in Figure

Monthly contribution of individual system components.

The hourly usage pattern of individual components is also important to know each components’ potential to satisfy load demand. Figure

Yearly Wind turbine output performance.

Yearly PV output performance.

Yearly battery bank performance.

Yearly Bio-Gen output performance.

Sensitivity analysis is important as it helps us to predict the behavior of the system under different conditions. For the present hybrid renewable energy system sensitivity analysis has been done with variation of wind turbine cost, PV cost, battery cost, bio-gen cost.

The net present cost and cost of energy of the hybrid system has been evaluated by variation in wind turbine cost. The wind turbine cost has been varied from 0.8 to 1.3 times to its present capital cost. The NPC and CoE of the system varied from $ 116,108 to $ 118,608 and $ 0.088/kWh to $ 0.093/kWh respectively. The variation in NPC and CoE with change in wind turbine cost are shown in Figure

CoE and NPC analysis of hybrid system with variation in wind turbine cost.

The net present cost and cost of energy of the hybrid system has been evaluated by variation in solar photovoltaic cost. The PV cost has been varied from 0.8 to 1.3 times to its present capital cost. The NPC and CoE of the system varied from $ 114,888 to $ 120,438 and $ 0.086/kWh to $ 0.097/kW/h respectively. The variation in NPC and CoE for change in PV cost are shown in Figure

CoE and NPC analysis of hybrid system with variation in PV cost.

The net present cost and cost of energy of the hybrid system has been evaluated by variation in battery cost. The battery cost has been varied from 0.8 to 1.3 times to its present capital cost. The NPC and CoE of the system varied from $ 115,884 to $ 118,944 and $ 0.88/kWh to $ 0.93/kWh respectively. The variation in NPC and CoE for change in battery cost are shown in Figure

CoE and NPC analysis of hybrid system with variation in battery cost.

The net present cost and cost of energy of the hybrid system has been evaluated by variation in bio generator cost. The generator cost has been varied from 0.8 to 1.3 times to its present capital cost. The NPC and CoE of the system varied from $ 115,698 to $ 119,223 and $ 0.88/kWh to $ 0.93/kWh respectively. The variation in NPC and CoE for change in battery cost are shown in Figure

CoE and NPC analysis of hybrid system with variation in bio-gen cost.

In the present study, a hybrid integrated energy system has been investigated using wind turbine, solar PV, and biogas generator as different source component to meet electric load demand. The design, modeling and operation of each system component had been described in detail. The optimization of the integrated system has been optimized by varying the size of the individual components as well as operational strategy. HOMER tool was used to optimize the overall system so as to have maximum renewable energy contribution with minimum net present cost. The most feasible system consists of 12 kW Photovoltaics, 3 kW wind turbine and 15 kW biogas generator with net present cost and cost of energy equal to $ 117,098.1 and $ 0.090/kWh unit respectively. Also, sensitivity analysis of hybrid renewable system has been done with variation in solar PV, wind turbine, battery and biogas generator cost. The IRES generates 71,826 kWh to meet AC load of 64,396 kWh per year. The capacity factor and percentage contribution of PV, wind turbine and biogas generator are 17.8%, 6.57%, 39.1% and 26%, 2.4%, 71.6% respectively. The results show that integrated system is very sensitive with variation in prices of components.

A_{w} |
Wind turbine swept area (m^{2}) |

C_{bat} |
Capacity of battery |

C_{VBG} |
Calorific value of biogas (MJ/kg) |

C_{NPC} |
Net present cost |

C_{ann} |
Annualized cost |

C_{rep} |
Component’s replacement cost |

E_{pv} |
Electrical output of PV system (W) |

E_{load} |
Energy demand (kWh) |

f | Expected inflation rate |

I_{sc} |
Short circuit current of PV module (A) |

I_{bat} |
Battery current (A) |

I_{T} |
Solar irradiance at tilted PV (W/m^{2}) |

I | Annual real discount rate |

i’ | Nominal discount rate |

N_{s} |
PV modules connected in parallel |

N_{p} |
PV modules connected in series |

P_{w} |
Power output of Wind turbine (kW) |

P_{Weff} |
Effective electrical power output of wind turbine (kW) |

P_{pv} |
Power output of solar PV array (W) |

P_{ar} |
Effective power output from PV array (W) |

P_{bio} |
Power generation by biogas generator (W) |

P_{load} |
Power load demand (W) |

R_{proj} |
Project lifetime |

R_{rem} |
Component’s remaining life |

R_{comp} |
Lifetime of the project |

S_{stc} |
Incident solar radiation at standard test condition (W/m^{2}) |

S_{mod} |
Solar radiation incident on the module (W/m^{2}) |

S_{inc} |
Solar radiation perpendicular to the sun (W/m^{2}) |

T_{PV,avg} |
Average Temperature of PV (°C) |

T_{amb} |
Ambient temperature (°C) |

t | Duration of operation |

V_{oc} |
Open circuit voltage of PV module (V) |

V_{ci}, |
Wind turbine cut in speed (m/s) |

V_{r} |
Wind turbine rated speed (m/s) |

V_{co} |
Wind turbine cut out speed (m/s) |

V_{bat} |
Terminal voltage of battery (V) |

AD | Daily autonomy |

CRC | Classroom complex |

CRF | Capital recovery factor |

DOD | Depth of discharge of battery |

FF | Fill factor of PV panel |

HOMER | Hybrid optimization of multiple energy resources |

IRES | Integrated renewable energy system |

NOCT | Nominal operating cell temperature (°C) |

PV | Photovoltaics |

SOC | State of charge of the battery |

β_{c} |
PV panel temperature coefficient |

γ_{c} |
Solar irradiance coefficient |

δ | Latitude degrees |

σ | Battery self-discharge rate |

η_{bat} |
Battery charge efficiency |

η_{bat} |
Battery efficiency |

η_{BG} |
Efficiency of biogas generator |

η_{gas} |
Efficiency of gasification of gasifier |

η_{w} |
Efficiency of wind turbine |

η_{inv} |
Efficiency of invertor |

η_{mod} |
PV module efficiency |

η_{stc} |
Efficiency of PV module at standard test condition |

The authors have no competing interests to declare.