Available Solar & Wind Resource

For this project Wind and Solar data is obtained using HOMER Pro, HOMER (Hybrid Optimization Model for Electric Renewables) is a simulation software tool used for analysing and optimizing hybrid renewable energy systems. HOMER, as a software tool for analysing and designing renewable energy systems, includes inbuilt solar and wind data for validation purposes. These datasets are typically derived from reliable sources such as meteorological databases, weather stations, or satellite data [1].

Figure 1: Monthly average wind speed

Figure 2: Average daily radiation

Figure 1 illustrates the average monthly solar global horizontal irradiance (GHI) and clearness index obtained from the NASA POWER database. The annual average GHI is recorded as 2.66 kWh/m2/day, with the peak occurring in June at 5.060 kWh/m2/day during the summer period, and the lowest in December at 0.380 kWh/m2/day during the winter period. The clearness index, which measures atmospheric clarity, is defined as the ratio of solar radiation transmitted through the atmosphere and reaching the Earth's surface to the amount of solar radiation incident on a horizontal plane at the top of the atmosphere. The highest clearness index value is observed in May at 0.477, while the lowest is recorded in December at 0.350. Figure 2 presents the average monthly wind speed data also sourced from the NASA POWER database. The annual average wind velocity is reported as 7.57 m/s, with the highest velocity occurring in January at 9.190 m/s, and the lowest velocity in July at 6.150 m/s.

System Configuration

Figure 3: Power system schematic

Model Configuration

The HOMER Model consist of the renewable generation source, storage, grid and the load. Sizing of the component was done to meet our Project KPOs as below:

Minimise cost of energy for local Industry
Maximise the proportion of demand met by local renewable generation
Minimise system complexity and capital cost
Contribute to the regeneration of derelict industrial buildings

Model Components

Figure 4: Wind turbine schematic

Wind Turbine

In the simulation conducted in HOMER, three AC wind turbines were utilized at a hub height of 30 meters. These turbines were chosen to fulfil a portion of the renewable energy requirement. The first turbine design involved a 1 MW capacity turbine, which accounted for 100% of the renewables. The second simulation incorporated a 0.8 MW turbine, representing 80% of the renewable fraction. Lastly, the third simulation featured a 0.5 MW turbine, fulfilling 50% of the renewable fraction.

To accurately model the performance, three parameters were considered during the design process. Firstly, a turbine performance loss of 20% was incorporated to account for inefficiencies in power conversion and transmission within the turbine system[2]. Secondly, environmental losses of 10% were incorporated to factor in variations in wind conditions that can impact power generation.

Figure 5: Inverter schematic

Converter

In the simulated hybrid systems, a converter is employed to convert the DC power generated by the PV array and stored in the battery into AC power at the AC bus bar. To achieve the desired power rating, the converters in the hybrid systems need to operate in parallel [3].

For the hybrid system model with a 200 kW PV solar capacity, a 60 kW converter unit is required. This can be achieved by running four 15 kW inverters in parallel.

Similarly, for the hybrid system model with a 500 kW PV solar capacity, a 250 kW converter unit is needed.

The sizing of the inverters is performed using the generic system converter component available in the HOMER library. During the design process, the following parameters are considered:

Power Rating
Efficiency
Parallel Operation [3]

Figure 6: Battery

Battery Storage

In our design, battery banks are utilized in the simulated hybrid systems to support renewable generation [4]. The storage plays a vital role in our design by fulfilling the following functions:

Ancillary Services: Battery banks are capable of providing ancillary services to the grid, including frequency regulation, voltage support, and reactive power control. By swiftly responding to grid signals, battery banks help maintain grid stability and enhance the overall quality of power.
Renewable Integration: Battery banks facilitate the smooth integration of renewable energy into existing power grids by acting as a buffer to balance supply and demand. They mitigate challenges associated with variable generation, such as solar intermittency or wind fluctuations. This enhances the reliability and compatibility of renewable energy sources with grid operations.
Microgrid Support: In microgrid systems, small battery banks play a crucial role in managing energy and ensuring stability by harmonizing the dynamics of supply and demand within the microgrid. They can provide backup power, support load sharing, and improve the resilience of the grid in isolated or remote locations.

Figure 7: Solar PV module

Solar PV

In our simulation, PV solar technology was incorporated to complement renewable energy generation, aiming to minimize supply variability and reduce the size of the storage system. For the project implementation, Trina Solar 500W PV modules were chosen.

In Scenario 2, the design necessitates the utilization of 400 modules arranged in a series-parallel configuration. This configuration enables efficient utilization of the PV modules' electrical characteristics to meet the requirements of the scenario.

In Scenario 3, a larger number of PV modules, specifically 1000 modules, will be required to achieve the desired renewable energy fraction. This increased number of modules ensures an adequate supply of solar energy to meet the energy demands of the scenario.

When sizing the PV system, various technical design parameters are taken into consideration to ensure optimal performance. Some of these parameters include:

System Efficiency: Evaluating the overall system efficiency, including PV module efficiency, inverter efficiency, and system losses, is crucial to estimate the actual energy production of the PV system accurately.
Temperature Coefficient: Considering the temperature coefficient of the PV modules is important as it affects their performance in varying temperature conditions. This parameter helps estimate the power output at different temperatures.
Array Orientation and Tilt Angle: Determining the optimal orientation and tilt angle of the PV modules ensures maximum energy capture based on the local solar path and latitude of the installation site.
Shading Analysis: Conducting a shading analysis helps identify potential obstructions, such as nearby buildings or vegetation, that might cast shadows on the PV modules. Minimizing shading ensures optimal energy production.

Simulated Systems

Simulation Results

Scenario 1

Figure 8: Monthly electricity generated

Figure 9: Self consumption & renewable fraction

Figure 10: Monthly energy export

Figure 9 presents the self-consumption and renewable energy fraction of the energy system, while Figure 8 displays the monthly generation from the system and Figure 10 shows the monthly export to grid. In this scenario, a self-consumption rate of 59.8% is achieved from an annual generation of 3.57 GWh. Additionally, it provides a net annual energy export to the grid of 1.16 GWh.

Scenario 2

Figure 11: Monthly electricity generation

Figure 12: Self consumption & renewable fraction

Figure 13: Monthly energy export

Figure 12 presents the self-consumption and renewable energy fraction of the energy system, while Figure 11 displays the monthly generation from the system and Figure 13 shows the monthly export to the grid. In this scenario, a self-consumption rate of 69.2% is achieved from an annual generation of 1.44 GWh. Additionally, the system it requires a net annual energy import from the the grid of 1.04 GWh.

Scenario 3

Figure 14: Monthly electricity generated

Figure 15: Self consumption & renewable fraction

Figure 16: Monthly energy imports

Figure 15 presents the self-consumption and renewable energy fraction of the energy system, while Figure 14 displays the monthly generation from the system and Figure 16 shows the monthly import from the grid. In this scenario, a self-consumption rate of 93% is achieved from an annual generation of 3.15 GWh. Additionally, it provides a net annual energy export to the grid of 0.64 GWh.

Comparison of Scenarios

Figure 17: Annual energy generation

Figure 18: Self consumption

Figure 19: Generation & load hourly simulation in June

Figure 17 illustrates the annual generation across all scenarios. Among them, scenario 1 exhibits the highest generation, while scenario 3 has the lowest generation. On the other hand, Figure 18 demonstrates that scenario 3 fulfils a significant portion of the annual primary load. In Figure 19, an hourly graph depicts the shortfalls of all scenarios during the second week of June. All scenarios require grid purchases to compensate for the low generation period. Furthermore, it is evident that scenario 1 and scenario 2 have a greater surplus generation compared to scenario 3.

References:

[1] Wilcox, S.M. “National Solar Radiation Database 1991–2010 Update: User’s Manual - NREL.” National Renewable Energy Lab, 2012, www.nrel.gov/docs/fy12osti/54824.pdf. [Accessed 7 May 2023].

[2] Temiloluwa, Akintayo, et al. “Assessment and Performance Evaluation of a Wind Turbine Power Output.” MDPI, 1 Aug. 2018, www.mdpi.com/1996-1073/11/8/1992. [Accessed 5 May 2023]

[3] Khadem, S.K., et al. “Parallel Operation of Inverters and Active Power Filters in Distributed Generation System-A Review.” Renewable and Sustainable Energy Reviews, 15 Sept. 2011, www.sciencedirect.com/science/article/pii/S1364032111002814. [Accessed 8 May 2023].

[4] Stenclik, Derek, et al. “Maintaining Balance: The Increasing Role of Energy Storage for Renewable Integration .” IEEE Explore, 17 Oct. 2017, ieeexplore.ieee.org/abstract/document/8070540/. [ Accessed 5 May 2023].

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