Abstract
Energy efficiency (EE) are recognized globally as a critical solution towards reduction of energy consumption, while the management of global carbon dioxide emission complement climate change. EE initiatives drive is a key factor towards climate change mitigation with variable renewable technologies. The paper aimed to design and simulate photovoltaic (PV), fuel cell stack (FCS) systems, and battery-super capacitor energy storage to enhance sustainable clean energy load demand and provide significant decarbonization potentials. An integration of high volume of data in real-time was obtained and energy mix fraction towards low carbon emission mitigation pathway strategy for grid linked renewables electricity generation was proposed as a solution for the future transport manufacturing energy supplement in South Africa. The interrelationship between energy efficiency and energy intensity variables are envisaged to result in approximately 87.6% of global electricity grid production; electricity energy demand under analysis can reduce the CO2 emissions by 0.098 metric tons and CO2 savings by 99.587 per metric tons. The scope serves as a fundamental guideline for future studies in the future transport manufacturing with provision of clean energy and sufficient capacity to supply the demand for customers within the manufacturing.
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1 Introduction
The increasing need for clean and sustainable technologies for energy sources in the power generation sector has motivated researchers to develop power generation sys-tems using renewable energy sources [1, 2, 3]. The quest for low carbon emission mitigation requires process technology diffusion. Energy efficiency for manufacturing plays a critical role in the economic growth, cost advantage against global competitors and provide a competitive advantage on a national scale. Additionally, interconnection of renewable energy sources into the distribution substation grid networks is constrained by the limitation of the network’s capacity and power flow behaviour [4, 5]. Microgrid captive energy supply and demand balances help to coordinate stable service within defined grid resources boundaries [6]. The provision for significant decarbonization potentials has motivated the quest for a conceptual design as a solution to enhance the integration of renewable energy sources into the grid linked networks. Researcher’s responses to greenhouse effect using solar PV, fuel cell stack and battery super capacitor energy storage with microgrid operation complexities increases when on a grid-connected mode, prompting cost increase. However, modelling in Matlab/Simulink along with electronic interfacing circuits are not widely used due to its disadvantages [7, 8]. Renewables energy penetration is enhanced by microgrid development in relations to dependable power supply, reduced transmission system expansion and ensures converter-interfaced generation units based on renewable power sources [9]. Recent research revealed that 80% savings are achieve in energy losses through localized control compared to centralized control [10]. The user’s need for sustainable lower energy cost, low carbon mitigation, reliable and resilient energy supply services, arising from issues regarding grid security and survivability [11]. The vast rise of energy demand necessitates actions towards grid optimization, conges-tions relief, and integration of ancillary services [12]. Energy efficient practices complement disaggregated econometric estimations of manufacturing operations. Energy demand through manufacturing operations can alter understanding of consumption from energy sources with direct implications on significant decarbonization potentials, while analysed data from microgrid are related to EE initiative, costs performance, and energy savings.
2 Methodology
2.1 Microgrid Design Configuration
The designed microgrid system uses monocrystalline PV modules is simulated with the substation in Matlab/Simulink, including interconnected solar PV cell, and energy storage rated at 100 295 kWp (kilowatts-peak). 50-kW fuel cell stack, and battery and super capacitor storage at 500 V dc voltage to provide backup for the system in an emergency [13]. The microgrid has sets of battery storage unit with individual lithium cells connected in series, with rated capacity of 21.7 kWh. Figure 1 shows the proposed microgrid system simulated configuration comprising solar PV array connection to DC bus through a dc/dc boost converter. PV array generates DC voltage by converting solar PV modules energy with variable irradiance temperature to electrical energy. And. Fuel cells stack generate a DC voltage by converting chemical energy to electrical energy with the inputs of hydrogen and oxygen. DC/DC converters step-up/step-down the voltage. A battery serves as energy storage device, connected to same dc bus via dc/dc bidirectional converter. The ac and dc buses are connected through ac/dc bidirectional converter. The controller collects the energy supply data and delivers through the communication link via control demands. The power converters coordinated the power flow signals for energy management system (EMS) through the power link. Energy storage (super capacitor) is used for storage of energy in case of insufficient power from photovoltaic array, since this source is weather dependent hence, the inputs cannot be control, therefore prompting the use of MPPT controller to extract maximum power from the PV. The output voltages are synchronized via the common DC bus, while the three-phase inverter converts direct current to alternating current for AC loads and the three-phase transformer steps up the voltage.
2.2 Microgrid Design Modeling
2.2.1 Mathematical Model of a Solar PV Cell
The solar cell circuit shown in Fig. 1 have Iph is the photon current (A), ID is the diode current (A), Rs is the series resistance (Ω), Rsh is the shunt resistance (Ω), and I, V are the current (A) and voltage (V) of the solar cell respectively. Applying Kirchhoff’s law IS as the saturation current (A); A is the thermal voltage (V); KB is the Boltzmann’s constant (J/K); q is the electronic charge (C) results in the following equation: the four parameters Iph, Rs, A and Is determined from the manufacturer data depicting \(A=\frac{mk{T}_{c}}{q}\)
To determine the parameters of a PV cell:
Thermal voltage at reference can be determined from manufacturer data provided at given reference conditions using VOC is the open circuit voltage, E.g is the cell material band gap and NS is the number of cells in series in one module. Photon current is equal to short circuit current.Rs is usually provided on the manufacturer data sheet if not provided calculated as
the photon current calculated as:
At reference conditions, saturation current at operating cell temperature calculated as:
2.2.2 Fuel Cell
Fuel cell systems modelling was set with proton exchange membrane fuel cell (PEMFC) normal parameters based on an electrochemical process that directly converts the chemical energy of the fuel to electrical power. Stack power settings range from Nominal (5000 watts) to Maximal (120400 watts). Fuel cells resistance of (0.66404 Ω), which work like batteries with Nerst voltage of one cell (En = 1.1342 V), but do not run down or need recharging. The fuel cell consists of two electrodes namely, hydrogen, which feed the anode and the air, fed to the cathode produce electricity and heat. Due to the activation losses and losses caused by diffusion and resistivity, the output voltage of series connected fuel cells expressed as:
where EO and A are empirical coefficients, Ifc and Io are the fuel exchange current in ampere, TD denotes the settling time of a cell due to a change in current, and R denotes the diffusion resistance., N is the number of cells connected in series to maximize the voltage.
2.2.3 Boost Converter
In the proposed microgrid system, the boost converter mainly used to step up the output voltage of the PV, an MPPT controller that uses perturb and observe technique optimizes the switching duty cycle. This MPPT system automatically varies the voltage across the terminals of the PV array to get the maximum possible power. The ratio of open circuit and short circuit voltage relationship for the inductor L is:
2.2.4 Buck Converter Components Design
The buck converter will be mainly used to step down the output voltage of a fuel cell stack, the fuel cell stack found in Simscape library with an output voltage of 625Â V. the DC voltage control block will be used to control the output voltage of the buck converter and the output of the DC voltage control will regulate the MOSFET ensuring that the output voltage does not exceed the reference voltage. Inductor size can be express as:
where fs is the switching frequency and \(\Delta {I}_{L}\) is the inductor current ripple, and the inductor ripple current is usually smaller than output current. Hybridization of a supercapacitor connected with a battery to a buck-boost converter is limited by a rate limiter block.
2.2.5 Low Carbon Emission Conversion from Energy Mix
Low carbon emission mitigation evaluation is relative to energy generation and consumption in manufacturing sector using applicable emission factor (7.0555*10^ (−4)) for energy mix carbon emissions estimation is model as.
Non-baseload CO2 output emission rate are used to convert reductions of kilowatts hours into avoided units of carbon dioxide emissions. Coal energy content of 25.532Â MJ/kg is adopted for the equivalent of emissions reductions from energy efficiency programmes assumed to affect non-baseload generation (power plants that brought online as necessary to meet demand).
3 Results and Discussion
Power electronic converters used in renewable energy sources mainly in hybrid microgrid system. However, certain precautions are considered in the circuit operations such as frequency variations and voltage synchronization from different sources during system performance evaluation. The results displayed some frequency variations that shows large current and voltage harmonics in the proposed hybrid microgrid system done under different circumstances to ensure reliable, efficient, and cost-effective solution for the intended users. The solar PV were tested with different temperatures and irradiances to see how the system behaves when operating at fixed temperature, while varying the irradiance and vice versa. The change in irradiance with constant temperature of 25 ℃. Cells per module (Ncell) is 96, number of series connected modules per string (5), using 5 series modules and 112 parallel strings. The maximum current and voltage [Solar irradiance (input)] obtained at 1000 W/m2, when irradiance decreases the current, power, and the voltage slightly decreases. The decrease in irradiance results to insufficient power generation. Operating temperatures are varied at 0, 25 and 50 ℃; Temperature coefficient of ISC is 0.061745%/℃; Light generated current IL at 6.0092 A, Maximum power (305.226 W); VOC (64.2 V), ISC (5.96 A), VMP (54.7 V), IMP (5.58 A); Diode saturation current IO (6.3014e−12 A), Diode ideality factor (0.94504), Shunt resistance Rsh (269.5934 Ω) and Series resistance Rs (0.37152 Ω).
3.1 Photovoltaic Simulated Results
The photovoltaic is simulated in different irradiances and temperatures; the signal builder with two input signals temperature and irradiance for PV array was utilized in the design. Figure 3 is the simulation of the PV array demonstrating the behavior of the PV array when temperature or irradiance changes. A drastically decrease in irradiance results to a drastically decrease in power delivered by the PV array this behavior validates the theory based on the solar energy. The mean voltage is almost constant since an MPPT controller extracting the possible maximum power from the PV array regulates the duty cycle (Fig. 2).
In a hydrogen fuel cell, a catalyst at the anode separates hydrogen molecules into protons and electrons, which take different paths to the cathode. The electrons go through an external circuit, creating a flow of electricity. The protons migrate through the electrolyte to the cathode, where they unite with oxygen and the electrons to produce water and heat. To increase the low voltage of a single fuel cell, many cells connected in series to form a fuel cell stack. The fuel cell signal variation {x_H2 = 99.95%] and {y_O2 = 21.0%}. Hydrogen utilization range between [Nominal 417.3 l pm and Maximum 1460 l pm]. The system temperature is set to [T = 338 K], Fuel supply pressure [Pfuel = 1.5 bar] and Air supply pressure [Pair = 1 bar].
The integrated system of photovoltaic assisted by fuel cell stack operating in parallel yielded the results shown in Fig. 3 after conversion from DC-AC through the universal bridge, the voltage is precisely the desired DC voltage at the DC bus. The time span of the scope was set to 0.5, the scope shows the current and voltage characteristics of the three-phase inverter. Large harmonics were observed during the operation of the system and due to such system behavior, it is not advisable to directly connect AC loads assuming that the voltage has been converted to AC this could possibly result to overheating of components connected directly to the converter, for AC loads to be properly connected these harmonics must be filtered. Figure 3 demonstrate the characteristics of current and voltage at the reduced time span (Fig. 4).
3.2 Low Carbon Emission Mitigation Pathway Strategy
The feasibility of a microgrid system based on a PV, FSC, and battery-super capacitor design has been demonstrated to enhance sustainable clean energy load demands. Comparing the results and the requirements of the user it has been observed that the simulation of this design yields the precise requirements, the designed microgrid system have the capability to deliver 170 kW which is within the range of the user’s requirements. From the simulated and tested results, we concluded that the requirements of the user were met. Table 1 is the simulated data from microgrid tied with PV, FSC, and battery-super capacitor in MATLAB/SIMULINK.
The hourly-amassed energy production is 160.55 kW/h for grid tie supply, 85.6 kW/h (53.3%) from Solar PV; 53.1 kW/h (33.0%) from fuel cell; 138.7 kW/h (86.39%) Solar PV and Fuel Cell combined. The simulated energy consumption is 137.7 kW/h (85.76%) of grid supply from Table 1 shows in Fig. 5, Fig. 6 and Fig. 7 for Grid tied supply, Solar PV, and Fuel Stack Cell respectively. Figure 8 is the sum of simulated hourly data of Energy Consumption.
The need for renewable energy mix fraction towards low carbon emission mitigation pathway strategy require reduction of fossil fuel usage in energy generation by 87.76% of grid electricity supply if the study is adopted. Applying country-specific energy carbon emission equivalent factors for coal mining derived from 2019 [14, 15, 16], coal energy content of 25.532Â MJ/kg. This study brought forward the argument of the importance of renewable energy use appropriateness design and simulation in comparison of interrelationship between energy efficiency and energy intensity variables f of the electricity energy demand under analysis, which can reduce the CO2 emissions by 0.098 metric tons and CO2 savings by 99.587 emission per metric tons. The substantial reduction of carbon emissions by the design and simulated systems could assist in decarbonizing the environments. The microgrid integrated energy system could mitigate CO2 emission from use of coal as fuel source to make the environment cleaner and ecofriendly. Moreover, the outcome of this paper can provide sustainable design strategy and guide for policy makers and investors in alternative energy mix (fuel source) towards a low-carbon carbon emission pathway for more profitable transition to enhanced energy microgrids.
4 Conclusion
The hybrid microgrid system proposed in this study has been successfully simulated and system performance analysis has been tested and evaluated when the system operating in off-grid mode with an AC load of 120Â kW. It has been observed that the system yields stable output voltage and current. Approximately 170Â kW real power added to the grid when operating in grid-connected mode. Hence, the proposed hybrid microgrid system considered as power generation for rural areas for the ongoing power crisis.
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Adenuga, O.T., Mpofu, K., Mathenjwa, T. (2023). Energy Efficiency for Manufacturing Using PV, FSC, and Battery-Super Capacitor Design to Enhance Sustainable Clean Energy Load Demand. In: Kim, KY., Monplaisir, L., Rickli, J. (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus . FAIM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-18326-3_26
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