Document Type : Original Article
Authors
1
Department of Industrial Management, Firuzkoh Branch, Islamic Azad University, Firozkoh, Iran
2
Department of Mathematics, Firuzkoh Branch, Islamic Azad University, Firozkoh,
Abstract
Introduction:
According to Article 8 of the approvals of the Supreme Energy Council of the country, all executive bodies subject to Article (5) of the Civil Service Law are required to provide five percent (5%) of their annual electricity needs through the construction of renewable power plants, and this amount at the end of the fourth year reach at least twenty percent (20%), at the same time, due to the restrictions on electricity consumption in the hot season of the year and power cuts in industries, the use of energy production equipment has become very important, and organizations are required to use of these equipments, in this research, optimization of the combined system consisting of solar photovoltaic panels and diesel generator as two independent decision variables and 7 responses or optimization objective function including system electricity consumption, system gas consumption, diesel fuel consumption, The reduction of environmental pollutants, the cost of maintenance and repairs, the cost of stopping production lines and also the return on investment are investigated as dependent variables of the research, an optimization method is used to achieve the best possible design in Transis software, in addition to finding To best combine the selected factors in the system, the response level method is used, the main purpose of the response level is to estimate and predict the effect of independent variables on the dependent variable. The results show that the effect of the change in the area of solar panels to produce electricity and the power of the diesel generator on the utility function has been selected to the optimal state, its value is 0.740, and it means that the combination of variables planned in the optimization section in The best optimal state has been reached, whose number is close to the highest possible value in the ideal state with a value of 1. Also, strategy 1, which includes the direct purchase of the total electricity demand from the grid and the direct sale of the total electricity produced by the system, is economically It seems more economical.
Materials and Methods:
The precise design of parallel systems including solar panels and distributed generation devices is very important so that all parameters are in their optimal state. Therefore, in this research, an optimization method is used to achieve the best possible design in Transis software. In this research, the experiment design method is used with the help of the response surface method, the response surface method is a statistical method that is used to investigate the interactions between independent variables in the processes and optimize them. The main purpose of the response level method is to estimate and predict the effect of independent variables on the dependent variable. For this purpose, mathematical models are used that describe the relationship between independent and dependent variables. In general, the system is first implemented in the Transis software, then the output obtained in the Design Expert software is performed using the response level design method. and again, these outputs are entered into Transis software and model optimization is done. According to the selected factors, the test design method (response level) designs and proposes a set of tests or simulations, which in the conducted research, 13 tests are performed, and these responses are a quadratic equation for pre The analysis of the relationship between the energy-economic responses will be chosen and will form the independent optimization factors that are used from equation 1:
y is the considered energy-economic response, z is the selected factor (factor) to optimize, i and j are the counters of the number of independent factors and N_f is the number of factors. Also, β's are unknown coefficients that will be obtained by regression analysis.
de_i is the desirability of answer i and N_r is the number of answers. It is necessary to explain that the purpose of multi-objective optimization is to maximize the combined utility.
The power consumption of power generation equipment, including pumps, compressed air compressors, production presses, welding equipment, determines the annual power consumption of the system. This is obtained through equation 3.
N_t is the number of time steps in the numerical solution for the entire duration of the simulation. PC is energy consumption in kJ h-1, f is a coefficient that indicates the on or off status of each component. When the consumer device is on, f is equal to one and when it is off, f is equal to zero.
Considering that an auxiliary boiler with natural gas fuel has been used to support the solar system and to recover the desiccant wheel, in order to increase the temperature of the working fluid to a certain temperature (T_set), the annual consumption of natural gas (ANGC) is obtained from equation 4 comes:
η_boiler is the efficiency of the boiler and LHV is the lower calorific value of the consumed natural gas.
Findings:
The response level method is used to obtain the best combination of the selected factors, the values predicted by the response level test design method for the factors in order to achieve the optimal system.
The highest value of the utility function or CD is equal to 0.725. This result shows that by using the optimal combination of the mentioned factors, the system reaches an optimal state (optimal system) and the value of the utility function approaches 0.725. By increasing the power of the diesel generator from 0 to 3000 kilowatts, the amount of total electricity consumption will decrease from about 7000000 kilowatt hours per year to about 2500000 kilowatt hours per year. In order to optimize the system, the test design method (response level) has been used. The most optimal point is in the area of solar panels equal to 16143.5 square meters and in cchp power equal to 2328.29 kilowatts. At this optimal point, the total electricity consumption is equal to -1327920 kWh per year. Increasing the power of cchp from 0 to 1600 kW leads to a sharp reduction in gas consumption, in this model gas consumption is reduced by 77.4%, which is equivalent to 1310000 cubic meters per year and will reach about 300000 cubic meters per year. Changes in gas consumption and cchp fuel consumption have opposite trends. In fact, it is not possible to reduce gas consumption and fuel consumption in CCHP at the same time, and their trends are opposite to each other. The payback period is less effective with the increase in the area of solar panels. On the other hand, increasing the power of cchp up to about 2000 kW will lead to a sharp decrease in the payback period. Also, increasing the power of cchp to more than 2000 to 3000 kW will lead to the return on investment period will increase. Due to the use of solar panels and cchp, the operation of energy production equipment is reduced and this will lead to a reduction in the time used in maintenance and repairs, as well as a reduction in the purchase of spare parts. Due to power cuts in industries during peak times and the problems of lack of support for production lines due to the stoppage of production lines, with the implementation of the plan to use solar panels and cchp, production line stops will be zero.
Discussion and Conclusion:
According to the simulation results of the multi-source system using the test design method (response surface), it showed that the solar panels and cchp in the optimal state are equal to 16143 square meters and 2328 kW, while the best performance is in optimal conditions. The optimal system has a total electricity consumption of 13,227,920 kilowatts, a total gas consumption of 559,488 cubic meters, a total diesel fuel consumption of 2,228,300 liters, an amount of environmental pollutants of 4,842 kilograms, and an investment return period of 2.29 years, maintenance and repair costs of $1,813, and production line shutdown costs. It is 4891880 dollars. From the analysis of the results, it can be concluded that the optimal combined system with cchp and solar panels is able to provide the total electricity required by the complex not only during peak hours (when the demand for electricity is high) but also during off-peak hours (when the demand for electricity is lower). is) is This system shows the ability to generate excess electricity at certain times that can be sold to the public power grid.
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