Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

Abstract

Summary This article proposes a multiobjective optimization model for renewable energy sources (RESs) and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems (CCHP). The hybrid CCHP system contains turbine, photovoltaic/thermal collectors, cooler/heater, supply setup, battery, and tank storage. The proposed hybrid method is the joint implementation of Garra Rufa Fish Optimization (GRFO) and Student Psychology Optimization Algorithm (SPOA); hence, it is named GRFO‐SPOA approach. An energy converters energy‐hub model along storage devices considers the properties of component of off‐design. The uncertainty of solar radiation together with building loads is exhibited at parametric manner and probability distributions. Assuming the uncertainty with system reliability and hybrid CCHP is enhanced to attain the feasible energetic, economic, and environmental benefit utilizing the GRFO‐SPOA method. To predict a new set, the GRFO‐SPOA method utilizes current datasets in the uncertainty modeling. The decision variables involves capacity of gas turbine and photovoltaic/thermal collectors, capacity of battery with water storing tank, and operational ratio of heat pump. The proposed method is implemented in MATLAB/Simulink; its efficiency is analyzed with other existing methods, like GA, SSA, and TSA technique. Once the confidence level of the system diminishes as of 0.99 to 0.50, the hybrid CCHP likened with traditional separate production system saves on average 13.7% of main energy and lessens 80% of acid gas emissions carbonic. The annual value saving rate is decreased because the confidence level of the system decreases and the uncertainty maximizes. The sensitivity analysis of the economist frontiers is executed on key economic parameters; therefore, it is obtained as an outcome of annual value saving rate is very sensitive to the value of fossil fuels, and the value of inversion of the star collectors has a stronger impact than that of turbine. The fist‐order statistical evaluation parameters, like mean, median, and SD, at 100 iterations for proposed technique is 0.61038, 0.5317, and 0.00543. Computation time utilizing 100, 150, 200, 250, and 500 trails of proposed technique is 48.1740 seconds, 51.2133 seconds, 71.0483 seconds, 60.00126 seconds, and 57.80132 seconds.

Theoretical Framework of Supply Chain Uncertainties

“…We'll highlight any unique sources of uncertainty that each model accounts for as we go through them. To begin, Elango, Prakash, and Umasankar [12] was among the first to identify three uncertainty sources: demand, supply and manufacturing. According to this concept, the uncertainty in the production process determines the timeliness with which orders may be completed.…”