Modeling and Optimization of Solar PV System With Pumped Hydro Energy Storage System

Implementation of modified AHP coupled with MOORA methods for modeling and optimization of solar photovoltaic (PV)-pumped hydro energy storage (PHS) system parameter is presented in this chapter. Work optimized the parameters, namely unmet energy (UE), size of PV-panel, and volume of upper reservoir (UR), to get economic cost of energy (COE) and excess energy (EE). The trail no.11 produces the highest assessment values compared to the other trails and provides EE as 16.19% and COE as 0.59 $/kWh for PV-PHS. ANOVA and parametric study is also performed to determine the significance of the parameters for PV-PHS performance. Investigation results indicate the effectiveness and significant potential for modeling and optimization of PV-PHS system and other solar energy systems.

Modeling and Optimization of Solar Flat Plate Collector

Solar flat plate collector (SFPC) is a heat exchanger that transforms radiant solar energy into thermal energy in the form of heated fluid. The performance of SFPC is very much dependent on operating/input and response/output parameter which mainly affects the efficiency of SFPC. This chapter presented the modeling and optimization of SFPC system parameters (solar radiation [I], wind velocity [V], ambient temperature [Ta], and Inlet Temperature [Ti]) for SFPC. Modified-fuzzy set theory with MOOSRA (M-FST-MOOSRA) was employed to optimize the SFPC system. Based on results, trail no. 14 (i.e., I = 825 W/m2, V = 1.4 m/s, Ta = 28.8oC, and Ti = 66.4oC) gave highest RPI among the other trail nos. and shows the optimal setting which results in higher efficiency and better performance for the SFPC. Further, parametric analysis is also done to determine the most important parameter followed by analysis of variance (ANOVA) analysis. Last, confirmatory test are conducted to verify and validate the proposed method with the experimental results.

Modeling and Optimization of Parabolic Trough Collector

Parabolic trough collector (PTC) is a concentrating collector widely used for steam cooking, water heating, and also steam power generation and desalination work. The performance of PTC is strongly depends on its process parameters and is a MCDM problem. Implementation of integrated method, that is, entropy with graph theory and matrix approach (E-GTMA) for modelling and optimization of PTC parameters to improve higher outlet temperature (To), higher heat gain (h), and higher thermal efficiency (ηth), is discussed in this chapter. Investigation results indicate the effectiveness of this technique for multi-objective optimization and determined optimal setting as Test no.10 for PTC. Additionally, parametric and ANOVA analysis is carried out to determine the significance and adequacy of the developed model. Last, validation of the proposed model and verification results is done via confirmatory tests, and tests results show comparable and acceptable w.r.t. experimental results.

Modeling and Optimization of Evacuated Tubular Solar Thermal Collector

Modeling and optimization of evacuated tubular solar thermal collector (ETSTC) is discussed using a modified simple additive weighting (M-SAW) method. To improve the system efficiency (η) and end day temperature (Tsfd), ETSTC parameter (i.e., start day temperature [Tsid], ambient temperature [Tad], global solar radiation on tilted surface [GT], and wind speed [Ws]) are optimized. The applied method is significantly improved the efficiency (η) and determined the best setting for ETSCT. Test no.10 is the optimal experimental trail run and corresponding collector efficiency is obtained as 43%. Further, experimental data are statistically tested via parametric, ANOVA analysis, and found satisfactory and acceptable. Last, confirmatory tests results show comparable and acceptable w.r.t. experimental results for the optimal setting obtained through proposed method. The proposed MCDM method can be recognized as potential use for modeling and optimization of other thermal systems.

Modeling and Optimization of Concentrated Solar Thermal System

This chapter discussed the modeling and optimization of concentrated solar thermal (CST) system using data envelopment analysis-based ranking (DEAR) method. Experimentation on CST under different environment (i.e., summer and winter season) of Silchar (Southern Assam, India) conditions is done. In order to improve the temperature (T), height of the receiver, that is, summer height and winter height (hs and hw) for summer and winter conditions, CST parameters are optimized. Study concludes that performance of CST under considered environments is feasible and use of Data envelopment analysis based ranking (DEAR) method found feasible and provides optimal results. Additionally, ANOVA analysis is carried out to determine the significance and adequacy of the developed model. The results show that all the response parameters are the major parameters for CST system and also model fitted data well fitted with experimental result within the 95% confidence level. At last, confirmatory test are conducted to verify and validate the proposed method with the experimental results.

Introduction

This chapter introduces to the solar energy in general and in perspective as far as the applications and utility of solar energy systems are concerned. It gives the brief overviews of all types of solar energy systems starting with their classifications followed by their modeling and optimization issues. Work sheds light on both solar thermal systems and solar photovoltaic (PV) systems. The performance measurement of these solar energy systems strongly depends on various system and operating parameters along with their ambient or climatic parameters, and it is a multi-criteria decision-making (MCDM) optimization problem. Based on the previous studies, the statistics as regards to the modeling and optimization of different solar energy systems and the basics of MCDM techniques with their classifications are discussed. The chapter ends with detailed summary with possible scope of future work on modelling and optimization of solar energy systems using various individuals plus hybrid MCDM techniques.