Decision Science and Operations Management of Solar Energy Systems

Description

Decision Science and Operations Management of Solar Energy System looks beyond developing a solar power plant by also considering the requirements necessary to manage effective power plant operation for the long-term. This book includes data of solar power plants and quantitative techniques of statistical analysis used to inform decision-making for solar energy systems, thus enabling readers to predict future individual solar power system forecasts using different technical and financial parameters. Including data visualization, descriptive statistics, sampling techniques, plant layout, manufacturing economics, inventory management and total quality management of solar energy system, this book covers new insights as well as established fundamentals. The detailed information in this reference bridges the gap between theory and practice in the operation of solar energy systems for researchers, professionals and students working in the area of solar and renewable energy. Front cover Half title Title Copyright Contents Chapter 1 Fundamental and basic principles 1.1 Introduction 1.2 Principles of solar energy system 1.2.1 How do these system works? 1.2.2 System component 1.2.3 Other components 1.3 Optimum design of solar energy system 1.4 Worldwide and Indian scenario of solar energy system 1.4.1 Worldwide scenario of solar energy system 1.4.2 Indian scenario of solar energy system 1.5 Fundamental of decision science 1.6 Fundamental of operational management process 1.6.1 Operations management functions 1.7 Conclusion 1.8 Exercise/question 1 References Chapter 2 Data visualization and descriptive statistics of solar energy system 2.1 Introduction 2.2 Basics of data visualization and descriptive statistics 2.2.1 3M concept of measurement of central tendency of solar energy system data 2.2.2 PQ Assessment of solar energy system data 2.2.3 MVS assessment of solar energy data 2.3 Frequency distribution of prefeasibility data of solar energy system 2.4 Quantitative and qualitative analysis of solar radiation data 2.4.1 Crosstabulation 2.4.2 Scatter plot 2.4.3 Two main approaches to qualitative data analysis 2.5 Measurement of central tendency and variability of solar energy data 2.5.1 Mean 2.5.2 Median 2.5.3 Mode 2.5.4 Measures of variability 2.5.5 Measures of variability 2.5.6 Z score 2.5.7 The empirical rules in terms of Z score 2.5.8 Coefficient of variation 2.6 Measures of shapes of solar energy data 2.6.1 Skewness 2.6.2 Extraterrestrial and terrestrial radiations 2.7 Conclusion 2.8 Exercise/question References Chapter 3 Facilities location and plant layout of solar energy system 3.1 Introduction 3.2 Factor affecting location decision of solar power plant 3.2.1 How latitudes affect temperature & solar radiation? 3.3 Location planning method of solar power plant 3.3.1 Location and performance assessment of solar system by PVSYST 7.1 3.4 Process–productmatrix of solar power plant 3.5 Performance measures of solar power plant layout design 3.6 Design of group technology solar plant layout 3.7 Conclusion 3.8 Exercise/ Question References Chapter 4 Productivity and manufacturing economics of solar energy system 4.1 Introduction 4.2 Aggregate operations planning of solar energy system 4.2.1 Alternatives for managing demand 4.2.2 Aggregate operation planning of grid connected solar energy system 4.3 Level, chase, and mixed strategy of solar energy system 4.4 Master operations scheduling (MOS) of solar energy system 4.4.1 MOS of solar product manufacturing industry 4.4.2 MOS of solar energy generation system 4.5 Dependent demand attributes of solar energy system 4.5.1 Planning a framework: solar panel building blocks 4.6 Manufacturing resource planning of solar energy component 4.7 Enterprise resource planning of solar energy system 4.7.1 Various types of solar ERP software 4.8 Conclusion 4.9 Exercise/question References Chapter 5 Assessment of solar energy system by probability and sampling distribution 5.1 Introduction 5.2 Discrete v/s continuous distribution of solar energy parameters 5.3 Binomial, poisson, and hypergeometric distribution of solar energy data 5.4 Assessment of solar energy system by sampling technique 5.4.1 Simple random sampling 5.4.2 Systematic sampling 5.4.3 Stratified sampling 5.4.4 Cluster sampling 5.5 Weibull distribution of solar energy parameters 5.5.1 Two-parameter Weibull distribution 5.6 Conclusion 5.7 Exercise/question References Chapter 6 Application of regression analysis and forecasting techniques in solar energy system 6.1 Introduction 6.2 Correlation and simple regression of solar energy parameter 6.2.1 Correlation 6.2.2 Simple regression analysis 6.2.3 Determining the equation of regression 6.2.4 Standard error 6.2.5 Coefficient of determination 6.3 Multiple regressions 6.3.1 Regression model with two independent variables 6.3.2 Error of the estimate 6.4 Time series forecasting 6.4.1 Time series components 6.4.2 The forecasting error measurement 6.4.3 Smoothing techniques 6.5 Exercise References Chapter 7 Inventory and total quality management of solar energy system 7.1 Introduction 7.2 Inventory planning of independent demand component 7.2.1 Mobile inventory barcode scanning 7.2.2 Types of inventory in solar energy system 7.3 Inventory control system of solar energy system 7.3.1 Selective control of inventory 7.4 Total quality management of solar system 7.4.1 Benefits of a quality management in solar energy system 7.4.2 Solar module quality assurance 7.4.3 SCADA based total quality management of solar energy system. 7.5 Quality certification and society of solar energy system 7.6 Conclusion 7.7 Exercise/question References Chapter 8 Case study: Solar–wind hybrid renewable energy system 8.1 Introduction 8.2 Study area 8.3 Solar radiation & wind velocity 8.3.1 Solar radiation 8.3.2 Wind velocity 8.4 Load profile of study area 8.5 Statistical assessment of datasets 8.5.1 Correlation between solar radiation and load demand and between wind velocity and load demand 8.5.2 Forecasting of clearness index, solar radiation, wind velocity, and load demand 8.6 Modeling of solar–wind hybrid renewable energy system 8.6.1 Modeling of PV system 8.6.2 Modeling of wind system 8.6.3 Modeling of diesel generator 8.6.4 Modeling of battery bank 8.7 Standalone hybrid renewable energy system 8.7.1 Photo-voltaic system 8.7.2 Wind turbine 8.7.3 Battery 8.7.4 Diesel generator 8.8 Objective function 8.8.1 Annual capital cost 8.8.2 Annual replacement cost 8.8.3 Annual O&M cost 8.9 Result and discussion 8.10 Life cycle analysis 8.10.1 Component description 8.10.2 Wind system life cycle calculation 8.10.3 Solar system life cycle calculation 8.11 Regression analysis 8.11.1 Load demand and clearness Index 8.11.2 Load demand and wind velocity 8.11.3 Load demand and solar radiation 8.12 Conclusion References Chapter 9 Data analysis of solar energy system with Python 9.1 Introduction 9.2 First level data analysis of solar energy data with Python library 9.2.1 Solar Data Operations in Numpy 9.2.2 Pandas data operations 9.2.3 Solar energy system data input as CSV file 9.2.4 Grouping of solar energy data 9.2.5 Solar energy data concatenation 9.3 Second level data analysis of solar energy data with Python library 9.3.1 Load and representation of solar data 9.3.2 Data visualization of solar energy data 9.4 Data assessment of solar radiation by linear regression analysis 9.5 Data assessment of solar energy system by logistic regression analysis 9.6 Data assessment of solar energy system by Naïve Bayes analysis 9.7 Data assessment of solar energy system by random forest 9.8 Data assessment of solar energy system by decision tree 9.9 Data analysis of solar energy system by support vector machine 9.10 Conclusion 9.11 Exercise/questions References Index IBC