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Group 7 load forecasting&harmonics final ppt

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Rahul Sharma

1) The document discusses load forecasting, including the objectives, factors influencing it, and types based on time frame. 2) Key factors in load forecasting include population, living standards, geographical location, cost of power, weather, time of day, and customer class. Accurate load forecasting helps utilities with generation planning and infrastructure development. 3) The document covers various load forecasting methods including time series analysis, regression/trend analysis, and correlation theory. Time series models include additive and multiplicative models, while regression analyzes trends using linear, exponential, power, and polynomial curves.

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Chinmaya Das Priyank Jain Rahul Sharma
TOPICS TO BE DISCUSSED • Major objectives of Load Forecasting. • Parameters influencing Load Forecasting . • Load Factor & Diversity factor. • Types of Load Forecasting based on time-frame. • Different factors involved in Load Forecasting. • System Peak forecasting. • Methods used for Load Forecasting . • G-S method of Load Flow study. • Load Duration Curve & its significance. • Harmonics are in a Power System & its effects on the Network .
BASIC DEFINITIONS [1] Load The power consumed by a Electrical Circuit. Forecasting The process of making statements about events whose actual outcomes have not yet been observed. Load forecasting An estimate of power demand at some future period.
LOAD FORECASTING • Load forecasting is a central and integral process in the planning and operation of electric utilities. • It involves the accurate prediction of both the magnitudes and geographical locations of electric load over the different periods (usually hours) of the planning horizon. • Accurate load forecasting holds a great saving potential for electric utility corporations.
PROGRESSIVE PATH • The basic quantity of interest in load forecasting is typically the hourly total system load. However, according to Gross and Galiana (1987), load forecasting is also concerned with the prediction of hourly, daily, weekly and monthly values of the system load, peak system load and the system energy. • Srinivasan and Lee (1995) classified load forecasting in terms of the planning horizon’s duration: up to 1 day for short-term load forecasting (STLF), 1 day to 1 year for medium-term load forecasting (MTLF), and 1±10 years for long-term load forecasting (LTLF).
FACTORS INFLUENCING LOAD FORECASTING Population Living Geographical Standard Location Cost of Future Plan Power
IMPORTANCE OF LOAD FORECASTING Forecasting gives magnitude and location of loads. Accurate model helps in 1)Economic size of plant and apparatus at correct time and place. 2)Generation authorities plan their water and fuel requirements and the generator allocation schedules.
IMPORTANCE OF LOAD FORECASTING 3)Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. 4)Load forecasts are extremely important for energy suppliers, ISOs, financial institutions, and other participants in electric energy energy generation, transmission, distribution, and markets.
PRESENT AIM… “The aim of the load forecasting is to make best use of electrical energy and reveled the conflict between supply and demand.” - International Journal of Systems Science,2009
OBJECTIVES OF LOAD FORECASTING • To know the peak load of system • Energy requirement in day, month and year • To know the load duration curve • To estimate the proper investment requirement • Supply side management • Demand side management
FACTORS FOR ACCURATE FORECASTS  Weather influence  Time factors  Customer classes
WEATHER INFLUENCE Electric load has an obvious correlation to weather. The most important variables responsible in load changes are: • Dry and wet bulb temperature • Dew point • Humidity • Wind Speed / Wind Direction • Sky Cover • Sunshine
[7]
TIME FACTORS In the forecasting model, we should also consider time factors such as: • The day of the week • The hour of the day • Holidays
CUSTOMER CLASS • Electric utilities usually serve different types of customers such as residential, commercial, and industrial. • The graphs show the load behavior in the above classes by showing the amount of peak load per customer, and the total energy.
LOAD CURVES
DEMAND FACTOR • The ratio of the maximum coincident demand of a system, or part of a system, to the total connected load of the system, or part of the system, under consideration. Demand Factor = maximum demand total connected load (of consumer)
LOAD FACTOR The total amount of energy the plant produced during a period of time and divide by the amount of energy the plant would have produced at full capacity. Load Factor = Total amount of energy the plant produced Plant’s Installed capacity
DIVERSITY FACTOR • The ratio of the sum of the individual maximum demands of the various subdivisions of a system to the maximum demand of the whole system. Diversity Factor = Σ Di ( i=1 to n) DG Where, • Di = maximum demand of load i, regardless of time of occurrence. • DG = coincident maximum demand of the group of n loads
SYSTEM POWER FACTOR The power factor of an AC electric power system is defined as the ratio of the real power flowing to the apparent power in the circuit. OR Measurement of cosine of angular difference between voltage and current. P = V* I cosφ Pf varies from 0 to 1. Pf = 0, when phase angle is 90. Pf =1, when phase angle is 0.
SYSTEM POWER FACTOR When power factor (φ=0, cosφ=1). When power factor (φ=90, cosφ=0)
LOAD FORECAST
LOAD CHARACTERISTICS • Diversity Factor 1.2-1.3 Domestic • Load Factor 10 -15% • Diversity Factor 1.1-1.2 Commercial • Load Factor 25-30%
TYPES OF LOAD • Diversity Factor ~1.1 Industrial • Load Factor 65-70% • Diversity Factor 1.0 Municipal • Load Factor 25-30%
Type of Demand Load Utilization Industry Factor Factor Factor Induction furnace 0.99 0.80 0.72 Steel Rolling Mills 0.80 0.25 0.72 Textile Industry 0.50 0.80 0.40 Gas Plant Industry 0.70 0.50 0.35 College & Schools 0.50 0.20 0.10 Paper Industry 0.50 0.80 0.40 Source[Electrical engineering portal]
Schematic of STLF Model Load Load at Temp Current Previous Previous Demand coming hour hour Hour 2 Hour Forecast Wind Cloud
DEMAND SUPPLY FORECAST : INDIA [5]
TYPES OF LOAD FORECASTING Short Term(1 hour- 1 week) Load Forecasting Long Middle Term(1 Term(Longer week -1 year) than Year)
DE-REGULATION AND FORECASTING • Load forecasting has always been important for planning and operational decision conducted by utility companies. • However, with the deregulation of the energy industries, load forecasting is even more important. • With supply and demand fluctuating and the changes of weather conditions and energy prices increasing by a factor of ten or more during peak situations, load forecasting is vitally important for utilities.
CONCEPTS  Time series (based on historical datas)  Trend analysis  Correlation Theory  Aritficial Neural Networks
TIME SERIES
TIME SERIES Time Series Model Additive Multiplicative (Y=T+C+S+I) (Y=T C S I) WHERE T = LONG TERM TREND C= CYCLICAL TREND (MAINLY OVER MANY YEARS) S = SEASONAL TREND (1 YEAR CYCLE) I = IRREGULARR MOVEMENTS(NOISE)* *IN PART DUE TO TEMPERATURE EFFECTS
TIME SERIES-COMPONENTS
REGRESSION / TREND ANALYSIS  Trend Analysis Study of behaviour of a time series or a process in the past and its mathematical modelling so that future behaviour can be extrapolated from it. Gives nature of relationships between the variables, where as correlation analysis measures the degree of relationship between the variables.
REGRESSION / TREND ANALYSIS Approaches to Trend Analysis 1.Fitting continuous mathematical functions through actual data 2.Fitting of a sequence on discontinuous lines or curves to data (short term forecasting)
REGRESSION/TREND ANALYSIS
TREND ANALYSIS
TREND ANALYSIS  Typical regression curves used in power system forecasting Linear y=A+Bx Exponential y=A(1+B)x Power y=AxB Polynomial y=A+Bx+Cx2 Method of Least squares  Can be used either to fit a straight line trend or a parabolic Trend
CORRELATION THEORY Scatter Diagram Karl Pearson’s coefficient of Correlation Correlation Methods Spearman’s Rank correlation coefficient Method of least squares 1) Coefficient of correlation is also called “goodness of fit” 2) Karl Pearson's coefficient of correlation ∑(X-Xav). (Y-Yav) r= ------------------------- (∑(X-Xav)2. ∑(Y-Yav)2)1/2
CORRELATION THEORY
ARTIFICIAL NEURAL NETWORKS • Artificial Neural Network(ANN) is based on Artificial Intelligence. • ANN, usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks • Neural networks offer the potential to overcome the reliance on a functional form of a forecasting model.
SUMMARY OF FUNDAMENTAL STEPS 1)Collection of data (reliably) 2)Draw a graph 3)Construct a long term trend 4)Seasonal index if it exists and de-seasonalize the data 5)Adjust data for the trend
FACTORS IN LOAD FORECASTING
FACTORS IN POWER SYSTEM LOADING Econometric Single Spatial Load Factor Forecasting Modeling Power System Loading Forecast of Strategic System Forecasting Peak Capacity Forecasting
ECONOMETRIC Growth in population (Long term trend) Growth of GNP (Long term variation) Business and economic cycle (cyclic Variation) Most of these factors effect the long term trend and not effect normal model based on past history.
GDP VS. ENERGY CONSUMPTION • Relation of GDP to energy consumption is an important indicator. • The elasticity of consumption with respect to GDP for India in 1980 – 1992 was 1.61. This implies that increase in GDP of 1 % will increase 1.61 % of electricity consumption.
INDIA
SINGLE FACTOR MODELING [2] Single factor modeling is based on a model that assumes one dominant factor, determines the model outcome.
DEFECTS IN SINGLE FACTOR MODELING Too General DEFECTS Not Biasing of Comprehensive Forecast as Rate of Values due to Growth Differs Uneven with Sectors Distribution
CAPACITY FORECAST MODEL As the forecast for electrical energy is on national level, in this the national projection is converted to regional peaks. From this the regional capacity requirements are made, removing the current generation and planned capacity addition there. Finally addition of planned retirement / decommissioning of units gives the net new capacity to be added
STRATEGIC FORECASTING Consumer’s Current Consumer’s Potential Demand Demand Strategic Forecasting Competitiveness in Availability of Market Alternatives
STRATEGIC FORECASTING IN INDUSTRY Strategic Strategic Management Management combines provides Future Econometric Assessment Technological Shaping of Detail Future
SPATIAL FORECASTING This method breaks down to geographically and consumer oriented forecasting. A land use map can be converted to electric load by using kW per acre of load curves on land use class basis.
Planning engineers are using GIS to visualize the distribution system’s load and forecast “what they are likely to see new addition to the system”. Timeline of Community Development Predictions by Spatial Location of Forecasting Direction of New infrastructure substation investment
LIMITATIONS OF SPATIAL FORECASTING  It cannot replace knowledge and experience of area engineers.  It cannot identify substation site to be purchased.
FORECASTING FOR GROWTH IN REGIONS : -  Urban areas – Increase in Specific Consumption  Agriculture – a. Projections of land irrigation b. Prospective agricultural consumers c. Availability of land water  Industrial – a. Diversification of business b. New consumers c. Change in production process
LOAD CURVE  A Load Curve is a curve showing the variation of load on the power station with respect to time .
TYPES OF LOAD CURVE Daily Load Curve Types of Load Curve Yearly Weekly Load Load Curve Curve
SIGNIFICANCE OF LOAD CURVE  Area under Load Curve = Units Generated  Highest Point of Load Curve = Maximum Demand  (Area Under Curve/Total No. of Hours)= Average Load  Load Factor = Average Demand/ Maximum Demand
LOAD DURATION CURVE • When the elements of a load curve are arranged in the order of descending magnitudes.
SIGNIFICANCE OF LOAD DURATION CURVE • The load duration curve gives the data in a more presentable form. • The area under the load duration curve is equal to that of the corresponding load curve. • The load duration curve can be extended to include any period of time.
LOAD FLOW STUDY • The power flow study (also known as load-flow study) is an important tool involving numerical analysis applied to a power system. • A power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (i.e. voltages, voltage angles, real power and reactive power).
SIGNIFICANCE OF LOAD FLOW STUDY • For planning future expansion of power systems as well as in determining the best operation of existing systems. • The principal information obtained from the power flow study is the magnitude and phase angle of the voltage at each bus, and the real and reactive power flowing in each line.
FORMULATION OF THE LOAD FLOW PROBLEM
FORMULATION OF THE LOAD FLOW PROBLEM where [Y] is the nodal admittance matrix
GAUSS-SEIDEL METHOD • The Gauss-Seidel Method is an iterative technique for solving the load flow problem, by successive estimation of the node voltages. • It is usually done with the help of MATLAB. • Can be used for quite complex equations.
GS METHOD FROM MATLAB(A SCREEN SHOT)
Group 7 load forecasting&harmonics final ppt
HARMONICS - INTRODUCTION • A sinusoidal component of a periodic wave or quantity having a frequency that is an integral multiple of the fundamental frequency. • Typical harmonics for a 50Hz system are, Single phase – 3rd, 6th, etc. Three phase – 5th, 7th, 11th, 13th, etc. • Harmonics should not be confused with spikes, dips, impulses, oscillations or other forms of transients.
WHY HARMONICS • These current result due to the fact that the device either has an impedance which varies during each half cycle of applied emf or it generate a back emf of non sinusoidal shape. Result - Distortion of the Wave shape .
The power company typically supplies a reasonably smooth sinusoidal waveform:
NONLINEAR DEVICES WILL DRAW DISTORTED WAVEFORMS, WHICH ARE COMPRISED OF HARMONICS OF THE SOURCE:
Group 7 load forecasting&harmonics final ppt
ORDER OF HARMONICS
PROPAGATION OF HARMONICS IN THE NETWORK INFLUENCE OF PHASE ANGLE OF HARMONICS[6]
TOTAL HARMONICS DISTORTION • The ratio of the sum of the power of all harmonic components to the power of the fundamental frequency • Pn :- Sum of all power • P1 :- Power of fundamental frequency
TOTAL HARMONICS DISTORTION • THD can be used to describe voltage or current distortion and is calculated as follows • where IDn is the magnitude of the nth harmonic as a percentage of the fundamental (individual distortion).
HARMONICS ARE GENERATED BY : Rectifiers  Inverters Induction furnaces Arc furnaces Fluorescent lamps TVs UPS & Computers etc.
ADVERSE EFFECTS OF HARMONICS • Fluctuation of voltage • Efficiency & capacity utilization of transformers, generators • High skin effect loss • High I2R loss • High failure rate in Motors, sophisticated electronics equipments.
MAXIMUM LIMITS OF VOLTAGE HARMONIC DISTORTION IN HT AND EHT SYSTEMS
HOW ARE HARMONICS MINIMIZED ? • Use three-phase drives wherever possible. • Use an additional inductance. • Make use of a harmonic filter.
FILTERS • A series- tuned harmonic filter consists of a capacitor bank with a reactor (inductor) in series with it. The series combination provides a low impedance path for a specific harmonic component, there by minimizing harmonic voltage distortion problems. • The filter is tuned slightly below the harmonic frequency of concern.
Group 7 load forecasting&harmonics final ppt
SYSTEM PEAK It is given by the formula :- Annual System Peak = Energy Requirement 8760 x Load Factor
QUESTION • The estimated Energy requirement and Load Factor of a particular Region for the year 2004 are 668132 GWh and 70% respectively. Calculate the annual peak demand. • Given : Energy Requirement - 668132 GWh Load Factor - 70%
• Thus putting given values in the formula: Ann. System Peak = Energy Requirement 8760 x Load Factor i.e. ASP= 668132 GWh = 668132 x 1000MWh 8760 x 0.7 6132 = 108958 MWh Thus,Annual Peak Demand = 108958MWh
DATE CHANGE DATE DATE DATE DATE DATE DATE CHANGE CHANGE CHANGE CHANGE CHANGECHANGE CHANGE VALUES HERE!!!
ACTUAL LOAD CURVE FOR THE WEEK 05SEPT 2010 TO 11 SEPT 2010 1200 1000 800 monday Tuesday Wednesday 600 Thursday FRIDAY Saturday Sunday 400 dte 200 0
REFERENCES • [1].International Journal of Systems Science, volume 33, number 1. • [2]. Electrical engineering portal • [3].US electric static schneider • [4].Department of Electrical and Electronics Engineering, S.V.U. College of Engineering, Tirupati, A.P., India • [5].India Energy Handbook. • [6] Dept. of Electrical,Electronic and Control Engineering, Ciudad Universitaria. Madrid. Spain. • [7] NRLDC.nic.in
Group 7 load forecasting&harmonics final ppt

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Group 7 load forecasting&harmonics final ppt

  • 1. Chinmaya Das Priyank Jain Rahul Sharma
  • 2. TOPICS TO BE DISCUSSED • Major objectives of Load Forecasting. • Parameters influencing Load Forecasting . • Load Factor & Diversity factor. • Types of Load Forecasting based on time-frame. • Different factors involved in Load Forecasting. • System Peak forecasting. • Methods used for Load Forecasting . • G-S method of Load Flow study. • Load Duration Curve & its significance. • Harmonics are in a Power System & its effects on the Network .
  • 3. BASIC DEFINITIONS [1] Load The power consumed by a Electrical Circuit. Forecasting The process of making statements about events whose actual outcomes have not yet been observed. Load forecasting An estimate of power demand at some future period.
  • 4. LOAD FORECASTING • Load forecasting is a central and integral process in the planning and operation of electric utilities. • It involves the accurate prediction of both the magnitudes and geographical locations of electric load over the different periods (usually hours) of the planning horizon. • Accurate load forecasting holds a great saving potential for electric utility corporations.
  • 5. PROGRESSIVE PATH • The basic quantity of interest in load forecasting is typically the hourly total system load. However, according to Gross and Galiana (1987), load forecasting is also concerned with the prediction of hourly, daily, weekly and monthly values of the system load, peak system load and the system energy. • Srinivasan and Lee (1995) classified load forecasting in terms of the planning horizon’s duration: up to 1 day for short-term load forecasting (STLF), 1 day to 1 year for medium-term load forecasting (MTLF), and 1±10 years for long-term load forecasting (LTLF).
  • 6. FACTORS INFLUENCING LOAD FORECASTING Population Living Geographical Standard Location Cost of Future Plan Power
  • 7. IMPORTANCE OF LOAD FORECASTING Forecasting gives magnitude and location of loads. Accurate model helps in 1)Economic size of plant and apparatus at correct time and place. 2)Generation authorities plan their water and fuel requirements and the generator allocation schedules.
  • 8. IMPORTANCE OF LOAD FORECASTING 3)Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. 4)Load forecasts are extremely important for energy suppliers, ISOs, financial institutions, and other participants in electric energy energy generation, transmission, distribution, and markets.
  • 9. PRESENT AIM… “The aim of the load forecasting is to make best use of electrical energy and reveled the conflict between supply and demand.” - International Journal of Systems Science,2009
  • 10. OBJECTIVES OF LOAD FORECASTING • To know the peak load of system • Energy requirement in day, month and year • To know the load duration curve • To estimate the proper investment requirement • Supply side management • Demand side management
  • 11. FACTORS FOR ACCURATE FORECASTS  Weather influence  Time factors  Customer classes
  • 12. WEATHER INFLUENCE Electric load has an obvious correlation to weather. The most important variables responsible in load changes are: • Dry and wet bulb temperature • Dew point • Humidity • Wind Speed / Wind Direction • Sky Cover • Sunshine
  • 13. [7]
  • 14. TIME FACTORS In the forecasting model, we should also consider time factors such as: • The day of the week • The hour of the day • Holidays
  • 15. CUSTOMER CLASS • Electric utilities usually serve different types of customers such as residential, commercial, and industrial. • The graphs show the load behavior in the above classes by showing the amount of peak load per customer, and the total energy.
  • 17. DEMAND FACTOR • The ratio of the maximum coincident demand of a system, or part of a system, to the total connected load of the system, or part of the system, under consideration. Demand Factor = maximum demand total connected load (of consumer)
  • 18. LOAD FACTOR The total amount of energy the plant produced during a period of time and divide by the amount of energy the plant would have produced at full capacity. Load Factor = Total amount of energy the plant produced Plant’s Installed capacity
  • 19. DIVERSITY FACTOR • The ratio of the sum of the individual maximum demands of the various subdivisions of a system to the maximum demand of the whole system. Diversity Factor = Σ Di ( i=1 to n) DG Where, • Di = maximum demand of load i, regardless of time of occurrence. • DG = coincident maximum demand of the group of n loads
  • 20. SYSTEM POWER FACTOR The power factor of an AC electric power system is defined as the ratio of the real power flowing to the apparent power in the circuit. OR Measurement of cosine of angular difference between voltage and current. P = V* I cosφ Pf varies from 0 to 1. Pf = 0, when phase angle is 90. Pf =1, when phase angle is 0.
  • 21. SYSTEM POWER FACTOR When power factor (φ=0, cosφ=1). When power factor (φ=90, cosφ=0)
  • 23. LOAD CHARACTERISTICS • Diversity Factor 1.2-1.3 Domestic • Load Factor 10 -15% • Diversity Factor 1.1-1.2 Commercial • Load Factor 25-30%
  • 24. TYPES OF LOAD • Diversity Factor ~1.1 Industrial • Load Factor 65-70% • Diversity Factor 1.0 Municipal • Load Factor 25-30%
  • 25. Type of Demand Load Utilization Industry Factor Factor Factor Induction furnace 0.99 0.80 0.72 Steel Rolling Mills 0.80 0.25 0.72 Textile Industry 0.50 0.80 0.40 Gas Plant Industry 0.70 0.50 0.35 College & Schools 0.50 0.20 0.10 Paper Industry 0.50 0.80 0.40 Source[Electrical engineering portal]
  • 26. Schematic of STLF Model Load Load at Temp Current Previous Previous Demand coming hour hour Hour 2 Hour Forecast Wind Cloud
  • 27. DEMAND SUPPLY FORECAST : INDIA [5]
  • 28. TYPES OF LOAD FORECASTING Short Term(1 hour- 1 week) Load Forecasting Long Middle Term(1 Term(Longer week -1 year) than Year)
  • 29. DE-REGULATION AND FORECASTING • Load forecasting has always been important for planning and operational decision conducted by utility companies. • However, with the deregulation of the energy industries, load forecasting is even more important. • With supply and demand fluctuating and the changes of weather conditions and energy prices increasing by a factor of ten or more during peak situations, load forecasting is vitally important for utilities.
  • 30. CONCEPTS  Time series (based on historical datas)  Trend analysis  Correlation Theory  Aritficial Neural Networks
  • 32. TIME SERIES Time Series Model Additive Multiplicative (Y=T+C+S+I) (Y=T C S I) WHERE T = LONG TERM TREND C= CYCLICAL TREND (MAINLY OVER MANY YEARS) S = SEASONAL TREND (1 YEAR CYCLE) I = IRREGULARR MOVEMENTS(NOISE)* *IN PART DUE TO TEMPERATURE EFFECTS
  • 34. REGRESSION / TREND ANALYSIS  Trend Analysis Study of behaviour of a time series or a process in the past and its mathematical modelling so that future behaviour can be extrapolated from it. Gives nature of relationships between the variables, where as correlation analysis measures the degree of relationship between the variables.
  • 35. REGRESSION / TREND ANALYSIS Approaches to Trend Analysis 1.Fitting continuous mathematical functions through actual data 2.Fitting of a sequence on discontinuous lines or curves to data (short term forecasting)
  • 38. TREND ANALYSIS  Typical regression curves used in power system forecasting Linear y=A+Bx Exponential y=A(1+B)x Power y=AxB Polynomial y=A+Bx+Cx2 Method of Least squares  Can be used either to fit a straight line trend or a parabolic Trend
  • 39. CORRELATION THEORY Scatter Diagram Karl Pearson’s coefficient of Correlation Correlation Methods Spearman’s Rank correlation coefficient Method of least squares 1) Coefficient of correlation is also called “goodness of fit” 2) Karl Pearson's coefficient of correlation ∑(X-Xav). (Y-Yav) r= ------------------------- (∑(X-Xav)2. ∑(Y-Yav)2)1/2
  • 41. ARTIFICIAL NEURAL NETWORKS • Artificial Neural Network(ANN) is based on Artificial Intelligence. • ANN, usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks • Neural networks offer the potential to overcome the reliance on a functional form of a forecasting model.
  • 42. SUMMARY OF FUNDAMENTAL STEPS 1)Collection of data (reliably) 2)Draw a graph 3)Construct a long term trend 4)Seasonal index if it exists and de-seasonalize the data 5)Adjust data for the trend
  • 43. FACTORS IN LOAD FORECASTING
  • 44. FACTORS IN POWER SYSTEM LOADING Econometric Single Spatial Load Factor Forecasting Modeling Power System Loading Forecast of Strategic System Forecasting Peak Capacity Forecasting
  • 45. ECONOMETRIC Growth in population (Long term trend) Growth of GNP (Long term variation) Business and economic cycle (cyclic Variation) Most of these factors effect the long term trend and not effect normal model based on past history.
  • 46. GDP VS. ENERGY CONSUMPTION • Relation of GDP to energy consumption is an important indicator. • The elasticity of consumption with respect to GDP for India in 1980 – 1992 was 1.61. This implies that increase in GDP of 1 % will increase 1.61 % of electricity consumption.
  • 47. INDIA
  • 48. SINGLE FACTOR MODELING [2] Single factor modeling is based on a model that assumes one dominant factor, determines the model outcome.
  • 49. DEFECTS IN SINGLE FACTOR MODELING Too General DEFECTS Not Biasing of Comprehensive Forecast as Rate of Values due to Growth Differs Uneven with Sectors Distribution
  • 50. CAPACITY FORECAST MODEL As the forecast for electrical energy is on national level, in this the national projection is converted to regional peaks. From this the regional capacity requirements are made, removing the current generation and planned capacity addition there. Finally addition of planned retirement / decommissioning of units gives the net new capacity to be added
  • 51. STRATEGIC FORECASTING Consumer’s Current Consumer’s Potential Demand Demand Strategic Forecasting Competitiveness in Availability of Market Alternatives
  • 52. STRATEGIC FORECASTING IN INDUSTRY Strategic Strategic Management Management combines provides Future Econometric Assessment Technological Shaping of Detail Future
  • 53. SPATIAL FORECASTING This method breaks down to geographically and consumer oriented forecasting. A land use map can be converted to electric load by using kW per acre of load curves on land use class basis.
  • 54. Planning engineers are using GIS to visualize the distribution system’s load and forecast “what they are likely to see new addition to the system”. Timeline of Community Development Predictions by Spatial Location of Forecasting Direction of New infrastructure substation investment
  • 55. LIMITATIONS OF SPATIAL FORECASTING  It cannot replace knowledge and experience of area engineers.  It cannot identify substation site to be purchased.
  • 56. FORECASTING FOR GROWTH IN REGIONS : -  Urban areas – Increase in Specific Consumption  Agriculture – a. Projections of land irrigation b. Prospective agricultural consumers c. Availability of land water  Industrial – a. Diversification of business b. New consumers c. Change in production process
  • 57. LOAD CURVE  A Load Curve is a curve showing the variation of load on the power station with respect to time .
  • 58. TYPES OF LOAD CURVE Daily Load Curve Types of Load Curve Yearly Weekly Load Load Curve Curve
  • 59. SIGNIFICANCE OF LOAD CURVE  Area under Load Curve = Units Generated  Highest Point of Load Curve = Maximum Demand  (Area Under Curve/Total No. of Hours)= Average Load  Load Factor = Average Demand/ Maximum Demand
  • 60. LOAD DURATION CURVE • When the elements of a load curve are arranged in the order of descending magnitudes.
  • 61. SIGNIFICANCE OF LOAD DURATION CURVE • The load duration curve gives the data in a more presentable form. • The area under the load duration curve is equal to that of the corresponding load curve. • The load duration curve can be extended to include any period of time.
  • 62. LOAD FLOW STUDY • The power flow study (also known as load-flow study) is an important tool involving numerical analysis applied to a power system. • A power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (i.e. voltages, voltage angles, real power and reactive power).
  • 63. SIGNIFICANCE OF LOAD FLOW STUDY • For planning future expansion of power systems as well as in determining the best operation of existing systems. • The principal information obtained from the power flow study is the magnitude and phase angle of the voltage at each bus, and the real and reactive power flowing in each line.
  • 64. FORMULATION OF THE LOAD FLOW PROBLEM
  • 65. FORMULATION OF THE LOAD FLOW PROBLEM where [Y] is the nodal admittance matrix
  • 66. GAUSS-SEIDEL METHOD • The Gauss-Seidel Method is an iterative technique for solving the load flow problem, by successive estimation of the node voltages. • It is usually done with the help of MATLAB. • Can be used for quite complex equations.
  • 67. GS METHOD FROM MATLAB(A SCREEN SHOT)
  • 69. HARMONICS - INTRODUCTION • A sinusoidal component of a periodic wave or quantity having a frequency that is an integral multiple of the fundamental frequency. • Typical harmonics for a 50Hz system are, Single phase – 3rd, 6th, etc. Three phase – 5th, 7th, 11th, 13th, etc. • Harmonics should not be confused with spikes, dips, impulses, oscillations or other forms of transients.
  • 70. WHY HARMONICS • These current result due to the fact that the device either has an impedance which varies during each half cycle of applied emf or it generate a back emf of non sinusoidal shape. Result - Distortion of the Wave shape .
  • 71. The power company typically supplies a reasonably smooth sinusoidal waveform:
  • 72. NONLINEAR DEVICES WILL DRAW DISTORTED WAVEFORMS, WHICH ARE COMPRISED OF HARMONICS OF THE SOURCE:
  • 75. PROPAGATION OF HARMONICS IN THE NETWORK INFLUENCE OF PHASE ANGLE OF HARMONICS[6]
  • 76. TOTAL HARMONICS DISTORTION • The ratio of the sum of the power of all harmonic components to the power of the fundamental frequency • Pn :- Sum of all power • P1 :- Power of fundamental frequency
  • 77. TOTAL HARMONICS DISTORTION • THD can be used to describe voltage or current distortion and is calculated as follows • where IDn is the magnitude of the nth harmonic as a percentage of the fundamental (individual distortion).
  • 78. HARMONICS ARE GENERATED BY : Rectifiers  Inverters Induction furnaces Arc furnaces Fluorescent lamps TVs UPS & Computers etc.
  • 79. ADVERSE EFFECTS OF HARMONICS • Fluctuation of voltage • Efficiency & capacity utilization of transformers, generators • High skin effect loss • High I2R loss • High failure rate in Motors, sophisticated electronics equipments.
  • 80. MAXIMUM LIMITS OF VOLTAGE HARMONIC DISTORTION IN HT AND EHT SYSTEMS
  • 81. HOW ARE HARMONICS MINIMIZED ? • Use three-phase drives wherever possible. • Use an additional inductance. • Make use of a harmonic filter.
  • 82. FILTERS • A series- tuned harmonic filter consists of a capacitor bank with a reactor (inductor) in series with it. The series combination provides a low impedance path for a specific harmonic component, there by minimizing harmonic voltage distortion problems. • The filter is tuned slightly below the harmonic frequency of concern.
  • 84. SYSTEM PEAK It is given by the formula :- Annual System Peak = Energy Requirement 8760 x Load Factor
  • 85. QUESTION • The estimated Energy requirement and Load Factor of a particular Region for the year 2004 are 668132 GWh and 70% respectively. Calculate the annual peak demand. • Given : Energy Requirement - 668132 GWh Load Factor - 70%
  • 86. • Thus putting given values in the formula: Ann. System Peak = Energy Requirement 8760 x Load Factor i.e. ASP= 668132 GWh = 668132 x 1000MWh 8760 x 0.7 6132 = 108958 MWh Thus,Annual Peak Demand = 108958MWh
  • 87. DATE CHANGE DATE DATE DATE DATE DATE DATE CHANGE CHANGE CHANGE CHANGE CHANGECHANGE CHANGE VALUES HERE!!!
  • 88. ACTUAL LOAD CURVE FOR THE WEEK 05SEPT 2010 TO 11 SEPT 2010 1200 1000 800 monday Tuesday Wednesday 600 Thursday FRIDAY Saturday Sunday 400 dte 200 0
  • 89. REFERENCES • [1].International Journal of Systems Science, volume 33, number 1. • [2]. Electrical engineering portal • [3].US electric static schneider • [4].Department of Electrical and Electronics Engineering, S.V.U. College of Engineering, Tirupati, A.P., India • [5].India Energy Handbook. • [6] Dept. of Electrical,Electronic and Control Engineering, Ciudad Universitaria. Madrid. Spain. • [7] NRLDC.nic.in

Editor's Notes

  • #13: Weather conditions influence the load. In fact, forecasted weatherparameters are the most important factors in short-term load forecasts.Various weather variables could be considered for load forecasting. Temperatureand humidity are the most commonly used load predictors. Among the weather variables listed above, two composite weathervariable functions, the THI (temperature-humidity index) andWCI (windchill index), are broadly used by utility companies. THI is a measure ofsummer heat discomfort and similarly WCI is cold stress in winter.Most electric utilities serve customers of different types such as residential,commercial, and industrial.
  • #15: For short-term load forecasting several factors should be considered,such as time factors, weather data, and possible customers’ classes. Themedium- and long-term forecasts take into account the historical loadand weather data, the number of customers in different categories, theappliances in the area and their characteristics including age, the economicand demographic data and their forecasts, the appliance salesdata, and other factors. The time factors include the time of the year, the day of the week,and the hour of the day. There are important differences in load between weekdays and weekends. The load on different weekdays also can behave differently. For example, Mondays and Fridays being adjacent to weekends, may have structurally different loads than Tuesday throughThursday.