PARTNERSHIP TO ADVANCE CLEAN ENERGY DEPLOYMENT (PACE-D 2.0 RE) TECHNICAL ASSISTANCE PROGRAM April 2020 Tool Demonstration: Demand Forecasting PACE D 2.0 RE Team
Agenda • Demand Forecasting • Demand Forecasting Tool: Why? • About Tool • Parameters Considers for Demand Forecasting • Results • Online Demo Slide No. 2
Demand Forecasting • Prediction of future energy demand requires an intuitive and wise judgment Long T erm Forecasting: • The forecast needs to be revised at regular • Plays a fundamental role in economic planning of new intervals to take care of new policies and changes generating capacity and in socio-economic trends. transmission networks. • The demand forecast is used as a basis for system Spans over 5 to 20 years. • Medium T erm Forecasting: development, and for determining tariffs for the Used mainly for the scheduling • future. of fuel supplies, maintenance Over-forecasts lead to more plant than is • program, financial planning and required – Unnecessary capital expenditure tariff formulation • Spans over 1 month to 5 years • Under-forecasts prevent optimum economic growth – Lead to installation of many costly and expensive to-run generators. Slide No. 3 3
Demand Forecasting Tool: Why? Slide No. 4
About Tool • The demand forecasting can be performed at DISCOM level for all categories. • The various consumer categories, like residential, commercial, industrial etc., can be considered for forecasting. The methods that have been provided in the software to arrive at the best forecast values are: Univariate: Multivariate: PEUM: • CAGR • Econometric Method Decomposes the sales of Trend Analysis ARIMA electricity into its elemental • • ANN component of consumption • Slide No. 5
Parameters Considered for Demand Forecasting Demand is forecasted under two scenarios: • ✓ Business As Usual ✓ Scenario with Drivers Business As Usual Scenario with Drivers Based on the energy sales and econometric In this impact of drivers is considered on • • data, the demand is forecasted for all the BAU scenario to forecast the demand. consumer categories. • Drivers: Open Access (OA), Captive Power CAGR, Trend, and Econometric for long Plants (CPP), Distributed Energy Sources • term forecasting (DER), and Electric Vehicles (EVs). • ARIMA and ANN for medium term forecasting Further, sensitivity and probabilistic analysis is done to study the variation in demand. Slide No. 6
Results: Long Term Forecasting (APDCL) APDCL: Net demand requirement in MU 25000 NET DEMAND (MU) Net demand 20000 Long term demand • 15000 forecasting: 2020 to 2040 10000 • Based on the average load 5000 factor of previous 3 years, the peak demand is 0 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 estimated. APDCL: Peak demand in MW • On an average, % deviation 5000 of demand projections w.r.t Peak demand PEAK DEMAN (MW) 4000 energy sales approved under 3000 AERC MYT Order 2018 is 4.8% . 2000 1000 0 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 Source: BAU Report Slide No. 7
Results: Medium Term Forecasting (APDCL) APDCL-Monthly net demand in MU APDCL-Monthly peak demand in MW Jun Jun 2020 2020 Sep Sep Dec Dec Mar Mar Jun Jun 2021 2021 Sep Sep Dec Dec Mar Mar Jun Jun 2022 Sep 2022 Sep Dec Dec Mar Mar Jun Jun 2023 Sep 2023 Sep Dec Dec Mar Mar Jun Jun 2024 Sep 2024 Sep Dec Dec Mar Mar 0 500 1000 1500 2000 2500 3000 0 200 400 600 800 1000 1200 Peak demand Net demand Slide No. 8 Source: BAU Report
Results: Hourly Load Profiles (APDCL) The load profile for the day of each month having peak demand is shown for the year 2020. Slide No. 9 Source: BAU Report
Probabilistic Analysis Probabilistic Energy Sales at Varying Standard Deviation of Independent Variable for the Year 2030. 16000 15000 14000 Total Energy Sales (MU) 13000 12000 11000 10000 9000 8000 7000 6000 -100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100% Variation in the Standard Deviation of Independent Variables (%) Risk Based Resource Plan Identification Source: Probabilistic Analysis Report Slide No. 10
Tool Highlights: Configuration of DISCOM • The DISCOM and associated consumer categories can be configured as a one-time activity. • The historical energy sales observed for each consumer category can be uploaded into the tool. • The SCADA data can be directly imported into the tool for capturing the hourly load profile and the load factor observed. Slide No.11
Tool Highlights: Scenario Creation Several scenarios can be created in the tool to analyse various aspects and carry out sensitivity studies to understand the impact of various policies and drivers on the total demand. Slide No. 12
Tool Highlights: Forecast Results The results obtained for each category by different forecasting methods can be visualized both graphically and in tabular form to identify the most suitable forecast results Slide No. 13 Results obtained for LT4 Commercial
Training Videos • Configuration of • Introduction to Tool • Dependent Variables • Pre-requisites • Independent • Logging-in and Variables DISCOM configuration • Load Profile • T&D Losses Data Modeling Getting Started 2 1 • Forecast Methods • View & Analyse • Policy Configuration Results Summary • Scenario-specific data • Drivers Configuration configuration • Category-wise Fitted Distributed Energy ➢ Curve Resources • Execution Open Access ➢ • Consolidated Results Captive Power Plants ➢ • Detailed PDF Report ➢ Electric Vehicles • Probability Analysis Impact of Scenario Creation Analysis of results 3 4 5 Policies & Drivers Slide No. 14
Brief Demonstration of the Tool & Discussion Slide No 15
Video Tutorial Link for first video is provided Video Tutorials Link Getting Started https://drive.google.com/drive/folder s/1mE35s7G7X7- z4Oa4q_jsPelUnUsqM9kq?usp=sharin g The video can be streamed on your respective laptop and mobile.. Slide No 16
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