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course | Electrical Load and Energy Forecasting, Planning & Demand Side Management

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EEC-933 | Electrical Load and Energy Forecasting, Planning & Demand Side Management

Course Sector : Electrical Engineering

Duration
Date from
Date to Course Venue Course fees Book a course
10 Days2025-05-052025-05-16Online$3,100 Book now
10 Days2025-09-012025-09-12Dubai$8,500 Book now
10 Days2025-12-222026-01-02kuala lumpur$8,950 Book now
10 Days2025-12-072025-12-18salalah$8,500 Book now

Course Introduction

This course will assist power system professionals in planning and operating a power system with increasing penetration of Demand Side Management (DSM) Programs renewable resources. Renewable generation impacts both the planning and the operating criteria. These criteria are examined in detail along with the new requirements for generation reserve margins. Distributed generation and smart meter infrastructures are discussed in light of commercial and residential programs. Weather volatility impacts the demand profile. Weather normal methodologies are presented along with short and long terms forecasting models. Demand Side Management programs are a fine balancing act between demand, supply and customer choice (and incentives).

The Power of Agile Virtual Devices. 

The course presents various facets of demand side management and the broader smart grid roadmaps for customers. The Internet of Things/ The Internet of Everything is a game changer through the Power of Agile Virtual Devices. The course presents how new Devices bring new Analytics and innovative approaches to Demand Side Management.  


Course objective

  •  Set Power System Planning Objectives
  •      Gain knowledge about Load Forecasting Methodologies
  •      Identify Factors affecting Load Forecasting
  •      Have a good understanding of different kinds of Short Term and Long Term Planning
  •      Examine the principle of Energy supply & demand forecasting with a National and Regional perspectives
  •      Assess the market conditions that effect power and energy generation, transmission and distribution
  •       Understand the critical role of energy demand forecasting in the market.
  •      Utilize the various methodologies and technologies to meet the challenges of energy demand forecasting

Course Outline | DAY 01

Module (01) Bulk Electricity System

  • 1.1               Generation System
  • 1.2               Transmission System
  • 1.3               North American Electricity Reliability Council (NERC) STANDARDS and REQUIREMENTS

Module (02) Energy Markets

 

  • 2.1               Real Time Market
  • 2.2               Day Ahead Market
  • 2.3               Bilateral Contracts
  • 2.3.1        Terms and Structures of Renewable Contracts
  • 2.3.2        FIT in Tariffs in Canada
  • 2.4               Physical versus Financial Transactions
  • 2.4.1          Generator Offers into Market
  • 2.4.2          Dispatchable Loads

Course Outline | Day 02

Module (03) Load Forecasting Methodology

 

  • 3.1            3Weather Forecasts
  • 3.2            Simulation, Optimization, Sensitivity Analysis
  • 3.3            Forecasting Methodologies and statistical techniques, Neural-Net modeling, End-Use modeling, Metrix ND & LT)
  • 3.4            Weather Normalization and Probability Distribution Tools

 

Module (04) Load Demand Patterns

 

  • 4.1            Short Term Load Forecasting Models: Operating Time Frame
  • 4.2            Long Term Load Forecasting Models: Planning Time Frame
  • 4.3            Weather Normal load forecasting 

Course Outline | Day 03

Module (05) Volumetric Risk

 

  • 5.1            Weather Response
  • 5.2            Weather Derivatives
  • 5.3            How can data be used for decision making?
  • 5.4            What data will be needed in future?
  • 5.5            What data should be collected (and analyzed) now?

Module (06) Long Term Planning

 

  • 6.1            Reliability Criteria
  • 6.2            Generation Reserve Margin
  • 6.3            Loss of Load Probability
  • 6.4            Loss of Load Expectation
  • 6.5            Variable generation and their impact on the LOLP, LOLE
  • 6.6            Capacity Planning
  • 6.6.1        Renewable Generation
  • 6.6.2        Demand Management
  • 6.6.3        Case Study

Course Outline | Day 04

Module (07) Short Term Planning

 

  • 7.1          Day Ahead
  • 7.2          Real Time
  • 7.3          Secure Islanding of Power Plants
  • 7.4          Operating Reserves in a System With Renewable Generation

 

Module (08) System Controls for Power Quality for Demand

 

 

  • 8.1          Power Electronics
  • 8.1.1        Static VAR Compensators
  • 8.1.2        Voltage and VAR Control
  • 8.1.3        Smart Inverters 

Course Outline | Day 05

Module (09) Demand Side Management and the Physical System

 

  • 9.1          Load Frequency Control
  • 9.2          Load Pricing Control & Load Shedding

Module (10) Demand Side Management

 

  • 10.1      Energy Asset Programs: Integrate technical and financial elements under a single contract agreement
  • 10.2      Utility Service Agreement
  • 10.3      Customer Benefits
  • 10.4      Data Management and Analytics for Utilities
  • 10.5      Big Data: How can Data be used for Decision Making?
  • 10.6      Dispatchable Loads 

Course Outline | Day 06

 Module (11) Characteristics of Wind and Solar Power Generation

  • 11.1      Short Term variability of a Photovoltaic Plant
  • 11.2      Commercial
  • 11.3      Residential
  • 11.4      Impact of Renewables on the Demand Side Management Programs
  • 11.5      Adjustment Energy Cost

 

Module (12) Energy Storage

 

  • 12.1      State of the art in energy storage
  • 12.2      Fly Wheels
  • 12.3      Type of Storage
  • 12.4      Electrical Vehicles
  • 12.5      Impact on Demand Side Management Programs 

Course Outline | Day 07

Module (13) Smart Meter Infrastructure

 

  • 13.1      Smart Meters: its evolution
  • 13.2      Smart Meters: the new generation
  • 13.3      Metering Infrastructure Standards
  • 13.4      Advanced Metering Infrastructure (AMI)

Module (14) Smart Meter Ecosystem

 

  • 14.1      Utility data analytics industry ecosystem
  • 14.2      Smart meters and advanced metering infrastructure (AMI) for energy storage
  • 14.3      Advanced grid Controls and Sensors
  • 14.4      Accessible energy data for end-use demand response
  • 14.5      Innovative policies and adaptive social infrastructure (still in development) 

Course Outline | Day 08

Module (15) Interoperability

 

  • 15.1      Controllers for Local Energy Networks
  • 15.2      Residential Energy Management Systems
  • 15.3      Financial Impacts of Smart Energy Technologies
  • 15.4      The Transactive Energy Model

 

Module (16) Advanced Grid Applications

 

  • 16.1      Components of Smart Enabling Demand Response, Storage and Distributed Energy Resources 

Course Outline | Day 09

Module (17) Architectural Platforms

 

  • 17.1      Roadmap and Architecture
  • 17.2      Centralized versus Un-centralized Architecture
  • 17.3      Systems Interface Architecture
  • 17.4      Plug-In Electric Vehicle Charging Infrastructure

Module (18) Internet of Things (of Everything) – IOT/IOE

 

  • 18.1      The Next Generation of IOT
  • 18.2      Move from Thermostat to Full Control
  • 18.3      The Future of IOT/IOE 

Course Outline | Day 10

Module (19) Internet of Things World Forum Reference Model

 

  • 19.1      Why it impacts the DSM Programs
  • 19.2      Two Way Controls: Remote and at Home
  • 19.3      The Power of Virtual Devices
  • 19.4      SMART DEVICES
  • 19.5      Smart Home of the Future : Telecom, Energy, Health
  • 19.6      The Home as an Enterprise
  • 19.7      Data Centric Architecture
  • 19.8      More Big Data

 

Module (20) Semantic Technologies and the Internet of Services

 

  • 20.1      Business Objects
  • 20.2      Business Services in the Smart Grid
Course Certificates
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BOOST’s Professional Attendance Certificate “BPAC”

BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.

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