Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

192
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
192
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

220
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
220
Load-frequency control01:28

Load-frequency control

165
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
165
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

116
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
116
Multimachine Stability01:25

Multimachine Stability

153
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
153

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Effects of audio guided loving kindness meditation on psychological well being and laboratory stress responsiveness in healthy university students.

Scientific reports·2026
Same journal

Adaptive cognitive driven cross modal network for few shot fine grained recognition.

Scientific reports·2026
Same journal

Tegoprazan-based dual therapy versus bismuth-containing quadruple therapy for Helicobacter pylori eradication: a prospective, multicenter, open-label, non-inferiority, randomized controlled trial.

Scientific reports·2026
Same journal

Primary tumor resection prior to peptide receptor radionuclide therapy is associated with improved survival in metastatic gastroenteropancreatic neuroendocrine tumors: a systematic review and meta-analysis.

Scientific reports·2026
Same journal

Sleep duration among medical students and its association with bronchial asthma, anxiety, and depression.

Scientific reports·2026
Same journal

MGMT deficiency augments STING-mediated inflammatory responses accompanied by metabolic alterations in macrophages.

Scientific reports·2026

Related Experiment Video

Updated: Jul 4, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.3K

Numerical algorithm for environmental/economic load dispatch with emissions constraints.

Christos Bakos1, Angelos Giakoumis2

  • 1Department of Information and Electronic Engineering, International Hellenic University (IHU), Sindos, Thessaloniki, Greece. bakoschristos233@gmail.com.

Scientific Reports
|February 9, 2024
PubMed
Summary

This study introduces an environmental/economic load dispatch algorithm that optimizes electricity generation costs while meeting emissions constraints. The Python-based program benefits the environment, power companies, and consumers by considering emissions trading costs.

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

7.9K

Related Experiment Videos

Last Updated: Jul 4, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.3K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

7.9K

Area of Science:

  • Electrical Engineering
  • Environmental Science
  • Computational Optimization

Background:

  • Traditional economic load dispatch (ELD) often overlooks environmental impacts and emissions constraints.
  • The increasing relevance of emissions trading systems necessitates their integration into power generation cost calculations.
  • Balancing economic factors with environmental regulations is crucial for sustainable energy production.

Purpose of the Study:

  • To develop and present a numerical algorithm for Environmental/Economic Load Dispatch (EELD) that incorporates emissions constraints and the impact of emissions trading.
  • To implement this algorithm in a Python computer program for practical application.
  • To evaluate the algorithm's effectiveness on a multi-unit fossil-fueled power system.

Main Methods:

  • Multi-objective optimization techniques are employed to balance economic and environmental objectives.
  • The algorithm integrates fuel costs with the costs associated with emissions allowances under a trading system.
  • A Python program is developed to apply the algorithm to a system with six generating units and constraints on NOx, SO2, and CO2 emissions.

Main Results:

  • The developed algorithm successfully calculated the optimal schedule for generating units.
  • Testing across various weighting factors demonstrated the algorithm's robustness.
  • The application to a six-unit system showed significant environmental and economic benefits.

Conclusions:

  • The proposed EELD algorithm is effective in optimizing power generation considering both economic and environmental factors.
  • The Python implementation provides a fast, cost-effective, and environmentally friendly solution for power system operation.
  • The algorithm's consideration of emissions trading costs leads to benefits for power companies and consumers alike.