Planned production of thermal units for reducing the emissions and costs using the improved NSGA II method

Neyestani, Mehdi; Hesari, Sadegh; Hatami, M.

doi:10.1016/j.csite.2019.100397

Cited by 4 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation ( 2) is utilized for any other generator form for simulating prices. These kinds of generators make use of sunlight to produce electricity through the use of photovoltaic modules, also called solar panels, with the possibility of having an additional solar tracking system to give the best orientation possible to the panels towards the Sun to make the best of solar energy [42]. The equation of the power generated by a photovoltaic plant is represented in the following Equation (3).…”

Section: Generation Plantsmentioning

confidence: 99%

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Garcia,

Águila,

Ortiz

et al. 2024

Energies

View full text Add to dashboard Cite

This research incorporates an electricity market model based on a stochastic allocation of distributed resources and the analysis of an optimal demand response for a smart microgrid. This research develops a methodology that allows the application and comparison of various demand-response mechanisms and the analysis of the differences between them and the case of no-demand response, emphasizing economics, environmental care, demand curves, and other factors. By enabling more active participation by residential users of the smart microgrid, these demand-response methods help to flatten the demand curve and support the goals set by the electricity market model. Both conventional and non-conventional generators compete in the electricity market, with renewable energy sources preferred to encourage green generation. Conventional generators are required to supply electricity gradually, starting with the lowest pollution level. In addition, conventional generators are compensated for dispatch, system reliability, and availability. In addition, random variables are used in this study to predict initial load, solar radiation analysis, and biomass input before resources are optimized to meet demand.

show abstract

Section: Generation Plantsmentioning

confidence: 99%

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Garcia,

Águila,

Ortiz

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Zhao et al [11] presented a dynamic economic dispatch model of a microgrid which uses a variant of NSGA-II algorithm with an external penalty function for dealing with the constraint conditions. An improved NSGA-II method is adopted for planning the production of thermal unit for minimizing the cost and emissions [12].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Generation Cost, Environmental Impact, and Reliability of a Microgrid Using Non-dominated Sorting Genetic Algorithm-II

Sohel¹,

Rahman²

2020

IJSDP

View full text Add to dashboard Cite

Over the past years, energy sectors have accomplished considerable progress in the transition from conventional fossil-based energy to low-carbon energy production, and microgrids are playing important roles in this sustainable energy transition. One of the key challenges for microgrids is to deliver power with the least possible cost and that too with such an approach that the environmental impact is the lowest and the overall system reliability is high enough. For this reason, generation cost, emission entities, and system reliability need to be efficiently optimized. Towards this goal, an online multi-objective technique has been employed to optimize cost, emission and system reliability taking these three factors in pairs at a time. The optimization model is designed using the non-dominated sorting genetic algorithm-II (NSGA-II), and the algorithm has been employed for several double objective scenarios considering reliability as an objective and later as a constraint. To evaluate the performance of the proposed approach, the simulation results are compared with the relative parameters from a different model that uses the strength pareto evolutionary algorithm (SPEA). The results show that the proposed technique satisfies the multi-objective optimization goals and provides good trade-offs between the conflicting objective functions while finding the optimal dispatch.

show abstract

Invisible Poisoning: Highly Stealthy Targeted Poisoning Attack

Chen

Zheng

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Planned production of thermal units for reducing the emissions and costs using the improved NSGA II method

Cited by 4 publications

References 18 publications

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

Optimization of Generation Cost, Environmental Impact, and Reliability of a Microgrid Using Non-dominated Sorting Genetic Algorithm-II

Invisible Poisoning: Highly Stealthy Targeted Poisoning Attack

Contact Info

Product

Resources

About