Assessment of wind power impact on power system transmission losses

Uski, Sanna; Kim, Inna

doi:10.1109/isgteurope.2014.7028975

Cited by 9 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If excess electricity is still available, it is not used supercapacitors for being charged. The balanced equation can be written as (3), Where and are the powers required for charging the battery and super-capacitor.…”

Section: ) Model 1 (Surplus Power)mentioning

confidence: 99%

See 1 more Smart Citation

Simulation, Optimization, and Analysis Approach of Microgrid Systems

Ali¹

2022

Preprint

View full text Add to dashboard Cite

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

show abstract

Section: ) Model 1 (Surplus Power)mentioning

confidence: 99%

“…The researchers have worked a lot on renewable energy resources (RERs) that are outside the grid system [3]. These RERs are growing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Simulation, Optimization, and Analysis Approach of Microgrid Systems

Ali¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, due to their high dependence upon weather stochasticity, these renewable energy resources often fail to produce an adequate amount of energy [3,4]. In such a scenario, an ideal solution is formed by integrating renewable and nonrenewable energy resources to exploit their collective capacities, known as hybrid renewable energy source (HRES) [5]. HRES is a combination of different diesel generators used to meet the required load [1].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Qayyum¹,

Jamil²,

Ahmad³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure. An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system. The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization (PSO) named recurrent PSO (rPSO). Firstly, rPSO algorithm minimizes the installation cost for nanogrid. Thereafter, the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources. State-of-the-art optimization models, including Genetic Algorithm (GA), bat and different Mathematical Programming Language (AMPL) solvers, are used to evaluate the model. The study's outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.

show abstract

“…Which, integrally, they don't have the necessary action to resolve this issue [1]. And, the usage of long-distance transmission line power distribution causes to lead the lines losses and it reduces the existing grid's efficiency [2]. The power generators like fossil fuel and coal are frequently emitting the carbon dioxide to the atmosphere [3].…”

Section: Introductionmentioning

confidence: 99%

Solar Powered Dc Nano-Grid with Multi Agent Control Strategy

2019

IJITEE

View full text Add to dashboard Cite

Smart homes are typical examples of DC Nano-grids wherein multi-agent strategy is required for coordinating different entities to harness flexible load and storage to maximize the integration of intermittent renewable generation. This paper proposes a novel multi-agent approach for DC Nano-grids in smart homes with an aim to simultaneously maximize comfort levels and renewable integration. In the proposed approach, there are three agents: flexible loads, batteries, and renewable energy sources which interact among them for meeting the control objectives. The agents are coordinated using a centralized controller and based on its decision the flexibility is harnessed to the grid. The novelty of the approach is that the different agents communicate only to the central controller and not among themselves which reduces the communication among them. The advantage of the proposed approach is their ability to handle DC Nano-grids and using an agent-based approach within a residential building. The proposed multi-agent approach is illustrated on a lab-level DC Nano-grids pilot developed by the authors. Our results show that achieves maximum overall energy efficiency and minimum electricity bill and smooth control of various modes of operation.

show abstract

Assessment of wind power impact on power system transmission losses

Cited by 9 publications

References 6 publications

Simulation, Optimization, and Analysis Approach of Microgrid Systems

Simulation, Optimization, and Analysis Approach of Microgrid Systems

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Solar Powered Dc Nano-Grid with Multi Agent Control Strategy

Contact Info

Product

Resources

About