scholarly journals Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3500 ◽  
Author(s):  
Bishwajit Dey ◽  
Fausto Pedro García Márquez ◽  
Sourav Kr. Basak
Keyword(s):  
Low Voltage ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Energy Resources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
The Impact

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down time of the dispatchable DERs are considered during the scheduling process. Adjustable loads are assumed to the residential loads which either operates throughout the day or for a particular period during the day. The impact of these loads on the generation cost of the microgrid system is studied. A novel hybrid approach considers the grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) to minimize the overall generation cost of the microgrid system. It has been found that the generation costs rise 50% when the residential loads were included along with the fixed loads. Active participation of the utility incurred 9–17% savings in the system generation cost compared to the cases when the microgrid was operating in islanded mode. Finally, statistical analysis has been employed to validate the proposed hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Crow Search Algorithm (MGWOSCACSA) over other algorithms used.

scholarly journals Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Bishwajit Dey ◽  
Biplab Bhattacharyya ◽  
Saurav Raj ◽  
Rohit Babu
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Test System ◽  
Grey Wolf Optimizer ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Abstract Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

scholarly journals Harmony Search Algorithm Based Management of Distributed Energy Resources and Storage Systems in Microgrids

2020 ◽  
Vol 10 (9) ◽  
pp. 3252 ◽  
Author(s):  
Oğuzhan Ceylan ◽  
Mustafa Erdem Sezgin ◽  
Murat Göl ◽  
Maurizio Verga ◽  
Riccardo Lazzari ◽  
...  
Keyword(s):  
Low Voltage ◽  
Search Algorithm ◽  
Renewable Energy Sources ◽  
Harmony Search ◽  
Energy Resources ◽  
Test Facility ◽  
Distributed Energy Resources ◽  
Optimization Approach ◽  
Distributed Energy ◽  
Islanded Operation

Microgrids are composed of distributed energy resources (DERs), storage devices, electric vehicles, flexible loads and so on. They may either operate connected to the main electricity grid (on-grid operation) or separated from the grid (islanded operation). The outputs of the renewable energy sources may fluctuate and thus can cause deviations in the voltage magnitudes especially at islanded mode. This may affect the stability of the microgrids. This paper proposes an optimization model to efficiently manage controllable devices in microgrids aiming to minimize the voltage deviations both in on-grid and islanded operation modes. RSE Distributed Energy Resources Test Facility (DER-TF), which is a low voltage microgrid system in Italy, is used to verify the algorithm. The test system’s data is taken through an online software system (REDIS) and a harmony search based optimization algorithm is applied to control the device parameters. The experimental results show that the harmony search based optimization approach successfully finds the control parameters, and can help the system to obtain a better voltage profile.

scholarly journals Collaborative Strategy for Grey Wolf Optimization Algorithm

2018 ◽  
Vol 12 (7) ◽  
pp. 73 ◽  
Author(s):  
Esra F. Alzaghoul ◽  
Sandi N. Fakhouri
Keyword(s):  
Optimization Algorithm ◽  
State Of The Art ◽  
Particle Swarm ◽  
Search Space ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Particle Swarm Optimizer ◽  
Minimal Points ◽  
Search Feature ◽  
Sine Cosine Algorithm

Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.

scholarly journals Impact of the Renewable Energy Development in Lithuania‘s Energy Economy

2019 ◽  
Vol 8 (4) ◽  
pp. 75
Author(s):  
Rita Bužinskienė
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Research Problem ◽  
Energy Sector ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Linear Regression Models ◽  
Energy Productivity ◽  
Energy Economy ◽  
The Impact

Paper is characterized by scientific novelty as it involves a very scarce research problem in Lithuanian‘s energy sector, assessing the impact of renewable energy resources on the energy economy. Renewable energy sources have a multiplier effect in spurring the economy and the development of not only the energy sector but also all the supporting activities related to such industry. The impact of the development of renewable energy is one of the factors that develop the quality of technology innovation development. This study includes the impact of renewable energy on the energy economy, using multiple linear regression models. The results of the study have shown that renewable energy resources: wind, sun, water, geothermal and biomass can not always be used together because they compete with each other and therefore reduce the efficiency of the energy economy. In this context, three combinations of renewable energy resources have been developed, which have been adapted to assess the impact of the energy economy on energy productivity and energy intensity. It has been found that the combination of resources of the second model (M2) RE is significant for the efficiency of the energy economy.Keywords: Renewable energy resources; Energy economy; Impact of efficiency

scholarly journals A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

2021 ◽  
Author(s):  
Prathap Siddavaatam
Keyword(s):  
Network Architecture ◽  
Design Space Exploration ◽  
Synthesis Process ◽  
Benchmark Problems ◽  
The Internet ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Exploration Process ◽  
Multi Objective ◽  
The Impact

Today, Internet of Things (IoT) is a major paradigm shift that will mark an epoch in communication technology such that every physical object can be connected to the Internet. With the advent of 5G communications, IoT is in urgent need of optimized architectures that can efficiently support wide ranging heterogeneous multi-objective requirements of communication, hardware and security aspects. The optimization challenges are rooted in the technology and how the information is acquired and manipulated by this technology. My research in this thesis provides a description of compelling challenges faced by IoT and how to mitigate these challenges by designing resource-aware communication protocols, resource- constrained device hardware with low computing power and low-powered computational security enhancements. This thesis lays the foundation for optimizing these challenging IoT paradigms by introducing a novel Delta-Diagram based synthesizing model. The Delta- Diagram provides a road-map linking the behavioral and structural domains of a given IoT paradigm to generate respective optimizer domain parameters, which can be utilized by any optimizer framework. The fundamental part of the communication synthesizer is a mathematical model, developed to obtain the best possible routing paths and communication parameters among things. The ultimate aim of the entire synthesis process is to devise a design automation tool for IoT, which exploits the interrelations between different layer functionalities. This thesis also proposes a novel cross-layer Grey wolf optimizer for IoT, which outperforms some of the contemporary optimizer algorithms such as Particle Swarm, Genetic Algorithm, Differential Evolution optimizers in solving unimodal, multi-modal and composition benchmark problems. The purpose of this optimizer is to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of delta diagram synthesis enabled network architecture. In addition, the Grey wolf optimizer for IoT plays a crucial role in design exploration of system on chip architecture for IoT device hardware. The results generated by the optimizer yielded the most optimum feasible solutions in the design space exploration process of the IoT.

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