Design of a Management Algorithm for Energy Trading in Microgrids

2020 ◽  
Vol 13 (7) ◽  
pp. 1028-1040
Author(s):  
Dimosthenis Verginadis ◽  
Athanasios Karlis
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Power Grids ◽  
Energy Sources ◽  
Future Research ◽  
Energy Trading ◽  
Management Algorithm

Background: The scope of this paper is to study the energy trading in microgrids. Microgrids are low voltage or medium voltage distribution networks, which consist of energy storage systems, electric loads, e.g. electric vehicles and Renewable Energy Sources (RES). Methods: Legacy energy grids are being transformed by the introduction of small to medium sized individual or cooperative, mostly RES invested energy producers and prosumers. Electric vehicles penetrate the market and modern power grids integrate them as ancillary services providers when there are peak domestic loads, as well as in order to balance grid voltage aiming to increase system reliability, compensating for renewable energy sources’ intermittency and volatility in energy production. Results: An elaborate management algorithm is proposed in this paper, to balance demand and local renewable energy sources microgrid supply. Conclusion: Finally, the results of simulations of different scenarios, including economic parameters and proposals for future research are presented.

scholarly journals Influence of FACTS Device Implementation on Performance of Distribution Network with Integrated Renewable Energy Sources

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5516
Author(s):  
Filip Relić ◽  
Predrag Marić ◽  
Hrvoje Glavaš ◽  
Ivica Petrović
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Low Voltage ◽  
Flow Direction ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Distribution Networks ◽  
Energy Sources ◽  
Voltage Profile ◽  
Facts Devices

In the modern power system, Flexible Alternating Current Transmission System (FACTS) devices are widely used. An increased share of the distributed generation (DG) and the development of microgrids change the power flows in the existing distribution networks as well as a conventional power flow direction from the transmission to the distribution network level which may affect the overall stability aspects. The paper shows the FACTS devices’ implementation influence on the performance of the distribution network with integrated renewable energy sources (RES) observing the aspects of the oscillatory stability and the low-voltage motor starting. The FACTS devices, in particular the static var compensators (SVC), have been allocated according to a novel algorithm proposed in the paper. The algorithm uses an iterative process to determine an optimal location for implementation and rating power of SVC considering active power losses minimization, improvement of the voltage profile and maximizing return of investment (ROI) of FACTS devices. Novel constraints—transformer station construction constraint, SVC industrial nominal power value constraint and the constraint of distribution system operator (DSO) economic willingness to investment in the distribution network development are considered in the proposed algorithm. The analysis has been performed on 20 kV rural distribution network model in DIgSILENT PowerFactory software.

Probabilistic operation management of automated distribution networks in the presence of electric vehicles and renewable energy sources

2020 ◽  
Vol 39 (5) ◽  
pp. 7035-7051
Author(s):  
Navid Parsa ◽  
Bahman Bahmani-Firouzi ◽  
Taher Niknam
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Forecast Error ◽  
Renewable Energy Sources ◽  
Probabilistic Method ◽  
Distribution Networks ◽  
Test System ◽  
Operation Management ◽  
Energy Sources ◽  
Modified Bat Algorithm

Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ-modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework.

A dual-axis tracking photovoltaic power plant as an educational tool

2016 ◽  
Vol 54 (3) ◽  
pp. 189-207 ◽  
Author(s):  
Dubravko Frankovic ◽  
Vedran Kirincic ◽  
Vladimir Valentic
Keyword(s):  
Renewable Energy ◽  
Power Plant ◽  
Power Efficiency ◽  
Low Voltage ◽  
Tracking System ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Design Stage ◽  
Energy Sources ◽  
Photovoltaic Power

Renewable energy sources have become a considerable part of electric transmission networks as well as medium and low voltage distribution networks. Understanding the overall process from design stage up to the installation stage, followed by the commissioning and startup of renewable energy sources plants is essential knowledge that electric engineers nowadays should posses. Therefore, in the first part of the article activities, conducted at the Faculty of Engineering, University of Rijeka, Croatia, necessary for the installation of a fully operational, grid connected photovoltaic power plant with dual-axis tracking system have been described. Consequently, upon photovoltaic plant’s installation and commissioning, students are able to have ‘hands-on’ on a fully functional photovoltaic power plant and perform supervised, ‘live’ measurements and compare it with previously calculated values. Therefore, new – dedicated laboratory sessions have been introduced in an existing subject to make the most of the photovoltaic installation in the teaching process. In the second part, the article is mainly focused on the newly introduced laboratory sessions as well as on the educational framework and methodology. Some of the experiments that our students are able to perform include alternating current and direct current operating values measurements (photovoltaic string and inverter voltages, currents, power, efficiency, etc.), environmental parameters measurements (irradiance, air temperature, wind direction, velocity, etc.) and grounding parameters measurements (soil resistivity, photovoltaic plant’s grounding resistance). The acquired knowledge gained from the activities performed during our educational photovoltaic plant project realization give us the ability to propose a methodology that can be used as the key model for other universities and faculties.

Stabilisation of low voltage distribution networks with renewable energy sources

2013 ◽  
Vol 46 (28) ◽  
pp. 455-460 ◽  
Author(s):  
Z. Bradáč ◽  
F. Zezulka ◽  
P. Marcon ◽  
Z. Szabó ◽  
Karel Stibor
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Energy Sources ◽  
Voltage Distribution

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

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