scholarly journals Design of Battery Storage System for Malaysia Low Voltage Distribution Network with the Presence of Residential Solar Photovoltaic System

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4887
Author(s):  
Meysam Shamshiri ◽  
Chin Kim Gan ◽  
Junainah Sardi ◽  
Mau Teng Au ◽  
Wei Hown Tee
Keyword(s):  
Low Voltage ◽  
Reverse Flow ◽  
Control Strategies ◽  
Storage System ◽  
Distribution Networks ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Network Losses ◽  
Solar Photovoltaic System ◽  
Solar Generation

The recent proliferation of residential solar photovoltaic systems has prompted several technical challenges to the operation of low voltage (LV) distribution networks. More specifically, the mismatch of the solar generation and demand profiles, particularly during the midday when the demand is low and solar generation is high, can lead to network overvoltages and increased network losses. In addition, the solar photovoltaic system is not able to reduce the system’s maximum demand, given the residential LV network would normally have an evening peak when the sun goes down. In this regard, this paper examines two different control strategies in designing the battery energy storage system. One aims to eliminate reverse flow caused by the surplus solar energy and the other aims for peak demand reduction.

A High Gain Novel Double-Boost Converter for DC Microgrid Applications

2020 ◽  
Vol 29 (15) ◽  
pp. 2050246 ◽  
Author(s):  
B. N. Ch. V. Chakravarthi ◽  
G. V. Siva Krishna Rao
Keyword(s):  
Control Strategies ◽  
High Gain ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Dc Microgrid ◽  
Boost Converters ◽  
Power Point ◽  
Solar Photovoltaic System ◽  
Conversion Stage ◽  
And Control

In solar photovoltaic (PV)-based DC microgrid systems, the voltage output of the classical DC–DC converter produces very less voltage as a result of poor voltage gain. Therefore, cascaded DC–DC boost converters are mandatory for boosting the voltage to match the DC microgrid voltage. However, the number of devices utilized in the DC–DC conversion stage becomes higher and leads to more losses. Thereby, it affects the system efficiency and increases the complication of the system and cost. In order to overcome this drawback, a novel double-boost DC–DC converter is proposed to meet the voltage in DC microgrid. Also, this paper discusses the detailed operation of maximum power point (MPP) tracking techniques in the novel double-boost DC–DC converter topology. The fundamental [Formula: see text]–[Formula: see text] and [Formula: see text]–[Formula: see text] characteristics of solar photovoltaic system, operational details of MPP execution and control strategies for double-boost DC/DC converter are described elaborately. The proposed converter operation and power injection into the DC microgrid are verified through the real-time PSCAD simulation and the validation is done through the experiment with hardware module which is indistinguishable with the simulation platform.

Mitsubishi FA-Based Solar Photovoltaic Tracking Control System

2012 ◽  
Vol 468-471 ◽  
pp. 928-932
Author(s):  
De Jun Miao ◽  
Yi Zong Dai
Keyword(s):  
Conversion Efficiency ◽  
Tracking Control ◽  
Control Strategies ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Input Control ◽  
Maximum Conversion ◽  
Maximum Conversion Efficiency ◽  
Solar Photovoltaic System ◽  
And Control

A sort of two axes auto- tracking solar photovoltaic system based on Mitsubishi FA productions to solve the problem of low conversion efficiency in existing systems. It is discussed that how to design frames of input、control、execution 、functions and control strategies. The method of timing light intensity comparison is proposed to achieve automatic tracking of solar cells. This system can regulate automatically the horizontal angle and the vertical angle of the battery board by controlling circuits of sensors, plc, transducer and amplifier. Sound results are shown by tracking maximum conversion efficiency of this system.

scholarly journals Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3937 ◽  
Author(s):  
Sangyoon Lee ◽  
Dae-Hyun Choi
Keyword(s):  
Energy Storage ◽  
Smart Home ◽  
Storage System ◽  
Energy Storage System ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Air Conditioner ◽  
Indoor Temperature ◽  
Q Learning ◽  
Solar Photovoltaic System

This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%.

Operation of a Hydrogen Storage-Based Smart DC Microgrid

2022 ◽  
pp. 145-172
Author(s):  
Mahesh Kumar
Keyword(s):  
Hydrogen Storage ◽  
Low Voltage ◽  
Storage System ◽  
Energy Storage System ◽  
Photovoltaic System ◽  
Control Technique ◽  
Dc Microgrid ◽  
Hydrogen Storage System ◽  
Solar Photovoltaic System ◽  
Battery Energy

In this chapter, the author presents the operation and power management of the hydrogen storage-based smart DC microgrid (DCMG). In this microgrid, several renewable distributed generations (DGs) such as wind turbine, solar photovoltaic system, solid oxide fuel cell (SOFC), and battery energy storage system are interconnected together and to the various DC and AC loads to form a ring-type low voltage distribution network. An additional storage as Hydrogen storage system has been connected to the dc microgrid for balancing the power at all times in the DCMG, under islanded mode operation, for all practical cases. An architecture of the hydrogen storage-based DC microgrid is suggested mainly for the remote rural area. For the regeneration of the electricity from the stored hydrogen, a SOFC DG system is also used in the proposed DCMG. A control technique is also developed for the operation of the hydrogen storage-based DCMG. The proposed DCMG system provides a reliable and high-quality power supply and will supply the power to all loads (both DC and AC) simultaneously.

Impact of Photovoltaic System Integration on Low Voltage Network

2020 ◽  
Vol 10 (10) ◽  
pp. 90-95
Author(s):  
Agbesi K. Moses ◽  
Sayawu Yakubu Diaba ◽  
Emmanuel A. Frimpong ◽  
Emmanuel K. Anto ◽  
Mohammed S. Elmustrati
Keyword(s):  
Power Distribution ◽  
System Integration ◽  
Low Voltage ◽  
Reverse Flow ◽  
Distribution Networks ◽  
Photovoltaic System ◽  
Power Distribution Networks ◽  
Voltage Level ◽  
Fixed Power

In this paper, we perform an analysis of the Photovoltaic penetrations on Low Voltage Power Distribution Networks. A case study of the East Legon Power Distribution Services M05/52 LV Network. The impacts of high photovoltaic penetration on voltage level and power factors are explored. We fixed power quality analyzers at the distribution substation and at the customers’ nodes; to capture the feeder loads and the voltage profiles. Simulations are done at varying photovoltaic penetrations levels to determine the strike of increasing photovoltaic incursion on the low voltage power network. We present results to show that, the optimum penetration level is achieved at the point where minimum loss reduction is recorded, reverse flow not present and power factor limit is not violated.

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