Capacity Optimization of Energy Storage Unit in Distributed Generation System

2012 ◽  
Vol 608-609 ◽  
pp. 1116-1119
Author(s):  
Cai Yun Guo ◽  
Hong Bin Wu
Keyword(s):  
Energy Storage ◽  
Distributed Generation ◽  
Search Algorithm ◽  
Generation Model ◽  
Storage Unit ◽  
Energy Storage Unit ◽  
Optimal Capacity ◽  
Battery Energy ◽  
Pv Generation ◽  
System Power

The photovoltaic(PV) generation model and the wind power generation model are introduced in this paper. Taking the best economy and reliability of system operation as the objective functions and the system power balance and battery storage performance indices as the constraints, the optimal capacity of battery energy storage can be determined with the Tabu search algorithm. With the example system, the simulation results show that the proposed models and the algorithm are correct.

scholarly journals Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3453
Author(s):  
Eugenio Borghini ◽  
Cinzia Giannetti ◽  
James Flynn ◽  
Grazia Todeschini
Keyword(s):  
Energy Storage ◽  
Distribution Systems ◽  
Energy Generation ◽  
Distribution Networks ◽  
Electricity Demand ◽  
Data Driven ◽  
Low Carbon ◽  
Storage Unit ◽  
Energy Storage Unit ◽  
Pv Generation

The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.

Optimization Location Selection Analysis of Energy Storage Unit in Energy Internet System Based on Tabu Search

2019 ◽  
Vol 29 (07) ◽  
pp. 941-954
Author(s):  
Haitian Chen ◽  
Yan Zhao ◽  
Yanwei Ji ◽  
Shunjiang Wang ◽  
Weichun Ge ◽  
...  
Keyword(s):  
Energy Storage ◽  
Tabu Search ◽  
Industrial Revolution ◽  
Search Algorithm ◽  
Reference Value ◽  
Tabu Search Algorithm ◽  
Storage Unit ◽  
Energy Internet ◽  
Location Selection ◽  
Energy Storage Unit

Energy Internet has become the theme of the new round of industrial revolution. Energy storage, as a key technical support for the development of energy Internet, has always been of concern to numerous people, since the energy Internet consists of various energy networks that can provide energy support for different energy subnetworks. Therefore, the energy storage unit is in a crucial position in the entire energy network. This paper points out the importance of various energy storage technologies in the energy Internet. An energy storage unit location analysis method based on Tabu search algorithm is proposed to reduce the network energy loss, pressing mimizing network loss as constraint on the location of the energy storage unit as a search target. The Tabu search algorithm is programmed using Matlab and is used to search for the location of energy storage unit in the IEEE example. Besides, the optimal node solution is obtained, which verifies the feasibility of this algorithm to analyze the location selection of energy storage unit in the energy Internet. This paper has some reference value for the coordinated optimization of energy storage units in the energy Internet.

scholarly journals Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Shahzad Nazir ◽  
Sami ud Din ◽  
Wahab Ali Shah ◽  
Majid Ali ◽  
Ali Yousaf Kharal ◽  
...  
Keyword(s):  
Energy Storage ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Energy Coupling ◽  
Energy Utilization ◽  
Energy Sources ◽  
Storage Unit ◽  
Random Forest Regression ◽  
Energy Storage Unit ◽  
Combined Cooling

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

scholarly journals The study of the isolated power supply system operation with controlled distributed generation plants, energy storage units and drive load

2022 ◽  
Vol 23 (5) ◽  
pp. 184-194
Author(s):  
Yu. N. Bulatov ◽  
A. V. Kryukov ◽  
K. V. Suslov
Keyword(s):  
Control System ◽  
Energy Storage ◽  
Distributed Generation ◽  
Power Supply ◽  
Power Supply System ◽  
Supply System ◽  
Storage Unit ◽  
Energy Storage Unit ◽  
Operating Modes ◽  
Speed Controller

THE PURPOSE. Investigation of the operating modes of an isolated power supply system with controlled distributed generation plants, energy storage units and a drive load. Determination of the influence of the proposed prognostic controller of a distributed generation plant on the control parameters and quality indicators of the control process under various operating modes of an isolated power supply system.METHODS. The studies were carried out on a computer model of an isolated power supply system of an industrial enterprise with a turbine generator plant, a wind generator plant and a high-power electric storage unit, for which a fuzzy control system and a prognostic controller were used. The simulation was performed in MATLAB using Simulink and SimPowerSystems packages.RESULTS. The article describes a computer model of an isolated power supply system, as well as a structural diagram of the proposed autoprognostic speed controller. The simulation results showed that the combined use of an energy storage unit and an auto-prognostic generator rotor speed controller makes it possible to ensure the stability and survivability of an isolated power supply system, increasing its damping properties. The use of a fuzzy control system of a wind-generating plant made it possible to ensure its stable operation in all considered modes.CONCLUSION. The auto-prognostic speed controller, which does not require special settings, and the energy storage unit provide high quality control indicators in normal and emergency modes. It is advisable to conduct further studies to coordinate the actions of the control system of the electric energy storage unit and the auto-prognostic speed controller of the distributed generation plant.

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