scholarly journals Retail Electricity Pricing Strategy via an Artificial Neural Network-based Demand Response Model of an Energy Storage System

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Hyun-Kyeong Hwang ◽  
Ah-Yun Yoon ◽  
Hyun-Koo Kang ◽  
Seung-Il Moon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Demand Response ◽  
Storage System ◽  
Energy Storage System ◽  
Pricing Strategy ◽  
Response Model ◽  
Electricity Pricing ◽  
Artificial Neural

An artificial neural network module for simulation of the energy storage system of a hybrid electric vehicle

2001 ◽  
Vol 215 (8) ◽  
pp. 919-932 ◽  
Author(s):  
S. R. Bhatikar ◽  
R. L. Mahajan ◽  
K Wipke ◽  
V Johnson
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Storage System ◽  
Energy Storage System ◽  
Ann Model ◽  
Artificial Neural ◽  
Hybrid Electric

A hybrid electric vehicle (HEV) is a complex system integrating interactive subsystems of disparate degrees of complexity. The simulation of an HEV thus poses a challenge. An accurate simulation requires highly accurate models of each subsystem. Without these, the system has a poor overall performance. Typically, modelling problems are not amenable to physical solutions without simplifying assumptions that impair their accuracy. Conventional empirical models, on the other hand, are time consuming and data intensive and falter where extensive non-linearity is encountered. An artificial neural network (ANN) approach to simulation of an HEV is presented in this paper. An ANN model of the energy storage system (ESS) of an HEV was deployed in the ADVISOR simulation software developed by the National Renewable Energy Laboratories (NREL) of the US Department of Energy. The ANN model mapped the state of charge (SOC) and the power requirement of the vehicle to the voltage and current at the ESS output An ANN model was able accurately to capture the complex, non-linear phenomena underlying the ESS. A novel performance-enhancing technique for design of ANN training data, Smart Select, is described here. It resulted in a model of 0.9978 correlation (R2 error) with data. ANNs can be data hungry. The issue of knowledge sharing between ANN models to save development time and effort is also addressed in this paper. The model transfer technique presents a way of levering the expertise of one ANN into the development of another for a similar modelling task. Lastly, integration of the ANN model of the ESS into the ADVISOR software, on the MATLAB software platform, is described.

scholarly journals A novel artificial neural network for power quality improvement in AC microgrid

2021 ◽  
Vol 12 (4) ◽  
pp. 2151
Author(s):  
Debani Prasad Mishra ◽  
Amba Subhadarshini Nayak ◽  
Truptasha Tripathy ◽  
Surender Reddy Salkuti ◽  
Sanhita Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Power Quality ◽  
Power Supply ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Sources ◽  
Artificial Neural

The microgrid concept provides a flexible power supply to the utility where the conventional grid is unable to supply. The microgrid structure is based on renewable energy sources known as distributed generators (DGs) and the power network. Nevertheless, the power quality (PQ) is a great challenge in the microgrid concept. Particularly the inclusion of renewable energy sources into the conventional grids increases the problems in the quality of power, like voltage sag/swell, oscillatory transient, voltage flickering, and voltage notching which reduces the quality and reliability of the power supply. In this paper, a microgrid is considered which consists of PV cells as DG, battery energy storage system (BESS), and a novel control strategy known as the nonlinear autoregressive exogenous model (NARX). The proposed controller is an improved artificial neural network (ANN). The various case studies like sag/swell, unbalanced condition, and voltage deviation have been simulated with the model. The comprehensive simulation results are compared with the proportional-integral (PI) controller. Hence in this paper, the robustness of the proposed controller has been studied through different situations.

scholarly journals Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3372 ◽  
Author(s):  
Heung-Jae Lee ◽  
Seong-Su Jhang ◽  
Won-Kun Yu ◽  
Jung-Hyun Oh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Heavy Load ◽  
Battery Energy Storage ◽  
Battery Energy ◽  
Load Conditions

This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers, is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB. As a result, the proposed ANN PSS shows a promising robust nonlinear performance.

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