• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.1864-1873
• /
• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.741-746
• /
• 1987

Solar cell which transforms the light energy into the electric energy from Sun comes into prominence as a new energy for next generation. However, it is difficult to obtain the stable output voltage and current from the solar cell due to the uncertainty in weather conditions, etc, In the present paper, two types of control laws are considered for regulating the input voltage in a photovoltaic energy storage system such as the system with the super conducting magnetic energy storage. (1) Oone is the design of optimal controller. (2) The other is that of weighted minimum prediction error controllers (weighted one-step ahead controllers). Simulation study for the above controllers is performed to see how they work and to get preliminary knowledge in the regulation of the input voltage to the experimental photovoltaic energy storage system.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.87-106
• /
• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.1597-1598
• /
• 2015

Regenerative power utilization is one of the most interesting issue in electric railway systems. Generally, technologies to utilize regenerative power from railway vehicles are railway substation with regenerative inverter, on-station energy storage systems, and on-board energy storage systems. In this paper, the electric power loss for those technologies is calculated and compared using DC electric railway system analysis algorithm.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.994-995
• /
• 2008

It is possible to suppress voltage drops, power loading fluctuations and regeneration power lapses for DC railway systems by applying an energy storage system. A electric double layer capacitor (EDLC) of the rapid charge/discharge type has been developed and used in wide ranges. It has a long life, high efficiency and maintenance free/low pollution features as a new energy storage element. In this paper, an efficient charge and discharge control method of a bidirectional DC-DC converter using the supercapacitor is proposed.

• Journal of Power Electronics
• /
• v.18 no.4
• /
• pp.1111-1126
• /
• 2018

In modern power systems, distributed generators (DGs) result in high stress on system frequency stability. Apart from the intermittent nature of DGs, most DGs do not contribute inertia or damping to systems. As a result, a new control method referred to as a virtual synchronous machine (VSM) has been proposed, which brought new characteristics to inverters such as synchronous machines (SM). DGs employing an energy storage system (ESS) provide inertia and damping through VSM control. Meanwhile, energy storage presents some physical constraints in the VSM implementation level. In this paper, a VSM mathematical model is built and analyzed. The dynamic responses of the output active power are presented when a step change in the frequency occurs. The influences of the inertia constant, damping factor and operating point on the ESS volume margins are investigated. In addition, physical constraints are proposed based on these analyses. The proposed physical constraints are simulated using PSCAD/EMTDC software and tested through RTDS experiment. Both simulation and RTDS test results verify the analysis.

• Journal of Power Electronics
• /
• v.13 no.4
• /
• pp.516-527
• /
• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.2043-2050
• /
• 2017

This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.326-332
• /
• 2018

In this paper, the design process and the control method of the power conversion system (PCS) that consists of a bidirectional DC-DC converter and a 3-level T-type inverter for an energy storage system is presented. Especially the design method of the output LCL filter for a 3-lvel T-type inverter without complex mathematical process are proposed. The validity of the control method and design process in this paper are verified through simulation and experimental analysis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.6
• /
• pp.63-69
• /
• 2015

Energy Storage Systems (ESSs) are essential in the future power systems because they can improve power usage efficiency. In this paper, we propose the countermeasure for improving the power quality using coordinated control of BESS(Battery Energy Storage System) on EV connected system. To verify the performance of proposed scheme, we simulate on the actual power system of KEPCO and compare the results of voltage variation, frequency variation, and load factor with those of uncoordinated control. From the simulation results, we confirm that frequency and voltage deviation are significantly reduced with proposed coordinated control of BESS.