Abstract

A strategy to operate a power conversion system (PCS) to minimize the electricity rate of an energy storage system (ESS) is formulated. The ESS operation method is determined considering the power management system (PMS). The primary functions include peak-cut, peak-shifting, and frequency regulation typically related to electricity rates. Thus, the battery is charged and discharged when the price is low and high, respectively, thereby monetizing the battery. However, the ESS incurs a high cost for the batteries and PCS. Therefore, ESSs that reuse electric vehicle (EV) batteries are being actively developed. Many researchers have attempted to maximize the profit of ESSs by developing algorithms to calculate the optimal ESS capacity by performing a power load analysis of electricity consumers. An ESS selected based on this calculation can be operated through the PMS. This ESS can use the battery state of charge (SoC), ranging from 10–90%, to conduct a feasibility analysis using the net present value, which reflects the current electricity rate. This feasibility analysis is performed considering the difference between the initial investment cost of the ESS and the profit obtained from the power generation of the ESS. In South Korea, many policies have been implemented to encourage the installation of ESSs. The ESS promotion policy was implemented until 2020 to reduce the electricity rate, including the contracted capacity of batteries. However, since 2021, this policy has been transformed to reduce the electricity rate based on the daily maximum power generation. Thus, the conventional method of increasing the battery capacity is not suitable, and the profitability should be increased using limited batteries. For ESSs, PCSs composed of single and parallel structures can be used. When installing a large capacity ESS, a PCS using silicon (Si) is adopted to reduce the unit cost of the PCS. The unit price of a silicon carbide (SiC) device has recently decreased significantly. Thus, in this study, a PCS using this SiC device was developed. Moreover, an algorithm was formulated to minimize the electricity rate of the ESS, and the operation of a modular type PCS based on this algorithm was demonstrated.

Highlights

  • With the widespread use and diversification of renewable energy sources (RES) and increase in the related investment, the proportion of new and renewable energy in conventional power systems is gradually increasing; wind and solar energy are the most rapidly growing new and renewable energy sources worldwide [1]

  • According to the electricity rate system, electricity rates vary depending on the time interval and load, and the electricity rate of the energy storage system (ESS) can be reduced depending on the discharge amount during the maximum load period

  • The proposed approach forminimization cost minimization is on based ratic programming (QP).(QP)

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Summary

Introduction

With the widespread use and diversification of renewable energy sources (RES) and increase in the related investment, the proportion of new and renewable energy in conventional power systems is gradually increasing; wind and solar energy are the most rapidly growing new and renewable energy sources worldwide [1]. This study formulates a strategy for PCS operation by adopting the objective function of minimizing the electricity rate of the ESS, considering the PCS efficiency. If a system is implemented with a single PCS, the efficiency during charging and discharging differs considerably This study addresses this problem by considering PCS efficiency to minimize the electricity the ESS are divided into those with single and multi-parallel structures. We propose an algorithm that enables the PCS to be operated the ESS is significantly higher than that of the PCS, the PCS can be operated at its maxiat the same efficiency regardless of load, in which the difference in efficiency occurs durmum capacity This method increases the initial investment cost of the ESS.

PCS Operation
Electricity Rate System and ESS Discount System
Minimized Electricity Bills for ESS
D T to infer the exact
Algorithm for PCS Operation
Simplified
Results
Figure
Conclusions
Full Text
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