• Journal of IKEEE
• /
• v.25 no.2
• /
• pp.388-398
• /
• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.7
• /
• pp.1125-1129
• /
• 2015

Increasing peak load is one of the major concerns about operation of urban railroad systems. Since ESSs (Energy Storage Systems) have a great potential for shaving the peak load, there has been a growing interest in the use of ESS for peak load reduction. Also, ESS can be optimally scheduled to minimize the electricity purchasing cost under a given ToU (Time-of-Use) tariff by taking advantage of electricity price difference between peak and off-peak time. This paper presents a Mixed Integer Programming (MIP)-based ESS scheduling method to minimize the electricity purchasing cost under a ToU tariff for an urban railroad system.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.267-278
• /
• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.720-723
• /
• 1996

The recent summer power peak crisis has been caused by excessive use of cooling loads at daily peak time in summer. The yearly load shape of KEPCO has gradually became very steep valley. Under this situation, more efficient DSM(Demand Side Management) tools are fully required for summer peak clipping and shaving. In this paper, the KEPCO's Jeju-Do model project for DSM, especially for Demand Controller, is presented. Demand Controller was evaluated to have the very high economical efficiency against the investment in equipment, as compared with another DSM tools. There were some serious problem to apply Demand Controller to many customers in the aspect to synchronization with KEPCO's watthour meter. But these problems have solved by Keyin's new Demand Controller using vision algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.9
• /
• pp.1199-1205
• /
• 2014

A power system configuration has been increasingly advanced with a number of generating units. In particular, renewable energy resources are widely introduced due to the environmental issues. When applying the renewable energy sources with the ESS (Energy Storage System), the ESS is the role of a potential generating resource in the power system while mitigating the output volatility of renewable energy resources. Thus, for applications of the ESS, the surrounding environment of it should be considered, which means that capacity and energy of the ESS can be affected. Moreover, operation strategy of the ESS should be proposed according to the installation purpose as well as the surrounding environment. In the paper, operation strategy of the ESS is proposed considering load demand and the output of renewable energy resources on a hourly basis. Then, the cost of electrical energy is minimized based on the economic model that consists of capital cost, operation cost, fuel cost, and grid cost for a year. It is sure that peak-shaving effects can be achieved while satisfying the minimum cost of electrical energy.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.2
• /
• pp.211-216
• /
• 2014

Construction of power plants and T&D facilities is so difficult because of the civil complaints and the additional cost according to the concerning field conditions. Therefore, various researches and methods have been considered to get solutions in the demand side and energy storage systems have been in the spotlight because of the various functions such as peak shaving, load shifting, and power system stabilizing, and so on. Residential small size batteries are considered in this paper and the economic analysis is carried out to evaluate the reasonable subsidy levels for the deployment of energy storage systems. Various economic parties are considered to find reasonable subsidy level comparing each other, which parties consist of utilities, participants and non-participants in general. The evaluation is based on California Standard Practice Test and the results are able to be used as subsidy guidelines.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.2
• /
• pp.73-79
• /
• 1987

This paper describes a new algorithm to evaluate the production cost for a generation system including energy-limited hydroelectric plants. The algorithm is based upon the analytical production costing model developed under the assumption of Gaussian probabilistic distribution of random load fluctuations and plant outages. Hydro operation and pumped storage operation have been dealt with in the previous papers using the concept of peak-shaving operation. In this paper, the hydro problem is solved by using a new version of the gradient projection method that treats the upper / lower bounds of variables saparately and uses a specified initial active constraint set. Accuracy and validity of the algorithm are demonstrated by comparing the result with that of the peak-shaving model.

• Proceedings of the KIPE Conference
• /
• 1999.07a
• /
• pp.226-229
• /
• 1999

The recent problems such as the rapid increase o electricity consumption, the large variation of electrical loads, and the sitting difficulty for new power plants could become a barrier to stable electrical power supply. Consequently, an electrical load management technology has become important, by which an electricity can be stored during off-peak time and efficiently used during peak time. The technology provides a variety of direct or indirect benefits which include, for utilities, reduction of new power plants, economical electricity production, and improved efficiency and reliability of power system and for consumers, low prices o electricity. The study is to develop a proto-type load management system and its application technology for a peak shaving. In the system, conventional batteries are used as energy storage device.

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.300-301
• /
• 2015

Accurate and robust load forecasting model plays very important role in power system operation. In case of short-term electric load forecasting, its results offer standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve accuracy of load forecasting. This paper proposes a newly forecasting model for weather sensitive season including temperature and Cooling Degree Hour(C.D.H) data as an input. This Forecasting model consists of previous electric load and preprocessed temperature, constant, parameter. It optimizes load forecasting model to fit actual load by PSO and results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows better performance than comparison groups.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.7
• /
• pp.1151-1160
• /
• 2016

In recent years, the energy storage systems such as LiB, NaS, RFB(Redox-Flow Battery), Super- capacitor, pumped hydro storage, flywheel, CAES(Compressed Air Energy Storage) and so on have received great attention as practical solutions for the power supply problems. They can be used for various purpose of peak shaving, load leveling and frequency regulation, according to the characteristics of each ESS(energy storage system). This paper will focus at 1 MWh RFB system, which is being developed through the original technology project of energy material. The output of ESS is mainly characterized by C-rate, which means that the total rated capacity of battery will be delivered in 1 hour. And it is a very important factor in the ESS operation scheduling. There can be several options according to the operation intervals 15, 30 and 60minutes. The operation scheduling is based on the optimization to minimize the daily electricity cost. This paper analyzes the cost-saving effects by the each operating time-interval in case that the RFB ESS is optimally scheduled for peak shaving and load leveling.