• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.305-312
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• 2014

This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.141-146
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• 2014

This paper suggests a way of generating one-day load curves for performance improvement of peak shaving in a power system. This Peak Shaving algorithm is a long-term scheduling algorithm of PMS (Power Management System) for BESS (Battery Energy Storage System). The main purpose of a PMS is to manage the input and output power from battery modules placed in a power system. Generally, when a Peak Shaving algorithm is used, a difference occurs between predict load curves and real load curves. This paper suggests a way of minimizing the difference by making predict load curves that consider weekly normalization and seasonal load characteristics for smooth energy charging and discharging.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.754-760
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• 2012

This paper describes peak shaving algorithms as long-term cycle scheduling in the power management system (PMS) for MW-scale battery energy storage systems (BESS). The purpose of PMS is basically to manage the input and output power from battery modules placed in the systems. Assuming that an one-day ahead load curve is provided, off-line peak shaving algorithms can be employed, but applying the results of the off-line algorithm may result in the difference in the real-time performance because there is uncertainty in the provided load curve. This paper adopts fuzzy based LP (linear programming) algorithms for describing the peak shaving algorithm in PMS and discusses a solution technique and real-time operation strategies using the solution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.404-410
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• 2016

This paper proposed a program of an energy storage system(ESS) for peak shaving of high-speed railway substations The peak shaving saves cost of equipment and demand cost of the substation. To reduce the peak load, it is very important to know when the peak load appears. The past data based load profile forecasting method is easy and applicable to customers which have relatively fixed load profiles. And an optimal scheduling method of the ESS is helpful in reducing the electricity tariff and shaving the peak load efficiently. Based on these techniques, MS. NET based peak shaving program is developed. In case study, a specific daily load profile of the local substation was applied and simulated to verify performance of the proposed program.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.981-987
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• 2017

In this paper, we estimate the economic benefits of Energy Storage Systems (ESSs) for peak load shaving in an urban railway substation using the annual cost. The annual investment cost of ESSs is estimated using Net Present Value (NPV) and compared with the cost reduction of electricity by the ESS. The optimal capacities of the battery and Power Converting System (PCS) are determined for peak load shaving. The optimal capacity of the ESS and the peak load shaving is determined to maximize the profit by the ESS. The proposed method was applied to real load data in an urban railway substation, and the results show that electric power costs can be reduced. Other aspects of the ESS, such as the lifetime and unit price of the battery, are also investigated economically.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1752-1758
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• 2014

This paper evaluates economic benefits of ESS (Energy Storage System) for peak load shaving in the substation of urban railway. For this purpose, the method to determine capacities of ESS and PCS (Power Conditioning System) for peak load shaving is proposed. Then the economic benefits of those devices are evaluated using net present value method. The proposed methods are applied to actual measurements of loads in the substation of urban railway. The results show that electric power costs can be reduced and the economic benefits of ESS for peak load shaving mainly depends on the price of battery.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.27-34
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• 2014

The paper proposed a sizing method of an energy storage system(ESS) for peak shaving of high-speed railway substations based on load profile patterns of substations. A lithium based battery ESS was selected since it can produce high-power at high speed that peak shaving requires, and also takes up a relatively smaller space for installation. Adequate size of the ESS, minimum capacity which can technically meet a peak shaving target, was determined by collectively considering load patterns of a target substation, characteristics of the ESS to be installed, and optimal scheduling of the ESS. In case study, a local substation was considered to demonstrate the proposed sizing method. Also economic analysis with the determined size of ESS was performed to calculate electricity cost savings of the peak shaving ESS, and to offer pay-back period and return on investment.

• Journal of Wind Energy
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• v.13 no.2
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• pp.13-22
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• 2022

In this study, the resonant motion of a floating offshore wind system due to negative damping was mitigated by peak shaving algorithms of the NREL ROSCO controller, and the decreased gains of the blade pitch controller by the gain detuning method for the floating system was increased to the gains of onshore baseline controller to improve the power performance of the turbine. To check the performance of the controller, the dynamic responses between the existing gain-detuned pitch controller and the ROSCO controller with peak shaving control for an OC4 floating offshore wind system were compared. As a result of DLC1.1 at near-rated wind speed, when peak shaving was applied, the average generator power decreased by 1.9%, but it was confirmed that the standard deviation was reduced and stability was improved with fast pitch regulation. In addition, since peak shaving reduces the maximum thrust of the rotor, was confirmed that not only are the loads of the blades and tower reduced, but the surge motion of the floater is also reduced, and the tension of the mooring lines is reduced.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.337-338
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• 2015

본 논문은 BESS 기반 전력피크 대응 peak shaving 알고리즘에 대하여 설명한다. 전력망 응용에 적용되는 BESS는 다양한 C-rate (charge rate)을 갖으며, 이를 고려한 알고리즘 개발이 요구된다. 상대적으로 높은 C-rate을 갖는 BESS의 경우 단주기 응용으로 적용될 수 있으나, 보다 효과적인 활용방안은 두 가지의 이상의 목적을 위한 운용이 요구된다. 더욱이 전력피크 예측을 위한 정교한 부하예측 방법이 필요하다. 본 논문에서는 단주기 및 장주기 응용을 모두 고려하였을 때 요구되는 peak shaving 알고리즘 설계 및 Algebraic Prediction을 이용한 부하예측에 대하여 논하고자 한다.