Effect of BESS Response on Frequency and RoCoF During Underfrequency Transients

Brogan, Paul; Best, Robert; Morrow, D. John; McKinley, Kenneth W.; Kubik, M. L.

doi:10.1109/tpwrs.2018.2862147

Cited by 87 publications

(58 citation statements)

References 9 publications

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“…The UPS systems are fully activated when the system frequency dropped below 49.5 Hz within a period 1.45 s. This ramping in the UPS power corresponds to 0.2% droop response. The total energy delivered to the grid before the frequency reaches nadir takes the form given in (15 This expression indicates that the ameliorating impact on the frequency deviation is directly proportional to additional energy provided during the FFR service before the frequency nadir is reached [37]. It is important to ensure the state of charge (SoC) of the UPS to remain within the upper and lower bands to prolong the battery life time as well as to guarantee the operational safety of the system.…”

Section: ) Methods III -Dynamic Response Through Ups Systemsmentioning

confidence: 99%

Manipulation of Static and Dynamic Data Center Power Responses to Support Grid Operations

et al. 2020

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This research investigates three frameworks for data centers to deliver fast frequency response services from their UPS storage systems, HVAC cooling units, and the ability of off-gridding the entire data center during transient frequency events. The aim of this is to provide dynamic injection during transients in real time for grid operators and for power sellers in hour ahead markets. A static and a dynamic model was developed in DIgSILENT PowerFactory. In the static model, the data centers are off-gridded in response to emergency frequency signals from system operator in a centralized manner, whereas in the dynamic model the UPS systems and shiftable cooling units power consumption was altered continuously in the data centers in response to local frequency measurements considering different real-time scenarios. The performance of the proposed operational frameworks is validated, and calibrated to actual frequency events that occurred in the Irish power system. The sensitivity analysis demonstrates that both static and dynamic load controls can significantly improve system frequency metrics, i.e., frequency nadir and rate of change of frequency during transients. The key findings show that demand response can only make a substantial frequency improvement if a large amount of energy delivered within the timeframe of inertial response. INDEX TERMS Battery and UPS, data centers, demand-side management, fast frequency response, inertia response, load modeling, power system dynamics, wind power generation. NOMENCLATURE PLo,j Pre-fault active power of a load at j th bus. QLo,j Pre-fault reactive power of a load. PL,j Active and reactive power of a load. QL,j Reactive power of a load at j th bus. vo,j Pre-fault voltage of j th bus. vj Post-fault voltage of j th bus. ui, uj Committed status. E Operational inertia floor constraint. Hi Inertia floor constraint of i th generators. Hsys Post-event system inertia constant. Si Stated nominal power limit. fo Nominal system frequency. fi Regional nominal frequency. fCOI Frequency at center of inertia.

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Section: ) Methods III -Dynamic Response Through Ups Systemsmentioning

confidence: 99%

Manipulation of Static and Dynamic Data Center Power Responses to Support Grid Operations

et al. 2020

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“…However, the number of applications for longer times (e.g., 2-5 h) and sizes 2 of 20 up to 500 kW is already significant, and applications for bigger sizes (also in the range 1-10 MW) are increasing. In the transmission system, the positive impacts of BESSs for providing a fast response to frequency deviations [3], mitigating under-frequency transients [4], and being exploited for energy arbitrage [5] have been observed. The integration of BESSs with appropriate control strategies has been shown to be able to improve the frequency stability [6].…”

Section: Introductionmentioning

confidence: 99%

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

et al. 2019

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Proper planning of the installation of Battery Energy Storage Systems (BESSs) in distribution networks is needed to maximize the overall technical and economic benefits. The limited lifetime and relatively high cost of BESSs require appropriate decisions on their installation and deployment, in order to make the best investment. This paper proposes a comprehensive method to fully support the BESS location and sizing in a low-voltage (LV) network, taking into account the characteristics of the local generation and demand connected at the network nodes, and the time-variable generation and demand patterns. The proposed procedure aims to improve the overall network conditions, by considering both technical and economic aspects. An original approach is presented to consider both the planning and scheduling of BESSs in an LV system. This approach combines the properties of metaheuristics for BESS sizing and placement with a greedy algorithm to find viable BESS scheduling in a relatively short time considering a specified time horizon, and the application of decision theory concepts to obtain the final solution. The decision theory considers various scenarios with variable energy prices, the diffusion of local renewable generation, evolution of the local demand with the integration of electric vehicles, and a number of planning alternatives selected as the solutions with top-ranked objective functions of the operational schedules in the given scenarios. The proposed approach can be applied to energy communities where the local system operator only manages the portion of the electrical grid of the community and is responsible for providing secure and affordable electricity to its consumers. 417Regarding the calculation of the load profiles, it takes into account the real contractual delivery 418 powers to each load. Two nodes are considered as commercial consumers, while the other ones 419 refer to residential customers. For the profiles, due to lack of complete information from the actual 420 profiles, relevant (residential and commercial) profiles have been taken from an open-source 421 dataset (OpenEI) [36], normalized and multiplied by the nominal powers.422 3.2.2 PV generation 423 The considered PV production, in the simulation, is calculated based on yearly solar irradiance 424 and the nominal installed power [37]. Yearly solar irradiance data is obtained through collected 425 data from third party weather service provider [38] for specific location of study case with the 426 coordination of 46.4746° N, 11.2479° E.427 3.2.3 EVs relevant information 428 EVs are considered in some scenarios, through the load profile caused by their charging. 429Within this analysis, the number of EVs that determines the EV charging profile, is set to 10 in the 430 beginning of the scenarios with EV and that number increments gradually by rate of one EV per 431year. The nominal power of the EV chargers are considered to be 3.6 kW, i.e., a standard power 432 level of charging stations, and charging events occur mainly during the night...

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“…In the Australian electricity market, FFR is defined as "any type of rapid active power increase or decrease by generation or load, in a timeframe of less than two seconds, to correct supply-demand imbalances and assist with managing frequency" [2]. There have been several studies of FFR encompassing a wide range of technologies [3][4][5][6]. In one study [6], it was shown that wind generators (WG) can provide IR for a very short duration (~10 s).…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Nguyen-Hong

Nakanishi

2019

Energies

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Nowadays, the hybrid wind–diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm—a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

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Effect of BESS Response on Frequency and RoCoF During Underfrequency Transients

Cited by 87 publications

References 9 publications

Manipulation of Static and Dynamic Data Center Power Responses to Support Grid Operations

Manipulation of Static and Dynamic Data Center Power Responses to Support Grid Operations

Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks

Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

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