Design of a hybrid hierarchical demand response control scheme for the frequency control

Bao, Yu-Qing; Li, Yang; Hong, Yi; Wang, Beibei

doi:10.1049/iet-gtd.2015.0628

Cited by 60 publications

(75 citation statements)

References 13 publications

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“…The effect of DR in the frequency control has been studied in many earlier researches. To provide primary frequency control, the manipulated responsive DR for the frequency control should be based on the frequency deviation [10,23]. For a negative frequency deviation ∆f < 0, the amount manipulated DR is determined by:…”

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

“…However, we can define different ∆f th for different appliances properly so that their aggregated frequency response characteristic is similar to Equation (14), as can be seen in Figure 4. The overall control framework can be based on a hierarchical framework [10] that combines the centralized and decentralized control framework together. In such a control framework, individual controllers measure the system frequency and compute control signals by themselves; meanwhile, the control parameters are given by the control centre.…”

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

“…In such a control framework, individual controllers measure the system frequency and compute control signals by themselves; meanwhile, the control parameters are given by the control centre. The response delay is very small and can be neglected [10]. More details of this DR control strategy can refer [10].…”

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

“…The response delay is very small and can be neglected [10]. More details of this DR control strategy can refer [10]. be based on the frequency deviation [10,23].…”

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

“…During recent years, increasing attention have been paid to DR in the frequency control. A variety of control strategies, e.g., decentralized control strategies [6,7], centralized control strategies [8,9], hybrid hierarchical control strategies [10], proportional-integral (PI) control method [11,12], linear quadratic regulator method [13], H ∞ control method [14], etc., have been developed. Some researches incorporate DR with other resources in the frequency control, e.g., wind generators [15], storages [16], etc.…”

Section: Introductionmentioning

confidence: 99%

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Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Bao

Wang

et al. 2017

Energies

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Abstract:The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples.

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Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

“…The response delay is very small and can be neglected [10]. More details of this DR control strategy can refer [10]. be based on the frequency deviation [10,23].…”

Section: Calculation Of the Frequency Nadirmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Bao

Wang

et al. 2017

Energies

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Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Barik

Das

2019

Int Trans Electr Energ Syst

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Summary This work is the earliest attempt towards coordinated regulation of both frequency and voltage of an isolated multisource‐based hybrid microgrid with demand response support using a modified selfish herd optimisation. It is a maiden effort to propose a 50‐Hz system frequency‐based hybrid microgrid model, utilising biorenewable sustainable energy sources like sunrays, wind, waste waters, and agricultural and domestic wastes, with available support from demand response contributors for a dual objective of waste and power management. The intermittency of these resources due to climatic variations creates a great challenge to meet the timely consumer demands of the microgrid with reliable quality power supply. To overcome these issues, the performances of the proposed system are studied considering different typical Indian‐climatic scenarios round the year with real‐time recorded data. Initially, the system performances are compared proposing a new objective function called integral square of weighted absolute error in order to minimise the net deviation in system apparent power, using six controllers tuned with four‐popular/recent optimisation algorithms and four‐objective functions in MATLAB. Then, the simulation results are analysed for five different extreme scenarios of both source and load variations in the proposed system using a novel quasi‐oppositional selfish herd optimisation and reported adaptive performances with demand response support. Finally, the sensitivity of the proposed system with variation in system inertia and load damping factor are analysed to study the robustness of the proposed system.

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An optimized fuzzy sliding based active disturbance rejection control for simultaneous cyber‐attack tolerant and demand response participation program

Rouhani

Mojallali

Baghramian

2021

Int Trans Electr Energ Syst

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The simultaneous investigation of false data injection cyber-attack (FDICA) and demand response (DR) participation in smart power systems are dealt with in this paper. For this purpose, simultaneous and separate detection of FDICA and determination of the changes in the power consumption for participation in DR (P DR ) are necessary. To balance between generation and consumption, P DR must be equal to the load disturbances (LDs). Activedisturbance rejection control (ADRC) with the extended state observer (ESO) is implemented to detect FDICA and LDs. But, ESO only detects the sum of FDICA and LDs without the ability to separate them. Therefore, a nonlinear sliding mode observer (NSMO) is implemented to detach FDICA from LDs. In order to improve the NSMO estimation accuracy, the modified salp swarm algorithm (SSA) is implemented to design NSMO parameters. Moreover, to achieve better performance of ADRC, a fuzzy tuner is applied to the online tuning of controller parameters. The proposed optimized fuzzy sliding-based modified active-disturbance rejection control (FSADRC) is tested for load frequency control of the IEEE 9 bus standard test system and the 10 machines List of Symbols and Abbreviations: FDICA i t ð Þ, false data injection cyber-attack in the area i; LDs i , load disturbances in the area i; Δω i , frequency deviation in area i; Δω j , frequency deviation in area j; ΔP tie,ij , transferred power between areas i and j; β i , frequency bias in the area i; D i , equivalent damping in the area i; R, droop coefficient; T ij , the synchronizing power coefficient between areas i and j; H i , equivalent inertia constant in the area i; T t,ji jth, time constant of turbine in the area i; T G,ji jth, time constant of governor in the area i; R j,i jth, droop coefficient in the area i; ΔP c , input signals to the primary control loop; ΔP valve,i , deviation in the valve of the governor in the area i; ΔP mech,i , deviation in the mechanical power of the turbine in the area i; T G , governor time constant; T t , turbine time constant; ΔP tie,i , transferred power in the area i; A s,i , system matrix for the area i; A s,ij , relation matrix between states in the areas i and j; B s,i , known input matrix in the area i; B Ld , LDs matrix; C s,i , output matrix in the area i; u s,i , system inputs (the controller output) in the area i; y s,i , system output in the area i; x s,i , system states in the area i; x s,j , system states in the area j; A s , system matrix for a multi area AGC; B s , known input matrix for a multi area AGC; B Ld , LDs matrix for a multi area AGC; C s , output matrix for a multi area AGC; D AT , FDICA distribution matrix for a multi area AGC; P DR , changing in the power consumption due to the participation in DR program; P AGG , available DR power; B AGG , DR matrix; D ATÀi , FDICA distribution matrix for the area i; D AT_decom , FDICA distribution matrix after decomposition; ν s,n t ð Þ, discontinuous output error injection term to retain a sliding motion; δ s , a small positive number; X 1 k , ...

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Design of a hybrid hierarchical demand response control scheme for the frequency control

Cited by 60 publications

References 13 publications

Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

An optimized fuzzy sliding based active disturbance rejection control for simultaneous cyber‐attack tolerant and demand response participation program

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