Frequency-based load control in power systems

Zhao, Changhong; Topcu, Ufuk; Low, Steven H.

doi:10.1109/acc.2012.6315283

Cited by 21 publications

(4 citation statements)

References 16 publications

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“…However, there are a lot of techniques used to predict and control the frequency of grid network to maintain stability, such as frequency response analysis, the dynamic simulation of the grid network also the steady state analysis of the grid network, which assumes that the grid is stable, load flow analysis [29] which is majorly used for power and voltage calculation can also be used to estimate the frequency of the grid network for different load values and since load values can be predicted in any power system network, then the corresponding frequency values can be determined. However, many literature [30][31][32][33][34][35][36][37][38][39] deals with frequency control in a power system network. The machine learning approach can be used to predict the frequency of the network based on the network's historical data and network topology.…”

Section: Annmentioning

confidence: 99%

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Makanju,

Shongwe,

Famoriji

2024

IEEE Access

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Prediction in the power system network is very crucial as expansion is needed in the network. Several methods have been used to predict the load on a network, from short to long time load prediction, to ensure adequate planning for future use. Since the power system network is dynamic, other parameters, such as voltage and frequency prediction, are necessary for effective planning against contingencies. Also, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This review analyzed different machine learning approaches used for load, frequency, and voltage prediction in power systems and proposed a machine learning predictive approach using network topology behavior as input variables to the model. The analysis of the proposed model was tested using a regression model, Decision tree regressor, and long short-term memory. The analysis results indicate that with network topology behavior as input to the model, the prediction will be more accurate than when isolated variables of a particular Bus in a network are used for prediction. This work suggests that network topology behavior data should be used for prediction in a power system network rather than the use of isolated data of a particular bus or exogenous data for prediction in a power system. Therefore, this research recommends that the accuracy of different predictive models be tested on power system parameters by hybridizing the network topology behavior dataset and the exogenous dataset.

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Section: Annmentioning

confidence: 99%

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Makanju,

Shongwe,

Famoriji

2024

IEEE Access

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“…The distributed model predictive control method was proposed to control the output frequency of the power plant in order to decrease frequency fluctuations. The advantage of MPC is that it can be directly extended to multiple-input multiple-output (MIMO) systems, which can be quadratic, and take into account process constraints, making it impossible for variables to exceed predefined values [93,94].…”

Section: Mpc For Frequency Regulationmentioning

confidence: 99%

Review of Model Predictive Control of Distributed Energy Resources in Microgrids

et al. 2022

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In recent years, in response to increasing environmental concerns, advances in renewable energy technology and reduced costs have caused a significant increase in the penetration of distributed generation resources in distribution networks. Nonetheless, the connection of distributed generation resources to distribution networks has created new challenges in the control, operation, and management of network reliability. This article is a review on the model predictive control (MPC) for distributed energy resources (DER) in microgrids. The solutions of MPC for energy conversion of solar photovoltaic, wind, and energy storage systems are covered in detail. MPC’s applications for increasing reliability of grid-connected converters under (a)symmetrical grid faults are also discussed. The promising potentials of the applications of MPC to the stable multi-variable control performance of DERs are highlighted. This work reflects strong symmetry on MPC control strategies and provides guidance map for readers to facilitate future research works in these exciting fields.

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“…The availability of DR resources during peak load condition [19][20][21], and role of providing AS mainly in islanded distribution level based microgrids is given in [22][23][24][25][26]. A number of studies have been reported [5,6,8,[27][28][29][30][31][32][33] for efficacy of DR control loop for power grid frequency stabilization. The scope of DR loop offers additional merits to balance generation with load for LFC related issue.…”

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confidence: 99%

“…The general configuration for the analysis of effect of DR control loop on power system performance is not reported in [5,6,8,27,29,30,32,33]. Further, the robustness of controller against uncertainty and nonlinearities in the system have not been discussed in [5,6,8,9,[30][31][32][33][34]. Some specific load without generalization in the system is considered by [5,6] & [8,9].…”

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confidence: 99%

Impact of demand response for frequency regulation in two-area thermal power system

Singh

Samuel

Kishor

2016

Int. Trans. Electr. Energ. Syst.

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Summary In future power system operation, wide area monitoring applications will rely heavily on demand response (DR) because of its inherent characteristics mainly in ancillary services (AS). The DR is widely applied in power system; however, impact of DR loop with communication delay on dynamic performance of two‐area power system, mainly load frequency control (LFC), have not been reported by researchers. This paper presents the impact of DR control loop with communication delay in two‐area thermal power systems for improvement in frequency deviation profile. The optimal power sharing between supplementary and DR loop control is presented for different load variations. Further, efficacy of controller performance is also validated by introducing non‐linearities in generation in terms of generation rate constraint (GRC) and governor dead band (GDB). The simulations are performed with inclusion of DR for LFC control problem in (i) single‐area, (ii) two‐area thermal and (iii) thermal power system integrated with pumped hydropower system to improve the dynamic performance of system and hence minimize the frequency deviation. The controller in supplementary loop and DR loop is optimized using particle swarm optimization (PSO) technique. Copyright © 2016 John Wiley & Sons, Ltd.

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Frequency-based load control in power systems

Cited by 21 publications

References 16 publications

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Review of Model Predictive Control of Distributed Energy Resources in Microgrids

Impact of demand response for frequency regulation in two-area thermal power system

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