Assessment of the influence of distributed generation and demand side management on transmission system performance

Hayes, Barry; Collin, Adam J.; Acosta, Jorge; Djokić, Saša Ž.

doi:10.1049/cp.2010.0889

Cited by 11 publications

(26 citation statements)

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“…(2) where: P t,t+1 is state transition probability matrix, t and t+1 are current and next states, p ij is transition probability from state i to state j, and S is the set of all possible n different states. The state transition matrix is built using recorded wind data, and resulting model can generate synthetic wind speed (or power output) time series of any length, in accordance with methodology in [26][27][28].…”

Section: Wind Energy Modelmentioning

confidence: 99%

Comparison of two energy storage options for optimum balancing of wind farm power outputs

Hayes

Wilson²,

Webster³

et al. 2016

IET Generation, Transmission & Distribution

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Section: Wind Energy Modelmentioning

confidence: 99%

Comparison of two energy storage options for optimum balancing of wind farm power outputs

Hayes

Wilson²,

Webster³

et al. 2016

IET Generation, Transmission & Distribution

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“…They can be easily extracted from the proposed aggregate load model, and are used for system performance analysis in sister paper [1].…”

Section: A Exponential and Poly Nomial/zip Interpretations Of The Prmentioning

confidence: 99%

“…wind farms) in a sister paper [1], and models of micro and small-scale DG technologies (representing e.g. wind based microgeneration) developed in the second sister paper [2].…”

Section: Introductionmentioning

confidence: 99%

“…wind based microgeneration) developed in the second sister paper [2]. Full analysis with the proposed aggregate load models, including analysis of network power flows, voltage profiles, (over)loading of system components, harmonic analysis and effect of demand-side management (OSM), is, due to the space limitations in this paper, provided in sister paper [1]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…Generic load models for each load category are presented and discussed in Section IV, while the proposed aggregate load models (and corresponding polynomial/exponential interpretations) are discussed in Section V, including the results for the variations in their harmonic injections during the 24 hour period. Sections V and VI present discussion of relevant results from [1] and general conclusions of the presented work.…”

Section: Introductionmentioning

confidence: 99%

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Component-based aggregate load models for combined power flow and harmonic analysis

Collin

Acosta

Hayes

et al. 2010

7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 201

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Aggregate load models traditionally used for steady state analysis of power systems are based on standard constant impedance/current/power representation of static load component, with induction motor representation of dynamic load component. These traditional load models cannot accurately represent characteristics of non-linear power electronic loads, the impact of local distributed generation (DG) on the aggregate load demand, or the effects of demand manageable loads. Furthermore, the assessment of the contribution of non-linear loads and inverter-interfaced DG to the flow and propagation of harmonic currents is usually not possible using the traditional aggregate load models. This paper uses a component-based approach to build improved aggregate load models that are capable of preserving full information on electrical characteristics of aggregated load, enabling to use the same load models for both the analysis of power flows and voltage profiles, and analysis of harmonics. Furthermore, by modelling aggregate system load as a composition of general load categories, it is possible to identify the exact portion of available demand-manageable load in the aggregate demand, and then investigate the actual effects of the controlled changes in demand on system performance. The developed load models are implemented in two "sister papers" that accompany this one, where they are combined with the models of small/large DG to perform steady state analysis of network performance (power flows, voltage profiles, overloading of system components, harmonic analysis and a simple case of demand-side management). Index Terms-aggregate load model, component-based load model, current/voltage harmonics, demand side management, exponential load model, generic load model, load modelling, polynomial load model, total harmonic distortion.

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Power Flow Management With Demand Response Profiles Based on User-Defined Area, Load, and Phase Classification

Alam¹,

Shahjalal²,

Islam³

et al. 2020

IEEE Access

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In recent times, the electric power management based on customers' demand has drawn significant attention of smart-grid (SG) governors. The SG requires real-time management of dynamic load to maintain the quality of service (i.e., balance between supply and demand) by interfacing with users. In this paper, we propose an approach to respond to the active demand (AD) based on user-defined energy-management policy. An algorithm is also proposed for the smart-controller device (SCD) modeled with the load aggregator and connected to the customers. The total contracted load and the user ADs are determined based on area, load, and phase classification, which specify the individual energy consumption. The proposed scheme is implemented in MATLAB/Simulink using the load-information of the IEEE 30bus system, and the feasibility is assessed in IEEE 13 and IEEE 34 node test feeder systems. By applying the customer's controlled SCD device both the deficiency and redundancy of generation in terms of grid controllable load have been improved that lead the maximization of generation and distribution services. The voltage regulation and power factor of the particular area have been enhanced by integrating appropriate distributed generation and power factor improvement devices. The results garnered from the performance analysis show that the proposed scheme can optimize power generation based on the user-defined demand. INDEX TERMS Power flow analysis, energy management, smart grid, dynamic demand response, load aggregation model.

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Assessment of the influence of distributed generation and demand side management on transmission system performance

Cited by 11 publications

References 0 publications

Comparison of two energy storage options for optimum balancing of wind farm power outputs

Comparison of two energy storage options for optimum balancing of wind farm power outputs

Component-based aggregate load models for combined power flow and harmonic analysis

Power Flow Management With Demand Response Profiles Based on User-Defined Area, Load, and Phase Classification

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