Energy Systems

, Volume 3, Issue 4, pp 341–377 | Cite as

Wind integration in self-regulating electric load distributions



The purpose of this paper is to introduce and assess an alternative method of mitigating short-term wind energy production variability through the control of electric loads. In particular, co-located populations of electric vehicles and heat pumps are targeted to provide regulation-based ancillary services, as the inherent operational flexibility and autonomous device-level control strategy associated with these load-types provide an ideal platform to mitigate enhanced variability within the power system. An optimal control strategy capable of simultaneously balancing these grid-side objectives with those typically expected on the demand-side is introduced. End-use digital communication hardware is used to track and control population dynamics through the development of online aggregate load models equivalent to conventional dispatchable generation. The viability of the proposed load control strategy is assessed through model-based simulations that explicitly track end-use functionality of responsive devices within a power systems analysis typically implemented to observe the effects of integrated wind energy systems. Results indicate that there is great potential for the proposed method to displace the need for increased online regulation reserve capacity in systems considering a high penetration of wind energy, thereby allowing conventional generation to operate more efficiently.


Wind energy integration Demand response Heat pumps Electric vehicles Ancillary services Distributed energy resources Low carbon energy systems 

List of symbols


Area control error


Automatic generation control


Economic dispatch


Electric vehicle


Load aggregator


Linear program


Programmable communicating hysteresis-controller


Thermostatically controlled load


Unit commitment


Virtual generator model


\(\delta \)

Width of deadband space

\(\eta \)

Energy conversion efficiency

\(\Delta \epsilon \)

Deadband discretization

\(\epsilon _{\pm }\)

State-transition boundary


Weighting coefficient


Number of previous samples considered in moving average horizon

\(m_{\pm }\)

State-transition boundary index


Number of generators


Number of responsive loads


Number of decision intervals


Number of responsive load types


Maximum generator ramp-rate


Rated power of responsive load


Maximum generator output


Minimum generator output

\(\mathcal R \)

Deadband resolution


Discrete sampling period



Generator index


Responsive load index


Discrete-time sampling index


Deadband location index


Responsive load-type index


Continuous-time index


\(\epsilon \)

End-use state comparison

\(\Phi \)

Capacity factor of responsive load population

\(\phi \)

Power density distribution function

\(\sigma _{lf,r}\)

Load-following power gradient standard deviation

\(\sigma _{lf}\)

Load-following power standard deviation

\(\sigma _{reg,r}\)

Regulation power gradient standard deviation

\(\sigma _{reg}\)

Regulation power standard deviation


Online load-following reserve ramp capacity


Online load-following reserve capacity


Online regulation reserve ramp capacity


Online regulation reserve capacity


Battery charging trajectory


Measurement or model error


Battery energy fully charged


Load-following capacity contract


Load-following ramp capacity contract


Set-point index


Operational state of responsive load

\(\Delta P^*\)

Deviation from uncontrolled aggregate responsive load trajectory


Curtailed wind power


Total generation


Total load demand


Uncontrolled aggregate responsive load trajectory


Self-regulating responsive load trajectory


Target responsive load trajectory


Aggregate unresponsive load trajectory


Wind power


Base-load component


Total installed power in responsive load population


Load following component


Regulation component


Regulation capacity contract


Regulation ramp capacity contract


EV user’s desired charge time


Systemically controlled set-point change

\(\mathbf Z \)

Set of power-state vectors

\(\mathbf z \)

Power-state vector



The authors would like to thank Torsten Broeer and Dave Chassin for their helpful comments and insights. Financial support from the Pacific Institute for Climate Solutions (PICS), NSERC Wind Energy Strategic Network (WESNet), NSERC Hydrogen Strategic Network (H2Can), and the University of Victoria is gratefully acknowledged.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Institute for Integrated Energy SystemsUniversity of VictoriaVictoriaCanada

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