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Wind integration in self-regulating electric load distributions

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Abstract

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.

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Notes

  1. In this paper, functions with square braces are used to designate discrete time or sampled data, with round braces used to designate continuous variables.

  2. In the case of heat pumps, if the customer were to change the temperature set-point, temperature measurements could be made that exist well outside the \(2\delta \)-region. Therefore, these customers would be unfit, or ineligible to participate in this aggregate load model formulation. As these customers choose to maintain control authority, they would in turn forfeit the benefits associated with participation. Therefore, this customer’s PCH would not send a power-state vector to the LA until the end-use state was again within this interval.

  3. In many locations colder temperatures are accompanied by higher wind speeds, and therefore seasonal operation of the heat pump may in fact coincide with times at which regulation is most needed.

Abbreviations

ACE:

Area control error

AGC:

Automatic generation control

ED:

Economic dispatch

EV:

Electric vehicle

LA:

Load aggregator

LP:

Linear program

PCH:

Programmable communicating hysteresis-controller

TCL:

Thermostatically controlled load

UC:

Unit commitment

VGM:

Virtual generator model

\(\delta \) :

Width of deadband space

\(\eta \) :

Energy conversion efficiency

\(\Delta \epsilon \) :

Deadband discretization

\(\epsilon _{\pm }\) :

State-transition boundary

\(a_j\) :

Weighting coefficient

\(M\) :

Number of previous samples considered in moving average horizon

\(m_{\pm }\) :

State-transition boundary index

\(N_g\) :

Number of generators

\(N_i\) :

Number of responsive loads

\(N_k\) :

Number of decision intervals

\(N_p\) :

Number of responsive load types

\(\dot{P}_{max}\) :

Maximum generator ramp-rate

\(P_r\) :

Rated power of responsive load

\(P_{max}\) :

Maximum generator output

\(P_{min}\) :

Minimum generator output

\(\mathcal R \) :

Deadband resolution

\(T\) :

Discrete sampling period

\(g\) :

Generator index

\(i\) :

Responsive load index

\(k\) :

Discrete-time sampling index

\(m\) :

Deadband location index

\(p\) :

Responsive load-type index

\(t\) :

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

\(C_{lf,r}\) :

Online load-following reserve ramp capacity

\(C_{lf}\) :

Online load-following reserve capacity

\(C_{reg,r}\) :

Online regulation reserve ramp capacity

\(C_{reg}\) :

Online regulation reserve capacity

\(E\) :

Battery charging trajectory

\(e\) :

Measurement or model error

\(E_c\) :

Battery energy fully charged

\(L\) :

Load-following capacity contract

\(L_r\) :

Load-following ramp capacity contract

\(m_s\) :

Set-point index

\(n\) :

Operational state of responsive load

\(\Delta P^*\) :

Deviation from uncontrolled aggregate responsive load trajectory

\(P_C\) :

Curtailed wind power

\(P_G\) :

Total generation

\(P_L\) :

Total load demand

\(P_o\) :

Uncontrolled aggregate responsive load trajectory

\(P_R\) :

Self-regulating responsive load trajectory

\(P_T\) :

Target responsive load trajectory

\(P_U\) :

Aggregate unresponsive load trajectory

\(P_W\) :

Wind power

\(P_{base}\) :

Base-load component

\(P_{cap}\) :

Total installed power in responsive load population

\(P_{lf}\) :

Load following component

\(P_{reg}\) :

Regulation component

\(R\) :

Regulation capacity contract

\(R_r\) :

Regulation ramp capacity contract

\(T_s\) :

EV user’s desired charge time

\(u\) :

Systemically controlled set-point change

\(\mathbf Z \) :

Set of power-state vectors

\(\mathbf z \) :

Power-state vector

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Acknowledgments

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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Authors and Affiliations

  1. Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, P.O. Box 3055, STN CSC, Victoria, BC, V8W 3P6, Canada

    Simon Parkinson, Dan Wang, Curran Crawford & Ned Djilali

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  1. Simon Parkinson
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Correspondence to Curran Crawford or Ned Djilali.

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Parkinson, S., Wang, D., Crawford, C. et al. Wind integration in self-regulating electric load distributions. Energy Syst 3, 341–377 (2012). https://doi.org/10.1007/s12667-012-0060-2

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