Energy Systems

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

Wind integration in self-regulating electric load distributions

Article

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.

Keywords

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

List of symbols

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

Constants

\(\delta \)

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

Indexes

\(g\)

Generator index

\(i\)

Responsive load index

\(k\)

Discrete-time sampling index

\(m\)

Deadband location index

\(p\)

Responsive load-type index

\(t\)

Continuous-time index

Variables

\(\epsilon \)

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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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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