Page 464 - Decision Making Applications in Modern Power Systems

Multistage and decentralized operations of Chapter | 16 423

b d;bu t ðÞUp # B d tðÞ 2 R u tðÞ # b d;bu t ðÞUP d ð16:22Þ
agg agg
Eq. (16.16) shows the expression for calculating the total revenue from
day-ahead frequency regulation markets. The revenue consists of the
regulation-up capacity payment and regulation-down capacity payment.
Unlike the modeling approaches in previous research where day-ahead com-
mitments can be violated with penalties, we do not intend to violate the com-
mitment in any circumstances due to the performance regulations in
California ancillary service markets.
Due to the noncontinuity property of power boundaries, auxiliary binary
decision variables are defined to indicate the options to participate in the reg-
ulation up and down markets. Given regulation signals from CAISO, an
aggregate EV fleet, for example, will follow the signals, that is, increase or
decrease the aggregated power consumption of the EVs. The revenue is cal-
culated on the basis of the day-ahead bids, that is, the committed regulation
up and down capacities, rather than the actual increased or decreased power
consumption following real-world regulation signals, indicated by
Eq. (16.17). The negative (up), ρ , and positive (down), ρ down , utilization
up
factors represent the fraction of the committed regulation dispatched by the
CAISO control signal. Actual utilization factors collected in a real-world
demonstration project at the Los Angeles Air Force Base were used in the
d
simulations presented here. The baseline aggregate power B tðÞ is the origi-
d
nal power consumption profile assuming no regulation signals, whereas P tðÞ
d
is the actual power profile in Eqs. (16.18) (16.21). Here, B tðÞ is a decision
variable. Eqs. (16.22) and (16.23) model the constraints so that the aggregate
fleets can participate in the regulation up or down markets, or choose to stay
out of the markets. We also assume that the aggregated EV fleet can follow
all regulation signals, that is, the actual power consumption should always
stay in the power boundaries, which is modeled by Eqs. (16.24) and (16.25).
Note that the aggregator can make regulation up and down bids for the
same time periods, even one of them will not be called during implementa-
tion, but still getting benefits for the bids. In addition, the actual aggregate
power and the aggregated baseline profiles should both satisfy the aggregate
energy and power constraints, modeled in Eqs. (16.4) (16.7). The problem
is formulated as follows:
Problem 3—TOU charges with regulation markets

Objective minimizeC EC 1 C DC 2 R AS
Subject to (16.1) (16.9) and (16.16) (16.25)

16.3.6 Integration with PDR market

Aggregated EVs can also participate in the PDR market, where the fleet EVs
are treated as a virtual battery with flexibility to “sell” the power in the PDR