Information Technology Reference
In-Depth Information
<<abstract>>
@agent
@GME Market
Class
@Load
@GME Clearing
House
@Genco
<<interface>>
GME Market Operator
<< interface>>
Extended Roth-Erev Agent
<< interface>>
Variant Roth-Erev Agent
<< interface>>
Roth-Erev Agent
Fig. 3.
UML class diagram of agents in ABM IPEX
clearing procedure) they access the GME clearing house in order to retrieve market
results and to update their strategic decisions. They extend GAPEX Agent class;
-
Loads: They are aggregations of zonal loads and represent the demand side of the
electricity market as inelastic;
-
GME Market Operator: It clears the market and sends information on awarded
prices and quantities to the GME clearing house. It extends GAPEX Electricity
Market class;
-
GME Clearing House: It computes all payoffs for the Gencos, updates their market
accounts and stores all market information. It extends GAPEX Session class.
It is worth remarking that the aim of the proposed model is to represent and to study the
strategic behavior of Gencos in the power exchange. Accordingly, the Gencos are char-
acterized by sophisticated decision process (i.e., the Enhanced Roth-Erev reinforcement
learning algorithm presented and discussed in Section 4)) that accounts for the effect
of a repeated game. Furthermore, according to the hypothesis of a competitive electric-
ity market, the Gencos communicate directly only with the GME Market Operator and
GME Clearing House so to account that every Genco is only aware of its own strategies
and payoffs. Finally, all the other agents in the model are passive entities and they are
not endowed with any cognitive capability. Figure 3 shows the UML class diagram for
the agents modeled in the ABM IPEX:
At each iteration step, each
i
th
generator (
i
=1
,
2
, ..., N
) submits to the DAM a bid-
ding curve shown in Figure 4. The curve is described by the triple of
P
i
([
/MWh]),
Q
i
([MWh]),
Q
i
([MWh]), i.e., the bidding price, the minimum and the maximum produc-
tion power for
i
th
generator, respectively. After receiving all generators' bids, the DAM
clears the market by performing a social welfare maximization subject to the constraints
on the zonal energy balance (Kirchhoff's laws) and on inter-zonal transmission limits
(see [8] for details). The objective function takes into account only the supply side of
the market as the demand is assumed to be price-inelastic. The zonal splitting clearing
mechanism (i.e., DC optimal power flow procedure) allows one to determine both the
unit commitment for each generator and the Locational Marginal Price (LMP) for each
e
