6.2 Adaptive EVs Charging Control model
6.2.3 Charging Control Model
The charging control model follows the MAS architecture. Each entity is modelled as an autonomous agent, which interacts with other agents and tries
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to achieve its own goals. All the agents exist in an environment where time is measured in time-steps. During one time-step, an agent either performs a set of actions or waits for a triggering signal from another agent. A sequence of six operational phases occurs in every time-step, namely Initial, Forecasting, Planning, Normal, Emergency and Final.
In the Initial Phase the EVs/DG aggregator decides whether a new forecast for the demand of “Unresponsive” EVs and the renewable generation is required. The Forecasting Phase is executed on the first time-step of every 24 hours, and thus during Initial Phase the EVs/DG aggregator evaluates the current time-step. At the same time, the EVs agent compares the current time- step with its connection time-step in order to decide its next action. In case the current time-step is equal to the connection time-step the EVs agent enters its Planning Phase, otherwise it enters the Normal Phase.
During Forecasting Phase, the EVs/DG aggregator forecasts the two days- ahead demand of “Unresponsive” EVs and renewable generation for every LV feeder of the corresponding MV/LV transformer in a time-step resolution. The forecast model described in [104] is implemented, based on SVM and trained using historical data. The historical data contain information about the charging demand from “Unresponsive” EVs and the renewable generation profiles. Once the forecasts are available, the EVs/DG aggregator uses a typical NoEV demand profile for every LV feeder to calculate the total scheduled demand of the next day. Assuming that the day is divided in N time-steps, an array of N values was created for every LV feeder (
T
sch_DMD). The array contains the total scheduled demand for every time-step k, and was calculated using Eq. (6.1):Where:
k = 1…N
f = 1…Number of LV feeders on MV/LV transformer.
f k noEV f k spEV f k DER f k UnrespEV f k DMD sch
F
F
S
F
T
,_
,
,
Re,
, (6.1)119
k Unresp
F
is the forecasted charging demand from “Unresponsive” EVs for the time-step k.k n
F
Re is the forecasted renewable generation for the time-step k.k sp
S
Re is the total scheduled charging demand of “Responsive” EVs for the time-step k.k noEV
F
is the forecasted NoEV demand for the time-step k.Based on the total scheduled demand, the N virtual prices are calculated. A simple pricing mechanism was applied, where the virtual prices are defined in a way that they reflect the EVs/DG aggregator’s preference for EVs charging demand in a certain time-step (valley filling strategy). The EVs/DG aggregator decreases the virtual cost of charging during the time-steps with low expected demand, incentivising the EVs agents to charge accordingly. The pricing formula is presented in Eq. (6.2).
Where:
Pf is the thermal power limit of the corresponding LV feeder.
w is a profit factor related to the contractual agreement between the
EVs/DG aggregator and the EVs agents.
The profit factor w does not affect the behaviour of the model, but is related to the revenue targets of the EVs/DG aggregator. The actual contractual agreements between the EVs/DG aggregator and the EVs agents are out of the scope of this research and thus the factor w is assumed to be equal to 1.
In case there are new arrivals or connections of EVs agents, the agents enter in the Planning Phase. A queue is created (Schedule Queue) containing all the EVs agents that have just connected to their charging stations. The EVs agents of Schedule Queue solve their scheduling problem on a first-come first-served sequence based on the virtual prices sent from the EVs/DG
w
P
T
VP
f f k DMD sch f k
, _ , (6.2)120
aggregator. Therefore, the decentralised structure of the charging control model is demonstrated when each EVs agent defines its charging schedules individually. Each EVs agent solves the scheduling problem described by Eqs. (6.3) - (6.5).
n n n d t t f k n t VP t P , min (6.3) Subject to:
n n n n n n n eff bat in final d t t n C SOC SOC dt t P
(6.4)
n nom ch nt
P
P
. (6.5) Where:tn is the connection time of EVs agent n to feeder f.
dn is the charging duration of EVs agent n.
Pn(t) is the instantaneous charging power demand of EVs agent n.
VPk,f(t) is the virtual cost value of each time step k .
SOCfinaln is the desired SOC of EVs agent n.
SOCinn is the initial SOC of EVs agent n.
Cbatn is the battery capacity of EVs agent n.
δeffn is the efficiency of the charging station.
Pch.nomn is the nominal power rate of the charging station.
Eq. (6.4) expresses the energy requirements of EVs agent n. These requirements are satisfied during the connection period [tn, tn+dn]. The
instantaneous charging power Pn(t) must not exceed the power rating of the
charging station (Pch.nomn) for every t, as described in Eq. (6.5). Once the EVs agent defines its charging schedule, it informs the EVs/DG aggregator and leaves the Schedule Queue. When the EVs/DG aggregator receives a charging schedule from an EVs agent, it updates the total schedule demand Tsch_DMD
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of the corresponding feeder. The virtual price values are recalculated according to the updated
T
sch_DMD , waiting for the next EVs agent.In case there are no EVs agents in the Planning Phase, the Normal Phase follows. The EVs/DG aggregator monitors the actual NoEV demand, the demand from “Unresponsive” EVs agents and the renewable energy generation for the current time-step. In order to check for possible violations of the network technical constraints, power flow analysis is performed considering the scheduled EVs charging demand (from “Responsive” EVs agents) and the monitored information (real time power demand). In case there are no violations or forecasting errors, the EVs agents execute their charging schedule for the current time-step. If the technical constraints (transformer nominal ratings, voltage statutory limits, line thermal limits) are violated or the charging schedule is not optimal due to forecasting errors, the EVs/DG aggregator transmits an emergency signal to all connected “Responsive” EVs agents. The EVs charging schedule is not executed, and the Emergency Phase begins.
A Reschedule Queue is created with the “Responsive” EVs agents that are connected in that time-step. The EVs/DG aggregator calculates the amount of EVs charging demand that needs to be rescheduled (Prsch) in order to
eliminate the problem and updates the virtual prices correspondingly. The EVs agents reschedule their charging demand for the remaining period before their departure (including the current time-step) using Eqs. (6.3)-(6.5) sequentially. After its reschedule, each EVs agent updates the total scheduled charging demand of “Responsive” EVs (
S
Rek sp) and leaves the RescheduleQueue. When an EVs agent leaves the Reschedule Queue, the EVs/DG aggregator updates the Total Scheduled Demand (
T
sch_DMD) and re-evaluates the emergency condition. If the problem remains, the Prsch is recalculatedalong with new virtual prices, and the procedure is repeated for the next EVs agent in the Reschedule Queue. The procedure is terminated when either Prsch
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During Final Phase, the EVs agents compare the current time-step with their departure time-step (tn+dn), in order to either disconnect or repeat the
operation in the following time-step. At the same time, the EVs/DG aggregator returns to its initial state.
All the actions of the EVs/DG aggregator agent and the EVs agents during one time-step are presented in Figure 6.3. A sample of the MATLAB code used for scheduling EVs charging demand is presented in Appendix D.
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Figure 6.3: Flowchart of EVs Charging Control Model
New Forecast? Schedule Queue? Monitor Actual Unresponsive EVs Demand Actual Renewable Generation Actual NO EV demand Responsive EVs Demand Emergency?
Update Virtual Prices Update Total Schedule
Demand
Update Virtual Prices Update Total Schedule
Demand
Calculate Reschedule Power Prsch
Reschedule Queue?
Update Virtual Prices
Update Total Schedule Demand End Start Solve Scheduling Problem Is my Turn?
Execute Schedule Plan Start
End Update Schedule
Queue
Update Responsive EVs Demand Schedule
Emergency? Solve Rescheduling Problem Departure? Is my Turn? Arrival? NO EV typical profile Forecast Renewable Generation Forecast Unresponsive EVs Demand Leave Reschedule Queue YES NO NO YES NO YES NO YES NO YES NO YES Update Responsive EVs Demand Schedule
Leave Schedule Queue
YES NO
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