# The datafev framework
# Copyright (C) 2022,
# Institute for Automation of Complex Power Systems (ACS),
# E.ON Energy Research Center (E.ON ERC),
# RWTH Aachen University
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import pandas as pd
from datafev.algorithms.multi_cluster.rescheduling_milp import reschedule
[docs]def charging_routine(ts, t_delta, horizon, system, solver, penalty_parameters):
"""
This routine is executed periodically during operation of charger clusters.
It addresses the scenarios where EVs connected in clusters have previously defined charging schedules that may
require deviations due to the local power consumption constraints of clusters. The control architecture is
centralized; therefore, all clusters are controlled by a single decision-maker. The applied control is based
on MILP rescheduling.
Parameters
----------
ts : datetime
Current time.
t_delta : timedelta
Control horizon.
horizon : timedelta
Optimization horizon of rescheduling.
system : data_handling.multi_cluster
Multi-cluster system object.
solver : pyomo SolverFactory object
Optimization solver.
penalty_parameters : dict
Cost parameters for capacity violation/devations.
Returns
-------
None.
"""
schedule_horizon = pd.date_range(start=ts, end=ts + horizon, freq=t_delta)
opt_horizon = list(range(len(schedule_horizon)))
opt_step = t_delta.seconds
################################################################################################
# Step 1: Identification of charging demand
# Clusters' individual power constraints/ violation tolerance
cluster_upperlimits = (
{}
) # Will contain the upper limit of (soft) power consumption constraint
cluster_lowerlimits = (
{}
) # Will contain the lower limit of (soft) power consumption constraint
cluster_violationlimits = (
{}
) # Will contain the violation tolerance of upperlimit/lowerlimits
# System level constraints power constraints
system_upperlimit = dict(
enumerate(system.upper_limit[schedule_horizon[:-1]].values)
)
system_lowerlimit = dict(
enumerate(system.lower_limit[schedule_horizon[:-1]].values)
)
# Dictionary containing EV charging demand parameters
pmax_pos = {} # Will contain the maximum power that can be withdrawn by the EVs
pmax_neg = {} # Will contain the maximum power that can be injected by the EVs
ch_eff = {} # Will contain charging efficiencies of the chargers hosting EVs
ds_eff = {} # Will contain discharging efficiencies of the chargers hosting EVs
bcap = {} # Will contain battery capacities of EVs
tarsoc = {} # Will contain target SOCs (at the end of rescheduling horizon)
deptime = {} # Will contain time until departures (in number of time steps)
inisoc = {} # Will contain current SOCs of EVs
minsoc = {} # Will contain maximum SOCs allowed by EVs
maxsoc = {} # Will contain minimum SOCs allowed by EVs
location = (
{}
) # Will contain the parameters indicating the location of EV in multi-cluster system
# Dictionaries contianing the penalty factors for the objective function of optimization model
rho_y = (
{}
) # Will contain cost parameters penalizing deviation from individual schedules of EVs
rho_eps = (
{}
) # Will contain cost parameters penalizing violation of (soft) power consumption constraints of clusters
# Loop through the clusters
clusters = []
for cc_id in system.clusters.keys():
cluster = system.clusters[cc_id]
if cluster.query_actual_occupation(ts) > 0:
# There are some connected EVs in this clusters, so this cluster must be taken into account in optimization
clusters.append(cc_id)
# Parameters defining the upper/lower limits of (soft) power consumption constraints of cluster
cluster_upperlimits[cc_id] = dict(
enumerate(cluster.upper_limit[schedule_horizon[:-1]].values)
)
cluster_lowerlimits[cc_id] = dict(
enumerate(cluster.lower_limit[schedule_horizon[:-1]].values)
)
# Parameter defining how much the upperlimit/lowerlimit can be violated
cluster_violationlimits[cc_id] = cluster.violation_tolerance
# Cost parameter penalizing deviation from individual optimal charging schedules of EVs
rho_y[cc_id] = penalty_parameters["rho_y"][cc_id]
# Cost parameter penalizing violation of (soft) power consumption constraints of clusters
rho_eps[cc_id] = penalty_parameters["rho_eps"][cc_id]
# Loop through the chargers
for cu_id, cu in cluster.chargers.items():
ev = cu.connected_ev
if ev != None:
# There is an EV connected in this charger
ev_id = ev.vehicle_id
# with a schedule of
sch_inst = cu.active_schedule_instance
cu_sch = cu.schedule_soc[sch_inst]
if cu_sch.index.max() < schedule_horizon.min():
cu_sch[schedule_horizon.min()] = cu_sch[cu_sch.index.max()]
cu_sch = cu_sch.reindex(schedule_horizon)
cu_sch = cu_sch.fillna(method="ffill")
# parameters defining the charging demand/urgency
bcap[ev_id] = ev.bCapacity
deptime[ev_id] = (ev.t_dep_est - ts) / t_delta
inisoc[ev_id] = ev.soc[ts]
minsoc[ev_id] = ev.minSoC
maxsoc[ev_id] = ev.maxSoC
tarsoc[ev_id] = cu_sch[ts + horizon]
ch_eff[ev_id] = cu.eff
ds_eff[ev_id] = cu.eff
# maximum power that can be withdrawn/injected by the connected EV
pmax_pos[ev_id] = min(ev.p_max_ch, cu.p_max_ch)
pmax_neg[ev_id] = min(ev.p_max_ds, cu.p_max_ds)
# Parameter indicating the EVs' positions in the multi-cluster system
location[ev_id] = (cc_id, cu_id)
################################################################################################
if len(bcap) > 0:
# The system includes connected EVs
################################################################################################
# Step 2: Solving (MILP-based) rescheduling problem to centrally decide how the chargers will operate now
p_schedule, s_schedule = reschedule(
solver,
opt_step,
opt_horizon,
bcap,
inisoc,
tarsoc,
minsoc,
maxsoc,
ch_eff,
ds_eff,
pmax_pos,
pmax_neg,
deptime,
location,
system_upperlimit,
system_lowerlimit,
clusters,
cluster_upperlimits,
cluster_lowerlimits,
cluster_violationlimits,
rho_y,
rho_eps,
)
################################################################################################
################################################################################################
# Step 3: Charging
for cc_id in system.clusters.keys():
for cu_id in system.clusters[cc_id].chargers.keys():
cu = system.clusters[cc_id].chargers[cu_id]
if cu.connected_ev != None:
ev_id = cu.connected_ev.vehicle_id
cu.supply(ts, t_delta, p_schedule[ev_id][0])
################################################################################################