import random as rd import copy class Particle(): def __init__(self, socs:list, times:list, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, x_min=-100, x_max=100, alpha=0.1): # Problem specific attributes self.nb_vehicles = nb_vehicles # Number of vehicles handles for the generations of position x self.delta_t = delta_t # delta_t for update purposes self.nb_of_ticks = nb_of_ticks # Accounting for time evolution of the solution (multiplied by delta_t) self.socs = socs # States of charges for the particle current position (self.x) self.times = times # Minima and maxima of a position value self.x_min = x_min self.x_max = x_max # Limitation of the velocity self.alpha = alpha self.r1 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of oneself self.r2 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of other particles # Particle attributes self.x = self.generate_position() # Position Vector (correspond to one solution for the problem) self.clean_position() # Staying coherent with problem modelisation for a_i,t self.v = self.generate_velocity() # Velocity self.p_best = self.x # Best known position (starting with initial position x) # Evalution attributes self.f_best = [(self.nb_of_ticks * self.nb_vehicles * self.x_max * 100) for _ in range(3)]# Hundred times the max grid power should be large enough to be out of scope (equivalent to inf) self.f_current = [0,0,0] # [f1,f2,f3] def update_position(self): for tick in range(self.nb_of_ticks): for i in range(self.nb_vehicles): new_pos_i_t = self.x[tick][i] + self.v[tick][i] self.x[tick][i] = new_pos_i_t self.clean_position() def update_velocity(self, leader_pos, c1, c2, w=0.4): for tick in range(self.nb_of_ticks): for i in range(self.nb_vehicles): new_vel_i_t = w * self.v[tick][i] + (self.p_best[tick][i] - self.x[tick][i]) * c1 * self.r1[i] + (leader_pos[tick][i] - self.x[tick][i]) * c2 * self.r2[i] self.v[tick][i] = new_vel_i_t # BELOW: Modifying position values to keep logical states def clean_position(self): for tick in range(self.nb_of_ticks): for i in range(self.nb_vehicles): arriving = self.times[i][0] leaving = self.times[i][1] # x[arriving][i] != 0 and x[leaving][i] == 0 if not(tick >= arriving and tick < leaving): self.x[tick][i] = 0.0 # Done after evaluation to correct out of bounds position def keep_boudaries(self,max_power): for tick in range(self.nb_of_ticks): current_power = self.get_current_grid_stress(tick) # As long as there is too much power, we cut supplies from charging vehicles (keeping discharging other vehicles at same current rate) while current_power > max_power: for i in range(self.nb_vehicles): if self.x[tick][i] > 0: self.x[tick][i] = self.x[tick][i] * 0.9 current_power = self.get_current_grid_stress(tick) def generate_position(self): pos = [] for _ in range(self.nb_of_ticks): x_tick = [] for _ in range(self.nb_vehicles): x_tick.append(rd.randrange(self.x_min, self.x_max +1, 1)) pos.append(x_tick) return pos # Randomize a velocity vector for each tick def generate_velocity(self): vel = [] vel_coeff = abs(self.x_max - self.x_min) for _ in range(self.nb_of_ticks): v_tick = [] for _ in range(self.nb_vehicles): v_tick.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha) vel.append(v_tick) return vel # Function objective def evaluate(self,elec_prices,socs,socs_req,times): f1 = self.f1(elec_prices) f2 = self.f2(self.socs,socs_req,times) f3 = self.f3() # Keeping in memory evaluation of each objective for domination evaluation f_current = [] f_current.append(f1) f_current.append(f2) f_current.append(f3) self.f_current = f_current def update_best(self): current_better = (self.f_current[0] <= self.f_best[0]) and (self.f_current[1] <= self.f_best[1]) and (self.f_current[2] <= self.f_best[2]) if current_better: # Not strict superiority yet current_dominates = (self.f_current[0] < self.f_best[0]) or (self.f_current[1] < self.f_best[1]) or (self.f_current[2] < self.f_best[2]) if current_dominates: self.p_best = copy.deepcopy(self.x) self.f_best = self.f_current[:] # Calculate the price of the electricity consumption in the grid SUM(1_to_T)(Epsilon_t * A_t * delta_t) def f1(self,elec_prices): result = 0 for tick in range(self.nb_of_ticks): grid_stress_tick = self.get_current_grid_stress(tick) result += elec_prices[tick] * grid_stress_tick * self.delta_t return result # User's insatisfaction def f2(self,socs,socs_req,times): result = 0 for i in range(self.nb_vehicles): leaving = times[i][1] stress = socs_req[i] - socs[leaving][i] result += max(0, stress) return result # Network Stress def f3(self): current_max = self.nb_vehicles * self.x_min for tick in range(self.nb_of_ticks): current_max = max(current_max, self.get_current_grid_stress(tick)) return current_max def get_current_grid_stress(self, tick:int): assert tick < self.nb_of_ticks # Make sure the tick exist in the position x current_grid_stress = 0 for i in range(self.nb_vehicles): current_grid_stress += self.x[tick][i] return current_grid_stress def updating_socs(self, initial_socs, capacities): # Calcul de l'évolution temporelle for tick in range(self.nb_of_ticks - 1): # On s'arrête à l'avant-dernier pour calculer le suivant for i in range(self.nb_vehicles): # SoC(t+1) = SoC(t) + (Puissance(t) * delta_t / Capacité) energy_added = (self.x[tick][i] * (self.delta_t / 60)) # Mise à jour du tick suivant basé sur le tick actuel # On utilise initial_socs comme base si c'est une liste de listes [tick][vehicule] self.socs[tick+1][i] = self.socs[tick][i] + (energy_added / capacities[i]) self.socs[tick+1][i] = max(0.0, min(1.0, self.socs[tick+1][i]))