#the higher the commission fees- the less the trading quantities that occur (makes sense if you think of cost function).
#the higher the trade commission fees- the lower the trading price, but because of lower trade quantities (zero if possible) - less economic benefit for prosumers
Commission_Fees = 0 #- for decentralized models (for economic comparison)
#Commission_Fees = 0.5 #- for centralized models 50c/kwh or might use 1c/kwh as done in [12] - using 1c/kwh gets almost the same price as having 0 commission fees and trades are at a slightly lower quantity
#using 50c/kwh commission fee results in vastly lower prices but basically 0 traded quantities
penalty_factor = 0.01
preferences = Commission_Fees * np.ones(num_agents - 1)
part = np.ones([num_agents, num_agents])
for i in range(num_agents):
for j in range(num_agents):
if (i == j):
part[i][j] = 0
player = func.createPlayers(agents, part, preferences, penalty_factor,
(num_agent - 1))
Trades = np.zeros([num_agents])
optimal = False
while (optimal == 0):
print('Performing Local Optimisation')
temp = player[0].optimize(Trades)
prices = player[0].y
primal_res = player[0].Res_primal * 10000
primal_res = round(primal_res)
dual_res = player[0].Res_dual * 10000
dual_res = round(dual_res)
#print(primal_res)
# #print(dual_res)
sender = str(web3.eth.accounts[num_agent - 1])
#print(web3.eth.defaultAccount)
preferences = Commission_Fees* np.ones(len(agents)-1)
Trades = np.zeros([num_agents, num_agents])
Prices = np.zeros([num_agents, num_agents])
#array of trade partners- set index to 1 if trade is happening e.g. part[0][1] = 1 if customer 0 is trading with customer 1
part = np.ones([num_agents, num_agents])
for i in range(num_agents):
for j in range(num_agents):
if (i == j):
part[i][j] = 0
print('Part', part)
#create players for this optimisation run - uses Prosumer class
players = func.createPlayers(agents, part, preferences, penalty_factor)
prim = float('inf')
dual = float('inf')
iteration = 0
SW = 0
simulation_time = 0
prim_previous = 0
lapsed = 0
start_time = time.time()
ouch = 0
prim_list = []
window = 25
#local optimisation function
while ((prim > residual_primal or dual>resiudal_dual) and iteration< max_Iter):
iteration+=1
temp = np.copy(Trades)
