#!/usr/bin/env python
# coding: utf-8

# # Multi-period Dispatch Simulation

# Multi-period dispatch economic dispatch (ED) and unit commitment (UC) is also available.
# 
# In this case, we will show a 24-hour ED simulation.

# In[1]:


import ams

import datetime


# In[2]:


print("Last run time:", datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))

print(f'ams:{ams.__version__}')


# In[3]:


ams.config_logger(stream_level=20)


# ## Load Case

# In[4]:


sp = ams.load(ams.get_case('5bus/pjm5bus_demo.xlsx'),
              setup=True,
              no_output=True,)


# ## Reginonal Design

# The disaptch models in AMS has develoepd with regional structure, and it can be inspected in device ``Region``.

# In[5]:


sp.Region.as_df()


# In device ``Bus``, the Param ``zone`` indicates the zone of the bus.
# Correspondingly, the region of generator and load are determined by the bus they connected.

# In[6]:


sp.Bus.as_df()


# ## Multi-period Dispatch Base

# In AMS, multi-period dispatch involves devices in group ``Horizon``.
# This group is developed to provide time-series data for multi-period dispatch.

# In[7]:


sp.Horizon.models


# We can get the idx of StaticGens.

# In[8]:


sp.StaticGen.get_idx()


# In ``EDTSlot``, Param ``sd`` refers the load factors of each region in each time slot, and Param ``ug`` represents the generator commitment status in each time slot.
# 
# To be more specific, EDT1 has ``sd=0.0793,0.0``, which means the load factor of region 1 is 0.0793 in the first time slot, and 0.0 in the second time slot.
# 
# Next, EDT1 has ``ug=1,1,1,1``, and it means the commitment status of generator PV_1, PV_3, PV_5, and Slack_4 are all online.

# In[9]:


sp.EDTSlot.as_df()


# ## Solve and Result

# In[10]:


sp.ED.init()


# In[11]:


sp.ED.run(solver='ECOS')


# All decision variables are collected in the dict ``vars``.

# In[12]:


sp.ED.vars


# As we can see, the generator output ``pg`` is a 2D array, and the first dimension is the generator index, and the second dimension is the time slot.

# In[13]:


sp.ED.pg.v


# Partial results can be accessed with desired time slot.
# In the retrieved result, the first dimension is the generator index, and the second dimension is the time slot.

# In[14]:


sp.ED.get(src='pg', attr='v', idx='PV_1', horizon=['EDT1'])


# Or, get multiple variables in mutliple time slots.

# In[15]:


sp.ED.get(src='pg', attr='v', idx=['PV_1', 'PV_3'], horizon=['EDT1', 'EDT2', 'EDT3'])