device-kit 1.0.4

Flow device economical optimization modeling kit

Device Kit

device_kit is a Python package containing a collection of scalar vector flow device models for convex optimal flow simulations. The package defines a common abstract interface for a device, a base set of concrete devices, and a way of constructing new aggregate devices from collection of related sub-devices. Under the hood it's a big 2d numpy array, with discrete time slot along one dimension, and leaf device along the other. device_kit makes it easier to express and manage the constraints and cost functions, and solve the system.

The device_kit was originally written for the purpose modeling controllable microgrid or household electrical devices, and then optimizing flows over a day-ahead time horizon.

The core classes of `device_kit`. Most the model complexity is in sub classes of `Device`. A handful of sub-types are provided in this package such as `IDevice`. `DeviceSet` connects a network of devices.	Example radial microgrid model-able with `device_kit`.
Example microgrid connection home simulation from unoptimized to optimized.	Example aircon utility based simulation from unoptimized to optimized. Temperature goes up as cost goes down. Utility curve varies between aircon scenarios.

Installation

pip install "git+https://github.com/sgpinkus/microgrid_device_kit"`

Synopsis

A simple example of using device_kit to model a collection of devices and then solve for some constraint over the joint flows - most commonly balanced flows:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import device_kit
from device_kit import *

def random_uncontrolled():
  return np.maximum(0, 0.5+np.cumsum(np.random.uniform(-1,1, 24)))

def generator_cost_curve():
  return np.stack((np.sin(np.linspace(0, np.pi, 24))*0.5+0.1, np.ones(24)*0.001, np.zeros(24)), axis=1)

def make_model():
  ''' Small power network model. '''
  np.random.seed(19)
  model = DeviceSet('site1', [
      Device('uncontrolled', 24, (random_uncontrolled(),)),
      IDevice2('scalable', 24, (0.5, 2), (0, 24), d0=0.3),
      CDevice('shiftable', 24, (0, 2), (12, 24)),
      GDevice('generator', 24, (-10,0), cbounds=None, cost_coeffs=generator_cost_curve()),
      DeviceSet('sub-site1', [
          Device('uncontrolled', 24, (random_uncontrolled(),)),
          SDevice('buffer', 24, (-7, 7), c1=1.0, capacity=70, sustainment=1, efficiency=1)
        ],
        sbounds=(0,10) # max capacity constraint.
      ),
    ],
    sbounds=(0,0) # balanced flow constraint.
  )
  return model

def main():
  model = make_model()
  (x, solve_meta) = device_kit.solve(model, p=0) # Convenience convex solver.
  print(solve_meta.message)
  df = pd.DataFrame.from_dict(dict(model.map(x)), orient='index')
  df.loc['total'] = df.sum()
  pd.set_option('display.float_format', lambda v: '%+0.3f' % (v,),)
  print(df.sort_index())
  print('Utility: ', model.cost(x, p=0))
  df.transpose().plot(drawstyle='steps', grid=True)
  plt.ylabel('Power (kWh)')
  plt.xlabel('Time (H)')
  plt.savefig('synopsis.png');

if __name__ == '__main__':
  main()

Gives:

Optimization terminated successfully
                                 0      1      2      3      4      5      6      7      8      9      10     11     12     13     14     15     16     17     18     19     20     21     22     23
site1.generator              -3.850 -2.744 -1.964 -1.630 -1.303 -1.455 -0.991 -1.040 -1.084 -1.140 -0.927 -0.770 -0.770 -0.783 -0.808 -0.848 -0.907 -0.991 -1.110 -1.282 -1.542 -1.964 -2.698 -3.249
site1.scalable               +0.991 +0.664 +0.663 +0.551 +0.632 +0.500 +0.664 +0.500 +0.500 +0.500 +0.500 +0.663 +0.663 +0.663 +0.663 +0.663 +0.664 +0.664 +0.664 +0.664 +0.664 +0.663 +0.698 +1.249
site1.shiftable              +2.000 +0.793 +0.696 +0.000 +0.000 +0.000 +0.100 +0.000 +0.000 +0.000 +0.000 +0.108 +0.108 +0.120 +0.145 +0.119 +0.244 +0.327 +0.446 +0.618 +0.878 +1.301 +2.000 +2.000
site1.sub-site1.buffer       +0.128 +0.052 +0.052 +0.026 +0.044 -0.091 +0.052 -0.015 -0.076 -0.138 -0.033 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 +0.000 -0.000
site1.sub-site1.uncontrolled +0.730 +1.016 +0.553 +1.054 +0.627 +1.046 +0.176 +0.555 +0.660 +0.767 +0.460 +0.000 +0.000 +0.000 +0.000 +0.066 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000
site1.uncontrolled           +0.000 +0.218 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.011 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000
total                        +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000 +0.000
Utility:  -13.021871014972055

Output plot.

Overview

In the scope of this package, a device is something that consumes and/or produces some kind of scalar valued commodity (ex. electricity, gas, fluid) over a fixed, discrete, and finite future planning/scheduling horizon (ex. every hour of the next day, or every minute of the next hour, etc). For each device what is modeled is simply:

1. The non-negotiable hard constraints on the commodity flow to/from the device.
2. Costs (or soft constraints) for different feasible states of flow to/from the device.

All the concrete device models provided are currently all weakly convex and tune-able. The concrete set of device implementations can be used to model quite a wide range of scenarios within the bounds of convexity.

Low level devices exist in a network which acts as a conduit for commodity flow between devices (ex. an electrical bus). This package is limited (by design) to modeling radially structured (in other words, rooted tree structured) networks to an arbitrary nesting level (the simple flow networks we're interested in modeling have a radial structure or can be treated as such at the level of abstraction we're interested in). "Networks" are implemented as composite devices containing other atomic or composite devices, down to some eventual leaf node device level (see figure).

Implemented Devices

SHORT NAME	LONG NAME	COST FUNCTION AND CONSTRAINTS
CDevice	Cummulative Device	Linear with cummulative consumption over time bounds
GDevice	Generator Device	Quadractive cost function
IDevice	Instantaneous Device	Parametized quadratic section see here
IDevice2	Alternative Instantaneous Device	Parametized quadratic section see here
MFDevice	Multiflow Device	Constraints s.t. can take flow from one of two source (ex. heat and electricity) in perfect subs
PVDevice	Photo-voltaic Device	Simple model of PV with some surface area and efficiency factor
SDevice	Storage Device	Generic battery or thermal storage device with a few parameters
TDevice	Thermal Device	Does a thing
ADevice	Any Device	Takes an arbitrary cost function and constraints
...	Other Device	Experimental

Class Structure

There is two important classes: Device and DeviceSet. The UML class diagram in the figure shows how they are related. All devices sub-class BaseDevice which is the key abstract representation of a device. A device's consumption/production/both, herein called flow, is a 2D R by C numpy array. For "atomic" devices of class Device, R is always 1, and C is the fixed length (__len__) of the Device. A collection of devices is also implemented as a sub-type of BaseDevice and this is how R can be greater than 1. The DeviceSet class is used to represent collections of devices, such as a network or sub-network. DeviceSet allows devices to be organized into an arbitrarily deep rooted tree of devices. An example is shown in the figure. Atomic device always occur at the leaves. All internal nodes, and the root of the tree are DeviceSet instances.

All devices are intended to be stateless: they are not actively consuming producing anything. Rather they are modeling the preferences and constraints for the flow of a commodity (the map(flow) method shown in the UML diagram allows you to map an actual flow matrix onto a collection of devices). Devices are also intended to be immutable (but technically they are not currently strictly immutable).

Flexibility Modeling Details

Device's encapsulate a flexibility model. Flexibility has two components preferences or soft constraints and (hard) constraints.

For convenience, the Device base class provides for two very common rudimentary baked-in constraints:

• Device.bounds for interval (also called instantaneous) bounds for each given interval of the time-horizon and,
• Device.cbounds for cumulative bounds across the entire time-horizon.

Preferences are expressed via the Device.cost(flow) function which expresses how much the device "likes" the given state of flow (note utility = -cost). The Device base class has no preferences: Device.cost() just returns 0. It is the main job of a Device sub-type to define preferences and/or additional more complex constraints that describe more nuanced device models. sub-types do this by overriding Device.cost() (preferences) and Device.constraints (constraints).