OPTESS

The development of this toolbox is still in early stage. It is not intended for productive use at the moment.

OPTESS is a framework around a (MI)LP Optimization to dimension an energy storage system or hybrid energy storage system. It builds the model with the help of pyomo and provides routines and classes for pre- and postprocessing. Exemplarily, the toolbox can take a load profile of a factory and calculate minimal storage (regarding energy, for a fixed power) to perform a specified power cut. Or, it can take the load profile of a photo voltaik plant with some consumers in a microgrid and calculate minimal storage to achieve a certain throughput and self consumption rate. The model assumes an idealised storage subject to efficiency and self discharge losses and not specific storage technologies with a fixed power to energy ratio.

Requirements

Python 3.4 or later

Additional Python Packages:

• pyomo
• numpy
• scipy
• matplotlib
• overload

Installation

Information below is outdated.

Download source code and install via pip install . or install directly from pypi.org servers via pip install optess. Note, that the last one might be an outdated version.

Getting Started

Information below is outdated.

Program flow is centered around two main classes, OptimizeSingleEES and OptimizeHybridEES, depending on whether a single energy storage shall be optimized or a hybrid energy storage (The latter one does not work at the moment). These classes gather inputs, delegate calculation and gather outputs, or in other words, administrate the whole calculation process with pre- and postprocessing.

An optimization setting is defined by:

• a load profile (class Signal)
• one or two storages (class Storage)
• an optimization aim (class Objective)
• in case of HESS: a strategy or boundary for control (class Strategy)
• A solver (class Solver)

For all inputs, factories are prepared to easily get started.

    import factories

    signal = factories.datafactory('alt')
    storage = factories.storagefactory('2.low')
    objective = factories.objectivefactory('std0-3')
    solver = 'glpk'

Then, the optimization object can be initialized:

    from optimize_ess import OptimizeSingleESS

    optim = OptimizeSingleESS(signal, storage, objective, solver)

To get the results, simply call it as a property

    res = optim.results

An alternative way to set up an optimization is the usage of a factory for the complete setting:

    opt_setup = factories.singlesetupfactory('alt.low', '2')

Which returns a tuple which can be directly unpacked into OptimizeSingleESS:

    optim = OptimizeSingleESS(*opt_setup)

All objects provide pprint() and pplot() functions to easily analzye, visualize and show the different objects, e.g.

    optim.pplot()
    optim.signal.pprint()
    optim.results.pplot()

Known Issues

Information below is outdated.

Hybrid EES Optimization is virtually useless at the moment as the model is erroneous.

Todo

• Add plotting and printing capabilities to:
  • abstractoptimees
  • storage
  • signal
  • objective
  • results (single, hybrid)
• Add Docstrings
• Rework Factories
• Fill Error Classes with code
• Debug HybridBuilder
• Validity Checking for PyomoResult
• Objective.validate(Signal)

License

This software is licensed under GPLv3, excluding later versions.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

For details see the license file $OPTMIZE-EES/LICENSE.

GPLv3 explicitely allows a commercial usage without any royalty or further implications. However, any contact to discuss possible cooperations is appreciated.

Author

optimize-ees - MILP optimization to find minimal (hybrid) energy storage
Copyright (C) 2018
Sebastian Günther
sebastian.guenther@ifes.uni-hannover.de

Institut für Elektrische Energiesysteme
Fachgebiet für Elektrische Energiespeichersysteme

Institute of Electric Power Systems
Electric Energy Storage Systems Section

https://www.ifes.uni-hannover.de/ees.html