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Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES)

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Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES)

Simplicity and Transparency in Power Systems Planning

The openTEPES model has been developed at the Instituto de Investigación Tecnológica (IIT) of the Universidad Pontificia Comillas.

The openTEPES model presents a decision support system for defining the integrated generation, storage, and transmission expansion plan (GEP+SEP+TEP) of a large-scale electric system at a tactical level (i.e., time horizons of 10-20 years), defined as a set of generation, storage, and electric and hydrogen networks dynamic investment decisions for multiple future years.

It is integrated in the open energy system modelling platform helping modelling Europe’s energy system.

It has been used by the Ministry for the Ecological Transition and the Demographic Challenge (MITECO) to analyze the electricity sector in the latest Spanish National Energy and Climate Plan (NECP) 2023-2030 in June 2023.

Reference

A. Ramos, E. Quispe, S. Lumbreras “OpenTEPES: Open-source Transmission and Generation Expansion Planning” SoftwareX 18: June 2022 10.1016/j.softx.2022.101070.

Description

openTEPES determines the investment plans of new facilities (generators, ESS, and lines) for supplying the forecasted demand at minimum cost. Tactical planning is concerned with time horizons of 10-20 years. Its objective is to evaluate the future generation, storage, and network needs. The main results are the guidelines for the future structure of the generation, storage, and transmission systems.

The openTEPES model presents a decision support system for defining the integrated generation, storage, and transmission expansion plan of a large-scale electric system at a tactical level, defined as a set of generation, storage, and network investment decisions for future years. The expansion candidate, generators, ESS and lines, are pre-defined by the user, so the model determines the optimal decisions among those specified by the user.

It determines automatically optimal expansion plans that satisfy simultaneously several attributes. Its main characteristics are:

  • Dynamic (perfect foresight): the scope of the model corresponds to several periods (years) at a long-term horizon, 2030, 2035 and 2040 for example.

    It represents hierarchically the different time scopes to take decisions in an electric system:

    • Load level: one hour, e.g., 01-01 00:00:00+01:00 to 12-30 23:00:00+01:00

    The time division allows a user-defined flexible representation of the periods for evaluating the system operation. Moreover, it can be run with chronological periods of several consecutive hours (bi-hourly, tri-hourly resolution) to decrease the computational burden without losing accuracy. The model can be run with a single period (year) or with several periods (years) to allow the analysis of the system evolution. The time definition allows also to specify disconnected representative periods (e.g., days, weeks) to evaluate the system operation. The model can be run with a single period (year) or with several periods (years) to allow the analysis of the system evolution. The time definition can also specify disconnected representative periods (e.g., days, weeks) to evaluate the system operation. The period (year) must be represented by 8736 hours because several model concepts representing the system operation are based on weeks (168 hours) or months (made of 4 weeks, 672 hours).

  • Stochastic: several stochastic parameters that can influence the optimal generation, storage, and transmission expansion decisions are considered. The model considers stochastic medium-term yearly uncertainties (scenarios) related to the system operation. These operation scenarios are associated with renewable energy sources, energy inflows and outflows, natural water inflows, operating reserves, inertia, and electricity, hydrogen, and heat demand.

The objective function incorporates the two main quantifiable costs: generation, storage, and transmission investment cost (CAPEX) and expected variable operation costs (including generation, consumption, emission, and reliability costs) (system OPEX).

The model formulates a two-stage stochastic optimization problem, including generation, storage, and electric, hydrogen, and heat network binary investment/retirement decisions, generation operation decisions (commitment, startup, and shutdown decisions are also binary), and electric line-switching decisions. The capacity expansion considers adequacy system reserve margin and minimum and maximum energy constraints.

The very detailed operation model is an electric network-constrained unit commitment (NCUC) based on a tight and compact formulation, including operating reserves with a DC power flow (DCPF), including electric line switching decisions. Electric network ohmic losses are considered proportional to the electric line flow. It considers different energy storage systems (ESS), e.g., pumped-hydro storage, battery, demand response, electric vehicles, solar thermal, alkaline water electrolyzer, etc. It allows analyzing the trade-off between the investment in generation/transmission/pipeline and the investment and/or use of storage capacity.

The model allows also a representation of the hydro system based on volume and water inflow data considering the water stream topology (hydro cascade basins). If they are not available it runs with an energy-based representation of the hydro system.

Also, it includes a representation of Power to Hydrogen (P2H2) by setting the hydrogen demand satisfied by the production of hydrogen with electrolyzers (consume electricity to produce hydrogen) and a hydrogen network to distribute it. Besides, it includes a representation of Power to Heat (P2H) by setting the heat demand satisfied by the production of heat with heat pumps (consume electricity to produce heat) and a heat network to distribute it. If they are not available it runs with just the other energy carriers.

The main results of the model can be structured in these topics:

  • Investment: (generation, storage, hydro reservoirs, electric lines, hydrogen pipelines, and heat pipes) investment decisions and cost

  • Operation: unit commitment, startup, and shutdown of non-renewable units, unit output and aggregation by technologies (thermal, storage hydro, pumped-hydro storage, RES), RES curtailment, electric line, hydrogen pipeline, and heat pipe flows, line ohmic losses, node voltage angles, upward and downward operating reserves, ESS inventory levels, hydro reservoir volumes, power, hydrogen, and heat not served

  • Emissions: CO2 emissions by unit

  • Marginal: Locational Short-Run Marginal Costs (LSRMC), stored energy value, water volume value

  • Economic: operation, emission, and reliability costs and revenues from operation and operating reserves

  • Flexibility: flexibility provided by demand, by the different generation and consumption technologies, and by power not served

Results are shown in csv files and graphical plots.

A careful implementation has been done to avoid numerical problems by scaling parameters, variables and equations of the optimization problem allowing the model to be used for large-scale cases, e.g., the European system with hourly detail.

Installation

There are 2 ways to get all required packages under Windows. We recommend using the Python distribution Miniconda. If you don’t want to use it or already have an existing Python (version 3.8 | 3.9 recommended, 2.7 is supported as well) installation, you can also download the required packages by yourself.

GitHub Repository (the hard way)

  1. Clone the openTEPES repository.

  2. Launch the Anaconda prompt

  3. Set up the PATH by cd "C:\Users\<username>\...\openTEPES". (Note that the path is where the repository was cloned.)

  4. Install openTEPES via pip by pip install .

Solvers

GLPK

As an easy option for installation, we have the free and open-source GLPK solver. However, it takes too much time for large-scale problems. It can be installed using: conda install -c conda-forge glpk.

CBC

The CBC solver is our recommendation if you want a free and open-source solver. For Windows users, the way to install the CBC solver is downloading the binaries from this link of the CBC solver, copy and paste the cbc.exe file to the Python folder of the Anaconda or Miniconda environment. In linux it can be installed using: conda install -c conda-forge coincbc.

Gurobi

Another recommendation is the use of Gurobi solver. However, it is commercial solver but most powerful than GLPK and CBC for large-scale problems. As a commercial solver it needs a license that is free of charge for academic usage by signing up in Gurobi webpage. It can be installed using: conda install -c gurobi gurobi and then ask for an academic or commercial license. Activate the license in your computer using the grbgetkey command (you need to be in the university domain if you are installing an academic license).

Mosek

Another alternative is the Mosek solver. Note that it is a commercial solver and you need a license for it. Mosek is a good alternative to deal with QPs, SOCPs, and SDPs problems. You only need to use conda install -c mosek mosek for installation and request a license (academic or commercial). To request the academic one, you can request here. Moreover, Mosek brings a license guide. But if you are request an academic license, you will receive the license by email, and you only need to locate it in the following path C:\Users\(your user)\mosek in your computer.

HiGHS

The HiGHS solver can also be used. For Windows users, the way to install the HiGHS solver is downloading the binaries from this link of the HiGHS solver, copy and paste the highs.exe file to the Python folder of the Anaconda or Miniconda environment. This solver is activated by calling the openTEPES model with the solver name ‘appsi_highs’.

GAMS

The model openTEPES can also be solved with GAMS and a valid GAMS license for a solver. The GAMS language is not included in the openTEPES package and must be installed separately. This option is activated by calling the openTEPES model with the solver name ‘gams’.

Get started

Developers

By cloning the openTEPES from this link repository, you can create branches and propose pull-request. Any help will be very appreciated.

Continue like the users for a simple way of executions.

Users

If you are not planning on developing, please follows the instructions of the Installation.

Once installation is complete, openTEPES can be executed in a test mode by using a command prompt. In the directory of your choice, open and execute the openTEPES_run.py script by using the following on the command prompt (Windows) or Terminal (Linux). (Depending on what your standard python version is, you might need to call python3 instead of python.):

openTEPES_Main

Then, four parameters (case, dir, solver, and console log) will be asked for.

Remark: at this step only press enter for each input and openTEPES will be executed with the default parameters.

After this in a directory of your choice, make a copy of the 9n or sSEP case to create a new case of your choice but using the current format of the CSV files. A proper execution by openTEPES_Main can be made by introducing the new case and the directory of your choice. Note that the solver is glpk by default, but it can be changed by other solvers that pyomo supports (e.g., gurobi, mosek).

Then, the results should be written in the folder who is called with the case name. The results contain plots and summary spreadsheets for multiple optimised energy scenarios, periods and load levels as well as the investment decisions.

Note that there is an alternative way to run the model by creating a new script script.py, and write the following:

from openTEPES.openTEPES import openTEPES_run

openTEPES_run(<case>, <dir>, <solver>)

Tips

  1. A complete documentation of the openTEPES model can be found at https://opentepes.readthedocs.io/en/latest/index.html, which presents the mathematical formulation, input data and output results.

  2. Try modifying the TimeStep in oT_Data_Parameter_<case>.csv and see their effect on results.

  3. Using 0 or 1, the optimization options can be activated or deactivated in oT_Data_Option_<case>.csv.

  4. If you need a nice python editor, think about using PyCharm. It has many features including project management, etc.

  5. We also suggest the use of Gurobi (for Academics and Researchers) as a solver to deal with MIP and LP problems instead of GLPK.

Run the Tutorial

It can be run in Binder:

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Expected Results

Network map with investment decisions

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