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The intermittent nature of the output of most renewable
energy resources (RES), its non homogeneous distribution
and the deployment of a large share of this generation
is expected to result in a significant increase in the
power flows among areas in large-scale systems. As a
result of this, the development of the network of the
system should be planned in an integrated way and the
number of operation snapshots to consider in the planning
process should probably be high. Identifying main optimal
transmission network corridors to reinforce and the
extent of reinforcements needed in them, and other operation
variables affected by the existence of the grid, like
investment cost of grid additions, network losses incurred,
CO2 emissions produced, overall production by technology
and fuel production costs is a major challenge for large-scale
systems. Different future RES generation strategies
associated with different RES targets may also strongly
influence this network development.
Transmission expansion planning (TEP) determines the
investment plans of new facilities (lines and other
network equipment) 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 network needs. The main results are the guidelines
for future structure of the transmission network.
TEPES model presents a decision support system for defining
the transmission expansion plan of a large-scale electric
system at a tactical level. A transmission expansion
plan is defined as a set of network investment decisions
for future years. The candidate lines are pre-defined
by the user so the model determines the optimal decisions
among those specified by the user, or identified automatically
by the model. Candidate lines can be HVDC or HVAC circuits.
The model determines automatically optimal expansion
plans that satisfy simultaneously several attributes.
Their main characteristics are:
- Dynamic: the scope of the model corresponds
to several years at a long-term horizon, 2020 or 2030
for example.
The model represents hierarchically the different
time scopes to take decisions in an electric system:
Year, Period, Sub-period and Load level.
This time division allows a flexible representation
of the intervals where to evaluate the system operation.
For example, by a set of non chronological isolated
snapshots or by a set of representative days for
different seasons of the year or by a stepwise load-duration
curve covering the duration of a year. Currently,
hourly representation of the operation of the electric
power system of a year is usually done.
- Stochastic: several stochastic parameters
that can influence the optimal transmission expansion
decisions are considered. The model considers stochastic
scenarios related to operation and to reliability.
The operation scenarios are associated to: renewable
energy sources, electricity demand, hydro inflows,
and fuel costs. The reliability scenarios evaluate
N-1 generation and N-1 transmission contingencies.
- Multicriteria: the objective function incorporates
some of the main quantifiable objectives: transmission
investment and variable operation costs (including
generation emission cost), reliability cost associated
to N-1 generation and transmission contingencies.
The optimization method used is based on a functional
decomposition between an automatic transmission plan
generator (based on optimization) and an evaluator of
these plans from different points of view (operation
costs for several operating conditions, or reliability
assessment for N-1 generation and transmission contingencies).
The model is based on Benders decomposition where the
master problem proposes network investment decisions
and the operation subproblem determines the operation
cost for this investment decisions and the reliability
subproblems determine the not served power for the generation
and transmission contingencies given that investment
decisions.
The operation model (evaluator) is based on a DC load
flow although a simpler transportation representation
is allowed for some or all the lines. Network losses
can also be considered. By nature, the transmission investment
decisions are binary although can also be treated as
continuous ones. The current network topology is considered
as the starting point for the network expansion problem.
The main results of the model can be structured in
these topics:
- Investment: investment decisions and cost
- Operation: output of different units and
technologies (thermal, storage hydro, pumped storage
hydro, RES), fuel consumption, RES curtailment, hydro
spillage, hydro reservoir scheduling, line flows,
line ohmic losses, node voltage angles
- Emissions: CO2
- Marginal: Short-run Marginal Costs, Transmission
Load Factors (TLF)
- Reliability: ENS (Energy Not Served)
- Cost to go function or future cost function
The results are grouped by node, zone, area and region.
TEPES is written in GAMS taking advantage of grid computing
and sensitivity analysis with a Microsoft Excel input/output
interface and requires a licence for one of the optimization
solvers linked with GAMS. Some of the optimization solvers
are free for academics, but must be registered with
a degree-issuing academic institution. TEPES can be
installed under Microsoft Windows 10 64 bits or under
Linux or MacOS.
A new open and simplified version of the model written
in Python/Pyomo can be found in:
Open
Generation and Transmission Operation and Expansion
Planning Model with RES and ESS (openTEPES)
This model is currently being used and improved in
some European research projects:
The model is also aimed at computing the benefits of
transmission expansion projects by a methodology developed
at IIT and also by the two possible approaches proposed
by ENTSO-E: the Take Out One at a Time (TOOT)
and the Put In one at a Time (PINT).
Some TEP related publications:
 F. Holz, T. Scherwath, P. Crespo del Granado, Ch. Skar, L. Olmos, Q. Ploussard, A. Ramos, A. Herbst A 2050 perspective on the role for Carbon Capture and Storage in the European power system and industry sector. Energy Economics, October 2021 10.1016/j.eneco.2021.105631
S. Lumbreras, H. Abdi, A. Ramos, and M. Moradi Introduction:
The Key Role of the Transmission Network in
the book S. Lumbreras, H. Abdi, A. Ramos (eds.) Transmission
Expansion Planning: The Network Challenges of the Energy
Transition Springer, 2020 ISBN 9783030494278
10.1007/978-3-030-49428-5
R. Espejo, G. Mestre, F. Postigo, S. Lumbreras, A.
Ramos, T. Huang andE. Bompard Exploiting
graphlet decomposition to explain the structure of complex
networks: the GHuST framework Scientific Reports
10, Jul 2020 10.1038/s41598-020-69795-1
Rafael Espejo A
Complex-Network Approach to Support Transmission Expansion
Planning. PhD Thesis. Universidad Pontificia
Comillas. October 2019. (Summary).
Directors: Andrés Ramos, Sara Lumbreras.
Q. Ploussard, L. Olmos and A. Ramos A
search space reduction method for transmission expansion
planning using an iterative refinement of the DC Load
Flow model IEEE Transactions on Power Systems
35 (1): 152-162, Jan 2020 10.1109/TPWRS.2019.2930719
Th. Marge, S. Lumbreras, A. Ramos, and B.F. Hobbs Integrated
offshore wind farm design: Optimizing micro-siting and
cable layout simultaneously Wind Energy 10.1002/we.2396
Quentin Ploussard Efficient
reduction techniques for a large-scale Transmission
Expansion Planning problem. PhD Thesis. Universidad
Pontificia Comillas. May 2019. (Summary).
Directors: Luis Olmos, Andrés Ramos.
R. Espejo, S. Lumbreras, A. Ramos, T. Huang, and E.
Bompard An
Extended Metric for the Analysis of Power-Network Vulnerability:
The Line Electrical Centrality IEEE PowerTech.
Milano, Italy. June 2019
S. Lumbreras, A. Ramos Better
Transmission Networks for a Smarter Global System
in the book A. Tascikaraoglu and O. Erdinc (eds.)
Pathways
to a Smarter Power System. Academic Press 2019
ISBN 9780081025925
R. Espejo, S. Lumbreras, A. Ramos A
Complex-Network Approach to the Generation of Synthetic
Power Transmission Networks IEEE Systems Journal
13(3), 3050-3058, Sep 2019 10.1109/JSYST.2018.2865104
R. Espejo, S. Lumbreras, A. Ramos Analysis
of transmission-power-grid topology and scalability,
the European case study Physica A: Statistical
Mechanics and its Applications 509, 383-395, Nov 2018
10.1016/j.physa.2018.06.019
Q. Ploussard, L. Olmos and A. Ramos An
efficient network reduction method for transmission
expansion planning using multicut problem and Kron reduction
IEEE Transactions on Power Systems 33 (6): 6120-6130,
Nov 2018 10.1109/TPWRS.2018.2842301
J.G. Dedecca, S. Lumbreras, A. Ramos, R.A. Hakvoort,
P.M. Herder Expansion
Planning of the North Sea Offshore Grid: Simulation
of Integrated Governance Constraints Energy
Economics 72, 376-392, May 2018 10.1016/j.eneco.2018.04.037
A. Held, M. Ragwitz, F. Sensfuß, G. Resch, L.
Olmos, A. Ramos, M. Rivier How
can the renewables targets be reached cost effectively?
Policy options for the development of renewables and
electricity networks Energy Policy 116, 112-126,
May 2018 10.1016/j.enpol.2018.01.025
S. Lumbreras, A. Ramos, F. Banez-Chicharro, L. Olmos,
P. Panciatici, C. Pache, J. Maeght Large-scale
Transmission Expansion Planning: from zonal results
to a nodal expansion plan IET Generation, Transmission
& Distribution 11 (11), 2778-2786, Aug 2017 10.1049/iet-gtd.2016.1441
S. Lumbreras, F. Banez-Chicharro, A. Ramos Optimal
Transmission Expansion Planning in Real-Sized Power
Systems with High Renewable Penetration Electric
Power Systems Research 49, 76-88, Aug 2017 10.1016/j.epsr.2017.04.020
F. Banez-Chicharro Methodology
for Benefit Analysis of Transmission Expansion Projects
PhD Thesis. Universidad Pontificia Comillas. June
2017 (Summary).
F. Banez-Chicharro, L. Olmos, A. Ramos and J.M. Latorre
Beneficiaries
of Transmission Expansion Projects of an Expansion Plan:
An Aumann-Shapley Approach Applied Energy 195,
382-401, June 2017 10.1016/j.apenergy.2017.03.061 F. Banez-Chicharro, L. Olmos, A. Ramos and J.M. Latorre
Estimating
the benefits of transmission expansion projects: an
Aumann-Shapley approach Energy 118 (1): 1044-1054,
Jan 2017 10.1016/j.energy.2016.10.135
Q. Ploussard, L. Olmos and A. Ramos An
operational state aggregation technique for transmission
expansion planning based on line benefits IEEE
Transactions on Power Systems 32 (4): 2744-2755 Oct
2017 10.1109/TPWRS.2016.2614368
S. Lumbreras, A. Ramos How
to Solve the Transmission Expansion Planning (TEP) Problem
Faster: Acceleration Techniques Applied to Benders Decomposition
IET Generation, Transmission & Distribution 10:
2351-2359, Jul 2016 10.1049/iet-gtd.2015.1075
S. Lumbreras, A. Ramos The
new challenges to transmission expansion planning. Survey
of recent Lab and literature review Electric
Power Systems Research 134: 19-29, May 2016 10.1016/j.epsr.2015.10.013
S. Lumbreras, D.W. Bunn, A. Ramos, M. Chronopoulos
Real
Options Valuation Applied to Transmission Expansion
Planning Quantitative Finance 16(2): 231-246 February 2016 10.1080/14697688.2015.1114362 S. Lumbreras, A. Ramos, L. Olmos, F. Echavarren, F.
Banez-Chicharro, M. Rivier, P. Panciatici, J. Maeght,
C. Pache Network
Partition Based on Critical Branches for Large-Scale
Transmission Expansion Planning IEEE PowerTech.
Eindhoven, The Netherlands. June 2015 10.1109/PTC.2015.7232344
F. Bañez, L. Olmos, A. Ramos, J.M. Latorre
Benefit allocation of transmission expansion plans
based on Aumann-Shapley IIT-14-050A, October
2014
S. Lumbreras, A. Ramos, P. Sánchez Offshore
Wind Farm Electrical Design using a Hybrid of Ordinal
Optimization and Mixed Integer Programming Wind
Energy 18(12): 2241-2258 Dec 2015 10.1002/we.1807
S. Lumbreras Decision
Support Methods for Large-Scale Flexible Transmission
Expansion Planning PhD Thesis. Universidad Pontificia
Comillas. June 2014 (Summary).
C. Duro Transmission
Expansion Planning using a Genetic Algorithm.
Final project of Engineering Degree. Universidad Pontificia
Comillas. June 2014 (Resumen)
S. Lumbreras, A. Ramos, P. Sánchez Automatic
Selection of Candidate Investments for Transmission
Expansion Planning International Journal of
Electrical Power and Energy Systems 59: 130-140, July
2014 10.1016/j.ijepes.2014.02.016
S. Lumbreras, A. Ramos Transmission
Expansion Planning using an Efficient Version of Benders’
Decomposition. A Case Study IEEE PowerTech. Grenoble, France. June 2013 10.1109/PTC.2013.6652091
M. Banzo and A. Ramos Optimization of AC Electric
Power Systems of Offshore Wind Farms in the book
P. Pardalos, S. Rebennack, M.V.F.Pereira, N.A. Iliadis,
and V. Pappu (eds.) Handbook
of Wind Power Systems. Springer 2014 ISBN 9783642410796
S. Lumbreras, A. Ramos and S. Cerisola A
Progressive Contingency Incorporation Approach for Stochastic
Optimization Problems IEEE Transactions on Power
Systems 28 (2): 1452-1460, May 2013 10.1109/TPWRS.2012.2225077
S. Lumbreras and A. Ramos Optimal
Design of the Electrical Layout of an Offshore Wind
Farm: a Comprehensive and Efficient Approach Applying
Decomposition Strategies IEEE Transactions on
Power Systems 28 (2): 1434-1441, May 2013 10.1109/TPWRS.2012.2204906
S. Lumbreras and A. Ramos Offshore
Wind Farm Electrical Design: A Review Wind Energy
16 (3): 459-473 April 2013 10.1002/we.1498
Ch. Egeruoh Long-Term Transmission Expansion Planning for Nigerian Deregulated
Power System. A systems approach Master Thesis.
Universidad Pontificia Comillas. July 2012
A. Ramos, S. Lumbreras A Dynamic Stochastic
Transmission Planning Model Solved by Efficient Benders’
Decomposition 9th International Conference on
Computational Management Science (CMS2012) London, UK
April 2012
S. Lumbreras, A. Ramos A Benders' Decomposition
Approach for Optimizing the Electric System of Offshore
Wind Farms IEEE PowerTech. Trondheim,
Norway June 2011 (Presentation)
10.1109/PTC.2011.6019371
M. Banzo and A. Ramos Stochastic
Optimization Model for Electric Power System Planning
of Offshore Wind Farms IEEE Transactions on
Power Systems 26 (3): 1338-1348 Aug 2011 10.1109/TPWRS.2010.2075944
P. Sánchez-Martín, A. Ramos, J.F. Alonso Probabilistic
mid-term transmission planning in a liberalized market
IEEE Transactions on Power Systems 20 (4): 2135-2142
Nov 2005 10.1109/TPWRS.2005.856984
P. Sánchez-Martín, A. Ramos Modeling Transmission
Ohmic Losses in a Stochastic Bulk Production Cost Model
October 1997
J.I. Pérez Arriaga, A. Ramos, G. Latorre Volumen
1: Guía de usuario del programa de planificación estática
de la red de transporte a largo plazo PERLA. Volumen
2: Casos ejemplo. IIT-93-053 Julio 1991 Preparado
para Red Eléctrica de España, S.A.
G. Latorre, A. Ramos, J.I. Pérez Arriaga, J.F. Alonso,
A. Sáiz PERLA: Un modelo
de planificación estática a largo plazo de la red de
transporte. Opciones de modelado y análisis de idoneidad
II Jornadas Hispano Lusas de Ingeniería Eléctrica 1:
3.46-3.54 Coimbra, Portugal Julio 1991
J.F. Alonso, A. Sáiz, L. Martín G. Latorre, A. Ramos,
I.J. Pérez-Arriaga PERLA: An Optimization
Model for Long-Term Expansion Planning of Electric Power
Transmission Networks IIT-91-009 January 1991
G. Latorre, J.I. Pérez Arriaga, A. Ramos, J. Román,
J.F. Alonso, A. Sáiz Un modelo de planificación estático
de la red de transporte a largo plazo I Jornadas
Hispano Lusas de Ingeniería Eléctrica 2: 521-533 Vigo,
España Julio 1990
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