Thesis Machine Learning Aided Column Generation for Solving Transmission Network Expansion Planning
| dc.contributor.department | IngenierÃa Electrica | |
| dc.contributor.guia | Alvarez Malebran, Ricardo Javier | |
| dc.coverage.spatial | Campus Santiago San JoaquÃn | |
| dc.creator | OteÃza Canales, Juan Pablo | |
| dc.date.accessioned | 2025-01-22T14:48:02Z | |
| dc.date.available | 2025-01-22T14:48:02Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Many countries around the world are experiencing an energy transition towards low-carbon economies, in order to reduce greenhouse gas emissions and combat climate change. One of the main sectors called to lead this transition is the electricity sector, mainly through the massive incorporation of renewable energies and the decommission of coal-fired power plants. In Chile, according to the long-term Chilean energy policy, the target is to reach net zero emissions by 2040 and supply 100% of the electrical demand from renewable energies by 2050. The Chilean commitment to combat climate change already started in 2011 by promoting the introduction of renewable energies in the electrical sector. Since then, the installed capacity of variable renewable energies (VRE) increased from 4% of the total capacity in 2011, to more than 40% in 2023. Under this global trend, the transmission infrastructure is key for achieving a cost-effective and secure energy transition. Indeed, to have an adequate transmission capacity is fundamental to accommodate new renewables and facilitate their integration into the market, supply the growing demand in a cost-effective way and enhance competition to ensure market efficiency. Increasing transmission capacity is not an easy task, due to growing difficulties in acquiring new rights-of way, the opposition of local communities to the construction of new lines and long processes of environmental licensing, among others. The aforementioned challenges considerably extend the development time of new transmission line projects. Delays in the commission of expansion projects can have significant technical and economic repercussions. Key concerns include unexpected curtailment of renewable energy sources (RES), where new RES investments can experience unexpected, diminished returns if the transmission capacity if not available on time for the delivery of their energy into the grid, increasing energy costs for consumers and greater transmission losses caused by the sub optimal operation of the transmission network due to underdevelopment. Flexible technological solutions can alleviate these issues by enhancing the network capacity and reliability in shorter time and without the need of installing new transmission lines. Amongst these alternative technological solutions, we can find line reconductoring, HVAC to HVDC reconversion, Flexible AC Transmission Systems (FACTS), Battery Energy Storage System (BESS) installed as grid boosters and others. What all these solutions have in common is shorter development times, lower investment cost and less challenging regulatory processes compared to the investment in new transmission lines. Consequently, nowadays there are plenty of technological options to increase the transmission capacity. However, the main challenge consists of determining which technology, when and where should be incorporated into the system to minimize total system costs while maintaining security, social and/or environmental constraints. To answer this question, a usual approach is to address this challenge as an optimization problem, the transmission network expansion planning (TNEP) problem. The TNEP is usually formulated as a mixed-integer linear problem (MILP), where integer variables are related to the investment decisions, while continuous variables are related to the power system operation. In more complex formulations, integer variables can also comprise operational variables such as the generator's unit commitment. The TNEP can also be formulated as a nonlinear problem (MINLP), for example to consider power system losses and/or AC power flows [6]. Furthermore, the use of stochastic and/or robust models has increased in the last years, due to the growing uncertainty involved in the network planning. In the past, when power systems were vertically integrated and the generation matrix was dominated by conventional fossil-fuel based generation, there was little uncertainty regarding future generation capacity and power feed-in. Therefore, the majority of TNEP formulations were deterministic. However, the deregulation of the electricity markets, along with the massive introduction of renewable energies, brought new uncertainties regarding future generation capacity and availability. The level of uncertainty regarding future generation capacity increased even more with the introduction of renewable energies, because of their short construction lead times (1 - 2 years), compared to conventional fossil-fuel based generation (3-5 years), thus increasing the need of adopting stochastic or robust TNEP formulations. Furthermore, it is expected that the use of stochastic and robust model will increase in the future, in order to incorporate new sources of uncertainties, for examples, those related to long-term effects of the climate change. In all cases, the TNEP problem is NP-hard, which means that it cannot be solved in polynomial time. For large-scale realistic-size power system models, solving the TNEP problem demands significant amounts of computational resources and could even require several days to be solved using a state-of-the-art optimization algorithm. Furthermore, the appearance of emergent technologies that increase the operational flexibility of power systems have introduced new challenges for the TNEP. Examples of such technologies are FACTS, storage devices, special protection schemes, energy storage-based grid booster and HVDC links, as well technological option to increase the use of existing transmission network assets such as line uprating and HVDC conversion. For each new technology, novel formulations capable of capturing their value in the power system are required, which usually means having more complex formulations, more variables, and constraints, thus resulting in models which are even more challenging to solve. In particular, proper models of energy storage systems require the introduction of time-coupling constraints to account for the energy balance, thus significantly increasing the complexity of the TNEP problem. Note that, to capture the benefits of short-term storage systems such as batteries, requires increasing the time resolution of the models (for example, representative days instead of isolated operating conditions), which in turn increase the computational burden of the TNEP formulation. | |
| dc.description.program | MagÃster en Ciencias de la IngenierÃa Eléctrica | |
| dc.format.extent | 35 páginas | |
| dc.identifier.uri | https://cris.usm.cl/handle/123456789/2313 | |
| dc.language.iso | en | |
| dc.publisher | Universidad Técnica Federico Santa MarÃa | |
| dc.rights | open access | |
| dc.subject | Machine learning | |
| dc.subject | Transmission Planning | |
| dc.subject | Decomposition techniques | |
| dc.title | Machine Learning Aided Column Generation for Solving Transmission Network Expansion Planning | |
| dspace.entity.type | Tesis |
