Publications

The transition to a zero-carbon economy requires significant advances in the design and operation of power systems. As fossil fuel power plants are phased out in favour of renewable energy sources, the industry faces pressing challenges in maintaining grid stability and reliability while minimising environmental impacts and costs. Traditional transmission expansion planning methods, which often rely on decomposition techniques that barely represent the future operation of power systems, fail to capture the complex interdependencies between generation, transmission, and distribution infrastructure, resulting in less-than-ideal expansion strategies. Rather than introducing a broad, large-scale model, this thesis delineates a nuanced approach to transmission expansion planning that aims to improve model performance and ensure its robustness and scalability while thoughtfully incorporating elements of local and utility-scale flexibility. The novelty of this work lies not only in the scope of local and utility-scale flexibility modeling, but also in the meticulous manner in which these components are woven into power transmission system expansion planning. The research is underpinned by three core objectives: the formulation of advanced optimisation methods designed to efficiently address complex issues across different system configurations and operational scenarios; the careful merging of the representation of local and utility-scale flexibility - highlighting the importance of distributed energy resources (DERs) and integrating utility-scale storage solutions into the transmission planning framework; and the creation of durable, scalable models that can serve as future-proof blueprints for system development. By pursuing these goals, the thesis aims to contribute significantly to the development of more efficient, resilient, and sustainable power systems. Through careful modeling and analysis, it seeks to provide actionable insights and recommendations for optimising transmission system development amidst the low-carbon transition, underscoring the importance of targeted innovation in addressing the evolving challenges facing the sector.

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

Long-term energy planning increasingly relies on mathematical models offering quantitative insight to support complex policy decisions. However, the increase in their use has meant the proliferation of tools developed at different institutions, with various scopes, dealing with specific aspects of the economy, the power sector, or the climate, with mismatches in temporal or geographic resolution. All this creates a need for using several models concurrently, integrating them to generate a complete perspective on the implications of policy decisions on the energy transition. This article proposes a methodology to categorize and combine energy models and develop a manipulation strategy to answer a target research question. Thus, it gives a formal structure to tasks carried out informally −and suboptimally- in virtually any energy planning project. This methodology is based on structured modeling, a formal mathematical theory conceived for representing and manipulating models. It assumes a soft-linking approach, meaning the models share information without integrating them within the same platform or code. This framework was developed within the European project openENTRANCE, which will develop, use, and disseminate an open, transparent, and integrated modeling platform for assessing low-carbon transition pathways in Europe. DOI.

This study presents an innovative optimization model for the self-scheduling of a hydrogen-based virtual power plant (H2-VPP) that aims to thrive in day-ahead energy and reserve markets. At its core, the model seeks to optimize profits by integrating a mix of renewable sources, battery storage, electrolyzers, and hydrogen storage, highlighting the model's focus on both electricity and hydrogen networks within a unified operational framework. Designed to navigate the complexities of a VPP, the model excels at strategically managing diverse resources for energy and reserve markets, emphasizing optimal operation of all assets. It accounts for the interplay between electricity and hydrogen production, storage, and demand, and addresses the time constraints critical to increasing revenues and ensuring balanced supply. A case study demonstrates the model's effectiveness, highlighting the role of hydrogen storage in increasing renewable integration and revenues. This underscores the model's ability to leverage the unique dynamics of electricity and hydrogen within the H2-VPP, confirming its potential in a rapidly evolving energy landscape.

This paper presents a cost-based TSO-DSO coordination model to quantify the value of local flexibility services and analyze its impact on the transmission grid expansion and the system operation. Flexibility is provided to the DC power flow transmission grid model by microgrids within the integrated AC power flow distribution grid model. The model’s objective is to minimize the overall cost of transmission investments and procured flexibility and is achieved using a bilevel optimization approach where the power exchanges on all connected grid interfaces are controlled. Case studies using a combined test system of the IEEE RTS-96 transmission network interfacing multiple 33-bus distribution grids were performed to validate the model and assess the values and impacts of local flexibility on the transmission system expansion. The results showed that the proposed model modified the investment plan and dispatch of flexibility resources reducing the investment and operation cost of the transmission system.

The expansion of the transmission network will be a key enabler of the energy transition. However, the high level of technical detail involved in network studies, where a DCPF and the consideration of discrete investment are necessary, meant that it was accessible only to very specialized researchers. OpenTEPES changes the picture by providing an open-access tool with full functionality. OpenTEPES determines the investment plans for new power facilities (generating units and lines) necessary to supply future demand at minimum cost. OpenTEPES represents hierarchically the different time scopes involved in the planning decisions, from the medium to the very long term. It includes the uncertainty related to system operation, such as the availability of renewable energy sources and electricity demand, and multiple criteria such as investment cost or carbon emissions. OpenTEPES is a flexible tool that has been applied to planning projects in a European context. It has been developed as part of the H2020 project OpenENTRANCE and is now available open-source for the energy planning community.

The decarbonization of the energy sector puts additional pressure on the transmission network. The main cause for this is that renewable sources are often more abundant in geographical areas far away from the main demand centers, so new transmission lines are required to connect the new renewable energy capacity. In addition, by connecting different geographical zones, the transmission network could smooth the intermittency and the variability of renewable energy production. Thus, the changing energy landscape leads to a need to reinforce the transmission network through the Network Transmission Expansion Planning. Ideally, all the idiosyncrasies of the electricity system are considered in the operation and expansion planning process. However, several critical dimensions of the planning process are routinely ignored since they may introduce parameters that are difficult to quantify and complexity that state-of-the-art planning methods cannot handle. This paper identifies the most relevant elements related to the human factor, which have been grouped around the main topics: the human behind the technical, the human at the institutional level, and the human at the individual level. This paper also provides an additional formulation that can be used to upgrade existing models to include the human element and discusses the implications of these upgrades.

The open-source Python package pyam provides a suite of features and method for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

This study presents a methodology to solve simultaneously the alternating current (AC) transmission network expansion and reactive power planning problems, considering multiple stages and operating conditions. A mixed-integer non-linear programming model for the proposed planning problem is presented and rewritten with semidefinite structures. Then, the generalised Benders decomposition is used to separate the overall problem into an upper-level (master) problem and several lower-level (slaves) problems. The master problem is a mixed-integer linear programming problem that optimises the investment cost and constraints of the multistage expansion. Each slave problem minimises the operating costs associated with each stage and operating condition (normal operation or contingency), considering the AC power flow via semidefinite relaxation. With the proposed methodology, the global optimality of generalised Benders decomposition can be preserved due to the use of semidefinite relaxation in each slave problem. Garver's 6-bus system and an IEEE 118-bus system are used to show the precision and convergence to near-global optimal solutions with small relaxation gaps through the proposed approach.

This thesis aims to explore the potential of one of the most promising optimization techniques, known as semidefinite relaxation or SDP relaxation, to address optimization problems in power transmission systems. This is a relatively new technique of convex optimization that has been developing in the last decade. It have quickly taken the attention of diverse research groups because numerous nonlinear problems can be approached by SDP. The pursuit of this work is related to: 1) the exploration of the potentialities of SDP relaxation to provide tight relaxations in power system optimization problems that is one of the key points in this line of research; 2) to solve the expansion planning and operation problems of power systems using SDP relaxation without linearizations. Both of them are challenging combinatorial problems and continually become more complex due to the high penetration of renewable energy sources, deployment of distributed generation, storage units and the increasing share of stochastic loads. To detail, the first part presents theoretical aspects of convex relaxations, specifically focused on SDP based on optimal power flow (OPF). In particular, a comprehensive and unified framework of the different methods proposed in the literature to design the semidefinite relaxations is provided. The second part presents the application of SDP to the optimal expansion planning and operation of power systems. Where an introduction to expansion planning problems in power systems is provided, with a special emphasis on one of the most challenging problem, known as the Transmission Network Expansion Planning (TNEP) problem. This problem was selected both for being a hard-combinatorial problem and for including integer variables. In order to solve the TNEP, a methodology based on the generalized Bender’s decomposition (GBD) and SDP is presented. Finally, other challenging problem related to the power systems operation is provided. This problem is known in literature as the Stochastic Market Clearing (SMC) problem. During many years, the SMC was approached with linearizations and approximations, which are the reason why it was selected and show the possibility to use the SDP in order to have a better representation of the characteristics of the power systems operation. In these problems, the semidefinite relaxation was applied, and reinforced by addition of appropriate constraints because each problem has its own characteristic and needs that the semidefinite relaxation to be applied in a particular way.

This paper proposes a two-stage stochastic market clearing (SMC) model based on a semidefinite programming (SDP) relaxation. The SMC model aims at determining the day-ahead schedule (DA) and the real-time (RT) balance settlement that minimize the total expected production cost. The network capacity constraints are considered in the proposed model through an AC power flow formulation, while the uncertainty in the renewable-based generation is taking into account using a set of stochastic scenarios. In order to solve the proposed non-linear programming model, a SDP relaxation is used. An illustrative example (3-bus test system) and the IEEE Reliability 24-bus test system are used to show the effectiveness and accuracy of the proposed model. Results shown that the proposed SDP relaxation introduce a negligible error, when compared with the solution after solving the original non-linear model.