• Energy Research
• 2016-2025close

The development of offshore transmission and wind power generation in the North Sea of Europe is advancing fast, but there are significant barriers to an integrated offshore grid in the region. This offshore grid is a multi-level, multi-actor system requiring a governance decision-making approach, but there is currently no proven governance framework for it, or for the expansion planning of the European power system in general. In addition, existing offshore expansion planning models do not endogenously include governance considerations, such as country vetoes to integrated lines. We develop a myopic Mixed-Integer Linear Programming model of offshore generation and transmission expansion planning to study the effect of integrated governance constraints. These constraints limit investments in integrated lines: non-conventional lines linking offshore wind farms to other countries or to other farms. Each constraint affects the system (including the main transmission corridors), transmission technologies and welfare distribution differently. We apply our model to a long-term case study of the 2030–2050 offshore expansion pathways using data from the e-Highway2050 project. Results confirm that the offshore grid is beneficial to society. Integrated governance constraints induce a modest loss of social welfare, but do not change significantly the existing welfare distribution asymmetry between countries and actor groups. They do strongly affect the interaction of line technologies and types (conventional or integrated), so the impact of the integrated governance constraints is more visible on the grid topology than on welfare levels and distribution. We highlight the need to consider technology and type interactions in expansion planning, especially between multiterminal HVDC and integrated transmission lines. Also, an offshore governance framework should address the use of multiterminal HVDC in a non-integrated grid, but this is a second-best option compared to an integrated grid.

Artículos en revistas Performing optimal Transmission Expansion Planning (TEP) in real, large-scale power systems such as the European one can be an unmanageable task, especially when long-term time scopes and multiple scenarios are considered. Project e-Highway had the daunting objective of planning the European network for the very long term and under high renewable energy penetration. The project objectives included the development of a planning methodology capable of applying optimization to large-scale systems that are currently unmanageable in practice. This paper presents this approach, which is based on simplifying the system while keeping its main features and investment drivers. The simplified system is then expanded optimally for the full time scope. Last, the original system is expanded optimally for the first time horizon taking into account the constraints imposed by the full time-scope optimization of the simplified system. It illustrates the applicability of the method with a case study based on the European Union. info:eu-repo/semantics/publishedVersion

Off-grid systems play a prominent role in rural electrification planning. The problem of optimizing the generation design of a single off-grid system has received a significant amount of attention in the literature, and several software tools and algorithms have addressed it. However, methods and tools designed for individual mini-grids are not directly applicable to regional planning, where it is necessary to estimate the generation cost of potentially thousands of mini-grids. Conversely, most regional planning tools estimate the generation cost of mini-grids with rules of thumb or analytical expressions. These estimations are useful, but they lack the accuracy necessary to develop a rural electrification plan. This paper presents a method to estimate the generation cost of any potential off-grid system in a large-scale rural electrification planning problem, which is currently implemented in the Reference Electrification Model (REM). The method uses a master-slave decomposition that exploits the structure of the problem and combines continuous and discrete variables. The algorithm is illustrated with a case study that shows that a direct application of a discrete model may lead to suboptimal results in large-scale planning.

Artículos en revistas The deregulation of power markets and the high amount of renewable energy expected in the coming decades have originated new needs for the expansion of the transmission network. Transmission expansion planning (TEP), the problem that deals with identifying the optimal grid reinforcements, is therefore becoming increasingly relevant. TEP, notoriously difficult to solve, is also deeply affected by uncertainty in factors such as renewable generation. Approaches for TEP based on optimization have not been widely used given that their high computational requirements mean that they could not be efficient for large-scale, real systems. We present a model that performs optimal TEP efficiently in a Stochastic Optimization context. The model uses a modified version of Benders decomposition that benefits from several improvements that are described. It deals with the incorporation of contingencies by using a double architecture for Benders cuts and a progressive contingency incorporation algorithm. In addition, it is able to identify the potentially interesting candidate transmission lines automatically, which is especially interesting in large-scale problems. Finally, it incorporates some other enhancements to the decomposition, which enable a faster problem resolution. This paper describes the optimization model in detail as well as its implementation. This is completed with a realistic case study that illustrates that optimal TEP can be applied to large systems with high renewable penetration as long as efficient models and implementations are used. info:eu-repo/semantics/publishedVersion

Artículos en revistas Transmission Expansion Planning, TEP, is receiving an increased attention, primarily due to the large-scale grid upgrades that will be necessary to accommodate the forthcoming renewable generation or to increase cross-border capacity. The intermittency of renewables, together with the uncertainties inherent to long-term planning, make it advisable to use solution methods that cope with uncertainty explicitly. Stochastic Optimization and, in particular, Benders decomposition, is one of the most widely applied approaches in this context. However, large-scale planning can still present computational problems. Several techniques have been proposed to accelerate Benders decomposition. However, they appear disperse in the literature and usually without a clear application scope. Most of them have not been applied to TEP yet. This paper presents a comprehensive view on TEP applied to Benders decomposition and the techniques available to accelerate its resolution, together with semi-relaxed cuts, a technique proposed in previous work by the authors [1]. Then, for three case studies based on IEEE test cases, the most promising of these techniques are implemented and their effectiveness is compared. All test cases could save around 50% of solution time using simple and easy-to-implement techniques, showing that there is an interest in using these approaches in academic and practical TEP applications. info:eu-repo/semantics/publishedVersion

AbstractAn optimization framework for global optimization of the cable layout topology for offshore wind farm (OWF) is presented. The framework designs and compares closed‐loop and radial layouts for the collection system of OWFs. For the former, a two‐stage stochastic optimization program based on a mixed integer linear programming (MILP) model is developed, while for the latter, a hop‐indexed full binary model is used. The purpose of the framework is to provide a common base for assessing both designs economically, using the same underlying contingency treatment. A discrete Markov model is implemented for calculating the cable failure probability, useful for estimating the time under contingency for multiple power generation scenarios. The objective function supports simultaneous optimization of (i) initial investment (network topology and cable sizing), (ii) total electrical power loss costs and (iii) operation costs due to energy curtailment from cable failures. Constraints are added accounting for common engineering aspects. The applicability of the full method is demonstrated by tackling three differently sized real‐world OWFs. Results show that (i) the profitability of either topology type depends strongly on the project size and wind turbine rating. Closed loop may be a competitive solution for large‐scale projects where large amounts of energy are potentially curtailed. (ii) The stochastic model presents low tractability to tackle large‐scale instances, increasing the required computing time and memory resources. (iii) Strategies must be adopted in order to apply stochastic optimization for modern OWFs, intending analytically or numerically simplification of mathematical models.

In the framework of the Paris Agreement, the European Union (EU) will have to firmly set decarbonization targets to 2050. However, the viability on these targets is an ongoing discussion. The European Commission has made several propositions for energy and climate "roadmaps". In this regard, this paper contributes by analyzing alternative pathways derived in a unique modelling process. As part of the SET-Nav project, we defined four pathways to a clean, secure and efficient energy system taking different routes. Two key uncertainties shape the SET-Nav pathways: the level of cooperation (i.e. cooperation versus entrenchment) and the level of decentralization (i.e. decentralization versus path dependency). All four pathways achieve an 85-95% emissions reduction by 2050. We include a broad portfolio of options under distinct framework conditions by comprehensively analyzing all energy-consuming and energy-providing sectors as well as the general economic conditions. We do this by applying a unique suite of linked models developed in the SET-Nav project. By linking more than ten models, we overcome the traditional limitation of models that cover one single sector while at the same time having access to detail sectoral data and expertise. In this paper, we focus on the implications for the energy demand sectors (buildings, transport, and industry) and the electricity supply mix in Europe and compare our insights of the electricity sector to the scenarios of the recent European Commission (2018a) report "A clean Planet for all".

The application of complex-network techniques to the analysis of power grids can provide interesting complementary insights into power studies such as the generation of synthetic power grids. Synthetic power grids are realistic, albeit nonreal, case studies that are topologically consistent with respect to real power networks. As a preliminary step to the generation of synthetic power grids, we use complex-network techniques to analyze several European power networks from a topological point of view. We observe that real topologies are markedly heterogeneous. Then, we present our main contribution: a novel model that generates synthetic spatial power networks that mimics the historical evolution of power systems by taking into account economic and technical factors. The model is articulated in two steps—the first step focused on economic efficiency in which power demand is met and the second one is targeted at increasing network robustness while achieving topological attributes. The resulting synthetic networks are topologically consistent with real power grids. The parametrical nature of the proposed model allows the generation of different instances of consistent power networks, a very interesting feature for grid generation.