Abstract--Significant uncertainty surrounds the future development of electricity systems, primarily in terms of size, location and type of new renewable generation to be connected. In this paper we assess the potential for flexible network technologies, such as phase-shifting transformers, and nonnetwork solutions, such as energy storage and demand-side management, to constitute valuable interim measures within a long-term planning strategy. The benefit of such flexible assets lies not only in the transmission services provided but also in the way they can facilitate and de-risk subsequent decisions by deferring commitment to capital-intensive projects until more information on generation development becomes available. A novel stochastic formulation for transmission expansion planning is presented that includes consideration of investment in these flexible solutions. The proposed framework is demonstrated with a case study on the IEEE-RTS where flexible technologies are shown to constitute valuable investment options when facing uncertainties in future renewable generation development.

The ongoing decarbonisation of modern electricity systems has led to a substantial increase of operational uncertainty, particularly due to the large-scale integration of renewable energy generation. However, the expanding space of possible operating points renders necessary the development of novel security assessment approaches. In this paper we focus on the use of security rules, where classifiers are trained offline to characterize previously unseen points as safe or unsafe. This paper proposes a novel deep learning-based feature extraction framework for building security rules. We show how deep autoencoders can be used to transform the space of conventional state variables (e.g. power flows) to a small number of dimensions where we can optimally distinguish between safe and unsafe operation. The proposed framework is data-driven and can be useful in multiple applications within the context of security assessment. To achieve high accuracy, a novel objective-based loss function is proposed to address the issue of imbalanced safe/unsafe classes that characterizes electricity system operation. Furthermore, an R-vine copula-based model is proposed to sample historical data and generate large populations of anticipated system states for training. The superior performance of the proposed framework is demonstrated through a series of case studies and comparisons using the load and wind generation data from the French transmission system, which have been mapped to the IEEE 118bus system.

Abstract-The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.Index Terms-Clustering, customer classification, C-vine, decision trees, mixture models, pair-copula construction, smart meters. I. INTRODUCTIONlectricity market liberalization has largely unbundled the distribution and supply services in many jurisdictions, providing customers with the freedom to select their electricity supplier. In this competitive environment, retail companies can improve the commercial attractiveness of their product by formulating tariffs aimed at different customer types. An important part of the tariff design process is the identification of meaningful customer classes that exhibit different consumption patterns, enabling the development of diversifiable products. Moreover, electrical customer classification can also play a crucial role in load forecasting [1][2] and modeling [3], electricity market development [4], energy system planning and operation [5] and theft detection [6]. Naturally, information on customer type (e.g. industrial, commercial, residential) provides important information regarding the likely electricity consumption pattern and intensity. However, for further partitioning and exploratory analysis to be carried out effectively, high-frequency demand measurements are necessary [7]. As such, the advent of smart metering has led to large-scale availability of consumption data that render clustering analysis increasingly possible.Load profile clustering aims to allocate consumers into a small number of homogeneous groups, ensuring that elements of the same cluster are similar between them, while being dissimilar to elements of different clusters. A large number of clustering techniques have been proposed in the past and applied to electrical load ...

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A large number of offshore wind farms and interconnectors are expected to be constructed in the North Sea region over the coming decades, creating substantial opportunities for the deployment of integrated network solutions. Creating interconnected offshore grids that combine cross-border links and connections of offshore plants to shore offers multiple economic and environmental advantages for Europe's energy system. However, despite the growing consensus among key stakeholders that integrated solutions can be more beneficial than traditional radial connection practices, no such projects have been deployed yet. In this paper we quantify costs and benefits of integrated projects and investigate to which extent the cost-benefit sharing mechanism between participating countries can impede or encourage the development of integrated projects. Three concrete interconnection case studies in the North Sea area are analyzed in detail using a national-level power system model. Model outputs are used to compute the net benefit of all involved stakeholders under different allocation schemes. Given the asymmetric distribution of costs and benefits, we recommend to consistently apply the Positive Net Benefit Differential mechanism as a starting point for negotiations on the financial closure of investments in integrated offshore infrastructure.