Ioannis Konstantelos

Valuation of Flexible Transmission Investment Options Under Uncertainty

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 non-network 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.

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages high performance computing to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform’s workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10 000 parallel cores) is analyzed, including task timings and data flows. Finally, the generated decision trees are compared with test data to quantify the functional performance of the DSA platform.

Strategic Valuation of Smart Grid Technology Options in Distribution Networks

The increasing penetration of renewable distributed generation (DG) sources in distribution networks can lead to violations of network constraints. Thus, significant network reinforcements may be required to ensure that DG output is not constrained. However, the uncertainty around the magnitude, location, and timing of future DG capacity renders planners unable to take fully informed decisions and integrate DG at a minimum cost. In this paper, we propose a novel stochastic planning model that considers investment in conventional assets as well as smart grid assets such as demand-side response, coordinated voltage control and soft open points. The model also considers the possibility of active power generation curtailment of the DG units. A node-variable formulation has been adopted to relieve the substantial computational burden of the resulting mixed integer nonlinear programming problem. A case study shows that smart technologies can possess significant strategic value due to their inherent flexibility in dealing with different system evolution trajectories. This latent benefit remains undetected under traditional deterministic planning approaches which may hinder the transition to the smart grid.

Evaluating composite approaches to modelling high-dimensional stochastic variables in power systems

The large-scale integration of intermittent energy sources, the introduction of shiftable load elements and the growing interconnection that characterizes electricity systems worldwide have led to a significant increase of operational uncertainty. The construction of suitable statistical models is a fundamental step towards building Monte Carlo analysis frameworks to be used for exploring the uncertainty state-space and supporting real-time decision-making. The main contribution of the present paper is the development of novel composite modelling approaches that employ dimensionality reduction, clustering and parametric modelling techniques with a particular focus on the use of pair copula construction schemes. Large power system datasets are modelled using different combinations of the aforementioned techniques, and detailed comparisons are drawn on the basis of Kolmogorov-Smirnov tests, multivariate two-sample energy tests and visual data comparisons. The proposed methods are shown to be superior to alternative high-dimensional modelling approaches.

Option value of Soft Open Points in distribution networks

We propose a novel stochastic planning model that considers investment in conventional assets as well as in Soft Open Points, as a means of treating voltage and thermal constraints caused by the increased penetration of renewable distributed generation (DG) sources. Soft Open Points are shown to hold significant option value under uncertainty; however, their multiple value streams remain undetected under traditional deterministic planning approaches, potentially undervaluing this technology and leading to a higher risk of stranded assets.

Opportunities for Energy Storage: Assessing Whole-System Economic Benefits of Energy Storage in Future Electricity Systems

Any Cost-effective transition toward low-carbon electricity supply will necessitate improved system flexibility to address the challenges of increased balancing requirements and degradation in asset use. Energy storage (ES) represents a flexible option that can bring significant, fundamental economic benefits to various areas in the electric power sector, including reduced investment requirements for generation, transmission, and distribution infrastructure as well as reduced system operation and balancing costs. The additional flexibility offered by ES could also significantly reduce the requirement for investment in low-carbon generation capacity while achieving the established carbon intensity targets. Moreover, ES may present significant option value, as it can provide flexibility for dealing with uncertainty in future system development.

C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

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 toward identifying multidimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2613 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.

Application of novel nested decomposition techniques to long-term planning problems

Cost effective, long term planning under uncertainty constitutes a significant challenge since a meaningful description of the planning problem is given by large Mixed Integer Linear Programming (MILP) models which may contain thousands of binary variables and millions of continuous variables. In this paper, a novel multistage decomposition scheme, based on Nested Benders decomposition is applied to the transmission planning problem. The difficulties in using temporal decomposition schemes in the context of planning problems due to the presence of non-sequential investment state equations are highlighted. An efficient and highly-generalizable framework for recasting the temporal constraints of such problems in a structure amenable to nested decomposition methods is presented. The proposed schemes solution validity and substantial computational benefits are clearly demonstrated through the aid of case studies on the IEEE24-bus test system.

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

It's All About Grids: The Importance of Transmission Pricing and Investment Coordination in Integrating Renewables

In Great Britain, it is projected that an unprecedented amount of transmission investment will take place in the next decade and that these investments will be the largest transmission network reinforcements since the post-World War II expansion. In Figure 1, the projected range, to 2030, of onshore, offshore, and crossborder investments is presented against the estimated asset values. The value of the transmission assets is expected to more than double to 2030 with investments projected between 20 billion and 50 billion pounds across onshore (main transmission system), offshore (connecting mainly offshore wind farms) and cross-border interconnection transmission projects. The exact level and cost of these transmission investments will depend on a number of factors, including the location of new conventional and renewable energy sources (RES) plants (considering on- and offshore developments); decommissioning of existing ones; demand growth; cross-border trading of energy and ancillary services; and uptake of distributed generation, energy storage, demandside response, and other smart grid technologies.