DC-Approximated Power System Reliability Predictions with Graph Convolutional Neural Networks

The current standard operational strategy within electrical power systems is done following deterministic reliability practices. These practices are deemed to be secure under most operating situations when considering power system security, but as the deterministic practices do not consider the probability and consequences of operation, the operating situation may often become either too strict or not strict enough. This can in periods lead to inefficient operation when regarding the socio-economic aspects. With the continuous integration of renewable energy sources to the electrical power system coupled with the increasing demand for electricity, the power systems have been pushed to operating closer to their stability limit. This poses a challenge for the operation and planning of the power system. Research is therefore being invested into finding more flexible operational strategies which operates according to probabilistic reliability criteria, taking the probability of future events into consideration while also aiming to minimize the expected cost and defining limits for probabilistic reliability indicators. To reliably plan and operate the systems according to a probabilistic reliability criterion, numerical problems such as the Optimal Power Flow (OPF) and the Power Flow (PF) equations are used. These tools are helpful as they are used to determine the optimal way of producing and transporting power. These tools are also used in contingency analyses, where the effect of occurring contingencies is analyzed and evaluated. Due to the non-linearity of the PF equations, the solution is often found through iterative numerical methods such as the Gauss-Seidel method or the Newton-Raphson method. These numerical methods are often computationally expensive, and convergence to the global minimum is not guaranteed either. In recent years, various Machine Learning (ML) models have gathered a lot of attention due to their success in different numerical tasks, particularly Graph Convolutional Networks (GCNs) due to the model’s ability to utilize the topology and learn localized features. As the field of GCN is new, extensive research is being committed to identify the GCNs ability to work on applications such as the electrical power system. This thesis seeks to conduct preliminary experiments where Graph Convolutional Networks (GCN) models are used as a substitution for the numerical DC-OPFs which are used to determine values such as the system load shedding due to contingencies. The GCN models are trained and tested on multiple datasets on both a system- and a node-level, where the goal is to test the models' ability to generalize across perturbations of different system-parameters, such as the system load, the number of induced contingencies and different system topologies. The experiments of the thesis show that the GCNs can predict the load-shedding values across multiple system-parameter perturbations such as the number of induced contingencies, increasing load-variation and a modified system-topology with a high accuracy, without having to be retrained for those specific situations. Though, the further the system-parameters were perturbated, the less accurate the model's predictions became. This reduction in accuracy per system-parameter perturbation was caused by a change in the load-shedding pattern as additional parameters were perturbated, which the models were unable to comprehend. Lastly, this thesis also shows that the GCN models are substantially faster than the numerical methods which they seek to replace

Verification and validation of simulation models

Simulation Models;econometrics

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty

Risk-based assessment for distribution network via an efficient Monte Carlo simulation model

Given the fact that Smart Grid technologies are implemented mainly in distribution networks, it is essential to build a risk-based assessment tool which can model the operational characteristics of distribution networks operation. This thesis presented a distribution network model which captures the features of distribution network restoration, based on approximations of real-time switching actions. It enables the evaluation of complex distribution network reliability with active network control. The development of an explicit switching model which better reflects actual network switching actions allows for deliberate accuracy and efficiency trade-offs. Combined with importance sampling approach, a significant improvement in computational efficiency has been achieved with both simplified and detailed network switching models. The assessment model also provides flexibility for users to analyse system reliability with various levels of complexity and efficiency. With the proposed assessment tool, different network improvement technologies were investigated for their values of substituting traditional network constructions and impacts on network reliability performances. It has been found that a combination of different technologies, according to specific network requirements, provide the best solution to network investments. Models of customer interruption cost were analysed and compared. The study shows that using different cost models will result in large differences in results and lead to different investment decisions. A single value of lost load is not appropriate to achieve an accurate interruption cost quantification. A chronological simulation model was also built for evaluating the implications of High Impact Low Probability events on distribution network planning. This model provides the insights for the cost of such events and helps network planners justify the cost-effectiveness of post-fault corrections and preventive solutions. Finally, the overall security of supply for GB system was assessed to investigate the impacts of a recent demand reduction at grid supply points (for transmission networks) resulting from the fast growing of generation capacity in distribution networks. It has been found that the current security standard may not be able to guarantee an acceptable reliability performance with the increasing penetration of distributed generation, if further balancing service investment is not available.Open Acces

An Interactive Multi-Dimensional Flexibility Scheduling in Low-carbon Low-inertia Power Systems

Today, electrical energy plays a significant and conspicuous role in contemporary economies; as a result, governments should place a high priority on maintaining the supply of electrical energy. In order to assess various topologies and enhance the security of power systems, it may be useful to evaluate robustness, dependability, and resilience all at once. This is particularly true when there is a significant amount of renewable energy present. The R3 concept, which consists of these three interrelated characteristics, describes the likelihood that a power system would fail, the potential severity of the repercussions, and the speed at which the system will recover from a failure. This paper uses eight case studies created from the IEEE 24-bus RTS and thoroughly assesses the properties of reliability, robustness, and resilience to highlight the significance of the issue. The sequential Monte Carlo method is used to evaluate reliability, cascade failure simulations are used to evaluate robustness, and a mixed-integer optimization problem is used to study resilience. Different indicators related to each of the three assessments are computed. The significance of the combined analysis is emphasized as the simulation findings are described visually and statistically in a unique three-dimensional manner eventually.Comment: 9 pages, 6 figure

Mixing quantitative and qualitative methods for sustainable transportation in Smart Cities

L'abstract è presente nell'allegato / the abstract is in the attachmen

Risks and Prospects of Smart Electric Grids Systems measured with Real Options

fi=vertaisarvioitu|en=peerReviewed

Decision Support System for Container Port Selection using Multiple-Objective Decision Analysis

Ports are essential for maritime transportation and global supply chains since they are nodes that connect the sea- and land-based modes of transportation. With containerization and supply chains stimulating global trade, ports are challenged to adjust to changes in the market to create value to their customers. Therefore, this dissertation research focuses on the container port selection decision analysis to provide information to help shipping lines select the best port for their shipping networks. Since the problem is complex, dynamic, and involves multiple and conflicting criteria, the research proposes to use the multi-objective decision analysis with Value-Focused Thinking approach. The first chapter analyzes the port selection literature by timeline, journals, geographical location, and focus of the studies. Also, the research identifies the multiple criteria used in the port selection literature, as well as the models and approaches used for the analysis of the port selection decision problem. The second chapter develops a container port selection decision model for shipping lines using ports in West Africa. This model uses a multi-attribute value theory with valued-focused thinking and Alternative-Focused Thinking methodologies. The third chapter develops a port selection decision support system for shipping lines to select the best port in the U.S. Gulf Coast considering the impact of the Panama Canal’s expansion. The decision support system uses the multi-objective decision analysis with Value-Focused Thinking approach, incorporating the opinion of an industry expert for the development of the value model. It also includes a cost model to quantify the cost of the alternatives. A Monte Carlo simulation is used to help decision makers understand the value and cost risks of the decision. The contribution of this research is that it provides a tool to decision makers of the shipping lines industry to improve the decision making process to select the port that will add the most affordable value to the global supply chains of their customers. In addition, researchers can use the proposed methodology for future port selection studies in other regions and from the perspectives of other stakeholders