1.4
Problem Statement
Massive introduction of renewable generation, market deregulation, new opera- tion paradigms and other Smart Grid trends drive numerous changes in electric power systems, especially at the distribution level, that result in new challenges to Smart Grids dependability, and dependability of ADNs in particular. Hence, with power grid’s digitalization we also need new dependability management approaches and methods.
Efforts towards more dependable grid, that in great part rely on ICT infras- tructure, include, among others, new demand side management programs[8] for better power balance and real-time state estimation[29] for improved grid status awareness, and faster and better detection of disturbances and other problems. Complementing and extending these efforts, the goal of this thesis is to develop a methodology and to design methods for prediction of disturbances in ADNs us- ing voltage sags as a case study. With this we want to pave the way for proactive management methods to additionally enhance availability of electric power de- livery service, and thus to contribute to a more trustworthy grid. The motivation for adopting such an approach are numerous research results in failure predic- tion developed for industrial systems, such as, for example telecommunication systems[67], high-performance computing systems [68], as well as commercial computer systems that exploit predictions for enhancing availability including IBM XIV storage system [69], IBM predictive management [70] and HP Backup Navigator[71]. More examples of systems with proactive fault management may be found in[72].
The backbone of the approach is in the use of accurate grid-status data (e.g. from PMU devices whose number is steadily increasing) to predict a disturbance and to take proactive corrective actions (e.g. with a distributed storage, a con- trollable generator or a tap changer) to prevent it or to mitigate it.
1.4.1
Challenges
Even though it is appealing to assume that, with more ICT elements in power grids, proactive management methods, as used in computer systems, may be simply transferred to the new field of application, their adaptation comes with numerous challenges:
• Identification and classification of Smart Grid faults and failures. With grow- ing complexity and interdependency between cyber and physical Smart Grid infrastructures and higher penetration of RES’es, the number of faults
16 1.4 Problem Statement
and disturbances is expected to increase but also new types of faults are expected to appear. This requires to identify and to classify faults and dis- turbances as a part of a comprehensive approach to Smart Grid depend- ability.
• Determination of the efficiency of proactive approach for enhancing system’s
availability. Despite encouraging implementations of proactive approaches in computer systems, there are still no models and figures of merit that would allow to evaluate to what extent, for which failure prediction qual- ity, and under what conditions availability of a system may be enhanced with proactive (predictive) approaches. Such models and metrics must be developed to determine the efficiency of proactive approaches on sys- tem’s availability in general before proposing their application for enhanc- ing Smart Grid’s availability.
• Development of proactive methodology and methods for fault management in
Smart Grids. A proactive fault-management methodology has to be defined for Smart Grids and appropriate methods for prediction and mitigation of disturbances identified. This also includes acquisition of data for prediction algorithm training and evaluation.
• Quantification of power delivery service availability and evaluation of proac-
tive methods. Comparing the existing and the proposed Smart Grid fault- management methods requires their evaluation with respect to availability enhancement that has to be quantified.
In addition to these challenges, the fact that the Smart Grid as a concept is constantly developing and improving brings new problems due to lack of depend- ability standards in Smart Grids and standardized models for system simulation that consider its cyber and physical aspects to allow evaluation of different man- agement approaches. Another difficulty is that the data on disturbances in power grids are rarely, if at all, publicly available.
1.4.2
Goals and Objectives
To tackle these challenges and problems for the sake of achieving the goal of the thesis, we aim at meeting the following objectives that also drive the dissertation work-flow:
1. Propose definitions for Smart Grid dependability attributes, develop taxon-
17 1.4 Problem Statement
This should provide unambiguous communication platform between var- ious Smart Grid communities and, along the lines of the thesis goal, it should also help in identifying new types of faults and proper figures of merit of grid dependability and availability in particular.
2. Develop a model and metrics to evaluate to what extent availability of a sys-
tem may be enhanced with a proactive approach and how this depends on the quality of prediction. The model should be sufficiently general, easily ap- plicable and understandable so as to provide better insights into the effect of a proactive failure management on availability of a system.
3. Define a methodology for proactive management of disturbances in Smart
Grid (and Active Distribution Networks in particular) and identify methods for the implementation. As accurate and effective disturbance prediction is the core of the approach it deserves particular attention. This includes identification of requirements for monitoring infrastructure, identification and adaptation of methods for the selection of features and finally methods for the prediction (e.g. by adopting the ones reviewed in[72]).
4. Develop and implement a simulation environment to synthesize disturbance-
related data that can be used for disturbance analysis and prediction. In the absence of field data, generation of disturbance-related data through sim- ulation is an alternative. In this respect, fault injection, that is used in computer systems for the evaluation of fault-tolerance policies and predic- tion methods [73] may be employed. Simulations must be performed for relevant models (that also need to be defined) and for different settings including different load and generation profiles that simulate system dy- namics, and behavior of the systems in the presence of different types of faults. In this regard, faults identified in the scope of the Objective 1 should be used.
5. Case Study: Implement disturbance predictor and evaluate the approach for
management of voltage sags in Active Distribution Networks. Evaluate the approach defined in the scope of the Objective 3 for the case of prediction of voltage sags in distribution networks in the presence of distributed gen- eration and variable generation and load. Use the framework implemented in the scope of the Objective 4 for the generation of voltage sag data. Iden- tify an appropriate mitigation mean and quantify availability enhancement using the model from the Objective 2.