Maria Helena Osório Pestana de Vasconcelos

On-line dynamic security assessment based on kernel regression trees

This paper presents a new approach to perform online dynamic security assessment and monitoring of electric power systems exploiting a statistical hybrid learning technique-kernel regression trees. This technique, besides producing fast security classification, can still quantify, in real-time, the security degree of the system, by emulating continuous security indices that translate the power system dynamic behavior. Moreover it can provide interpretable security structures. The feasibility of this approach was demonstrated in the dynamic security assessment of isolated systems with large amounts of wind power production, like in the Crete island electric network (Greece). Comparative results regarding performances of decision trees and neural networks are also presented and discussed. From the obtained results, the proposed approach showed to provide good predicting structures whose performance stands up to the performance of the two other existing methods.

Automatic Generation Control operation with electric vehicles

This paper presents a methodology to make EV able to contribute to secondary frequency regulation, by participating in Automatic Generation Control (AGC). The aggregator entity is used in this methodology to act as intermediate between the AGC and the EV controllers. The case-study used is an approximation of the Portuguese transmission/generation network, including existing tie lines with Spain, to which a solid three-phase short circuit was applied. After the disturbance, the impact of EV in the AGC operation was studied for two different situations: EV contributing to secondary frequency regulation and EV behaving as uncontrollable loads. The results obtained for both situations were then compared, in terms of interconnection active power flows in the existing tie-lines and its re-establishment time.

Preliminary results from the More Advanced Control Advice Project for secure operation of isolated power systems with increased renewable energy penetration and storage

In this paper, preliminary results from MORE CARE, a European R&D project financed within the Energy Program are described. This project has, as its main objective, the development of an advanced control software system, aiming to optimize the overall performance of isolated and weakly interconnected systems in liberalized market environments by increasing the share of wind energy and other renewable forms, including advanced online security functions. The main features of the control system comprise advanced software modules for load and wind power forecasting, unit commitment and economic dispatch of the conventional and renewable units and online security assessment capabilities integrated in a friendly man-machine environment. Pilot installations of advanced control functions are foreseen on the islands of Crete, Ireland and Madeira.

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.

Simultaneous tuning of power system stabilizers installed in the VSC-based MTDC networks of large offshore wind farms

Using Voltage Source Converter (VSC) based High Voltage Direct Current (HVDC) technologies, in a Multi-terminal dc (MTDC) system, has been envisaged as an attractive solution for connecting the large offshore wind farms that have been planned to meet the EU renewable energy targets. The control and operation of VSC-HVDC technologies comprise a number of challenging tasks, aiming to assure an effective integration of MTDC systems in ac transmission systems. Stability studies play a key role within this framework Among them, small signal stability analysis is required. Therefore, in this paper, modal analysis is performed for assessing small signal stability, in terms of the electromechanical modes of oscillation, considering the combined AC-MTDC system. Also, this paper evaluates the interest of installing classical Power Systems Stabilizer (PSS) in the onshore VSC stations for providing additional damping to the electromechanical modes of oscillation. Simultaneous tuning is performed for adjusting the parameters of these PSS based controllers. For this purpose, an optimization based approach exploiting Evolutionary Particle Swarm Optimization (EPSO) is proposed. Modal analysis and time domain simulations are performed to evaluate the effectiveness of the proposed solutions.

Managing forecast uncertainty in power system security assessment

Accounting for the increasing uncertainties related to forecast of renewables is becoming an essential requirement while assessing the security of future power system scenarios. The FP7 EU project iTesla tackles these needs and reaches several major objectives, including the development of a security platform architecture. In particular, the platform implements a complex stochastic dependence model to simulate a reasonable cloud of plausible “future” states — due to renewable forecast — around the expected state, and evaluates the security on relevant states sampling the cloud of uncertainty. The paper focuses on the proposed model of the uncertainty and its exploitation in power system security assessment process.