Cansin Yaman Evrenosoglu

The focus of this paper is how to account for the impacts of high shares of variable renewable energy sources such as wind farms and solar PVs on transmission grid steady-state operation in long-term planning of reactive power resources. The increased variability of nodal voltages due to the volatility of such sources will require adequate reactive support capability at the point of connection to the grid as well as throughout the grid if traditional reactive power supplies, i.e. conventional generation, are not available either because they are not dispatched at moments of high availability of wind and solar or they are already decommissioned. An optimization-based approach is proposed, namely a multi-period, multi-scenario corrective security-constrained optimal power flow, to identify the optimal size and location of reactive power sources for long-term planning in scenarios with increasing penetration of variable renewable sources. The proposed reactive power planning formulation optimizes the reactive power investments, considering different energy scenarios (multi-scenario) in a given planning horizon in discrete time steps (multi-period). The proposed formulation is demonstrated on a modified IEEE 14-bus system.

This paper investigates the value of bulk energy storage and long-distance interconnectors as enablers towards achieving very high penetration levels of variable renewables. This question is addressed by formulating an optimal grid infrastructure expansion planning problem. It receives input scenarios (such as evolution of generation mix and demand, wind, solar hourly profiles) and computes the optimal investments in cross-region transmission capacity, energy storage and additional generation as well as hourly operational variables, over a 40-year horizon. Illustrative results are presented for selected future scenarios of the European electricity system.

This paper presents a new method for short-term nodal voltage phasor forecasting in power systems to which a large number of renewable resources and microgrids are connected. Its motivation stems from the observation that such systems are characterized by an unconventional topology along with the presence of a number of buses with bursty power injection patterns, which in turn significantly impacts the temporal and spatial correlation of the nodal voltage phasors. Simulation results carried out on three IEEE test systems reveal that this new pattern affects the number of dominant buses, termed electrical hubs, and, subsequently, the time and spatial correlation among nodal voltage angles. They also reveal that the nodal voltage magnitudes exhibit time correlation, but no spatial correlation. In this paper, time series are represented by autoregressive (AR) processes while multivariate time series with spatial correlation are represented by vector autoregressive (VAR) processes. Statistical tests show that an order one for both models is adequate. The non-zero parameters of the VAR(1) models are identified using metrics on electrical connectivity, centrality, and node significance. The good performance of the proposed method is demonstrated on the IEEE 57-, 118-, and 300-bus test systems in presence of large-scale distributed wind farms and microgrids, and under loss of system observability.

Using one circuit of multi-circuit AC overhead lines for HVDC transmission has been proposed to increase the transmission capacity, to minimize system stability problems, and to avoid opening new transmission corridors. This paper presents a formulation to determine the optimal location and power rating of HVDC converters to convert one circuit of a multi-circuit AC line to DC. These configurations, where AC and DC circuits share the same tower, can also be referred to as `AC-DC hybrid lines.' The proposed method is similar to the formulation of one-stage transmission expansion planning. This problem is transformed to a mixed-integer linear programming by exploiting `disjunctive modelling.' In addition, the proposed formulation takes into account the investment cost of HVDC converters, cost of reconfiguration of existing AC circuits to DC, energy and reserve provision costs, and N-1 security criterion. This optimization model allows the system operator to investigate the impacts of HVDC lines on transmission capacity increase, and power flow controllability. The advantages of two different hybrid configurations are investigated. The proposed deterministic optimization model is effectively applied to the IEEE RTS 24-bus system.

This paper proposes a a new method for 1-hour state forecasting in electrical power systems with high intermittent wind energy penetration. The proposed method has the potential to be utilized as an auxiliary function in Energy Management System (EMS). The method utilizes Markov Models (MM), the Viterbi Algorithm (VA) and fuzzy logic. The proposed auxiliary function creates a predictive database for the power system states using the historical states as well as wind and load forecasts. The predictive database can be utilized to provide pseudo measurements to a static linear state estimator in the case of loss of observability, loss of power system communication network or cyber-attacks. Simulations for the IEEE 14-bus system using available load and wind data from the Bonneville Power Administration (BPA) websites show good correlation between the predictions and the actual data.

A paradigm shift in the renewable energy proliferation in the U.S. necessitates a paradigm shift in power system operations to accommodate large-scale intermittent power while keeping the grid reliable and secure. Energy management systems (EMS) will benefit from an auxiliary function, which integrates the wind and load forecasting to state estimation and forecasting. This auxiliary function will create a predictive database for the power system states using the historical states as well as wind and load forecasts. The predictive database can be utilized to provide pseudo-measurements to a static state estimator in the case of loss of observability and bad data processing, or it can be used for short-term congestion and ramping predictions. This paper proposes an auxiliary tool for look-ahead power system state forecasting in electrical power systems with high intermittent renewable energy penetration. The method utilizes Markov models (MMs) and the Viterbi algorithm (VA) with a grid of feasible power system states obtained and updated by using the past states. The proposed algorithm is evaluated on the IEEE 14-bus and 118-bus systems using wind and load data available from the Bonneville Power Administration (BPA). The results show good correlation between the predictions and the actual power system states.

This paper presents a machine learning-based faulty-line identification method in smart distribution networks. The proposed method utilizes postfault root-mean-square (rms) values of voltages measured at the main substation and at selected nodes as well as fault information obtained by fault current identifiers (FCIs) and intelligent electronic re-closers (IE-CRs). The information from FCIs and IE-RCs are first used to identify the faulty region in the network. The normalized rms values of voltages are then utilized as the input to the support vector machine (SVM) classifiers to identify the faulty-line according to the pre-determined fault type. The IEEE 123-node distribution test system is simulated in ATP software. MATLAB is used to process the simulated transients and to apply the proposed method. The performance of the method is tested for different fault inception angles (FIA) and different fault resistances with satisfactory results.

This paper presents a traveling-wave-based method for fault classification and localization for three-terminal power transmission systems. In the proposed method, the discrete wavelet transform is utilized to extract transient information from the recorded voltages. Support-vector-machine classifiers are then used to classify the fault type and faulty line/half in the transmission networks. Bewley diagrams are observed for the traveling-wave patterns and the wavelet coefficients of the aerial mode voltage are used to locate the fault. Alternate Transients Program software is used for transients simulations. The performance of the method is tested for different fault inception angles, different fault resistances, nonlinear high impedance faults, and nontypical faults with satisfactory results.