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Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm.

​© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., A settings-free and derivative-free optimization technique, called Jaya Optimization Algorithm, is investigated in this paper for the design of simple fuzzy PSSs (FPSSs) in a multimachine power system. Performance of this technique during the optimization process is compared to the one exhibited by a Bat Optimization Algorithm based approach. Considering a set of test cases for illustrative purposes, FPSSs tuned with both of these algorithms are applied to the study system and their performance in damping inter-area and local oscillations is analyzed against some commonly used lead-lag structures, including multi-band PSSs.

Power flows exceeding the thermal capacity on power system lines and severe rise on bus voltages are a potential problem in the future distribution grids where development of photovoltaic (PV) installations is expected. This problem could cause damage on components such as transformers and distribution lines provoking triggering the system protections that would eventually lead to system failures. The economic and environmental implications related to reinforcing of the existing network, make this option not the immediate solution for implementation. Installation of battery energy storage systems (BESS) and control of active power curtailment (APC) would be another option to phase the upcoming problems. In this paper a forecast for the year 2035 in a real residential area in Switzerland was investigated, where large amount of PV panels are expected to be installed. This project investigates different solutions based on BESS and APC to deal with the inevitable increase of power flow in the network. The solutions presented here, prevent the overload in a distribution system transformer and avoid excess power flows on the lines. For a given set of demand and PV production curves, simulations in Matlab were performed and computations of optimal power flows were calculated. The solutions explored on this work consider economical aspects such as discounted cash flow method that finds the optimal size of BESS installed. Results also show that combining BESS with curtailment is the most viable solution.

In this work, a new framework for control of power flow of an energy storage is proposed. As part of the framework, an advanced controller for manipulating the power flow of an energy storage system, a photovoltaic (PV) source, and the utility grid is developed. The new controller relies on the Model-based Predictive Control (MPC) concept. The proposed controller realizes an optimal control scheme that maximizes the local self-consumption of renewable energy source. At the same time, the energy consumption supplied by the electric grid is minimized subject to its price posted in the grid. The presented approach is evaluated through computer-aided simulations using data available from a real installation in Switzerland. The data consists of photovoltaic panels, industrial building as a load as well as a battery energy storage system (BESS).

This paper presents an algorithm for determining an optimum size of Energy Storage System (ESS) via the principles of exhaustive search for the purpose of local-level load shifting including peak shaving (PS) and load leveling (LL) operations in the electric power system. An exhaustive search method is employed to perform the ESS capacity (Q ESS ) and power (P ESS ) optimization. The sizing process involves two distinct steps. In the first step the search for a feasible ESS parameter space in which the requirements of PS and LL are fulfilled and in the second step the search for an optimum point in the feasible space with respect to the cost benefit. Finally, the search is expanded to find a set of storage capacity, Q ESS and peak power limits, P limit 's for each month, in order to perform the load shifting throughout a one year. To validate the ESS size optimization, an appropriate model is created for time-domain simulations. The model consists of variable load, a simple state-space ESS model and a rule-based controller which operates the ESS using a set of rules. A number of time-domain simulations were performed to validate the correctness of the ESS size optimization. It appears that the proposed optimization algorithm produces results that meet the requirements in the peak shaving and load leveling operations.

The effectiveness of using both local and remote (wide-area) feedback signals for power oscillation damping (POD) controllers is first demonstrated. The challenge is then to guarantee a minimum level of dynamic performance with only the local signals following a sudden loss of remote signals. A case study on the Nordic equivalent system is presented to show that the closed-loop response could deteriorate if the remote signals are lost. A fault-tolerant control (FTC) design methodology is presented to solve this problem and ensure an acceptable performance level even in case of loss of remote signals. The FTC design methodology is based on simultaneous regional pole-placement for normal and loss of (remote) signals conditions. First the problem is solved non-iteratively using a Linear Matrix Inequality (LMI) approximation and then it is shown that, although this procedure is linear and easy to implement, it has a drawback: the value of one of the control matrices is fixed before calculating the others. An iterative procedure is presented instead to ameliorate this problem and potentially improve the damping of the system. Case studies on the Nordic equivalent system confirm that the proposed iterative fault tolerant controller (FTCit) is able to improve performance against the non-iterative fault tolerant controller (FTC) and produce acceptable performance in case of loss of the remote signals while the response with a CC is unacceptable if a fault occurs.

In this paper is presented a semi-active fault-tolerant control (SAFTC) as an alternative to reconfigurable or self-repairing fault-tolerant architectures with application to power systems. The final goal is to achieve stability and a minimum level of system performance following the completely loss of the feedback signals in the controller, which in this case are remote wide-area signals. The proposed controller is designed under the framework of Linear Matrix Inequalities (LMIs) to achieve pole placement. The semi-active design is compared against and ordinary or non-fault-tolerant control design to highlight that following the loss of a wide-area signal in the control, the closed loop response can lead to instability in the non-fault-tolerant case. The results are validated through nonlinear simulations results using a reduced version of the Nordic power system where the effectiveness of the proposed approach is demonstrated.