Ganesh Kumar Venayagamoorthy

Wide-Area Measurement Based Dynamic Stochastic Optimal Power Flow Control for Smart Grids With High Variability and Uncertainty

Summary form only given. To achieve a high penetration level of intermittent renewable energy, the operation and control of power systems need to account for the associated high variability and uncertainty. Power system stability and security need to be ensured dynamically as the system operating condition continuously changes. A wide-area measurement based dynamic stochastic optimal power flow (DSOPF) control algorithm using the adaptive critic designs (ACDs) is presented in this paper. The proposed DSOPF control replaces the traditional AGC and secondary voltage control, and provides a coordinated AC power flow control solution to the smart grid operation in an environment with high short-term uncertainty and variability. The ACD technique, specifically the dual heuristic dynamic programming (DHP), is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The proposed DSOPF controller dynamically drives the power system to its optimal operating point by continuously adjusting the steady-state set points sent by the traditional OPF algorithm. A 12-bus test power system is used to demonstrate the development and effectiveness of the proposed DSOPF controller.

Intelligent Local Area Signals Based Damping of Power System Oscillations Using Virtual Generators and Approximate Dynamic Programming

This paper illustrates the development of an intelligent local area signals based controller for damping low-frequency oscillations in power systems. The controller is trained offline to perform well under a wide variety of power system operating points, allowing it to handle the complex, stochastic, and time-varying nature of power systems. Neural network based system identification eliminates the need to develop accurate models from first principles for control design, resulting in a methodology that is completely data driven. The virtual generator concept is used to generate simplified representations of the power system online using time-synchronized signals from phasor measurement units at generating stations within an area of the system. These representations improve scalability by reducing the complexity of the system “seen” by the controller and by allowing it to treat a group of several synchronous machines at distant locations from each other as a single unit for damping control purposes. A reinforcement learning mechanism for approximate dynamic programming allows the controller to approach optimality as it gains experience through interactions with simulations of the system. Results obtained on the 68-bus New England/New York benchmark system demonstrate the effectiveness of the method in damping low-frequency inter-area oscillations without additional control effort.

One Step Ahead: Short-Term Wind Power Forecasting and Intelligent Predictive Control Based on Data Analytics

The intelligent integration of wind power into the existing electricity supply system will be an important factor in the future energy supply in many countries. Wind power generation has characteristics that differ from those of conventional power generation. It is weather dependent in that it relies on wind availability. With the increasing amount of intermittent wind power generation, power systems encounter more and more short-term, unpredicted power variations. In the power system, supply and demand must be equal at all times. Thus, as levels of wind penetration into the electricity system increase, new methods of balancing supply and demand are necessary.

Dynamic Energy Management System for a Smart Microgrid

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrids system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

Two-Level Dynamic Stochastic Optimal Power Flow Control for Power Systems With Intermittent Renewable Generation

High penetration of intermittent renewable energy imposes new challenges to the operation and control of power systems. Power system security needs to be ensured dynamically as the system operating condition continuously changes. The dynamic stochastic optimal power flow (DSOPF) control algorithm using the Adaptive Critic Designs (ACDs) has shown promising dynamic power flow control capability and has been demonstrated in a small system. To further investigate the potential of the DSOPF control algorithm for large power systems, a 70-bus test power system with different generation resources, including large wind plants, is developed. A two-level DSOPF control scheme is proposed in this paper to scale up the DSOPF algorithm for this 70-bus system. The lower-level area DSOPF controllers control their own area power network. The top-level global DSOPF controller coordinates the area controllers by adjusting the inter-area tie-line flows. This two-level architecture distributes the control and computation burden to multiple area DSOPF controllers, and reduces the training difficulty for implementing the DSOPF control for a large power network. Simulation studies on the 70-bus power system with large wind variation are shown to demonstrate the effectiveness of the proposed two-level DSOPF control scheme.

Decentralized Asynchronous Learning in Cellular Neural Networks

Cellular neural networks (CNNs), as previously described, consist of identical units called cells that are connected to their adjacent neighbors. These cells interact with each other in order to fulfill a common goal. The current methods involved in learning of CNNs are usually centralized (cells are trained in one location) and synchronous (all cells are trained simultaneously either sequentially or in parallel depending on the available hardware/software platform). In this paper, a generic architecture of CNNs is presented and a special case of supervised learning is demonstrated explaining the internal components of a cell. A decentralized asynchronous learning (DAL) framework for CNNs is developed in which each cell of the CNN learns in a spatially and temporally distributed environment. An application of DAL framework is demonstrated by developing a CNN-based wide-area monitoring system for power systems. The results obtained are compared against equivalent traditional methods and shown to be better in terms of accuracy and speed.

Swarm Intelligence for Transmission System Control

Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems.

Cellular computational networks-A scalable architecture for learning the dynamics of large networked systems

Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application.

Modeling and simulation of hybrid distributed generation and its impact on transient stability of power system

The present grid is accommodating mixed energies resulting into increasing complexities and instabilities. The dynamic performance is measured by also considering the impact the integration of new technologies such as distributed generation (DG) and hybrid distributed generation (HDG) have on the grid. Hybrid distributed generation with one or more renewable (stochastic) energy sources interact with the existing grid during import and export of power generation. This interaction contributes more fault current therefore making the system vulnerable to instability more than a single energy source. This study investigates the dynamic impact of hybrid Wind/ PV/small Hydro power on transient stability. To investigate this impact, a detail modeling of grid connected wind / solar PV and small hydropower system with single machine infinite system is carried out. The simulation was done in DIgSILENT power factory software. The configuration of the proposed typical grid connected hybrid distributed generation (HDG) consists of variable speed Wind turbine with doubly -fed induction generator (DFIG), solar PV and small hydropower system. The wind turbine is integrated through PWM converter into the existing Grid while the solar PV incorporated into the system consists of DC sources integrated through PWM inverter and the hydro power is directly connected through a synchronous generator.

Online coherency analysis of synchronous generators in a power system

In multi-machine power systems, synchronous generators tend to oscillate in several coherent groups, each group being equivalent to a virtual generator. Coherency analysis is fundamental to wide area control of large power systems. Usually, coherency analysis is carried out in an offline mode. However, in response to various events at different operating conditions, the coherent groups may differ. Thus, it is important to develop the analysis to be online. In this paper, K-harmonic means clustering (KHMC) approach is introduced for online analysis. This approach is insensitive to initialization of group centers and is fast. Besides, a second algorithm is developed to automatically determine the optimal number of groups during the online analysis for KHMC. Simulation results are presented using the IEEE 68-bus 16-machine power system. The results indicate that KHMC approach correctly identifies the group centers and assigns each generator to its corresponding group.