Application Of Energy Management System Research Articles

Employment of Corrective Switching in Congestion Management for Eliminating Multiple Overloads in Power Systems

There are many papers proposing Corrective Switching Technique for eliminating overloads in electrical branches, but most of them consider just one overloaded branch. However, considering the increasing demand of the power system, contingencies in a branch may cause multiple overloads in electrical grid. This way, the aim of this paper is to present a methodology to systematize the use of Corrective Switching in congestion management for eliminating multiple overloads in power system. For congestion management, two methodologies will be employed for supporting Corrective Switching Technique: Relief Function and Loading Characteristics. These will provide the background of the algorithm to be used in the power system for eliminating the overloads. It is important to mention that the configuration can be obtained quickly, since the calculations for decision support are linear, requiring just a low computational effort. This way the on-line application in Energy Management Systems becomes feasible.

계통운영시스템 계통해석 프로그램 정확도 향상에 관한 연구

This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

Model-based predictive control of office window shades

In the automation of interior window shading devices, a control system that relies on a prediction of environmental conditions and a building's thermal response can provide savings to space-conditioning loads beyond what can be achieved using a reactive approach. The development of these control strategies can be difficult because of the uniqueness of each building. A simplified model-based predictive control (MPC) method for window shades is proposed. To this end, a control-oriented model representing the heat transfer problem in a perimeter office space was developed. The parameters of the model were estimated using the ensemble Kalman filter (EnKF). The energy-savings potential of the EnKF-based MPC approach for window shades was investigated using EnergyPlus simulations. This was accomplished by implementing the control-oriented model into the energy management system application of EnergyPlus. Simulations were conducted to assess the energy saving potential of using the EnKF-based MPC for roller blinds in a south-facing perimeter office space in Ottawa, Canada. The simulation-based results indicate the potential for about 35% reduction in electricity usage for space conditioning over manually operated interior roller blinds.

Development of an occupancy learning algorithm for terminal heating and cooling units

A significant portion of the North American workforce reports having the ability to alter their daily arrival and departure times for work. As a result, personal preferences translate into individual occupancy profiles. To accommodate these diverse personal schedules, building operators tend to use conservatively short temperature setback periods. In this paper, a year's worth of data gathered by motion sensors placed in private offices in an academic building were analyzed. The predictability of the recurring occupancy patterns was assessed. Drawing upon this, an adaptive occupancy-learning control algorithm which learns the arrival and departure times recursively and adapts the temperature setback schedules accordingly, was developed. Later, the algorithm was implemented in the Energy Management System (EMS) application of the building performance simulation (BPS) tool EnergyPlus. Simulations conducted with this tool indicate that a 10–15% reduction in the space heating and cooling loads can be achieved by applying individual and dynamically evolving temperature setback periods.

계통운영시스템 계통해석 프로그램 검증 방안에 관한 연구

Network analysis applications in energy management systems play a key role in the economic and reliable operation of power systems. In order to provide operators with useful network information, the accurate results of topology processing, state estimation, power flow, and contingency analysis must be simulated. This paper proposes a validation methodology of network analysis applications in energy management systems. The energy management systems was checked to ensure that it meets the originally specified functions based on the proposed methodology. In addition, the performance of state estimation is evaluated with the reference of the proposed methodology. The proposed methodology is being conducted by energy management systems and the Korean power systems have been utilized for the test systems.

Adaptive three-phase power-flow solutions for smart grids with plug-in hybrid electric vehicles

Abstract This paper introduces an adaptive three-phase power flow method for smart grids with plug-in hybrid electric vehicles (PHEVs). The proposed method is developed based on the loop frame of reference. The operations of smart grids are inherently unbalanced because of the incomplete three-phase feeder arrangements, nonsymmetrical conductor spacing of three-phase underground cables and overhead lines, unbalanced loads, and a variety of distributed energy resources (DERs). Therefore, the proposed method was developed based on the actual phase (a–b–c) frame, rather than the sequence-component frame. To adapt the network topologies of smart grids, two solution strategies are used, one for radial smart grids and the other for non-radial smart grids. To demonstrate the validity and capability of the proposed algorithm, four IEEE feeder systems and an actual Taiwan Power Company (Taipower) distribution system are used as benchmarks for comparison purposes. The test results show that the proposed method is accurate, efficient, and adaptable, and it therefore has good potential for smart grid energy management system (EMS) applications.

Power System Dynamic State Estimation With Synchronized Phasor Measurements

The dynamic state estimation (DSE) applied to power systems with synchrophasor measurements would estimate the system's true state based on measurements and predictions. In this application, as phasor measurement units (PMUs) are not deployed at all power system buses, state predictions would enhance the redundancy of DSE input data. The significance of predicted and measured data in DSE is affected by their confidence levels, which are inversely proportional to the corresponding variances. In practice, power system states may undergo drastic changes during hourly load fluctuations, component outages, or network switchings. In such conditions, the inclusion of predicted values could degrade the power system state estimation. This paper presents a mixed-integer programming formulation of DSE that is capable of simultaneously discarding predicted values whenever sudden changes in the system state are detected. This feature enhances the DSE computation and will not require iterative executions. The proposed model accommodates system-wide synchronized measurements of PMUs, which could be of interest to smart grid applications in energy management systems. The voltage phasors at buses without PMUs are calculated via voltage and current measurements of adjacent buses, which are referred to as indirect measurements. The guide to the expression of uncertainty in measurement is used for computing the confidence level of indirect measurements based on uncertainties associated with PMU measurements as well as with transmission line parameters. Simulation studies are conducted on an illustrative three-bus example and the IEEE 57-bus power system, and the performance of the proposed model is thoroughly discussed.

An Alternative Method for Power System Dynamic State Estimation Based on Unscented Transform

An efficient, timely, and accurate state estimation is a prerequisite for most energy management system (EMS) applications in power system control centers. The emerging wide-area measurement systems (WAMSs) offer new opportunities for developing more effective methods to monitor power system dynamics online. Recently, alternative methods for power system state estimation have caught much attention. Due to the nonlinearity of state transition and observation equation, linearization and Jacobian matrix calculation are indispensible in the existing methods of power system state estimation. This makes WAMS' high performance compromised by burdensome calculation. In order to overcome the drawbacks, this study tries to develop an effective state estimation method without the linearization and Jacobian matrix calculation. Firstly, unscented transformation is introduced as an effective method to calculate the means and covariances of a random vector undergoing a nonlinear transformation. Secondly, by embedding the unscented transformation into the Kalman filter process, a method is developed for power system dynamic state estimation. Finally, some simulation results are presented showing accuracy and easier implementation of the new method.

Potential of General Purpose Graphic Processing Unit for Energy Management System

This paper investigates the potential of General Purpose Graphic Processing Unit (GPGPU) for the server and HMI parts of Energy Management System (EMS). The HMI investigation focuses on the applicability and performance improvement of GPGPU for scattered data interpolation algorithms typically used to visually represent the overall state of a power network. The server side investigation focuses on fine grain parallelization of EMS applications by targeting the sparse linear solver. The different performance evaluations show the high potential of GPGPU for the HMI part with a speedup factor up to 100 at the cost of acceptable approximations, while the benefit on the server side varies from a speedup factor of up to 300 to 0 depending on the application.

Genetic Algorithm‐Based Artificial Neural Network for Voltage Stability Assessment

With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system′s proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance‐based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6‐bus and IEEE 30‐bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.

Measurements get together

This article deals with the synchronized measurement technology that has the potential of becoming the backbone for real-time monitoring system. The secure and reliable operation of modern power systems is an increasingly challenging task due to the ever-increasing demand for electricity, the growing number of interconnections, penetration of variable renewable energy sources, and deregulated energy market conditions. Power companies in different parts of the world are therefore feeling the need for a real-time wide area monitoring, protection, and control (WAMPAC) system. Synchronized measurement technology (SMT) has the potential of becoming the backbone of this system. The major advantages of using SMT are that 1) the measurements from widely dispersed locations can be synchronized with respect to a Global Positioning System (GPS) clock, 2) voltage phase angles can be measured directly, which was so far technically infeasible, and 3) the accuracy and speed of energy management system (EMS) applications increases manifold.

Fast voltage contingency analysis of power systems using model trees

This paper presents a model tree (MT) based approach for fast voltage contingency ranking, of most severe contingencies for online applications in energy management systems. To measure the severity of contingencies, a bus voltage violation based scalar performance index is used. Model trees are developed to estimate the real value of voltage performance index (VPI) and rank the single line outage contingencies, according to their severity. Full AC power flow is performed, to generate the training and testing patterns for the proposed MT under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, the MT method gives fast and accurate ranking of contingencies for unknown load patterns, hence it is suitable for online applications in energy management systems.

A Hybrid Decision Tree Model for Fast Voltage Contingency Screening and Ranking

This paper presents a new decision tree (DT) based approach for fast voltage contingency screening and ranking for on-line applications in energy management systems. The hybrid decision tree model is developed to learn all the selected contingencies simultaneously, therefore fewer DTs are required. To reduce the size and improve the accuracy of the decision tree, the K-class problem is converted into the set of K two-class problems, and separate decision tree modules are trained for each of the two class problems. All the selected contingencies are presented to the filter module, which is trained to separate them in critical and non-critical contingency classes, which reduces the burden on ranking modular DT. The critical contingencies screened out by the filter module are presented to the ranking modular decision tree for their further ranking. To measure the severity of contingencies, bus voltage violation based scalar performance index is used. Full AC load flow is performed to generate the training and testing patterns for the proposed hybrid decision tree, under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, a hybrid decision tree method gives fast and accurate screening and ranking of contingencies for unknown load patterns.

SMP計算機を用いた系統制御システムの高速性とリアルタイム性の実現

High performance and real-time capability are very important for control systems including load dispatching office systems with energy management system (EMS) functions. Parallel computing and very large memory (VLM) introduced by general purpose symmetric multi-processor (SMP) computers are required to achieve high-performance computing for EMS applications. However, a performance decline was observed on concurrent execution of EMS applications in our early evaluation. VLM may cause non-preemption and I/O inversion problems that interfere with real-time processing. The main issues addressed in this paper are: (1) adoption guidelines of SMP computers to achieve high performance and (2) design guidelines to realize real-time capability with VLM environment from practical points of view. The verification results show that performance can be improved by 2 times by selecting SMP computers appropriately and that real-time processing can be realized by the guidelines. The results of this study are used in the newly developed load dispatching office system that has been in practical operation.

00/01908 Comparison of feature selection techniques for ANN-based estimation

Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating on-line voltage stability condition of a power system. In this paper, an approach based on parallel self-organizing hierarchical neural network is presented to predict a maximum loadability margin which is an indication of the power system’s proximity to voltage collapse. Parallel self-organizing hierarchical neural network (PSHNN) are multi-stage neural networks in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used, along with forward–backward training of stage neural networks. Input features for PSHNN are selected on the basis of entropy concept in one method while in the other method, real and reactive loads at critical buses are considered as the inputs for PSHNN. The proposed PSHNN based methods are compared by estimating the maximum loadability margin at different loading conditions in IEEE 30-bus and a practical 75-bus Indian system. Entropy based PSHNN learns faster, at the same time it provides more accurate loadability margin estimation as compared to that based on critical buses. It is found to be suitable for on-line applications in energy management systems.

Estimation of loadability margin using parallel self-organizing hierarchical neural network

Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating on-line voltage stability condition of a power system. In this paper, an approach based on parallel self-organizing hierarchical neural network is presented to predict a maximum loadability margin which is an indication of the power system’s proximity to voltage collapse. Parallel self-organizing hierarchical neural network (PSHNN) are multi-stage neural networks in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used, along with forward–backward training of stage neural networks. Input features for PSHNN are selected on the basis of entropy concept in one method while in the other method, real and reactive loads at critical buses are considered as the inputs for PSHNN. The proposed PSHNN based methods are compared by estimating the maximum loadability margin at different loading conditions in IEEE 30-bus and a practical 75-bus Indian system. Entropy based PSHNN learns faster, at the same time it provides more accurate loadability margin estimation as compared to that based on critical buses. It is found to be suitable for on-line applications in energy management systems.

Application of Variable-Structure Control to Air-Conditioning Systems

Abstract A modern discontinuous control strategy, based on variable-structure systems control, is proven to be vastly more efficient than the commonly used (optimal) on-off control without the need for additional hardware. It may be acheived, however, on the expence of a higher actuator switching frequency. A wider application in energy control and management systems is suggested.

Network applications in energy management systems

Four steady-state network applications are described: state estimation, security analysis, power flow, and optimal power flow. Unlike other energy management system (EMS) applications, they depend on power system network models. They may execute in EMS real-time and study environments, and also operator training simulators. The intent is to provide guide to current network application practice and implementation. >

New Computer Configuration and Major Software Redesign For Ontario Hydro's Energy Management System

Ontario Hydro is relocating its existing System Control Centre (SCC) and installing a new Energy Management System (EMS), which includes a distributed configuration for the computer system in order to meet more rigorous reliability requirements and utilize new technological advances. This paper will discuss the rationale for the distributed system, prioritization of the EMS application programs, and the acquisition/distribution of power system data. Failover strategy is provided to ensure high availability for the high priority application programs. An Integrated System Console which provides easy control of the distributed systems is discussed. Two essential software tools for the EMS environment, namely Alarm Management and Man/Machine software, are briefly discussed.

An operations-oriented formulation and solution to the load flow problem

This paper presents an operations-oriented formulation and solution to the load flow problem. In this approach all the unit real power outputs are assumed specified and the objective is to calculate the bus loads. This fundamental change is useful in some power system problems and is also closer to the actual problem facing a load flow user. Different solution approaches, including a decoupled method, are proposed to solve the above problem. The numerical and analytical properties of this new decoupled load flow approach are similar to those of the conventional decoupled method. The main advantages of this proposed approach are: all the bus generations can be specified, a slack or swing bus need not be defined, and losses are independent of a choice of reference bus. This fundamental change in the load flow formulation has various applications. These include transmission loss evaluation, loss penalty factor calculation, and study applications in energy management systems.