José Luis Rueda Torres

Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.

Python Scripting for DIgSILENT PowerFactory: Leveraging the Python API for Scenario Manipulation and Analysis of Large Datasets

The need to set up and simulate different scenarios, and later analyse the results, is widespread in the power systems community. However, scenario management and result analysis can quickly increase in complexity as the number of scenarios grows. This complexity is particularly high when dealing with modern smart grids. The Python API provided with DIgSILENT PowerFactory is a great asset when it comes to automating simulation-related tasks. Additionally, in combination with the well-established Python libraries for data analysis, analysis of results can be greatly simplified. This chapter illustrates the synergic relationship that can be established between DIgSILENT PowerFactory and a set of Python libraries for data analysis by means of the Python API, and the simplicity with which this relationship can be established. The examples presented here show that it can be beneficial to exploit the Python API to combine DIgSILENT PowerFactory with other Python libraries and serve as evidence that the possible applications are mainly limited by the creativity of the user.

Advanced smart grid functionalities based on PowerFactory

Part I: Fundamentals -- Special features of PowerFactory for Smart Grids -- DIgSILENT Simulation Language (DSL) -- DIgSILENT Programming Language (DPL) -- Interfacing PowerFactory with Third-party-Software -- Potentials for Co-simulation and HIL -- Part II: Applications for smart grid planning -- Co-simulation for modelling and simulation of hybrid systems -- Model identification and dynamic equivalencing i. Transmission systems ii. Active distribution networks -- EMT models of RES -- Offshore-onshore grids -- HVDC-HVAC systems -- Dynamic modelling of electric vehicles infrastructure -- Advanced electricity energy storage systems -- Part III: Applications for smart grid operation -- Real-time load measurement and management -- Diagnosis & notification of equipment condition -- Dynamic capability rating -- Fault current limiting in hybrid systems -- Smarter adaptive and enhanced protections -- Automated islanding and restoration -- Wide area monitoring, visualization, & control -- Customer Electricity Use Optimization -- Risk-based security assessment.

Probabilistic Load-Flow Using Analysis Using DPL Scripting Language

Load-flow analysis is an effective tool that is commonly used to capture the power system operational performance and its state at a certain point in time. Power grid operators use load-flow extensively on a daily basis to plan for day-ahead and dispatch scheduling among many other purposes. Also, it used to plan any grid expansion, alter or modernization. However, due to the deterministic nature and its applicability for only one set of operational data at a certain period, deterministic load-flow reduces the chances for predicting the uncertainty in power system. Researchers usually create a data model using probabilistic analyses techniques to produce a stochastic model that mimics the realistic system data. Combining this model with Monte Carlo methodology leads to form a probabilistic load-flow tool that is more powerful and potent to carry on many uncertainty tasks and other aspects of power system assessment. This chapter presents the DIgSILENT PowerFactory script language (DPL) implementation of a DPL script to perform probabilistic power flow (PLF) using Monte Carlo simulations (MCS) to consider the variability of the stochastic variables in the power system during the assessment of the steady-state performance. The developed PLF script takes input data from an external Microsoft Excel file, and then, the DPL can carry on a probabilistic load-flow and export the results using a Microsoft Excel file. The suitability of the implemented DPL is illustrated using the classical IEEE 14 buses.

Generic DSL-Based Modeling and Control of Wind Turbine Type 4 for EMT Simulations in DIgSILENT PowerFactory

In this chapter, to cope with new challenges arising from the increasing level of power injected into the network through converter interfaces, a new wind turbine (WT) as well as a VSC–HVDC control concept, which determines the converter reference voltage directly without the need for an underlying current controller, is presented and discussed. Additionally, alternative options for frequency support by the HVDC terminals that can be incorporated into the active power control channel are presented. The implementation steps performed by using DSL programming are presented for the case of EMT simulations. Simulation results show that the control approach fulfills all the operational control functions in steady state and in contingency situations supporting fault ride through and emergency frequency support, without encountering the problems arising from current injection control.

Introduction to Smart Grid Functionalities

Future power system has several challenges. One of them is the major changes to the way of supply and use energy; building a smarter grid lies at the heart of these changes. The ability to accommodate significant volumes of decentralized and highly variable renewable generation requires that the network infrastructure must be upgraded to enable smart operation. The reliable and sophisticated solutions to the foreseen issues of the future networks are creating dynamically intelligent application/solutions to be deployed during the incremental process of building the smarter grid. The smart grid needs more powerful computing platforms (centralized and dispersed) to handle large-scale data analytic tasks and supports complicated real-time applications. The implementation of highly realistic real-time, massive, online, multi-time frame simulations is required. The objective of this chapter is to present a general introduction to the DIgSILENT PowerFactory, the most important aspects of advanced smart grid functionalities, including special aspects of modelling as well as simulation and analysis, e.g. wide area monitoring, visualization, and control; dynamic capability rating, real-time load measurement and management, interfaces and co-simulation for modelling and simulation of hybrid systems. The chapter presents a very well-documented smart grid functionality, and limited cases are explained: Virtual Control Commissioning: connection to SCADA system via OPC protocol, direct connection to Modbus TCP devices, GIS integration with PowerFactory using API. The explained cases allow showing the full potential of PowerFactory connectivity to fulfil the growing requirements of the smart grids planning and operation.

Intervention scheme based heuristic optimizer for online reactive power management

This paper introduces a metaheuristic Intervention Scheme Based Mean Variance Mapping Optimization (MVMO-IB) algorithm for solving the Optimal Reactive Power Management (ORPM) problem. The intervention scheme, objectives and constraints used in the MVMO-IB have been derived through surveys conducted at transmission system control center and are supported using literature. Contingency analysis feature has also been included in the solver. The solver can search solutions for optimization variables of continuous, discrete and binary types. Simulation based studies are conducted on an 80-bus power system divided into three areas. Evaluations based on convergence speed, optimal solutions and closeness of results obtained through industrialized linear programming optimization solver are provided. Results show that MVMO-IB can be used to solve ORPM in the system operations domain.

Hybrid intervention scheme-based optimization algorithm for real-time management of reactive power resources

Abstract This article introduces a Hybrid Intervention Scheme Based Optimization (HIBO) algorithm solving an Optimal Reactive Power Management (ORPM) problem in real-time using a Mixed Integer Linear Programming (MILP) solver. The ORPM problem presented here contains a linear objective function containing four objectives separated using a set of static penalty factors for each area. The non-linear optimization problem has been assumed linear by localizing the search for solution, this is done by introducing a penalty on the change from the original state or the base case scenario. Thereby, optimizing the non-linear ORPM in linear steps makes it a fast solver for small changes in power system state. A contingency analysis (for N-1 voltage violations) is included for ensuring the safety and reliability of the power system. The results are used to update the ORPM problem or stop if the system is secure. The optimization variables used to represent transformer taps and shunt device switches are handled as discrete integers and remaining variables as continuous real numbers. The intervention scheme, objectives and constraints used in the HIBO have been derived through surveys conducted at a transmission system control center and are supported using literature. Validation of the HIBO algorithm was performed on the Dutch transmission network model after dividing it into four regional areas. Convergence characteristics of the HIBO algorithm are compared using results. From the results, it is concluded that the HIBO algorithm is a competitive optimization solver, suitable for deployment in the secondary voltage control scheme within system operations domain for transmission system operators.