Showing papers on "Power system simulation published in 2013"

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

Abstract: Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

A Microgrid Energy Management System Based on the Rolling Horizon Strategy

Abstract: A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.

Dynamic Averaged and Simplified Models for MMC-Based HVDC Transmission Systems

Abstract: Voltage-source converter (VSC) technologies are rapidly evolving and increasing the range of applications in a variety of fields within the power industry. Existing two- and three-level VSC technologies are being superseded by the new modular multilevel converter (MMC) technology for HVDC applications. The computational burden caused by detailed modeling of MMC-HVDC systems in electromagnetic transient-type (EMT-type) programs complicates the simulation of transients when such systems are integrated into large networks. This paper develops and compares different types of models for efficient and accurate representation of MMC-HVDC systems. The results show that the use of a specific type of model will depend on the conducted analysis and required accuracy.

Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network

Abstract: In this paper we present a unit commitment model for studying the impact of large-scale wind integration in power systems with transmission constraints and system component failures. The model is formulated as a two-stage stochastic program with uncertain wind production in various locations of the network as well as generator and transmission line failures. We present a scenario selection algorithm for selecting and weighing wind power production scenarios and composite element failures, and we provide a parallel dual decomposition algorithm for solving the resulting mixed-integer program. We validate the proposed scenario selection algorithm by demonstrating that it outperforms alternative reserve commitment approaches in a 225 bus model of California with 130 generators and 375 transmission lines. We use our model to quantify day-ahead generator capacity commitment, operating cost impacts, and renewable energy utilization levels for various degrees of wind power integration. We then demonstrate that failing to account for transmission constraints and contingencies can result in significant errors in assessing the economic impacts of renewable energy integration. Subject classifications: unit commitment; stochastic programming; wind power; transmission constraints. Area of review: Environment, Energy, and Sustainability.

Unified Stochastic and Robust Unit Commitment

Abstract: Due to increasing penetration of intermittent renewable energy and introduction of demand response programs, uncertainties occur in both supply and demand sides in real time for the current power grid system. To address these uncertainties, most ISOs/RTOs perform reliability unit commitment runs after the day-ahead financial market to ensure sufficient generation capacity available in real time to accommodate uncertainties. Two-stage stochastic unit commitment and robust unit commitment formulations have been introduced and studied recently to provide day-ahead unit commitment decisions. However, both approaches have limitations: 1) computational challenges due to the large scenario size for the stochastic optimization approach and 2) conservativeness for the robust optimization approach. In this paper, we propose a novel unified stochastic and robust unit commitment model that takes advantage of both stochastic and robust optimization approaches, that is, this innovative model can achieve a low expected total cost while ensuring the system robustness. By introducing weights for the components for the stochastic and robust parts in the objective function, system operators can adjust the weights based on their preferences. Finally, a Benders' decomposition algorithm is developed to solve the model efficiently. The computational results indicate that this approach provides a more robust and computationally trackable framework as compared with the stochastic optimization approach and a more cost-effective unit commitment decision as compared with the robust optimization approach.

A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch

Abstract: The existence of wind power forecast errors is one of the most challenging issues for wind power system operation. It is difficult to find a reasonable method for the representation of forecast errors and apply it in scheduling. In this paper, a probability distribution model named “versatile distribution” is formulated and developed along with its properties and applications. The model can well represent forecast errors for all forecast timescales and magnitudes. The incorporation of the model in economic dispatch (ED) problems can simplify the wind-induced uncertainties via a few analytical terms in the problem formulation. The ED problem with wind power could hence be solved by the classical optimization methods, such as sequential linear programming which has been widely accepted by industry for solving ED problems. Discussions are also extended on the incorporation of the proposed versatile distribution into unit commitment problems. The results show that the new distribution is more effective than other commonly used distributions (i.e., Gaussian and Beta) with more accurate representation of forecast errors and better formulation and solution of ED problems.

Advanced Algorithms for Wind Turbine Power Curve Modeling

Abstract: A wind turbine power curve essentially captures the performance of the wind turbine. The power curve depicts the relationship between the wind speed and output power of the turbine. Modeling of wind turbine power curve aids in performance monitoring of the turbine and also in forecasting of power. This paper presents the development of parametric and nonparametric models of wind turbine power curves. Parametric models of the wind turbine power curve have been developed using four and five parameter logistic expressions. The parameters of these expressions have been solved using advanced algorithms like genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), and differential evolution (DE). Nonparametric models have been evolved using algorithms like neural networks, fuzzy c-means clustering, and data mining. The modeling of wind turbine power curve is done using five sets of data; one is a statistically generated set and the others are real-time data sets. The results obtained have been compared using suitable performance metrics and the best method for modeling of the power curve has been obtained.

Modeling and Simulation of All-Electric Ships With Low-Voltage DC Hybrid Power Systems

Abstract: DC hybrid power systems are of interest for future low emission, fuel-efficient vessels. In spite of the advantages they offer onboard a ship, they result in a complex, interconnected system, which requires effective analysis tools to enable a full realization of the advantages. Modeling and simulation are essential tools to facilitate design, analysis, and optimization of the system. This paper reviews modeling of hybrid electric ship components including mechanical and electrical elements. Power electronic converters are modeled by nonlinear averaging methods to suit system-level studies. A unified model for bidirectional converters is proposed to avoid transitions between two separate models. A simulation platform using the derived models is developed for the system-level analysis of hybrid electric ships. Simulation results of power sharing among two diesel generators, a fuel cell module, and an energy storage system are presented for three modes of operation.

Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand

Abstract: This work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.

A Chance-Constrained Unit Commitment With an $n-K$ Security Criterion and Significant Wind Generation

Abstract: This paper presents a new approach for the joint energy and reserves scheduling and unit commitment with n-K reliability constraints for the day-ahead market. The proposed method includes a novel n-K criterion where demand must be met with a specified probability under any simultaneous loss of K generating units. A chance-constrained method is proposed with an α-quantile measure to determine the confidence level to meet the demand under K simultaneous contingencies. The chance-constrained optimization problem is recast as a mixed integer linear programming optimization problem. Wind and demand uncertainty are included into the model. The methodology proposed is illustrated with several case studies where the effect of increasing wind power penetration is analyzed showing the performance of our model.

Allocation of Hourly Reserve Versus Demand Response for Security-Constrained Scheduling of Stochastic Wind Energy

Abstract: This paper presents a stochastic method for the hourly scheduling of optimal reserves when the hourly forecast errors of wind energy and load are considered. The approach utilizes the stochastic security-constrained unit commitment (SCUC) model and a two-stage stochastic programming for the day-ahead scheduling of wind energy and conventional units with N-1 contingencies. The effect of aggregated hourly demand (DR) response is considered as a means of mitigating transmission violations when uncertainties are considered. The proposed mixed-integer programming (MIP) model applies the Monte Carlo method for representing the hourly wind energy and system load forecast errors. A 6-bus, 118-bus, and the Northwest region of Turkish electric power network are considered to demonstrate the effectiveness of the proposed day-ahead stochastic scheduling method in power systems.

A decomposition approach to the two-stage stochastic unit commitment problem

Abstract: The unit commitment problem has been a very important problem in the power system operations, because it is aimed at reducing the power production cost by optimally scheduling the commitments of generation units. Meanwhile, it is a challenging problem because it involves a large amount of integer variables. With the increasing penetration of renewable energy sources in power systems, power system operations and control have been more affected by uncertainties than before. This paper discusses a stochastic unit commitment model which takes into account various uncertainties affecting thermal energy demand and two types of power generators, i.e., quick-start and non-quick-start generators. This problem is a stochastic mixed integer program with discrete decision variables in both first and second stages. In order to solve this difficult problem, a method based on Benders decomposition is applied. Numerical experiments show that the proposed algorithm can solve the stochastic unit commitment problem efficiently, especially those with large numbers of scenarios.

Planning Pumped Storage Capacity for Wind Power Integration

Abstract: Pumped storage can provide some of the flexibility that power system operators need to balance load and generation in an uncertain environment, and thus enhance a power system's ability to incorporate wind power. Since the process of balancing wind power involves various combinations of wind generation and loads, the amount of pumped storage capacity needed should be evaluated using a substantial number of scenarios. This paper describes a chronological production simulation platform and its application in planning pumped storage capacity for the Jiangsu (China) provincial power system. The daily dispatching of various types of units is simulated using a unit commitment module. A simulation of wind farm operation is incorporated in this module to take into account the effect of its variability on daily dispatching. A detailed cost model for thermal generating units provides an accurate estimate of the benefits of pumped storage. Simulation results clearly show how much generation cost and wind power curtailment should be expected for different amounts of pumped storage capacity. A comparison between the operating and investment costs is then used to determine the optimal pumped storage capacity. Finally, various sensitivity analyses are performed to assess the effect of key parameters on this optimal capacity.

Modeling and Solution of the Large-Scale Security-Constrained Unit Commitment

Abstract: Security-constrained unit commitment (SCUC), as one of key components in power system operation, is being widely applied in vertically integrated utilities and restructured power systems. The efficient solution framework is to implement iterations between a master problem (unit commitment) and subproblems (network security evaluations). In industrial applications, both Lagrangian relaxation and mixed-integer programming are commonly applied for the unit commitment problem, and both linear sensitivity factor and Benders cut methods are used to generate additional constraints in the phase of network security evaluations. This paper evaluates capabilities and performances of each algorithm through technical discussion and numerical testing. Special topics on the large-scale SCUC engine development are also discussed in this paper, such as input data screening, inactive constrains elimination, contingency management, infeasibility handling, parallel computing, and model simplification. This paper will benefit academic researchers, software developers, and system operators when they design, develop and assess effective models and algorithms for solving large-scale SCUC problems.

Multi-FPGA digital hardware design for detailed large-scale real-time electromagnetic transient simulation of power systems

Abstract: Large-scale electromagnetic transient simulation of power systems in real-time using detailed modelling is computationally very demanding. This study introduces a multi-field programmable gate array (FPGA) hardware design for this purpose. A functional decomposition method is proposed to map FPGA hardware resources to system modelling. This systematic method lends itself to fully pipelined and parallel hardware emulation of individual component models and numerical solvers, while preserving original system characteristics without the need for extraneous components to partition the system. Proof-of-concept is provided in terms of a 3-FPGA and 10-FPGA real-time hardware emulation of a three-phase 42-bus and 420-bus power systems using detailed modelling of various system components and iterative non-linear solution on a 100 MHz FPGA clock. Real-time results are compared with offline simulation results, and conclusions are derived on the performance and scalability of this multi-FPGA hardware design.

Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty

Abstract: The penetration of wind energy sources to power systems has significantly increased in recent years. With variable and uncertain wind power output, the payment and market-clearing price (MCP) may vary in different cases. In this paper, a methodology to quantitatively model the payment cost minimization (PCM) considering the effects of wind power from a probabilistic viewpoint is presented. The autoregressive moving average (ARMA) method with normal distribution of wind forecast error is used to model a time series of wind speed. Based on the wind turbine power curve, the probability distribution of wind power output can be obtained. Then, Monte Carlo simulation (MCS) is used to produce random samples of wind speed, and the genetic algorithm is applied to solve PCM for each sample. The proposed methodology and its solution are verified with simulation studies of two sample systems. The probabilistic distribution results can give consumers an overview of how much they should pay in a probabilistic sense. Further, the simulation results can serve as a lookup table to provide useful input for more refined unit commitment, and also provide a benchmark for future research works on PCM considering wind power.

Simplified Averaged Models of DC–DC Power Converters Suitable for Controller Design and Microgrid Simulation

Abstract: This paper presents simplified nonlinear averaged large-signal and linear small-signal models of the three basic dc-dc converter topologies, boost, buck, and noninverting buck-boost, respectively, operating in peak current-mode control. Models have been derived for the continuous and discontinuous conduction mode. The modeling methodology used is the equivalent current injected method. The derived models have been compared to the existing full-order large-signal nonlinear models and have been found to exhibit simulation time reduction by a few magnitudes in complex distributed power systems, such as today's popular microgrids. The models developed have been experimentally verified on a custom-built 120-W boost converter prototype, showing great accuracy in steady state and in dynamical behavior in all operating points, as determined by the output resistance.

Identifying Wind and Solar Ramping Events

Abstract: Wind and solar power are playing an increasing role in the electrical grid, but their inherent power variability can augment uncertainties in the operation of power systems. One solution to help mitigate the impacts and provide more flexibility is enhanced wind and solar power forecasting; however, its relative utility is also uncertain. Within the variability of solar and wind power, repercussions from large ramping events are of primary concern. At the same time, there is no clear definition of what constitutes a ramping event, with various criteria used in different operational areas. Here, the swinging door algorithm, originally used for data compression in trend logging, is applied to identify variable generation ramping events from historic operational data. The identification of ramps in a simple and automated fashion is a critical task that feeds into a larger work of 1) defining novel metrics for wind and solar power forecasting that attempt to capture the true impact of forecast errors on system operations and economics, and 2) informing various power system models in a data-driven manner for superior exploratory simulation research. Both allow inference on sensitivities and meaningful correlations, as well as quantify the value of probabilistic approaches for future use in practice.

Blackout Model Considering Slow Process

Abstract: In this paper a blackout model that considers the slow process at the beginning of blackouts is proposed based on the improved ORNL-PSerc-Alaska (OPA) model. It contains two layers of iteration. The inner iteration, which describes the fast dynamics of the system, simulates the power system cascading failure, including the tree contact and failure of lines caused by heating. The simulation of protective relays and the dispatching center is also improved to make it closer to practical conditions. The outer iteration, which describes the long-term slow dynamics of the system, adds the simulation of tree growth and utility vegetation management (UVM). Typical blackout process with slow process is simulated on the Northeast Power Grid of China and self-organized criticality (SOC) characteristic of this system is analyzed with the proposed model. The effectiveness of the proposed model is verified by the simulation results.

Empirical characterization and modeling of electrical loads in smart homes

Abstract: Smart meter deployments are spurring renewed interest in analysis techniques for electricity usage data. An important prerequisite for data analysis is characterizing and modeling how electrical loads use power. While prior work has made significant progress in deriving insights from electricity data, one issue that limits accuracy is the use of general and often simplistic load models. Prior models often associate a fixed power level with an “on” state and either no power, or some minimal amount, with an “off” state. This paper's goal is to develop a new methodology for modeling electric loads that is both simple and accurate. Our approach is empirical in nature: we monitor a wide variety of common loads to distill a small number of common usage characteristics, which we leverage to construct accurate load-specific models. We show that our models are significantly more accurate than binary on-off models, decreasing the root mean square error by as much as 8X for representative loads. Finally, we demonstrate two example uses of our models in data analysis: i) generating device-accurate synthetic traces of building electricity usage, and ii) filtering out loads that generate rapid and random power variations in building electricity data.

Balancing System Survivability and Cost of Smart Grid Via Modeling Cascading Failures

Abstract: As a typical emerging application of cyber physical system, smart power grid is composed of interdependent power grid and communication/control networks. The latter one contains relay nodes for communication and operation centers to control power grid. Failure in one network might cause failures in the other. In addition, these failures may occur recursively between the two networks, leading to cascading failures. We propose a k-to- n interdependence model for smart grid. Each relay node and operation center is supported by only one power station, while each power station is monitored and controlled by k operation centers. Each operation center controls n power stations. We show that the system controlling cost is proportional to k. Through calculating the fraction of functioning parts (survival ratio) using percolation theory and generating functions, we reveal the nonlinear relation between controlling cost and system robustness, and use graphic solution to prove that a threshold exists for the proportion of faulty nodes, beyond which the system collapses. The extensive simulations validate our analysis, determine the percentage of survivals and the critical values for different system parameters. The mathematical and experimental results show that smart grid with higher controlling cost has a sharper transition, and thus is more robust. This is the first paper that focuses on on improving smart power grid robustness by changing monitoring strategies from an interdependent complex networks perspective.

Scenario-based security-constrained hydrothermal coordination with volatile wind power generation

Abstract: This paper presents the application of Mixed-Integer Programming (MIP) approach for solving the security-constrained daily hydrothermal generation Scheduling which takes into account the intermittency and volatility of wind power generation, which is called Security-Constrained Wind Hydrothermal Coordination (WHTC). In restructured power systems, Independent System Operators (ISOs) execute the Security-Constrained Unit Commitment (SCUC) program to plan a secure and economical hourly generation schedule for the daily/weekly-ahead market. The objective of security-constrained daily hydrothermal generation scheduling is to determine an optimum schedule of generating units for minimizing the cost of supplying energy and ancillary services with considering network security constraints. The problem formulation includes dynamic ramp-rate constraints for generation schedules and reserve activation, and minimum up-time and down-time of conventional units. Of particular interest in this study are considering more practical constraints and rigorous modeling of thermal and hydro units such as prohibited operating zones and valve loading effects. Furthermore, for the hydro plants, multi performance curve with spillage and time delay between reservoirs are considered. To assess the efficiency and powerful performance of mentioned method, a typical case study based on modified IEEE-118 bus system is investigated and the results are compared to each other in different test system.

Application of probabilistic wind power forecasting in electricity markets

Abstract: This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile-copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two-settlement electricity market with clearing of day-ahead and real-time markets for energy and operating reserves. At the day-ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast-starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real-time dispatch is based on the realized availability of wind power. To evaluate the model in a large-scale real-world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright © 2012 John Wiley & Sons, Ltd.

Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of Illinois

Abstract: Summary form only given. In this paper we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.

INSPIRE: Integrated co-simulation of power and ICT systems for real-time evaluation

Abstract: Future power systems in terms of Cyber - Physical Energy Systems (CPES) apply the integration of IT and physical processes using local and wide area communication networks. The smart grid is a typical example of the application of CPES and poses additional challenges to the engineering as these networks consist of two components: the power system itself and an underlying communication network applied for transmitting monitoring and control information. Therefore, performance evaluations of CPES need to take into account both networks in detail in order to provide meaningful results. In this paper, we introduce our simulation environment INtegrated co-Simulation of Power and ICT systems for Real-time Evaluation (INSPIRE), which is based on the Hybrid Simulator Architecture [1] and capable of evaluating both power system and communication network within a co-simulation framework. Besides the simulator architecture, we detail our time synchronization approach, which is applied for interconnecting communication and power system simulation. Secondly, we present reference scenarios and configuration settings for the combined simulation system. Finally, we introduce the first performance evaluation carried out using INSPIRE, covering characteristics of the communication network and highlighting the retroactive effects on the power system using an exemplary control algorithm.

Probabilistic Spinning Reserve Provision Model in Multi-Control Zone Power System

Abstract: Inter-zonal trading in multi-area power system (MAPS) improves the market efficiency and the system reliability by sharing the resources (energy and reserve services) across zonal boundaries. Actually, each area can operate with less reserve resources than would normally be required for isolated operation. The aim of this work is to propose a model that includes the problem of optimal spinning reserve (SR) provision into the security constraint unit commitment (SCUC) formulation based on the reliability criteria for a MAPS. The loss of load probability (LOLP) and the expected load not served (ELNS) are evaluated as probabilistic metrics in the case of a multi-control zone power system. Moreover, we demonstrate how these criteria can be explicitly incorporated into the market-clearing formulation. The non-coincidental nature of spinning reserve requirement across the zonal boundary is effectively modeled. Two system cases including a small-scale (six-bus) test system and the IEEE reliability test system (IEEE-RTS) are used to demonstrate the effectiveness of the presented model.

Residential demand response: An overview of recent simulation and modeling applications

Abstract: This paper reviews recent simulation and modeling applications of residential demand response including demand response enabled load models, home energy management systems, and multi-agent systems. Demand response implementation in residential sectors is a recent effort to improve efficiency of the electricity market and stability of the power system. The benefits are significant; however the investment and potential risks are nonnegligible. Simulation and modeling is a desirable way to identify and quantify impacts and benefits of demand response applications. The two main aims of these applications are to reduce electricity peak demand and to match the demand with renewable energy. The flexible demand aspect enables time-shift electricity consumption by bringing forward or delaying the use of appliances. Therefore, developing applicable residential load models and efficiency home energy management systems are critical issues to allow incorporation of dynamic electric use patterns. Multi-agent systems allows evaluating various components of further power system or smart grid including distributed generator, microgrid, distribution intelligence, etc..