Showing papers on "Power system simulation published in 2012"

Robust Unit Commitment With Wind Power and Pumped Storage Hydro

Abstract: As renewable energy increasingly penetrates into power grid systems, new challenges arise for system operators to keep the systems reliable under uncertain circumstances, while ensuring high utilization of renewable energy. With the naturally intermittent renewable energy, such as wind energy, playing more important roles, system robustness becomes a must. In this paper, we propose a robust optimization approach to accommodate wind output uncertainty, with the objective of providing a robust unit commitment schedule for the thermal generators in the day-ahead market that minimizes the total cost under the worst wind power output scenario. Robust optimization models the randomness using an uncertainty set which includes the worst-case scenario, and protects this scenario under the minimal increment of costs. In our approach, the power system will be more reliable because the worst-case scenario has been considered. In addition, we introduce a variable to control the conservatism of our model, by which we can avoid over-protection. By considering pumped-storage units, the total cost is reduced significantly.

Sizing of Energy Storage for Microgrids

Abstract: This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulated as a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper.

A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output

Abstract: In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems.

Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC

Abstract: In this paper, the coordinated integration of aggregated plug-in electric vehicle (PEV) fleets and renewable energy sources (wind energy) in power systems is studied by stochastic security-constrained unit commitment (Stochastic SCUC) model, which minimizes the expected grid operation cost while considering the random behavior of the many PEVs. PEVs are mobile and distributed devices with deferrable options for the supply/utilization of energy at various times and locations. The increased utilization of PEVs, which consume electricity rather than fossil fuel for driving, offers unique economic and environmental opportunities, and brings out new challenges to electric power system operation and planning. The storage capability of PEVs could help power systems mitigate the variability of renewable energy sources and reduce grid operation costs. Vehicle-to-grid (V2G) enables PEVs to have bi-directional power flows once they are connected to the grid, i.e., they can either inject power to, and draw power from, the grid which adds further complexity to power system operations. PEVs signify customers' random behavior when considering their driving patterns, locational energy requirements, topological grid interconnections, and other constraints imposed by the consumers. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of PEVs on the grid operation cost and hourly wind energy dispatch.

AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network

Abstract: With the growing wind power penetration in the emerging power system, an accurate wind power forecasting method is very much essential, to help the system operators, to include wind generation into economic scheduling, unit commitment, and reserve allocation problems. It also assists the wind power producers to maximize their benefits by bidding in the electricity markets. A statistical-based wind power forecasting without using numerical weather prediction (NWP) inputs is carried out in this work. The proposed approach consists of two stages. In stage-I, wavelet decomposition of wind series is carried out and adaptive wavelet neural network (AWNN) is used to regress upon each decomposed signal, to predict wind speed up to 30 h ahead. In stage-II, a feed-forward neural network (FFNN) is used for nonlinear mapping between wind speed and wind power output, which transforms the forecasted wind speed into wind power prediction. The effectiveness of the proposed method is compared with persistence (PER) and new-reference (NR) benchmark models and the results show that the proposed model outperforms the benchmark models.

Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC

Abstract: This paper compares applications of scenario-based and interval optimization approaches to stochastic security-constrained unit commitment (Stochastic SCUC). The uncertainty of wind power generation is considered in this study to compare the two approaches, while other types of uncertainty can be addressed similarly. For the simulation of uncertainty, the scenario-based approach considers the Monte Carlo (MC) method, while lower and upper bounds are adopted in the interval optimization. The Stochastic SCUC problem is formulated as a mixed-integer linear programming (MIP) problem and solved using the two approaches. The scenario-based solutions are insensitive to the number of scenarios, but present additional computation burdens. The interval optimization solution requires less computation and automatically generates lower and upper bounds for the operation cost and generation dispatch, but its optimal solution is very sensitive to the uncertainty interval. The numerical results on a six-bus system and the modified IEEE 118-bus system show the attributes of the two approaches for solving the Stochastic SCUC problem. Several convergence acceleration options are also discussed for overcoming the computation obstacles in the scenario-based approach.

GECO: Global Event-Driven Co-Simulation Framework for Interconnected Power System and Communication Network

Abstract: The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to today's power system. As wide area measurements and control techniques are being developed and deployed for a more resilient power system, the role of communication network is becoming prominent. Power system dynamics gets influenced by the communication delays in the network. Therefore, extensive integration of power system and communication infrastructure mandates that the two systems be studied as a single distributed cyber-physical system. This paper proposes a power system and communication network co-simulation framework (GECO) using a global event-driven mechanism. The accuracy is tunable based on the time-scale requirements of the phenomena being studied. This co-simulation can improve the practical investigation of smart grid and evaluate wide area measurement and control schemes. As a case study, a communication-based backup distance relay protection scheme is co-simulated and validated on this co-simulation framework.

Optimal Power Flow Solution Incorporating Wind Power

Abstract: This paper presents a solution of optimal power flow (OPF) incorporating wind power. A paradigm for modeling the cost of wind-generated electricity from a wind farm is proposed. Based on the Weibull wind speed distribution and wind turbine model represented by function approximation, the frequency distribution of wind farm power output to be the basis for modeling wind generation cost is established via applying Monte Carlo simulation. The proposed wind generation cost model consists of the opportunity cost of wind power shortage and the opportunity cost of wind power surplus, which reflect the cost of dispatching additional reserve capacity and the cost of environmental benefit loss, respectively, and it is integrated into the conventional OPF program. Furthermore, the small signal stability constraints are considered simultaneously as well during optimization. A self-adaptive evolutionary programming method is employed to solve the OPF with wind power involved. A case study is conducted based on the IEEE New England test system (10-Generator-39-Bus) as a benchmark. The simulation results demonstrate the effectiveness and validity of the proposed model and method.

Unified System-Level Modeling of Intermittent Renewable Energy Sources and Energy Storage for Power System Operation

Abstract: The system-level consideration of intermittent renewable energy sources (RES) and small-scale energy storage in power systems remains a challenge as either type is incompatible with traditional operation concepts. Noncontrollability and energy constraints are still considered contingent cases in market-based operation. The design of operation strategies for up to 100% RES power systems requires an explicit consideration of nondispatchable generation and storage capacities, as well as the evaluation of operational performance in terms of energy efficiency, reliability, environmental impact, and cost. By abstracting from technology-dependent and physical unit properties, the power nodes modeling framework presented here allows the representation of a technologically diverse unit portfolio with a unified approach, while establishing the feasibility of energy-storage consideration in power system operation. After introducing the modeling approach, a case study is presented for illustration.

Quantifying Spinning Reserve in Systems With Significant Wind Power Penetration

Abstract: The traditional unit commitment and economic dispatch approaches with deterministic spinning reserve requirements are inadequate given the intermittency and unpredictability of wind power generation. Alternative power system scheduling methods capable of aggregating the uncertainty of wind power, while maintaining reliable and economic performance, need to be investigated. In this paper, a probabilistic model of security-constrained unit commitment is proposed to minimize the cost of energy, spinning reserve and possible loss of load. A new formulation of expected energy not served considering the probability distribution of forecast errors of wind and load, as well as outage replacement rates of various generators is presented. The proposed method is solved by mixed integer linear programming. Numerical simulations on the IEEE Reliability Test System show the effectiveness of the method. The relationships of uncertainties and required spinning reserves are verified.

Commitment and Dispatch With Uncertain Wind Generation by Dynamic Programming

Abstract: Fluctuating wind production over short time periods is balanced by adjusting generation from thermal plants to meet demand. Thermal ramp rates are limited, so increased variation in wind output as wind penetration increases can add to system operating costs because of the need for more thermal operating reserves. Traditional deterministic modeling techniques fail to fully capture these extra costs. We propose a stochastic dynamic programming (SDP) approach to unit commitment and dispatch, minimizing operating costs by making optimal unit commitment, dispatch, and storage decisions in the face of uncertain wind generation. The SDP solution is compared with two other solutions: 1) that of a deterministic dynamic program with perfect wind predictions to find the cost of imperfect information, and 2) that of a simulation model run under a decision rule, derived from Monte Carlo simulations of the deterministic model, to assess the cost of suboptimal stochastic decision making. An example Netherlands application shows that these costs can amount to several percent of total production costs, depending on installed wind capacity. These are the conclusions of a single simplified case study. Nonetheless, the results indicate that efforts to improve wind forecasting and to develop stochastic commitment models may be highly beneficial.

A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output

Abstract: In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems.

Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems

Abstract: Time-domain scheduling simulation is the most effective tool for predicting the operational costs in wind-integrated power systems, because it can represent the inter-temporal constraints that limit the balancing actions of the thermal plant, storage, and demand-side measures. High wind penetrations demand just-in-time commitment decisions that reflect the uncertainties in the wind infeed, so that it is desirable to generate the scheduling decisions using stochastic unit commitment (SUC) with rolling planning. However, the computational burden can make such methods impractical in long simulations. We present an efficient formulation of the SUC problem that is designed for use in scheduling simulations of single-bus power systems. Unlike traditional SUC techniques, the proposed formulation uses a quantile-based scenario tree structure that avoids the need for exogenous operating reserves. We compare the performance of various tree topologies in year-long simulations of a large system. Simple quantile-based trees give statistically significant cost improvements over fixed-quantile deterministic methods and compare favorably with trees based on Monte Carlo-generated scenarios.

Conceptual Design of A Multi-Agent System for Interconnected Power Systems Restoration

Abstract: Outages and faults in interconnected power systems may cause cascading sequences of events, and catastrophic failures of power systems. How to efficiently manage power systems and restore the systems from faults is a challenging research issue in power engineering. Multi-agent systems are employed to address such a challenge in recent years. A centralized coordination strategy was firstly introduced to manage agents in a power system. Such a strategy usually adopts a single central coordinator to control the whole system for system management, maintenance, and restoration purposes. However, disadvantages such as deficiencies in robustness, openness, and flexibility prevent this strategy from extensive online applications. Consequently, a decentralized coordination strategy was proposed to overcome such limitations. But the decentralized coordination strategy cannot efficiently provide a global solution when serious faults spread out in a power system. In this paper, a conceptual multi-agent system design is introduced to express our proposal in power system modeling. A novel dynamic team forming mechanism is proposed to dynamically manage agents in power system with a flexible coordination structure, so as to balance the effectiveness and efficiency of the introduced multi-agent system. The results from simulations of case studies indicate the performance of the proposed multi-agent model.

Large-Scale Transient Stability Simulation of Electrical Power Systems on Parallel GPUs

Abstract: This paper proposes large-scale transient stability simulation based on the massively parallel architecture of multiple graphics processing units (GPUs). A robust and efficient instantaneous relaxation (IR)-based parallel processing technique which features implicit integration, full Newton iteration, and sparse LU-based linear solver is used to run the multiple GPUs simultaneously. This implementation highlights the combination of coarse-grained algorithm-level parallelism with fine-grained data-parallelism of the GPUs to accelerate large-scale transient stability simulation. Multithreaded parallel programming makes the entire implementation highly transparent, scalable, and efficient. Several large test systems are used for the simulation with a maximum size of 9,984 buses and 2,560 synchronous generators all modeled in detail resulting in matrices that are larger than 20, 000 × 20, 000.

A fuzzy inference model for short-term load forecasting

Abstract: This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity./[1] Forecasting is a significant element in economic system performance and its impact on network power control. Load forecasting with the uses of fuzzy implementation is faster and more accurate than conventional load forecasting methods that deal with huge amount of data and the long time needed to be processed/ [1]. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. The proposed fuzzy-based STLF method is applied on a real case study, and the results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes.

A Unified Control Strategy for Electronically Interfaced Distributed Energy Resources

Abstract: This paper proposes a unified control strategy that enables islanded and grid-connected operations of three-phase electronically interfaced distributed energy resources (DERs), with no need for knowing the prevailing mode of operation or switching between two corresponding control architectures. The proposed strategy benefits from both active feedback compensation and the droop method. It allows the employment of the same power circuit and control architecture for the islanded operation as those established and optimized for grid-connected power-electronic converter systems. The proposed strategy can be directly adopted for dispatchable systems (e.g., battery energy storage systems) or, alternatively, it can be embedded in a nested control loop for non-dispatchable systems. This paper presents the mathematical model on which the proposed strategy is based. Further, the effectiveness of the proposed strategy is demonstrated through time-domain simulation of a two-unit test microgrid in the PSCAD/EMTDC software environment.

Digital Hardware Emulation of Universal Machine and Universal Line Models for Real-Time Electromagnetic Transient Simulation

Abstract: Real-time electromagnetic transient simulation plays an important role in the planning, design, and operation of power systems. Inclusion of accurate and complicated models, such as the universal machine (UM) model and the universal line model (ULM), requires significant computational resources. This paper proposes a digital hardware emulation of the UM and the ULM for real-time electromagnetic transient simulation. It features accurate floating-point data representation, paralleled implementation, and fully pipelined arithmetic processing. The hardware is based on advanced field-programmable gate array (FPGA) using VHDL. A power system transient case study is simulated in real time to validate the design. On a 130-MHz input clock frequency to the FPGA, the achieved execution times for UM and ULM models are 2.5 μs and 1.42 μs, respectively. The captured real-time oscilloscope results demonstrate high accuracy of the emulator in comparison to the offline simulation of the original system in the EMTP-RV software.

Optimization-Based Bidding in Day-Ahead Electricity Auction Markets: A Review of Models for Power Producers

Abstract: We review some mathematical programming models that capture the optimal bidding problem that power producers face in day-ahead electricity auction markets. The models consider both price-taking and non-price taking assumptions. The models include linear and non-linear integer programming models, mathematical programs with equilibrium constraints, and stochastic programming models with recourse. Models are emphasized where the producer must self-schedule units and therefore must integrate optimal bidding with unit commitment decisions. We classify models according to whether competition from competing producers is directly incorporated in the model.

Maximum penetration level of wind generation considering power system security limits

Abstract: In this study, a method is proposed for determining the maximum penetration level of wind farms (WF) based on permanent magnet synchronous generators and doubly-fed induction generators into the power system; it takes power system transient stability as well as frequency security criteria into consideration. Considering the probabilistic nature of wind speed, probabilistic transient stability analysis is introduced and applied to determine the maximum penetration level of WFs. The effect of overall system reduced inertia because of increasing capacity of WFs is investigated. A fast approach for deriving system frequency drop after a sudden disturbance is employed, which is suitable for online frequency stability constrained unit commitment incorporating WFs. The feasibility and effectiveness of the proposed method is evaluated using the IEEE 9-bus test system.

A Comparison of Wind Power and Load Forecasting Error Distributions

Abstract: The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slowstarting conventional generators.

Real-Time Power Management of Integrated Power Systems in All Electric Ships Leveraging Multi Time Scale Property

Abstract: All-electric ships (AES), enabled by integrated power systems (IPS), have been pursued for both commercial and military applications to meet the increasing ship-board power demand and environmental sustainability initiatives. They necessitate real-time power management (PM) for dynamic reconfiguration to support system critical operations in the event of dynamic load change or IPS component failures. The nonlinear, large scale trajectory optimization problem associated with IPS, along with the non-analytical nature of IPS model, makes many existing methods inadequate in meeting the real-time requirements. In this paper, we develop a methodology that exploits time scale separation, a characteristic associated with IPS dynamics, to achieve real-time optimization. In parallel, a dynamic model of the IPS with gas turbine and fuel cell as power plants is developed that captures the relevant dynamics but is simple enough for real-time optimization. The tradeoffs between the computational efficiency and optimization accuracy are analyzed. The optimization results for IPS PM on a real-time simulator are reported, which illustrate the real-time feasibility of the proposed optimization strategy.

On the Transient Behavior of Large-Scale Distribution Networks During Automatic Feeder Reconfiguration

Abstract: The paper presents an in-depth analysis of the automatic reconfiguration and self-healing principles of the next generation (3G) smart grid of a real metropolitan distribution network. The large network is to be divided dynamically and remotely controlled into three smaller subnetworks to further increase the reliability of electrical power distribution secondary networks. When one subsection is experiencing difficulties, there is no longer the need to de-energize the entire network. A time-domain (EMTP) model has been developed and validated by comparing simulations with recordings of actual transient events. Different switching and fault scenarios are investigated using this model. Analysis of the results provides important conclusions on equipment rating, relay protection coordination, voltage regulation, switching and operation strategies which are discussed in the paper. A subset of these results is presented for illustration. This extensive study of a complex urban network suggests that: 1) before implementation of smart grid principles, it would be prudent to supplement steady-state analysis with time-domain analysis to avoid problems, such as installation of improperly rated equipment, and improper relay-protection coordination; and 2) EMTP-type programs may be used to conduct the time-domain analysis, despite the enormous number of elements contained in an urban network.

Modeling and optimization of a combined cooling, heating and power plant system

Abstract: In this paper, we develop a modeling and optimization procedure for minimizing the operating costs of a combined cooling, heating, and power (CCHP) plant at the University of California, Irvine, which uses co-generation and Thermal Energy Storage (TES) capabilities. Co-generation allows the production of thermal energy along with electricity, by recovering heat from the generators in a power plant. TES provides the ability to ‘reshape’ the cooling demands during the course of a day, in refrigeration and air-conditioning plants. Therefore, both cogeneration and TES provide a potential to improve the efficiency and economy of energy conversion. The proposed modeling and optimization approach aims to design a supervisory control strategy to effectively utilize this potential, and involves analysis over multiple physical domains which the CCHP system spans, such as thermal, mechanical, chemical and electrical. Advantages of the proposed methodology are demonstrated using simulation case studies.

Semidefinite programming for power system state estimation

Abstract: State Estimation (SE) plays a key role in power system operation and management. For AC power system state estimation, SE is usually formalized mathematically as a Weighted Least Square or Weighted Least Absolute Value problem, and solved by Newton's method. Although computationally tractable, Newton's method is highly sensitive to the initial point, as it is essentially a local search algorithm. In this paper, we propose a Semidefinite Programming (SDP) approach to effectively obtain a good initial state to improve the performance of the existing Newton's method. Our simulation results not only show that the SDP initial guess is much better than the currently used flat start on the IEEE standard bus systems, but also demonstrates approximately globally optimal results, with a lower bound provided in this paper.

Cyber security impacts on all-PMU state estimator - a case study on co-simulation platform GECO

Abstract: Traditional state estimators require longer scan time, leading to delayed, and inaccurate state estimation Given the increased deployment trend of phasor measurement units (PMUs), it is expected that all-PMU state estimation will eventually replace traditional or mixed state estimators at the control centers of power utilities Due to the repeated calibration of the voltage and current transformers at the measurement sites, and direct time-synchronized measurement of phasors, the estimated state by an all-PMU state estimator is not only accurate, but also available at a rapid rate, leading to the use of the system state for protection, stabilization, and even calibration of the measuring devices However, due to high reliance on an advanced communication network infrastructure for the delivery of large amount of measurements in real-time, the cyber attack surface of the power system is increased Deliberate cyber attacks or unintentional network failures can affect the state estimator leading to misoperations of the power system In this paper, we study the cyber security impacts on the all-PMU state estimator, using a power system and data network co-simulation method A linear state estimator for a model of the New England 39-bus system and the corresponding data network is built in a global event-driven co-simulation platform “GECO” which was developed and leveraged for our experimental setup The co-simulation of PSLF (power system simulator) and NS-2 (network simulator) is run with injection of attacks on the network The injected cyber attacks in the form of network failures or malicious data injection are simulated and their effects are observed We also, observe the robustness of the all-PMU state estimator, when the number of affected measurements is below a threshold