Showing papers on "Power system simulation published in 2020"

A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

Abstract: The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

Modeling Frequency Dynamics in Unit Commitment With a High Share of Renewable Energy

Abstract: The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency security margin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin.

Controller-Embeddable Probabilistic Real-Time Digital Twins for Power Electronic Converter Diagnostics

Abstract: In this article, an approach is proposed for the online diagnostic analysis of power electronic converters utilizing real-time, probabilistic digital twinning. Under this approach, a digital twin (DT) of a power converter is defined as a real-time, probabilistic simulation model with stochastic (random) variables, developed using generalized polynomial chaos expansion. The DT models are partitioned in perspective of control layers for power converter subsystems in the approach, with emphasis on the application and converter control layers. Real-time executed solvers of these divided probabilistic DT models are embedded into power converter controllers running on field programmable gate array (FPGA) computing devices. Using a monitoring system, the real-time probabilistic DTs at each control layer and the corresponding physical twins of the power converter are compared by the controllers to determine if the power converter is operating within probable behavior. Knowing the large computational cost of probabilistic modeling, the resource usage and timing of real-time DT solvers on modern FPGAs is reported for common power electronic converter topologies, showing the approach is feasible and able to perform probabilistic real-time simulation of smaller power converters in perspective of application and converter control layers using $\leq 2\; \mu s$ time steps, with as low as $\text{70}\;{\rm ns}$ steps; while staying embeddable within FPGA-based controllers. To highlight the capabilities of the proposed approach, a case study is presented using a probabilistic DT in the application layer controller of a pair of converters to comparatively monitor their behavior and corresponding controller action under hardware-in-the-loop testing.

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Abstract: Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming ...

Minimax-Regret Robust Co-Optimization for Enhancing the Resilience of Integrated Power Distribution and Natural Gas Systems

Abstract: Natural gas system that supplies flexible and distributed energy resources can play a significant role in enhancing the resilience of the power distribution system. In this paper, a minimax-regret robust resilience-constrained unit commitment model is proposed for enhancing the resilience of the integrated power distribution and natural gas system (IDGS). First, a tri-level robust cooperation optimization problem is formulated based on the minimax-regret robust criterion to enhance the distribution system resilience against worst N-k contingencies. The multi-stage extreme weather model is considered to obtain the spatial dynamics of extreme weather. Then, the proposed robust model is reformulated as a mixed-integer convex programming model by relaxing nonconvex power and natural gas flow equations into second-order conic (SOC) constraints. The SOC-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust optimization problem. The effectiveness of proposed robust optimization model is validated using several case studies applied to the 33-bus-6-node and 123-bus-20-node IDGSs.

Flexible Operation of Retrofitted Coal-Fired Power Plants to Reduce Wind Curtailment Considering Thermal Energy Storage

Abstract: The rapid expansion of wind power has triggered significant wind curtailment because the power system lacks flexibility to deal with the uncertainty and variability of wind power. The operational flexibility of coal-fired power plants is limited by the minimum stable firing rate in the boiler. Steam extraction and thermal energy storage could enable power output adjustment without changing the firing rate in the boiler. Thus, retrofitting existing coal-fired power plants with steam extraction and thermal energy storage is a promising option to accommodate the high penetration of wind power in the power system, especially in coal-dominated power system. This study investigated the operational flexibility of coal-fired power plants retrofitted with steam extraction and thermal energy storage. First, a linear operation model is proposed for retrofitted coal-fired power plants considering new characteristics and technical constraints. Second, a simulation framework based on stochastic unit commitment and rolling economic dispatch is developed to explore the benefits of steam extraction and thermal energy storage in wind-integrated power systems. The forecasted wind power data is used in stochastic unit commitment to evaluate start-up and shut-down cost, while real wind power data is adopted in rolling economic dispatch to calculate the actual production cost. The simulation results on the modified IEEE 24-bus system demonstrate the effectiveness of retrofitting coal-fired power plants with steam extraction and thermal energy storage for mitigating wind curtailment.

Distributionally Robust Unit Commitment in Coordinated Electricity and District Heating Networks

Abstract: Coordinated operations of electricity and district heating networks offer a potential for mitigating inherent variability of renewable energy sources (RES) in the ongoing transition to smart grids. This paper proposes a two-stage distributionally robust optimization (DRO) approach to determine the optimal day-ahead unit commitment in coordinated electricity and district heating networks with variable RES power output. The proposed formulation is to minimize the worst-case expected total cost over an ambiguity set comprising a family of probability distributions with given support and moments of RES power output. As such, the proposed DRO approach can overcome the limitations of stochastic programming in its inherent dependence of exact probability distributions along with a huge computational burden, but also becomes less conservative than classical robust optimization. The pertinent DRO model is eventually reformulated as a tractable mixed-integer second-order cone (SOC) programming after employing linear decision rules and the SOC duality. Simplified affine policies are utilized to further improve computational tractability and performance. Finally, case studies are conducted based on Barry Island electricity and district heating networks. The numerical results demonstrate the decision-making superiority of the proposed method as compared with deterministic, stochastic programming, and robust optimization approaches. They also validate the computational improvement of the proposed approach by employing simplified affine policies.

Distributed $Q$ -Learning-Based Online Optimization Algorithm for Unit Commitment and Dispatch in Smart Grid

Abstract: Economic dispatch (ED) and unit commitment (UC) problems need to be revisited in order to make a transition from a traditional power system to a smart grid. In this paper, we formulate the ED and UC problems into a unified form, which is also capable of characterizing the infinite horizon UC problem. Based on the formulation, a centralized $Q$ -learning-based optimization algorithm is proposed. The proposed algorithm runs in an online manner and requires no prior information on the mathematical formulation of the actual cost functions, thus being capable of dealing with situations for which such cost functions are too difficult to obtain. Then, the distributed counterpart of the centralized algorithm is developed by relaxing the demand for global information and balancing exploration and exploitation cooperatively in a distributed way. Theoretical analysis of the proposed algorithms is also provided. Finally, several case studies are presented to demonstrate the effectiveness of the proposed algorithms.

Review on the cost optimization of microgrids via particle swarm optimization

Abstract: Economic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.

Integrated Electricity– Heat–Gas Systems: Techno–Economic Modeling, Optimization, and Application to Multienergy Districts

Abstract: Multienergy systems (MES) can optimally deploy their internal operational flexibility to use combinations of different energy vectors to meet the needs of end-users and potentially support the wider system. Key relevant applications of MES are multienergy districts (MEDs) with, for example, integrated electricity and gas distribution and district heating networks. Simulation and optimization of MEDs is a grand challenge requiring sophisticated techno–economic tools that are capable of modeling buildings and distributed energy resources (DERs) across multienergy networks. This article provides a tutorial-like overview of the state-of-the-art concepts for techno–economic modeling and optimization of integrated electricity–heat–gas systems in flexible MEDs, also considering operational uncertainty and multiple grid support services. Relevant mixed integer linear programming (MILP) formulations for two-stage stochastic scheduling of buildings and DER, iteratively soft-coupled to nonlinear network models, are then presented as the basis of a practical network-constrained MED energy management tool developed in several projects. The concepts presented are demonstrated through real-world applications based on The University of Manchester MED case study, the details of which are also provided as a testbed for future research.

Data-Driven Screening of Network Constraints for Unit Commitment

Abstract: The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant problems solved by independent system operators for the daily operation of power systems. Given its computational complexity, this problem is usually not solved to global optimality for real-size power systems. In this paper, we propose a data-driven method that leverages historical information to screen out network constraints in the TC-UC problem. First, past data on demand and renewable generation throughout the network are used to learn the congestion status of transmission lines. Then, we infer the lines that will not become congested for upcoming operating conditions based on such learning and disregard their capacity constraints. This way, we formulate a reduced TC-UC problem that is easier to solve. Numerical results on a medium- and a large-size power system show that the proposed approach outperforms existing ones by significantly reducing the computational time while obtaining solutions that are equal or close to the one obtained with the original TC-UC problem. Furthermore, the purely data-driven method we propose can be seamlessly complemented with a constraint generation procedure to guarantee that the optimal solution to the original TC-UC problem is eventually recovered.

Resilience Constrained Day-Ahead Unit Commitment Under Extreme Weather Events

Abstract: Over the last years, extreme weather events have caused extensive damages in power systems, leaving millions of customers without electricity and therefore highlighting the necessity to enhance power system resilience. This paper proposes a resilience constrained day-ahead unit commitment framework for increasing resiliency of a power system exposed to an extreme weather event. The weather-dependent failure probabilities of the transmission lines are taken into account in order to decide the scheduling of generators that minimizes load shedding in the most efficient way, while respecting operating limits of the system. The problem is formulated as a tri-level optimization problem that is transformed to a bi-level problem using duality theory and linearization techniques. The problem is solved as a two-stage robust optimization problem using a Column & Constraint Generation based decomposition algorithm. The master problem provides the unit commitment and the subproblem identifies the worst damage scenario due to weather event. A Sequential Monte Carlo simulation of a modified IEEE Reliability Test System and IEEE 118-bus System is applied to illustrate and validate the effectiveness of the proposed framework.

A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

Abstract: The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

Power-Based Generation Expansion Planning for Flexibility Requirements

Abstract: Flexibility requirements are becoming more relevant in power system planning due to the integration of variable Renewable Energy Sources (vRES). In order to consider these requirements Generation Expansion Planning (GEP) models have recently incorporated Unit Commitment (UC) constraints, using traditional energy-based formulations. However, recent studies have shown that energy-based UC formulations overestimate the actual flexibility of the system. Instead, power-based UC models overcome these problems by correctly modeling ramping constraints and operating reserves. This paper proposes a power-based GEP-UC model that improves the existing models. The proposed model optimizes investment decisions on vRES, Energy Storage Systems (ESS), and thermal technologies. In addition, it includes real-time flexibility requirements, and the flexibility provided by ESS, as well as other UC constraints, e.g., minimum up/down times, startup and shutdown power trajectories, network constraints. The results show that power-based model uses the installed investments more effectively than the energy-based models because it more accurately represents flexibility capabilities and system requirements. For instance, the power-based model obtains less investment (6–12%) and yet it uses more efficiently this investment because operating cost is also lower (2–8%) in a real-time validation. We also propose a semi-relaxed power-based GEP-UC model, which is at least 10 times faster than its full-integer version and without significantly losing accuracy in the results (less than 0.2% error).

Do unit commitment constraints affect generation expansion planning? A scalable stochastic model

Abstract: Due to increasing penetration of stochastic renewable energy sources in electric power systems, the need for flexible resources especially from fast-start conventional generation units (e.g., combined cycle gas turbine plants) is growing. The fast-start conventional units are being operated more frequently in order to respond to the variability and uncertainty of stochastic generation. This raises two important technical questions: as it is common in the literature, is it still an appropriate simplification to ignore the operational unit commitment (UC) constraints of conventional units within the generation expansion planning optimization? And if not, which UC constraint impacts most the expansion planning outcomes? To answer these questions, this paper aims at measuring the planning inefficiency (i.e., the underestimation of need for new generation capacity) caused by ignoring each UC constraint. To this purpose, we develop a centralized network-constrained generation expansion planning model incorporating UC constraints. In particular, we model start-up and shut-down costs, minimum production level and hourly ramping limits of conventional units. Wind power production is considered as the only source of uncertainty, and is modeled through a set of scenarios. A two-stage stochastic programming tool is used, whose first stage determines the long-term expansion and short-term UC decisions over different hours of representative days, while the second stage models the real-time operation for accommodating imbalances arising from wind deviation under different scenarios. Since this problem is potentially hard to solve especially with a large number of representative days and scenarios, a multi-cut Benders’ decomposition algorithm is implemented. The well-functioning of the proposed model and the impact of each UC constraint on planning outcomes are evaluated using an extensive numerical study. In our case studies, the exclusion of ramping constraints from planning optimization causes large error and is the most distorting simplification.

Security-Constrained Unit Commitment With Natural Gas Pipeline Transient Constraints

Abstract: The interdependencies of power systems and natural gas networks have increased due to the additional installations of more environmental-friendly and fast-ramping natural gas power plants. The natural gas transmission network constraints and the use of natural gas for other types of loads can affect the delivery of natural gas to generation units. These interdependencies will affect the power system security and economics in day-ahead and real-time operations. Hence, it is imperative to analyze the impact of natural gas network constraints on the security-constrained unit commitment (SCUC) problem. In particular, it is important to include natural gas and electricity network transients in the integrated system security because the impacts of any disturbances propagate at two distinctly different speeds in natural gas and electricity networks. Thus, analyzing the transient behavior of the natural gas network on the security of natural gas power plants would be essential as these plants are considered to be very flexible in electricity networks. This paper presents a method for solving the SCUC problem considering the transient behavior of the natural gas transmission network. The applicability of the presented method and the accuracy of the proposed solution are demonstrated for the IEEE 118-bus power system, which is linked with the natural gas transmission system and the results are discussed in this paper.

Adaptive Dynamic Programming for Gas-Power Network Constrained Unit Commitment to Accommodate Renewable Energy With Combined-Cycle Units

Abstract: Due to the fast response capabilities, combined-cycle units could provide valuable flexibilities to cope with uncertainties from the renewable energy resources. However, combined-cycle units intensify the coupling between power and gas networks, so operational constraints of the two networks should be explicitly considered simultaneously. Indeed, neglecting the impact of any network may lead to infeasible solutions that will violate physical operational constraints. Compared with the previous stochastic unit commitment model with only combined-cycle units, this paper establishes a stochastic gas-power network constrained unit commitment model considering both combined-cycle units and gas network. Furthermore, an adaptive dynamic programming (ADP) to prevent the curse of dimensionality is proposed. Numerical results verify the effectiveness of the proposed model.

Analytical Reformulation for Stochastic Unit Commitment Considering Wind Power Uncertainty With Gaussian Mixture Model

Abstract: To capture the stochastic characteristics of renewable energy generation output, chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical reformulation for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenario-based methods are used to approximate probability with heavy computational burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.

Optimal scheduling of reconfigurable grids considering dynamic line rating constraint

Abstract: Power system automation is an effective tool from both economical and technical aspects to improve the optimal operation of power generators. In this regard, Security Constrained Unit Commitment (SCUC) incorporating Dynamic Thermal Line Rating (DTLR) of overhead transmission lines can boost the system security effectively. Using Transmission Switching (TS) tool in SCUC problems leads to cost reduction. Still, one of the main challenges arisen in TS problems is the excessive number of switching in lines, which decreases the lifespan of power switches. In this paper, an Improved Linear AC Optimal Power Flow (ILACOPF) is proposed by using TS and considering Heat Balance Equation (HBE) as a security constraint. Merging dynamic thermal line rating (considering the weather conditions) in SCUC with TS, besides decreasing the number of switching and increasing the lifespan of power switches, causes a remarkable reduction in operating costs. In this power flow, system losses are modeled by linear formulations. Moreover, a linear approximation of the heat losses due to power flow through lines is proposed. To solve the proposed model, Benders' decomposition approach is applied. The performance of the proposed framework has been evaluated on 6-bus and 118-bus IEEE test systems.

An Improved Genetic Algorithm Approach to the Unit Commitment/Economic Dispatch Problem

Abstract: The deployment of new technologies, the importance of accurately modeling the dynamics of the generating units and the introduction of new policies are making the solution of the Unit Commitment/Economic Dispatch problem more and more complicated.In the present scenario, traditionally followed scheduling criteria might not lead to the optimal fleet configuration any more. In addition, most of the widely used techniques have limited capabilities at modeling the nonlinear dynamics of committed power plants. When realistic power systems comprising of several tens of generating units are modeled, the resulting optimization problem turns to be computationally intensive for the current computing capabilities. In this paper, an improved version of a GA-based optimization algorithm is presented. A detailed methodology aimed at obtaining a more efficient version of the GA, and a more detailed and accurate description of the flexible operation flexibility of the power plants is described.

Robust Flexible Unit Commitment in Network-Constrained Multicarrier Energy Systems

Abstract: The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

Feasibility Identification and Computational Efficiency Improvement for Two-Stage RUC With Multiple Wind Farms

Abstract: The increasing penetration level of wind power challenges robust unit commitment with feasibilities and high computational burden. To meet these challenges, we propose two-fold advances for the two-stage robust unit commitment (TS-RUC), aiming at providing feasible solution and efficient decision tool for the TS-RUC with multiple wind farms. First, the feasibility identification method is proposed to ensure the tractability of the TS-RUC. The feasibility boundaries are determined based on values of two sets of introduced slack variables, the wind power curtailment and load shedding. Second, the disjunctive programming is used to improve the computational efficiency of the max-min problem, which is reformulated with convex hull relaxation (CHR) method to reduce constraints embedding binary uncertainty variables. Simulation results on the modified IEEE-118 bus system and Henan power grid demonstrate that the proposed improvement for the TS-RUC can be implemented for power systems with multiple wind farms and significant wind power. The feasibility identification can guarantee a feasible solution and the use of the CHR can improve computational efficiency.