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Synthetic power systems that imitate functional and statistical characteristics of the actual grid have been developed to promote researchers’ access to public system models. Developing time series to represent different operating conditions of these synthetic systems will expand the potential of synthetic power systems applications. This paper proposes a methodology to create synthetic time series of bus-level load using publicly available data. Comprehensive validation metrics are provided to assure that the quality of synthetic time series data is sufficiently realistic. This paper also includes an example application in which the methodology is used to construct load scenarios for a 10,000-bus synthetic case. [ABSTRACT FROM AUTHOR]

The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted. [ABSTRACT FROM AUTHOR]

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community. [ABSTRACT FROM AUTHOR]

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model. [ABSTRACT FROM AUTHOR]

Electromechanical modes are inherent to any interconnected power systems which provide a measure of the small-signal stability margin of the system. A number of algorithms have been developed for the estimation of these modes using synchrophasor measurements. However, most of these algorithms are not designed to operate in the presence of forced oscillations (FO). These FOs are results of periodic rogue input driving the system. When FOs are present, estimates of system modes can be biased depending on the frequency and the amplitude of the FOs. To tackle this problem, a new algorithm is proposed in this paper to estimate system modes in the presence of FOs. In the proposed method, the over-determined modified Yule–Walker method which is used to estimate autoregressive coefficients of an autoregressive moving average (ARMA) signal model is extended to an ARMA with exogenous input (ARMAX) model that incorporates the presence of FOs. Two versions of the proposed method are included in this paper based on the requirement of the information of the duration of FOs in the signal. Results obtained by implementing the proposed algorithm on simulated data and real-world data validate the effectiveness of both versions of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is devoted to the description of a new approach for large-scale electric power systems (EPS) simulation, so-called hybrid simulation approach. The design and research of EPS require solving a large number of non-trivial tasks. Due to the well-known EPS specificity, mathematical modeling is the only way to proceed. According to the hybrid approach, several simulation techniques (analogue, digital, and physical) are used to achieve the best results. An analogue part provides the elimination of methodological integration error, which appears in a case of numerical methods usage. A digital part allows the realization of control and parameter changing algorithms on software level. In a physical part, the connection and switching of modeled elements are provided in a way as in real EPS. In addition, the paper presents hardware features of the simulator—hybrid real-time power system simulator (HRTSim), which is an alternative to the digital simulators currently used. The important part of this paper is the result of comparative analysis of transient processes oscillograms obtained by HRTSim and real disturbance recorders in Tomsk region power system, as well as HRTSim and real-time digital simulator. [ABSTRACT FROM AUTHOR]

Load characteristics have substantial influence on the voltage stability of power systems. The self-restorative characteristic and stalling of induction motor loads can deteriorate the voltage stability, so it is necessary to develop accurate and efficient dynamic analysis methods for voltage stability analysis of systems with induction motor loads. In this paper, a set of methods based on holomorphic embedding is proposed, which is able to solve steady states and dynamics of a power system with induction motors. The paper is the first work that applies holomorphic embedding to dynamic problems of power systems. The test cases on the IEEE 14-bus system, the NPCC 140-bus system, and the Polish 2383-bus system verify both accuracy and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a new systematic method is proposed to analyze the system frequency in power systems under uncertain variability (UV). UV gradually increases in power systems, which originates from the renewable energy generation, random loads, frequency measurement, and communication channels, exerting noteworthy impacts on dynamic system frequency. The UV is modeled as a stochastic process in this paper. Then, the stochastic differential equations are used to describe the dynamic system frequency response (SFR) of a power system under UV, which is based on the SFR model. To assess system frequency dynamics under UV, an index of intra-range probability is put forward. Based on stochastic analysis theory, an analytic method is proposed to analyze the system frequency under UV, by which the intra-range probability can be analytically solved. Compared with Monte Carlo simulation in a typical SFR case and the Iceland electricity transmission network, the proposed method shows almost the same results using much less computation resource. Finally, insights for improving the SFR under UV are provided. The proposed method can be used to quickly verify whether the system frequency could withstand the UV and stay in the security range. [ABSTRACT FROM AUTHOR]

Time-domain simulation is the basis of dynamic security assessment for power systems. Traditionally, numerical integration methods are adopted by simulation software to solve nonlinear power system dynamics for any given contingency under a specific operating condition. An alternative simulation approach promising for online applications is to offline derive a semi-analytical solution (SAS) for power system differential equations and then online evaluate the SAS over consecutive time windows regarding the operating condition and contingency until the simulation result over a desired period is obtained. This paper proposes a general semi-analytical approach that derives and evaluates an SAS in the form of a power series in time to approximate the true solution. This paper provides an error-rate upper bound of the SAS for reliable use of adaptive time windows in evaluation of the SAS. To extend the semi-analytical approach for solving general power system differential-algebraic equations, this paper also proposes a dynamic bus method to efficiently link the SAS for dynamic components with the numerical solution of the network algebraic equations. Case studies on the New England 39-bus system and the Polish 2383-bus system test the performance of the proposed approach, compare it with other methods, and show the potentials of the proposed semi-analytical approach for online simulation. [ABSTRACT FROM AUTHOR]

Battery energy storage system (BESS) services will be of great help when operating power systems at high renewable energy penetrations. This paper demonstrates the requirements for effective frequency and inertia services. Some system operators currently apply scalars to financially reward improved performance. This paper demonstrates a method of validating and improving these scalars by considering the delay time and ramp time of the BESS. The importance of the method used to measure the rate of change of frequency is discussed in the paper. While the rate of change of frequency services improves with reduced delivery times, limits are identified where further reductions produce a negligible impact. This demonstrates the tangible benefits of a rapid response, and that current technology could meet the criteria. A variety of BESS capacities are trialed, which demonstrate consistency in performance and the occurrence of diminishing returns [ABSTRACT FROM AUTHOR]

Day-ahead scheduling has played a significant role in the operation of distribution systems, aiming at minimizing the total operation cost. With the excessive integration of information and communication technologies and intelligent electronic devices, distribution system operators may seek to optimally control the active management elements to increase the economic and technical performance of future smart distribution systems. This paper focuses on an integrated day-ahead scheduling scheme that is formulated as a mixed integer second-order cone programming problem to obtain the optimal solution by means of the relaxed power flow model (RPFM). The detailed technical constraints for all the elements are accommodated based on the RPFM-based optimization method. Meanwhile, active management cost and punishment cost of wind power curtailed and customer energy not supplied are also considered in the design objectives of this paper. In order to address the uncertainties, a stochastic robust optimization method is first introduced to decide the active management elements (including capacitor bank, electrical storage system, controllable load, and switch) as the robust decision scheme. Afterwards, the column-and-constraint generation algorithm is applied to solve the proposed stochastic robust two-stage optimization model. Numerical results based on the IEEE 33-bus, PG&E 69-bus, and practical 152-bus systems are obtained to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents parameter values and ranges of static load models used in a power system analysis for the representation of both individual electrical devices and aggregate system loads. The paper discusses and correlates load models and their parameters following statistical processing of the responses of around 100 transmission system operators and utilities around the world, who participated in a survey initiated by CIGRE Working Group C4.605, with the corresponding information on load models and their parameter values from the existing literature. According to the survey, the most frequently used static load models are voltage dependent, and voltage and frequency dependent exponential load models. The identified typical parameter values and ranges of these static load models, both for low voltage devices and for aggregate loads at higher voltage levels, are results of the analysis of a large number of data. Based on these results, the paper provides recommendations for their further use in power system studies and also introduces a novel method for obtaining the mean values and ranges of parameters of the aggregate system load models. [ABSTRACT FROM AUTHOR]

Power system operation will encounter numerous variabilities as the proliferation of renewable energy continues. Such random events will require the evaluation of corresponding variables and the assessment of their impacts on the monitoring and the control of stochastic power systems. In this paper, a global sensitivity analysis (GSA) method is proposed to perform a priority ranking of renewable energy variabilities that will affect the voltage stability of power systems. First, a probabilistic model for the load margin calculation is presented considering the renewable power generation. Then, GSA is applied to models with correlated random input variables and the importance index is designed for evaluating the impacts of such variables. Next, the stochastic response surface method is adopted in GSA to improve the computation efficiency of the proposed evaluation method. Finally, the overall procedure is presented to identify critical variables that can affect the variability of load margins in voltage stability analyses. The proposed method is applied to power systems with a large penetration of renewable power and the influences of critical variabilities on voltage stability are analyzed. In this paper, the proposed method is tested using the IEEE 9-bus and the IEEE 118-bus systems, and its accuracy is compared with those of the local sensitivity analysis method and the commonly used GSA methods. [ABSTRACT FROM PUBLISHER]

Today the dq0 reference frame is mainly used for modeling and control of traditional electric machines and small power sources. A current challenge is to merge various dq0-based models appearing in recent literature to obtain a complete model of a large power system. To this end, in this paper we propose a model describing the dynamics of large transmission networks based on dq0 quantities. The proposed model is based on a standard network topology, uses sparse system matrices, and is of minimal order. We also demonstrate how this model may be used to construct a small-signal description of a complete system that includes the transmission network, generators, and loads. Results are illustrated on the basis of a long transmission line, and using the 118-bus test case network. This paper is accompanied by a free software package. [ABSTRACT FROM PUBLISHER]

Short-term load forecasting has been playing an increasingly important role in electric power systems. Effective forecasting of the future electricity demand, however, is difficult in view of the complicated effects on load by a variety of factors. A hybrid method based support vector regression (SVR) with meteorological factors and electricity price is proposed to address such problem. First, the input used in this paper, are specific ratio value combinations of each characteristic parameter affecting the hourly electricity load. Second, SVR is used to analyze and develop a load forecasting model. Third, an improved adaptive genetic algorithm (IAGA) is utilized to optimize the specific ratio value combinations of each characteristic parameter, penalty factor C and Gaussian kernel function σ to accurately establish a forecasting model. The experimental results show that the proposed method can obtain better forecasting performance in comparison with other standard and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The screening curve method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. It calculates an economically adapted generation mix for a target load duration curve within a few seconds, but lacks many detailed considerations of the generation system. In order to improve SCM, many developments have been made in recent years. However, one of the biggest limitations had always been that the SCM assumed all capacity to be new with no existing capacity. This drawback made SCM less useful compared to other generation planning models. In this paper, we develop a direct way to model existing capacity in SCM. The proposed method is studied in this paper and is illustrated with an example. Finally, an ERCOT year 2030 case is simulated. [ABSTRACT FROM AUTHOR]

This paper presents a modeling approach for capturing closed-loop load behavior in distribution networks using bottom-up time-variant load models and incorporating load-flow feedback for thermal loads. Traditional load models are useful for providing insights into instantaneous changes in power, however, they do not capture the time-variant response to changes in voltage due to the effects of appliances with closed-control loops such as those which are thermostatically controlled. This paper proposes a modeling and co-simulation platform to simulate closed-loop load-modeling using state-of-the-art bottom-up load modeling techniques for the residential sector with a feedback model for time series steady-state load-flow. It incorporates time-variant load models and discrete state-space representation of thermostatically controlled appliances. The developed methodology is used in a conservation voltage reduction case study on an urban distribution network, allowing detailed assessment of the voltage response and analysis of changes in active and reactive power and network losses. [ABSTRACT FROM AUTHOR]

Modern aircrafts are evolving toward more electric aircraft (MEA), resulting in greater reliance on the electrical system for safe flight. On-board power system of MEA integrates a large number of power electronic converters, and it is reported that semiconductor devices and electrolytic capacitors in power converters are the most vulnerable links impacted by loading conditions; thus, reliability becomes a critical concern in an MEA power system. This paper proposes a hierarchical approach for systematic reliability modeling and evaluation for the on-board power system of MEAs. It consists of three hierarchical levels (HLs): component level (HL1), subsystem level (HL2), and system level (HL3). In HL1, failure rates of power electronic components are modeled considering relevant inner structure and loading conditions; in HL2, the reliability of individual subsystems such as converters are constructed; in HL3, the system reliability is quantified based on the network architecture and reliability of the subsystems. The impacts of different parts (components/subsystems) on the overall system are assessed effectively with the identification of the vulnerable parts. This also provides a guideline for reliability enhancement by using thermal control techniques, adding redundancies or performing maintenance on the vulnerable parts to ensure the satisfactory of system reliability requirements. The proposed method is demonstrated on the future MEA power system architectures (hybrid ac–dc architecture and HVdc architecture). [ABSTRACT FROM AUTHOR]

With substantially increasing penetration levels of wind power, electric power system flexibility is needed to address the variability and uncertainty of wind power output. Thus, it has become an urgent issue to obtain an optimal tradeoff between economics and reliability, and to price system uncertainties. This paper proposes a new electricity market-clearing mechanism based on locational marginal prices (LMPs) for pricing uncertain generation and load. The uncertainty contained locational marginal price (U-LMP) is derived from a distributionally robust chance-constrained optimal power flow model in which only the first-order and second-order moments of the uncertain sources’ probability distribution are needed. Compared with traditional LMPs, the proposed U-LMP formulation includes two new uncertainty components: transmission line overload uncertainty price and generation violation uncertainty price. These LMP uncertainty components are the price signals reflecting the system costs as a result of wind generation and demand uncertainty at different locations. Finally, using parametric case studies, the relationship among uncertainty levels, system generation cost, and LMP uncertainty components are established. Case studies performed on the PJM 5-bus and IEEE 118-bus systems verify the proposed U-LMP method. [ABSTRACT FROM AUTHOR]

This paper examines the operation of distribution networks that have large aggregations of thermostatically controlled loads (TCLs) providing frequency regulation to the bulk power system. Specifically, we assess the prevalence of distribution network constraint violations, such as over- or under-voltages and overloading of transformers. Our goal is to determine the set of constraints that are at increased risk of being violated when TCLs provide regulation. We compare network operation in two cases: first, with TCLs operating freely; and second, with TCLs controlled to track a regulation signal. Using GridLAB-D, we run power flow simulations of five real distribution networks. Our results indicate that voltage limits are at increased risk of violation when TCLs provide regulation because of increased voltage variation. Effects on transformer aging are more nuanced and depend on the method used for dispatching TCLs. Overall, we find that, for the networks studied, the set of constraints at risk of violation is a small subset of all network constraints. Therefore, network-protecting load control algorithms may only need to consider a small set of constraints, enhancing their computational efficiency. [ABSTRACT FROM AUTHOR]

This paper develops a computationally efficient method to bound the impact of multiple uncertain parameters in a dynamic load model. Load model trajectory sensitivity is first conducted on regional dynamics only (e.g., a large industrial load bus and its model for adequately representing the bus voltage dynamics) to identify critical and correlated load parameters. Systemwide trajectory sensitivity on the entire power system model is then evaluated for this reduced set of parameters, and finally, the impact of multiple uncertain parameters on the representation of power system dynamics is bounded. To reinforce this reasoning, we elaborate on the conceptual meaning of the load model trajectory sensitivity, its implication, and its applicability to the entire power grid analysis. This research also develops a fluctuation index of trajectory sensitivity to effectively rank and select the model parameters based on the impact of their perturbations on the system's dynamic performance. Case studies for the Korean power system demonstrate the validity and efficacy of the developed methods for adequately bounding the uncertainty impacts with reference to the comprehensive time-domain dynamic simulation approach. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized control based on static var compensator (SVC) devices to improve the frequency control of high voltage transmission systems. For a realistic structure preserving power system with voltage dependent loads, we propose a sufficient condition based on a strict Lyapunov energy function, which is utilized to design a nonlinear adaptive SVC controller. This controller only requires local bus measures and parameters. Furthermore, we also present an SVC with a conventional frequency droop controller. The performance of the two considered SVC control systems are evaluated on the well known IEEE New England 39-bus 10-machine power system and compared with conventional SVC controllers. [ABSTRACT FROM AUTHOR]

Combined transmission and distribution systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying co-simulation techniques for integrated simulation of the two systems. In this paper, two methods for dynamic co-simulation of CoTDS are proposed using parallel and series computation of the transmission system and distribution systems. The convergence of numerical methods in co-simulation is addressed from a fundamental mathematical perspective and the impact of the integration time-step is studied on proposed CoTDS dynamic co-simulation methods. Both these co-simulation methods are validated against total system simulation in a single time-domain simulation environment. The proposed CoTDS co-simulation method is also validated against commercial EMTP dynamic simulation. CoTDS dynamic co-simulation is further demonstrated on a New England 39-bus transmission system with ten load buses replaced by distribution systems including a 5780-node distribution system on one load bus for a detailed fault-induced delayed voltage recovery simulation study. [ABSTRACT FROM AUTHOR]

Cyber-attack is recognized as an emerging risk in smart grids. In this paper, we reveal a potential link between data attack and physical consequences and analyzed how the attacker can launch a malicious data attack to trigger sequential outages and thus impose large damages to the grid. In this attack mechanism, the attacker constructs an optimal false data injection attack to intentionally trigger a targeted branch outage sequence that trips multiple branches and then leads to subsequent failures. The studied attack mechanism integrates constructing an optimal data attack and identifying critical lines, and imposes a substantial security impact with a high chance of occurring. Simulations on the IEEE 118-bus system verify that the introduced attack mechanism and highlight the risk of such attacks in today's smart grids. [ABSTRACT FROM AUTHOR]

Multienergy systems are flexible energy systems that can benefit from energy resources to supply different energy demands. Due to the capabilities of multienergy systems in generating different energy carriers, these systems have been rapidly expanded in power systems. After restructuring in power system in recent years and appearance of competent energy markets, energy systems operated within such environments have been usually exposed to uncertainties of various parameters, such as price, demand, etc. In this paper, a novel optimization framework based on hybrid scenario-based/interval/information gap decision theory method is developed to investigate the optimal operation of smart energy hubs (S. E. Hubs) subject to economic priorities, technical constraints of the distribution network and uncertainties. Considering energy hubs equipped to smart facilities, demand-side management programs including price response and load response services have been available to motivate electrical consumers to revise their consumption pattern in order to satisfy economic priorities of energy hubs. By using the results of employed hybrid uncertainty modeling approach, the operator of S. E. Hubs can decide either to take risk-averse or risk-seeking strategy against the uncertainties. Uncertainty based integration of S. E. Hubs into distribution network is evaluated regarding the IEEE 33-bus test system and the results obtained from simulations are presented for comparison. [ABSTRACT FROM AUTHOR]

A shortage of large power system data sets, as well as the frequent restrictions on sharing such models, have led to newfound interest in creating synthetic data that can be easily shared among researchers. This paper considers the problem of forming a power system test case from the constituent parts of realistic power grid samples. Starting from a topology, and samples of generation, load, and branches, we assemble systems while respecting the constraints imposed by a typical Optimal Power Flow problem. Expressed in this manner, the problem involves solving for permutations of the input data. Since permutations matrices are binary, the problem is linearized, allowing for a Mixed Integer Linear Problem formulation. The problem is further decomposed using the Alternating Direction Method of Multipliers as well as an Evolutionary Algorithm to facilitate scaling to larger system sizes. A post processing step is used to add shunt elements for reactive power planning. The resulting systems demonstrate statistically similar power flow behavior to reference systems. Finally, new analysis avenues, opened by synthesizing test cases according to the proposed method, are briefly introduced by creating fictitious systems with different topology models and examining how these affect power flow behavior. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed optimal frequency control of power systems considering a network-preserving model with nonlinear power flows and excitation voltage dynamics. Salient features of the proposed distributed control strategy are fourfold, first, nonlinearity is considered to cope with large disturbances, second, only a part of generators are controllable, third, no load measurement is required, fourth, communication connectivity is required only for the controllable generators. To this end, benefiting from the concept of “virtual load demand,” we first design the distributed controller for the controllable generators by leveraging the primal-dual decomposition technique. We then propose a method to estimate the virtual load demand of each controllable generator based on local frequencies. We derive incremental passivity conditions for the uncontrollable generators. Finally, we prove that the closed-loop system is asymptotically stable and its equilibrium attains the optimal solution to the associated economic dispatch problem. Simulations, including small and large-disturbance scenarios, are carried on the New England system, demonstrating the effectiveness of our design. [ABSTRACT FROM AUTHOR]

The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the nonlinear dependence structure they exhibit. In this paper, we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modeling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches. [ABSTRACT FROM AUTHOR]

In this paper, the impact of load dynamics on the oscillatory stability of power systems is studied. The well-known composite load model is linearized to yield third order transfer functions for active and reactive power dynamics of loads. It is shown that the third order model exhibits a recovery response that is accompanied by damped oscillations. In studying the impact of loads’ transfer function parameters on the small signal stability of power systems, a resonance is observed between the load and the electromechanical modes of the system that significantly alters the electromechanical modes’ damping. The role of the active and reactive power dynamics, as well as the operating condition of the system on the impact from a dynamic load on electromechanical modes are investigated. For preliminary insights, a single machine infinite bus system (SMIB) is used initially to avoid the unnecessary complexities associated with large grids. The general conclusions drawn from the SMIB studies are then confirmed on a 14 generator model of the Australian grid. [ABSTRACT FROM AUTHOR]

Series FACTS devices are one of the key enablers for very high penetration of renewables due to their capabilities in continuously controlling power flows on transmission lines. This paper proposes a bilevel optimization model to optimally locate variable series reactor and phase shifting transformer in the transmission network considering high penetration of wind power. The upper level problem seeks to minimize the investment cost on series FACTS, the cost of wind power curtailment, and possible load shedding. The lower level problems capture the market clearing under different operating scenarios. Due to the poor scalability of $B\theta$ formulation, the shift factor structure of FACTS allocation is derived. A customized reformulation and decomposition algorithm is designed and implemented to solve the proposed bilevel model with binary variables in both upper and lower levels. Detailed numerical results based on 118-bus system demonstrate the efficient performance of the proposed planning model and the important role of series FACTS for facilitating the integration of wind power. [ABSTRACT FROM AUTHOR]

Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems. [ABSTRACT FROM AUTHOR]

The system frequency response (SFR) model describes the average network frequency response after a disturbance and has been applied to a wide variety of dynamic studies. However, the traditional literature does not provide a generic, analytical method for obtaining the SFR model parameters when the system contains multiple generators; instead, a numerical simulation-based approach or the operators’ experience is the common practice to obtain an aggregated model. In this paper, an analytical method is proposed for aggregating the multi-machine SFR model into a single-machine model. The verification study indicates that the proposed aggregated SFR model can accurately represent the multi-machine SFR model. Furthermore, the detailed system simulation illustrates that the SFR model can also accurately represent the average frequency response of large systems for power system dynamic studies. Finally, three applications of the proposed method are explored, with system frequency control, frequency stability, and dynamic model reduction. The results show the method is promising with broad potential applications. [ABSTRACT FROM AUTHOR]

Increasing penetration of distributed generators (DGs) and intelligent loads necessitates a transactive system, where a distribution system operator (DSO) is required to submit bidding curves for its net demand. This paper proposes a price-maker bidding approach for a DSO that captures distribution power flows, constraints, and uncertainties arising from DGs and loads. A neurodynamic algorithm is proposed to ensure that the bidding curve can be derived in real time. Simulation results justify the need for considering constraints and uncertainties in the bidding model and explain how these factors impact the bidding curve. [ABSTRACT FROM AUTHOR]

In order to support an appropriate level of resource adequacy in the long term and also to achieve a reliable operation in the short term, a new pricing design and mechanism is needed as a key success factor of real-time energy-only market. This paper proposes optimization-based mathematical models to explore operating reserve demand curves (ORDCs) and two price adders in real-time energy-only market, including dispatch correction and extended security-constrained economic dispatch. The Lagrangian multipliers of the dispatch correction problem are used to construct the ORDCs. Then, the derivative of the ORDCs’ social welfare is formulated as real-time online reserve price adders such as the scarcity adder and the price-lift adder. In case studies, we justify the effectiveness of the proposed ORDCs and two price adders by a simple example with two aggregated units and the Electric Reliability Council of Texas data. [ABSTRACT FROM AUTHOR]

This paper proposes a generic steady-state modeling and power-flow analysis approach for droop- and isochronously controlled microgrids. The proposed framework adopts symmetrical sequence component models, rather than phase-coordinate models, of microgrid elements. Such approach immensely reduces the power-flow execution time, as it breaks down the system model into independent equation sets with considerably reduced sizes. To render the proposed approach practical and generic, it integrates different types and control schemes of distributed generation (DG), including synchronous generator-based DG (SGDG) and electronically interfaced DG units. Furthermore, it incorporates unbalanced loads and feeders, transformer connections, different load characteristics, and configurations, as well as microgrid droop features. A novel power-flow algorithm based on a modified Newton–Raphson method is proposed to solve for the microgrid steady-state voltage magnitudes, angles, and frequency. The accuracy of the models and algorithm is verified through comparison with detailed time-domain simulations in MATALB/Simulink. Additionally, the proposed approach is shown to outperform the reported Newton-Trust Region approach in generality, accuracy, and performance. Two case studies, incorporating IEEE 123-node test microgrid, are further performed to examine the effectiveness of the proposed approach in solving complicated droop-controlled microgrids, and to examine the behavior of droop-controlled DGs in isochronous microgrids. [ABSTRACT FROM AUTHOR]

This paper develops an effective model for microgrid optimal scheduling with dynamic network reconfiguration. Network reconfiguration can effectively alter local power flow and thus provide an opportunity to reduce microgrid distribution network losses during grid-connected operation (supporting microgrid economic objectives) and to reduce potential load curtailments during the islanded operation (supporting microgrid reliability objectives). The proposed optimal scheduling model is decomposed into a grid-connected operation master problem and an islanded operation subproblem. A novel and highly accurate dynamic linear power flow model, with the ability of line switching, is developed and included in both problems. The optimal schedule determined in the master problem is assessed to meet the microgrid islanding feasibility in the subproblem. If infeasible, the decision variables are amended using the islanding cuts, which will accordingly revise the network reconfiguration, as well as the schedule of dispatchable units, energy storage, and adjustable loads. The simulation results on a test microgrid demonstrate the effectiveness and satisfying performance of the proposed model. [ABSTRACT FROM AUTHOR]

As the operational conditions of power systems change, the operating point (i.e., the equilibrium point) may approach the small-signal security boundary. Given the current operating condition as well as the load and generation variations, it is meaningful to compute the small-signal security margin (SSSM). In this paper, an effective method that consists of three stages to compute the SSSM is proposed and examined. In the first stage, which is an estimation stage, the critical mode crossing the damping ratio line is estimated. The SSSM is then computed by a test function or by a direct method in the second stage, which is a correction stage. The computational results are then verified in the third stage, which is a verification stage. Numerical results on a 10-machine and a 160-machine system (with the state variables being 1600) show that the proposed three-stage method is efficient and promising. [ABSTRACT FROM AUTHOR]

A probabilistic power flow (PPF) study is an essential tool for the analysis and planning of a power system when specific variables are considered as random variables with particular probability distributions. The most widely used method for solving the PPF problem is Monte Carlo simulation (MCS). Although MCS is accurate for obtaining the uncertainty of the state variables, it is also computationally expensive, since it relies on repetitive deterministic power flow solutions. In this paper, we introduce a different perspective for the PPF problem. We frame the PPF as a probabilistic inference problem, and instead of repetitively solving optimization problems, we use Bayesian inference for computing posterior distributions over state variables. Additionally, we provide a likelihood-free method based on the approximate Bayesian computation philosophy, that incorporates the Jacobian computed from the power flow equations. Results in three different test systems show that the proposed methodologies are competitive alternatives for solving the PPF problem, and in some cases, they allow for reduction in computation time when compared to MCS. [ABSTRACT FROM AUTHOR]

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts. [ABSTRACT FROM AUTHOR]

This paper proposes a novel voltage stability-constrained optimal power-flow (VSC-OPF) model utilizing a recently proposed sufficient condition on power-flow Jacobian nonsingularity. We show that this condition is second-order conic representable when load powers are fixed. Through the incorporation of the convex sufficient condition and thanks to the recent development of convex relaxation of OPF models, we cast a VSC-OPF formulation as a second-order cone program. An approximate model is introduced to improve the scalability of the formulation to larger systems. Extensive computation results on Matpower and NESTA instances confirm the effectiveness and efficiency of the formulation. [ABSTRACT FROM AUTHOR]

Power system static voltage stability region boundary (SVSRB) is a complicated hypersurface in parameter space. Due to inequality constraints such as generator reactive power limits, different critical points on the SVSRB may be related to different groups of active constraints and can be associated with different types of loadability limit, which makes it difficult to approximate the whole SVSRB at once or assemble many local approximations of the SVSRB. To overcome the difficulty, this paper proposes an approach for globally approximating the whole SVSRB considering generator reactive power limits at one go. First, a parametric optimization model incorporating all inequality constraints is formulated to describe the SVSRB, where the optimal solutions are the critical points on the SVSRB. The optimality condition, i.e., parametric Karush–Kuhn–Tucker conditions (KKT conditions), is also derived. Then, by applying the Galerkin method to the parametric KKT conditions in an interior-point-method-like manner, the whole SVSRB is approximated and represented using a single polynomial expression. Case studies in a 10-bus system and IEEE 300-bus system verify the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters. [ABSTRACT FROM AUTHOR]

Rescheduling methods for mitigation of poorly damped power swings depend on a model-based evaluation of eigenvalue sensitivities and are slow to implement. This paper proposes an approach for an online model-free implementation of modulation-type control using Wide Area Measurement System (WAMS). Such a control measure can act as a primary line of defense before the slower rescheduling-type measures are taken. The proposed control measure utilizes special properties of swing modes derived analytically from a simplified system model to 1) synthesize a modal feedback signal as the input to the controller and 2) tune the controller parameters for adequate damping improvement, in a model-independent manner for certain FACTS devices and HVDC links. For a power system stabilizer (damping controller in a synchronous machine excitation system), the extent of model information required is limited to that of the well-known GEP $(s)$ transfer function and inertia constant of the concerned synchronous machine. Case studies with detailed system models provide the necessary proof-of-concept. [ABSTRACT FROM AUTHOR]

Several transient stability software packages are widely used for power grid planning and operations. Prior research and software documentations have shown that packages can vary in the implementations of dynamic models, and hence could potentially yield different results for the same system simulation. This paper presents a systematic methodology to determine the sources of the discrepancies seen in results obtained from different transient stability packages. This methodology can be applied to various types of dynamic models and is illustrated with generator-associated dynamic models in this paper. Consistent metrics, data mining techniques, and engineering heuristics are used to determine the discrepancy sources. This hybrid top-down+bottom-up method has been implemented on a practical, large-system case to demonstrate its scalability and transferability to real-world system studies. [ABSTRACT FROM AUTHOR]

Neatly represented and properly classified load profiles are fundamental to many control optimization techniques of modern power systems, especially in a distribution area. This paper presents a novel load profile management software framework for boosting the efficiency of power systems operation. The proposed framework encodes and classifies load profiles in real-time. Imperfections as well as time-shifts in the input (measured power consumption levels) are tolerated by the suggested system, thus always providing accurate, fast and reliable output. The framework’s fully component based structure allows easy customizations of the encoding as well as the classification engines. The default encoding engine is based on an artificial neural network, a variant known as a deep learning auto-encoder comprised from stacked sparse auto-encoders. The default classifier engine is based on an implementation of a locality sensitive hashing algorithm. The developed methodology was tested on the real case of a set of anonymous customers supplied by a power distribution company. The paper also contains an elaboration about the experiences gained during the design, implementation and testing phase of this system as well as a detailed engineering use case of the framework’s applicability. [ABSTRACT FROM PUBLISHER]

Load modeling is essential for designing and operating power systems. This paper presents an approach for load modeling on smaller power systems that could be “islanded,” an approach that preserves the detail of a full differential equation simulation of relevant loads while requiring far less computation by employing behavioral models of important loads. Mixed domain models, e.g., stochastic, finite-state machine, and differential equation models, are employed to provide accuracy in a computationally tractable framework. Where simple load models may not be adequate, particularly for generation-constrained systems (in a paper by Sotiropoulos ), and full models are computationally unfavorable, this approach provides excellent results that enable “what-if” studies and flexible re-evaluation during power system design and operational assessment. Naval vessels, particularly warships with relatively large and increasing load power requirements, offer a unique laboratory for understanding isolated power grids. This paper examines the DDG-51 power distribution system as an example. [ABSTRACT FROM PUBLISHER]

Power system reliability measures for residential distribution networks have gained a lot of attention in recent years. This is due to the introduction of smart grids, where the quality of the delivered power becomes a crucial issue. In this paper, a new methodology to include cold load pickup (CLPU) events in the reliability assessment of power distribution systems is proposed. The developed methodology presents a platform to include CLPU events in the reliability assessment assuming an optimal systematic procedure is used to restore the affected load points. The methodology is applied on a test system using a state-of-the-art search technique named lightning search algorithm (LSA). The optimal plan takes into account electrical code protection system settings, system operation limits, and stochastic behavior of the load. The reliability indices for a base case without considering CLPU is compared with the case that considers CLPU assuming an optimal restoration. Both cases are compared further under fixed-restoration plans with different time intervals. The results demonstrate the effectiveness of the proposed methodology to improve the distribution network reliability by considering the CLPU events. [ABSTRACT FROM AUTHOR]

The activation of under frequency load shedding (UFLS) is the last automated action against the severe frequency drops in order to rebalance the system. In this paper, the setting parameters of a multistage load shedding plan are obtained and optimized using a discretized model of dynamic system frequency response. The uncertainties of system parameters including inertia time constant, load damping, and generation deficiency are taken into account. The proposed UFLS model is formulated as a mixed-integer linear programming optimization problem to minimize the expected amount of load shedding. The activation of rate-of-change-of-frequency relays as the anti-islanding protection of distributed generators is considered. The Monte Carlo simulation method is utilized for modeling the uncertainties of system parameters. The results of probabilistic UFLS are then utilized to design four different UFLS strategies. The proposed dynamic UFLS plans are simulated over the IEEE 39-bus and the large-scale practical Iranian national grid. [ABSTRACT FROM AUTHOR]

Within an alternating current (AC) optimal power flow (OPF) market clearing framework, market-settlement surplus (MS) is inevitable due to network losses, congestion, and reactive power. For a general AC-OPF model, decomposition of MS into its constituents is a challenge. Therefore, this paper proposes a solution for decomposing MS into its constituents, i.e., network losses, congestion, and reactive support services, by deriving analytical expressions for each of the components. An optimization problem, named pseudo-IV-OPF, is set up around the existing AC-OPF solution to derive expressions. The central idea involved in current and voltage OPF, that the current flow at sending end is exactly equal and opposite at receiving end, is exploited to derive the elements of MS by using Karush–Kuhn–Tucker conditions of optimality. It is important to note that a converged OPF solution, obtained from the standard AC-OPF model used by the utility, is an input to the proposed model. The efficacy of the proposed expressions is illustrated on various benchmark systems. [ABSTRACT FROM AUTHOR]

Since cascading outages are major threats to power systems, it is important to reduce the risk of potential cascading outages. In this paper, a risk management method of cascading outages based on Markovian tree search is proposed. With the tree expansion on the cascading outage risk, risk gradient is computed efficiently by a forward–backward tree search scheme with good convergence, and it is then employed in an optimization model to minimize control cost while effectively reducing the cascading outage risk. To overcome the limitation with linearization in computing risk gradient, an iterative risk management (IRM) approach is further developed. Tests on the RTS-96 3-area system verify the accuracy of the computed risk gradient and its effectiveness for risk reduction. Time performance of the proposed IRM approach is tested on the RTS-96 system, a 410-bus U.S.–Canada northeast system, and a 1354-bus mid-European system, and demonstrates its potentials for decision support on practical power systems online or on hourly basis. [ABSTRACT FROM AUTHOR]