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There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods., Comment: Author copy of the manuscript submitted to IEEE Trans on Power Systems

The paper develops an optimization method for assessing transmission network vulnerability to small changes in generation (as caused, for example, by wind forecast inaccuracy). The method computes the smallest deviation (in a weighted 2-norm sense) from the nominal generation pattern that would drive a particular line to a specified temperature, over a given time horizon. The 2-norm weighting matrix provides a means of capturing spatial and temporal coupling between generation sites and time intervals. The temperature constraint is second-order in voltage angle differences. The problem is therefore a quadratically-constrained quadratic program (QCQP). Solving the QCQP for each line in the network yields a set of candidate generation deviation patterns which may then be sorted to determine the lines that are most vulnerable to overloading. The paper develops a computationally efficient algorithm for solving this QCQP. An example explores line-overload vulnerability due to changes in wind patterns. Numerical results emphasize the framework's ability to incorporate evolving ambient and system conditions, as well as computational scaling properties.

Transient stability of a power system can be significantly affected by wind power generators due to their stochastic power output and complex dynamic characteristics. This paper proposes a robust optimization approach for coordinating generation dispatch and emergency load shedding against transient instability under uncertain wind power output. The problem is modelled as a two-stage robust optimization (TSRO) model considering transient stability constraints, where the first-stage is to optimize the generation dispatch (preventive control) before a contingency and the second-stage decision is the emergency load shedding (emergency control) after the contingency occurrence under the worst case of wind power variation. To solve this TSRO problem, this paper also proposes a solution algorithm which integrates transient stability assessment and transient stability constraint construction in a column and constraint generation framework. The proposed method is validated on the New-England 39-bus system and the Nordic32 system, which shows high computational efficiency and stability robustness against uncertain wind power.

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attackers action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel water-pouring algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods.

This paper presents a novel load shedding scheme with consideration of the active power ramping capability of Distributed Energy Resources (DERs) to address the challenges due to low inertia and diverse types of DERs in microgrids. In the paper, it is demonstrated that due to the small inertia in microgrids, even with sufficient reserve power, the frequency could rapidly drop to a low level and trigger the DERs' under frequency protection (thus the total system collapse), if the reserve active power is not ramped up at a sufficient rate. The proposed load shedding scheme addresses this challenge by considering not only the DERs' reserve, but also their speed in injecting active power to the system to determine the amount of load should be shed, so that critical frequency thresholds are not violated. The proposed load shedding scheme is tested using a realistic real time hardware-in-the-loop arrangement. The results show that the proposed scheme can correctly detect the cases when the DERs' responses are too slow and trigger the required load shedding actions, thus effectively containing the frequency above the critical threshold.

Aging asset management requires careful consideration of end-of-life of any asset. This is particularly serious with aging infrastructure which may require complete replacement of a large asset which has a long lead time. In many current End-of-Life analysis approaches to calculate unavailability, the period of interest is sub-divided into yearly cycles with the assumption that the asset is available at the start of the year. Although this assumption is adequate if the replacement time for the asset occurs within the year, with longer lead times this can create excessively optimistic availabilities. This paper presents an improved probabilistic tool for risk assessment due to End-of-Life failure unavailability which overcomes this deficiency. The End-of-Life methodology proposed in this paper is required when aging assets have long lead times to replacement and so the capital investment decision must be made several years ahead. The method is applied to evaluate the End-of-Life unavailability of Manitoba Hydro Bipole II HVdc converters. The results are corroborated using Monte Carlo simulation. Finally, the risk of End-of-Life is incorporated into an economic cost-benefit analysis corresponding to the presented unavailability evaluation method.

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations.

Due to the development of cyber-physical systems for modernizing power grids, vulnerability assessment has become an emerging focus in power system security studies. With the increasing deployment of cyber-enabled technologies in power systems, modern power system is prevalently exposed to a wide gamut of cybersecurity threats. Thus, there is an urgent need to develop effective cyber risk management mechanisms to mitigate the growing cyberthreats. Recently cyber insurance is emerging as a promising financial instrument for cyber risk management of critical infrastructures such as power grids. In this paper, a new cyber-insurance design framework is proposed to hedge against the risk of massive monetary losses due to potential cyberthreats. Traditionally, insurance companies serve as third-party risk-bearers offering aggregate design of the insurance policy which may stipulate high premiums. However, unusual loss patterns may still lead to excess financial risk for insurance companies. In this paper, coalitional insurance is introduced as a promising alternative or supplement to the traditional insurance plans provided by insurance companies. Under the proposed cyber-insurance model, several transmission operators form an insurance coalition, where the coalitional premiums are derived considering system vulnerabilities and loss distributions. The indemnity which covers the loss of TOs complies with the budget sufficiency. Overall, this study proposes a novel coalitional platform based cyber-insurance design that estimates the insurance premiums via cybersecurity modeling and reliability implication analysis.

This paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology. This work was supported by the Spanish Ministry of Innovation under Grant PID2019-104449RB-I00. Paper no. TPWRS-01989-2020. Publicado

High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

The low-frequency oscillations (LFOs) inherent in power systems will be impacted by the increasing penetration of renewable energy sources (RESs). This paper investigates the impact of virtual synchronous machine (VSM) based RESs on the LFOs in power systems. A detailed two-machine test-bed has been developed to analyze the LFOs which exists when VSMs replace synchronous generators. The characteristics of the LFO modes, and the dominant states have been comprehensively analyzed. Furthermore, this study analyzes the LFO modes which exists in an all-VSM grid. The role of the power system stabilizers in the all-VSM grid has been comprehensively evaluated. The IEEE benchmark two-area four-machine system has been employed to corroborate the results of the small-signal analysis, and observe the transient performance. The analysis in this paper have been performed in MATLAB/SIMULINK environment.

In this paper, we investigate whether and under which conditions jointly owning a variable renewable source of electricity (VRES) and an electricity storage generates economies of scope in competitive electricity markets. Using a simple stochastic optimization model that assumes frictionless markets, we analytically show that no economic benefit arises from combining the two assets. This finding is in contradiction to large parts of the literature, which claim that it is in the economic interest of owners of VRES to additionally own electricity storage. We also identify circumstances where our argument does not hold and the combination of storage and VRES could theoretically make economic sense on the level of individual agents. In the last part of the paper, we demonstrate in a numerical case study of the German market that even in cases where our theoretical results do not hold, tying together storage and VRES may produce suboptimal results.

As the penetration of utility-scale solar photovoltaic (PV) power plants increases, the inertia in the system is reduced and there will be increased primary frequency response requirements. To increase inertia and improve the primary frequency response, grid-forming inverters connecting PV to grid and energy storage systems (ESSs) may play an important role. Moreover, high-voltage direct current (HVdc) links can also be an enabler to transfer remote PV power generation and to improve grid stability. That is, with increased penetration of PV, discrete development of PV and ESS connecting to transmission ac grid and HVdc links is one of the solutions for stable operation of the grid. In this paper, an integrated concept for integration of PV and ESS to transmission ac grid and HVdc links is proposed that is named as multi-port autonomous reconfigurable solar power plant (MARS). The integrated development incorporates advanced control methods to provide inertial and primary frequency response, reactive power support, and transient stability to manage PV and ESS resources. In this paper, high-fidelity switched system model of the integrated system and grids are developed and detailed simulation results are provided to showcase the stable operation of the integrated system and provision of grid support functions.

With more variable and uncertain patterns of electricity production and consumption, the need for flexibility in the power grid is becoming increasingly crucial. Industrial energy systems have the potential to contribute to providing such flexibility. Yet, there is still a lack of effective methods to quantify the magnitude of available flexibility from industrial energy systems that can be optimally dispatched to support the operation of the power grid. This paper studies the flexibility provision from steam systems, which exist in many energy-intensive industries. A generic model of industrial steam systems with turbine-generators is presented to reflect its interactions with the power grid. Then, a hybrid physics-based and data-driven approach is developed to approximate the boundaries of the flexibility domain at different operating conditions of the steam systems. The proposed flexibility quantification method is applied to two real industrial steam systems in a paper mill and a steel mill. The results show that the proposed method can approximate the flexibility boundaries under uncertainty steam states and reflect key factors that affect the boundaries. Also, it is shown that neglecting the limits imposed by the steam network leads to an overestimation of flexibility boundaries at certain operating conditions.

State Estimation (SE) is the backbone of modern Energy Management System (EMS) due its capability of processing real time measurements and provide reliable information to system operators. Since its introduction to power systems in the 70's, SE has been widely used in transmission networks. Distribution grids on the other hand lack sufficient number of real time measurements, and for that reason SE has not been widely implemented on these systems. The recent increase in the number of renewable energy sources connected to the grid at lower voltage levels, the advent of Distribution Automation (DA) and Smart Grids necessitate closer monitoring of distribution networks and thus forcing utilities to upgrade their operations and deploy Advanced Distribution Management Sytems (ADMS). Therefore, Distribution System State Estimation (DSE) is paramount to provide real time monitoring of active distribution grids. This papers investigates the formulation of the three-phase distribution state estimation where in the most general case, none of the system buses may have balanced three phase voltages. In such a case, the choice and definition of the slack bus need to be revisited. Most of the existing work arbitrarily assign the root bus of the distribution feeder as the balanced three-phase reference which may not be realistic for today's active distribution systems. This paper addresses this shortcoming of the existing SE formulations by introducing a virtual reference bus. The proposed approach is effectively validated by simulations carried out on distribution test systems.

In the absence of demand-side bids for certain reliability products in the wholesale electricity markets, Independent System Operators (ISOs) traditionally use fixed demand requirements with penalty factors to clear the market. This approach does not allow proper tradeoffs between cost and reliability due to the inelasticity of the fixed requirements. Therefore, ISOs have been replacing the fixed requirements with sloped demand curves. However, the economic interpretation of demand curves, especially for multiple coupled products, is largely missing in both theory and practice. This could lead to the misrepresentation of the demand benefit in the market clearing objective. Using a two-product market model, Part I of this paper reveals two distinct interpretations of demand curves, each associated with a specific form of the market clearing problem. This implies that the construction of demand curves and the use of them in market clearing must be consistent with their economic interpretation. In Part II of this paper, we analyze the sequential and iterative market clearing processes currently implemented in some markets to demonstrate their solution sub-optimality. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is proposed.

Renewables, such as solar and wind generation, combined with storage are becoming a key part of modern grid. This paper develops probabilistic tools for analysis of grid reliability with such variable generation resources. The developed tools improve speed and accuracy of the reliability analysis compared to usual Monte Carlo methods. This is achieved by using an extension of well known convolution method applicable to interdependent variables. The interdependent distributions are obtained from historical data using Machine Learning of quantile models. The paper presents a novel approach to the analysis of reliability contribution of storage based on these models and related to Information Theory. The developed tools are demonstrated in several example scenarios for ISO-New England service area.

The increasing penetrations of renewable energy in the electricity sector and plug-in electric vehicles (PEVs) in the transportation sector have increased the interests in introducing new methods to deal with uncertainties in power system studies. In this paper, a new distributionally robust optimization (DRO) via scenario wise ambiguity set is proposed to develop a collaborative bidding strategy for intermittent resources such as hydroelectric generation, wind farms, solar farms and electric vehicle aggregator in the day-ahead energy market. The proposed scenario wise ambiguity set is based on Wasserstein distance and is capable of considering both distributional information and statistical distance metric information in the ambiguity set. In this context, the robust counterpart of proposed DRO applying scenario based affine recourse approximation is developed in this paper. The proposed methodology is applied on a 3-bus test system as well as IEEE 118-bus test system to corroborate the effectiveness of the novel DRO model.

This paper introduces a novel application of a stochastic filtering algorithm–unscented particle filter (UPF)–to estimate the inaccessible state variables of doubly fed induction generator (DFIG) connected to a multi-area power system with local phasor measurement units (PMUs). This dynamic estimation implementation bears more advanced features than the particle filter (PF) method since it can not only track the dynamic states more accurately and smoothly when the power system experiences sudden disturbances, but also manage to resolve the particle degeneration problem that exists in the PF algorithm. Moreover, the proposed UPF-based dynamic state estimation method is achieved in a decentralized manner and only uses local PMU measurements of voltage and current. Through a comparison study where popular stochastic filtering methods, unscented Kalman filter (UKF) and PF, are employed to achieve the same estimation purpose, this paper shows the superiority of the UPF algorithm particularly designed for state estimation over the other two existing algorithms in terms of accuracy and error tolerance.

This paper presents a market-based resource adequacy assessment framework to analyze the system generation portfolio that results in a competitive market environment. In particular, we apply the modeling framework to investigate the impact of high penetration levels of variable renewable energy resources and different market designs on achieving resource adequacy. The model captures the strategic capacity investment and retirement decision-making of profit-maximizing generation companies. In contrast to many previous studies, this paper considers markets for capacity, energy, and reserve products in detail; thus, the modeling framework allows improved analysis of generation expansion and revenue sufficiency in a competitive market environment. The modeling framework is based on Stackelberg leader–follower games and is formulated as a bi-level optimization problem, which is then transformed into a mathematical program with equilibrium constraints. Furthermore, we employ a Lagrangian decomposition algorithm to enhance computational performance. In a case study of the electric reliability council of texas (ERCOT) system, we compare the results from the proposed model with those obtained from a traditional centralized least-cost planning model. The results demonstrate the effectiveness of the proposed modeling framework and highlight the importance of considering strategic behavior in a competitive market framework when assessing resource adequacy.

This paper is committed to developing a thorough analysis on the electromechanical oscillations (EOs) of doubly-fed induction generator (DFIG) wind turbines (WTs), taking into consideration the implemented frequency control and drivetrain dynamics of the mechanical system. Some basic recommendations for mitigating EOs would be hence attained. To this end, a linearized model of DFIG WT is proposed to present the interplay between frequency control and the dynamics of WT's drivetrain and electrical system. Then, the conventional torque analysis method is used to establish the basic effects of the frequency control on the damping of the drivetrain and electrical system frequency dynamics. Therein, the adverse effects could incur detrimental EOs, viz., drivetrain torsional oscillations (TOs) and electrical system low-frequency oscillations (LFOs). To mitigate such TOs and LFOs, this paper proceeds with an investigation of different alternatives to stabilize the torsional dynamics of drivetrain and the frequency dynamics of electrical system, meanwhile, to minimize undesired impacts caused by their interactions. Simulation studies on two test systems validate: the basic concepts obtained from theoretical analysis and that the findings can be generalized to multi-machine systems.

This paper presents a two-timescale multi-objective coordinated volt/var optimization system for active distribution networks with renewable energy resources (RESs). In the hourly timescale, power loss and the number of control actions of on-load tap changers and (switchable) shunt capacitors for 24 h are minimized by coordinating on-load tap changers, (switchable) shunt capacitors, and reactive power outputs of RESs. A three-step method consisting of global search, user preference, and local search is adopted to solve the corresponding multi-objective mixed-integer nonlinear optimization problem. A rolling volt/var optimization is executed every 6 h, considering the day-ahead RES forecast errors. In the 15-min timescale, the RES reactive powers are optimized with RES uncertainties explicitly taken into consideration to minimize the pilot-bus voltage deviation under all representative scenarios. The pilot buses are those buses whose voltage magnitudes capture the network voltage profile. In this paper, a linear method is applied to quickly identify a set of pilot buses, and the impact of the identified pilot bus on the entire network voltage profile is evaluated. The IEEE 33-bus distribution network is used to evaluate the proposed two-timescale volt/var optimization formulations and methods with promising results.

In this paper, we propose an integral data rectification strategy for phasor measurement units (PMUs) in multi-area power systems, comprising local data processing and central data recovery modules. The local data processing is designed for the purpose of detecting and eliminating false PMU measurements, which is performed in a decentralized manner, based on our previously developed dynamic state estimation technique; whereas the data recovery is performed in a centralized manner at the control center, based on a newly proposed adaptive-phasor approach. The recovered PMU measurements are then utilized in a low-frequency oscillation damping enhancement scheme, which is achieved by a modified proportional-integral (PI) power system stabilizer (PSS) mechanism embedded in the automatic voltage regulator structure of a synchronous generator. Control parameters of the PI-PSS are optimized by maximizing the critical damping ratio of the power system. This paper is intended to make a contribution to the need of high-quality data transmission in modern power grids with contemporary measuring technologies.

With the increased uncertainty in the power system operation due to growing penetration of highly intermittent energy sources such as wind power, the need for the impact assessment of the renewable penetration on system operating risk and the quantification of benefits of using energy storage technologies is more than ever. A recovery-risk-analysis-based analytical framework for operating risk assessment of wind-integrated bulk power system following a major contingency disturbance is presented in this paper. Two new indices that quantify the recovery risk profile of the bulk power system and its load delivery points following major disturbances are introduced in this paper. The indices obtained from the proposed framework quantify the impact of increasing wind penetration on the system operating risk and the reliability benefits of using fast-responding energy storage system such as flywheel energy storage systems. The proposed methodology is illustrated through several case studies carried out in a test system.

The reduction in bus voltage magnitudes as the load demand grows may lead to sudden disconnection of loads and/or distributed generation units, in distribution grids, caused by undervoltage protection schemes. As proposed in this paper, this discontinuous behavior of distribution grids can be modeled as a sudden load variation in traditional static voltage stability assessment methods, such as the continuation power flow (CPFLOW). A discussion on the impacts of these discontinuities on the equilibrium diagram of the system is presented in this paper, as well as a set of numerical simulations showing that the traditional CPFLOW algorithm presents convergence problems caused by the discontinuities under analysis. From this perspective, this paper proposes an algorithm based on novel predictor/corrector and identification schemes, which are capable of successively calculating the discontinuities that exist in the equilibrium loci of the system under analysis, as well as the maximum loadability point and the type of bifurcation. A simplified modeling approach that eliminates the need for a complex (and computationally expensive), detailed description of distribution grids is also elaborated and incorporated into the proposed algorithm. The simulated examples show that the proposed algorithm adequately handles the problem, yelding more accurate results than the traditional CPFLOW algorithm.

This paper proposes a machine learning approach for placing control actuators to enhance dynamic performance in power systems with high penetration of renewable energy. The method transforms the placement problem into a classification problem by studying the relationship between system topological, physical and operational features, and dynamic performance related labels. The proposed framework includes four stages: power system setup; dataset creation; feature extraction and analytical model generation, and creating guidelines for system planners. Test results in the IEEE 39-bus system and the IEEE 118-bus system determine the most significant features including inertia, topological, and impedance distributions. This paper also contributes to providing general criteria and guidance for system planners to place the control resources for improving oscillation damping, voltage, and transient stability issues.

Experimental results and performance analyses of wind farms providing secondary frequency regulation is lacking. This is despite the increasing share of variable generators in electrical energy production. As variable generators displace traditional generators that provide ancillary services, there is a growing need for alternate providers of these ancillary services. Wind generators are technologically capable of providing ancillary services, such as frequency regulation. This paper presents experimental results of a secondary frequency regulation test carried out on a 10 MW wind farm with Type-V wind turbines. This is a 5 h test of the wind farm's ability to follow an external, historical automatic generation control signal. 1 MW of power is offered on the regulation market with up-regulation provided via a fixed curtailment value. The performance of the farm is evaluated using the performance score method developed by the Pennsylvania–New Jersey–Maryland (PJM) system operator. Performance scores are used to perform an economic analysis using 2017 hourly PJM price data. This results in an estimated additional income of $\$$ 7200 from participating in the regulation market versus providing energy alone. A sensitivity analysis reveals that profitability is more influenced by the turbine's performance scores than regulation and energy prices. This paper also examines some practical aspects of providing secondary regulation, such as pitch activity, power error, and operating power ranges.

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.

In recent years, under deregulated environment, electric utility companies have been encouraged to ensure maximum system reliability through the employment of cost-effective long-term asset management strategies. To help achieve this goal, this research proposes a novel statistical approach to forecast power system asset population reliability. It uniquely combines a few modified Weibull distribution models to build a robust joint forecast model. At first, the classic age based Weibull distribution model is reviewed. In comparison, this paper proposes a few modified Weibull distribution models to incorporate special considerations for power system applications. Furthermore, this paper proposes a novel method to effectively measure the forecast accuracy and evaluate different Weibull distribution models. As a result, for a specific asset population, the suitable model(s) can be selected. More importantly, if more than one suitable model exists, these models can be mathematically combined as a joint forecast model to forecast future asset reliability. Finally, the proposed methods were applied to a Canadian utility company for the reliability forecast of electromechanical relays and the results are discussed in detail to demonstrate the practicality and usefulness of this research., 8 pages, 8 figures

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method.

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 rate of change of frequency services improve with reduced delivery times, limits are identified where further reductions produce 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 trialled which demonstrate consistency in performance and the occurrence of diminishing returns.

The second part of this two-part paper discusses how to determine whether a device connected to the grid is providing inertial response and/or frequency control. The proposed technique is based on the index proposed in Part I of this paper. This part first discusses the dynamic behavior in terms of the rate of change of controlled power of a variety of non-synchronous devices that do and do not regulate the frequency. These include passive loads, energy storage systems and thermostatically controlled loads. Then a case study based on a real-world dynamic model of the all-island Irish transmission system discusses an application, based on a statistical analysis, of the proposed technique to wind power plants with and without frequency control. The properties and the robustness with respect to noise and other measurement issues of the proposed technique are also thoroughly discussed.

High-voltage direct current (HVDC) transmission lines are increasingly being installed in power systems around the world, and this trend is expected to continue with advancements in power electronics technology. These advancements are also bringing multiterminal direct current (MTDC) systems closer to practical application. In addition, the continued deployment of phasor measurement units makes dynamic information about a large power system readily available for highly controllable components, such as HVDC lines. All these trends have increased the appeal of modulating HVDC lines and MTDC systems to provide grid services in addition to bulk power transfers. This paper provides a literature survey of HVDC and MTDC damping controllers for interarea oscillations in large interconnected power systems. The literature shows a progression from theoretical research to practical applications. There are already practical implementations of HVDC modulation for lines in point-to-point configuration, although the modulation of MTDC systems is still in the research stage. As a conclusion, this paper identifies and summarizes open questions that remain to be tackled by researchers and engineers.

To uncover the mechanism by which voltage-dependent loads (VDLs) influence electromechanical dynamics, this paper mathematically deduces the damping torque induced by VDLs based on a single machine infinite bus system. Moreover, by continuously regulating the terminal voltage of a typical large-scale VDL, i.e., the aluminum electrolysis load, this paper proposes a novel load damping control architecture to dynamically modulate the power consumption of controlled loads so as to eliminate interarea oscillations in power systems. Specifically, the decentralized load damping controllers (LDCs) used in this architecture are designed by a proposed multistage mixed ${{{H}}_{2}} / {{{H}}_\infty }$ control approach to guarantee robustness against uncertainties (e.g., tie-line outages and wind generation fluctuations) as well as to ensure reasonable control efforts of controlled VDLs. Simulation results on the modified New England and New York interconnected system validate the method for analysis of VDLs’ effects on interarea oscillations and show the proposed load damping control strategy can satisfactorily damp interarea oscillations over multiple operating points.

This paper presents a method for harmoniously coordinating roles between generators and faster-acting energy storage systems (ESSs), e.g., batteries, to improve their frequency response and regulation services to the grid, particularly at the high wind penetration. This paper theoretically demonstrates that the proposed droop with the state of charge (SOC) feedback (DaSOF) provides a unified frequency control framework for distributed and energy-constrained ESS, seamlessly fitting the conventional primary and secondary frequency control practice. The ESS autonomously takes charge of high-frequency components of a frequency deviation and thus complements the frequency controls of the incumbent generators. By securing the SOC of the ESS at the desired level, the method provides the capability to provide other ancillary services from the energy-constrained ESS. These coordinated roles are represented and validated through time- and frequency-domain analyses. Rigorous case studies using DIgSILENT/PowerFactory demonstrate the efficacy of the proposed method to support the high level of variable renewable energy, which should help in improving the value of operating the ESS and scale up renewable energy.

Optimal placement and sizing of distributed energy resources (DERs) can be performed from various points of view. In this paper, a multiobjective function is employed to determine optimal place and size of DERs including combination of photovoltaic resources and a small-scale synchronous generator (SSSG) with an internal combustion engine as its prime mover. As the objective function, not only minimization of investment costs, operation costs, and system losses are used, but also the voltage of microgrid (μG) busses and current of lines are considered as constraints. In addition, this paper shows mechanical stresses can be imposed on the SSSG caused by generator swings after short circuit fault clearing. Furthermore, it may lead to mechanical fatigue in its associated shaft and prime mover. Meanwhile, it is revealed that the mechanical stresses depend on the DERs placement and size. Thus, a novel index representing the stresses on the SSSG shaft is applied in the multiobjective goal function. This decreases SSSG loss of life in a μG with overhead lines which has high rate of short circuit faults. Optimization studies are performed in a six-bus and four-DER μG using time-based simulations.

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.

This paper analyzes different models for evaluating investments in Energy Storage Systems (ESS) in power systems with high penetration of Renewable Energy Sources (RES). First of all, two methodologies proposed in the literature are extended to consider ESS investment: a unit commitment model that uses the System States (SS) method of representing time; and another one that uses a representative periods (RP) method. Besides, this paper proposes two new models that improve the previous ones without a significant increase of computation time. The enhanced models are the System States Reduced Frequency Matrix (SS-RFM) model which addresses short-term energy storage more approximately than the SS method to reduce the number of constraints in the problem, and the Representative Periods with Transition Matrix and Cluster Indices (RP-TM&CI) model which guarantees some continuity between representative periods, e.g. days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as benchmark. While both system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The RP-TM&CI model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

This paper presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks.

DC electric power systems, including HVDC, HVDC light, dc distribution systems, and dc microgrids, have a significant position in present and, especially, future electric power systems, namely, smart grids. One of the most significant issues of electric power systems, including dc electric power systems, is the calculation of accurate and fair energy costs of the consumers. In this paper, novel, simple, practical, and precise relations between economic and electrical variables in dc electric power systems are extracted. The novel relations prove that there are interesting relations between voltages of nodes, as electrical variables, and prices of nodes, as economic variables, in dc electric power systems. Applying the extracted novel relations in this paper, a new, fair, precise, practical nodal price, i.e., the fair nodal price, is determined to calculate the energy costs of consumers in dc electric power systems, including dc distribution systems and microgrids. Moreover, as an application of the extracted novel relations, a practical method to determine the fair nodal prices of all nodes and, as a result, the real energy cost of each consumer at each node of dc electric power systems is presented. The analyses results demonstrate the mentioned significant characteristics.

This two-paper series introduces a new multistage robust approach for solving the transmission expansion planning (TEP) problem under the uncertainty of loads and wind power productions. The proposed multistage robust TEP model is formulated using mixed binary and linear decision rules, which aims at finding a solution withstanding any realization of the uncertain parameters belonging to a set-based uncertainty model. The robustness of the optimal expansion plan can be adjusted by the budget of uncertainty. The first part of this two-paper series presents the extended and compact formulations for a deterministic TEP problem. Then, the compact formulation of the TEP problem under the uncertainty of loads and wind power productions is introduced as a multistage robust optimization problem, which is reformulated using mixed binary and linear decision rules. The extended formulation of these decision rules, as functions of uncertain parameters, is presented in this part as well.

The unit commitment is still a widely studied problem, especially when more renewable energy of stochastic character is being added to power systems. This paper proposes a model for weekly planning of systems involving hydro, wind, and thermal energy under a stochastic perspective. The proposed model follows the endogenous reserve determination criterion to achieve a simultaneous optimization of energy and reserves considering wind uncertainty, forced outages of equipment, a dc Flow model of the network, and cascaded head sensitive hydro systems, among others. The paper also develops a practical solution methodology based on outer approximation and benders decomposition. Testing has been conducted over four systems, and computational results demonstrate that the proposed model and solution are useful and effective. Fil: Lopez Salgado, Carlos Josue. Universidad Tecnológica Centroamericana; Honduras Fil: Año, Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina Fil: Ojeda Esteybar, Diego Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina

This paper extends the dynamic coherency determination (DCD) method by including Type-3 wind power plants (WPPs) models in the coherency evaluation process and quantifies impacts of the high depth of wind power penetration on the coherency phenomena of interconnected power systems. The method is based on frequency-deviation signals, measured at nongenerator buses and terminal buses of WPPs and synchronous generators. Salient features of the presented method are its ability to account for the hybrid nature of enhanced generic models of Type-3 WPPs and expanding notion of the power system coherency beyond the classical definition by including models of Type-3 WPPs. This paper also investigates impacts of the high depth of wind power penetration; i.e., up to 30%, on the coherency structure of the 16-machine/68-bus NPCC equivalent system. The studies are based on time-domain simulations in PSS/E software. The investigations reveal that Type-3 WPPs can introduce new coherent areas, significantly change areas’ boundaries and number of areas, and alter frequencies/dampings of interarea modes.

In the restructured power industry, bulk energy storage may play a crucial role to provide the flexibility required by system operators to cater for the unprecedented levels of uncertainty. Within the context of co-optimized electricity markets for energy and reserves under wind uncertainty, this paper addresses the incorporation of bulk energy storage units in day-ahead network-constrained energy and reserve scheduling. A novel two-stage robust optimization approach is presented whereby the nonconvex and time-coupled operation of storage devices is precisely modeled while accounting for the anticipativity of the two-stage setting. The resulting robust counterpart is cast as a mixed-integer trilevel program with lower-level binary variables. In order to address the nonconvexity of the recourse problem, this paper proposes the application of an exact nested column-and-constraint generation algorithm. Numerical results illustrate the effective performance of the proposed approach.