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This paper proposes an incentive-based coordination mechanism between a wind energy supplier (WES) and a conventional energy supplier (CES) to achieve a Pareto improvement. To comply with its day-ahead schedules and hedge against the intermittent wind energy generation, the WES is allowed to outsource a back up power capacity from the CES via making a bilateral contract. However, unanimous agreement cannot always be achieved since each party plays on its own interest. We employ the concept of swing option contracts to further encourage the suppliers to reach an agreement of the contract. On the one hand, the WES can leverage the uncertainty of wind output by covering possible energy shortage from the CES. On the other hand, the CES can optimally allocate its energy capacity by participating into the electricity market and offering capacity to backup the shortage of energy from the WES. The bidding problem for each supplier is formulated as a multi-stage stochastic programming model, with the objective of maximizing the expected profit while maintaining a low level of risk. Unlike the traditional two-stage approach, the proposed multi-stage model can effectively capture the impact of rebidding process in the real-time market. We incorporate conditional-value-at-risk as a risk measure to characterize the effect of risk perception of suppliers on their bidding decisions. Meanwhile, a game theory based approach is developed to obtain the contract items between the suppliers. Implementation results on real cases are provided to illustrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

This paper presents a framework to carry out multi-area stochastic reserve scheduling (RS) based on an ac optimal power flow (OPF) model with high penetration of wind power using distributed consensus and the alternating direction method of multipliers (ADMM). We first formulate the OPF-RS problem using semidefinite programming (SDP) in infinite-dimensional spaces that is in general computationally intractable. Using a novel affine policy, we develop an approximation of the infinite-dimensional SDP as a tractable finite dimensional SDP, and explicitly quantify the performance of the approximation. To this end, we adopt the recent developments in randomized optimization that allow a priori probabilistic feasibility guarantees to optimally schedule power generating units while simultaneously determining the geographical allocation of the required reserve. We then use the geographical pattern of the power system to decompose the large-scale system into a multi-area power network, and provide a consensus ADMM algorithm to find a feasible solution for both local and overall multi-area network. Using our distributed stochastic framework, each area can use its own wind information to achieve local feasibility certificates, while ensuring overall feasibility of the multi-area power network under mild conditions. We provide numerical comparisons with a new benchmark formulation, the so-called converted dc (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of clearing sequential electricity markets under uncertainty. We consider the European approach, where reserves are traded separately from energy to meet exogenous reserve requirements. Recently, proposed stochastic dispatch models that co-optimize these services provide the most efficient solution in terms of expected operating costs by computing reserve needs endogenously. However, these models are incompatible with existing market designs. This paper proposes a new method to compute reserve requirements that bring the outcome of sequential markets closer to the stochastic energy and reserves co-optimization in terms of cost efficiency. Our method is based on a stochastic bilevel program that implicitly improves the inter-temporal coordination of energy and reserve markets, but remains compatible with the European market design. We use two standard IEEE reliability test cases to illustrate the benefit of intelligently setting operating reserves in single and multiple reserve control zones. [ABSTRACT FROM AUTHOR]

Hardening components in transmission systems is a practice to improve system resilience against possible disturbances caused by natural disasters. In a power system with a very high penetration of renewable energy, the system hardening will be further complicated by the uncertainty and variability of renewable energy. In this paper, we study the transmission line hardening planing problem in the context of probabilistic power flows injected by the high penetration of renewable energy. We assume that the probabilistic information of renewable energy is incomplete and ambiguous and propose a data-driven approach to approximate the renewable uncertainty sets. We then extend the $N-1$ security criteria to multiple simultaneous contingencies and seek to prepare a hardening plan for the worst-case scenarios. A two-stage data-driven stochastic model is formulated by considering the joint worst-case wind output distribution and transmission line contingencies. Then, we apply the Column-and-Constraints generation method to solve the proposed model. To test the effectiveness of the proposed approach, we conduct experiments on 24-bus and 118-bus test systems. We numerically show that the data-driven approach can effectively address the uncertainty ambiguity and the proposed approach can produce effective hardening plans that improve the system resilience. [ABSTRACT FROM AUTHOR]

Substantial challenges in power systems operation and control as a result of the intermittent and stochastic nature of wind power generation can be significantly alleviated by proficient very short-term wind power prediction interval (WPPI) models. In WPPI models, minimization of cost functions is conducted to train prediction engines and consequently tune their parameters. The prevalent cost functions of prediction engines in WPPI models are mainly non-differentiable and non-convex, and therefore the training process becomes problematic. To transcend such a crucial barrier, this paper addresses a new very short-term WPPI framework based on a bi-level formulation and benefiting from a differentiable and convex cost function. The prediction engine is trained by classical global optimization of the cost function in the lower-level problem, while hyperparameters that control the quality of the WPPIs are injected thereto from the upper-level problem. The hyperparameters can be tuned such that the most useful WPPIs are constructed from the lower-level problem depending on the power system operator's preferences. Lessening the need to heuristically tune a large number of prediction engine parameters is the foremost contribution of this work to the WPPI literature. The superior performance of the proposed WPPI is verified in the multistep ahead prediction of real wind power generation data in comparison to well-tailored benchmark models. [ABSTRACT FROM AUTHOR]

Security-constrained economic dispatch is executed based on consecutive dispatch intervals in real time. The short-term variations of wind power can cause the system to enter an unsecured operating region inside a dispatch interval, which is referred to as the intra-interval security (IIS) problem in this paper. IIS may not be addressed by existing dispatch methods as the traditional dispatch is to ensure the security at the interval end. This can lead to IIS risks in an interval when a high-wind-penetrated system encounters large intra-interval variations (IIVs) of wind power. To address this issue, an IIS preventive dispatch approach is proposed in this study. This approach models the wind power IIVs and develops a preventive dispatch to ensure the IIS over the interval in terms of line overloads while taking into account the tradeoff between security and economics. The proposed approach is implemented in a linear-programming model whose global optimum can be efficiently obtained by adding Benders-like cuts. Case study on the IEEE 118-bus system proves that a high level of nonlinear IIVs can cause the IIS issue (inside of an interval) even with accurate wind forecasts at both interval ends. The simulations show that some lines can have severe overloads inside the dispatch interval in spite of having flows below their capacity limits at both interval ends, and demonstrate that the proposed approach can mitigate such intra-interval overloads even under the worst case IIV scenario. [ABSTRACT FROM AUTHOR]

High penetration of wind energy in the modern power system exposes the need of smoothing the fluctuating output power in an effective and conducive way. In this context, this paper proposes two novel control strategies that utilize the self-capability of permanent magnet synchronous generator-based wind turbine to realize power smoothing. The first strategy pursues to offer power smoothing support via simultaneous utilization of dc-link voltage control, rotor speed control, and pitch angle control. The second control strategy seeks to coordinate the three concerned individual control schemes in a hierarchical manner, where the power smoothing tasks are allocated to individual control modules or their combinations dynamically in line with WT's operation status. Both two strategies are able to provide power smoothing support by fully exploiting wind turbine's self-capability, whereas the second strategy has the merits on 1) reducing the activation frequency of pitch angle control, and 2) enhancing wind energy harvesting. Case studies of the proposed control strategies are carried out to compare and verify their effectiveness in achieving power smoothing. [ABSTRACT FROM AUTHOR]

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications. [ABSTRACT FROM AUTHOR]

The large scale integration of wind energy generation adds a new burden to frequency regulation because of the volatility of wind power. Incorporating wind turbines (WTs) into automatic generation control (AGC) is a new approach for maintaining a satisfactory frequency regulation performance, which requires the active power control (APC) of WTs. Conventionally, the APC of WTs is achieved by pitch angle control (PAC) or rotor speed control (RSC), which respectively show a slow response and a narrow regulating range. To improve the regulation performance of variable speed WTs, a coordinated APC strategy that includes the simultaneous activation of PAC and RSC and takes full advantage of rotor kinetic energy to absorb or release extra mechanical energy is proposed in this paper. The simulations focusing on both the WT and power system levels have shown the effectiveness of proposed control strategy. [ABSTRACT FROM AUTHOR]

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real-world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Therefore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management. [ABSTRACT FROM AUTHOR]

In this letter, a novel solution method of generalized robust chance constrained real-time dispatch (GRCC-RTD) considering wind power uncertainty is proposed. GRCC models are advantageous in dealing with distributional uncertainty, however, they are difficult to solve because of the complex ambiguity set. By constructing traceable counterparts of the robust chance constraints and using the reformulation linearization technique, the model is transformed into a deterministic linear programming problem, which can be solved efficiently by off-the-shelf solvers. Numerical results verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

A method based on the rational fractional representation is developed for parametric stability analysis of power systems, such as wind power integrated power systems. A fitted parametric state matrix with its parameters in rational fractions rather than traditional polynomials is shown to enjoy improved accuracy. The generalized linear fractional transformation is used to develop the standard linear M (s)-Δ feedback system for robust stability analysis. The mapping theorem is then applied to compute the value set plots of the determinant of the return difference matrix, i.e., det (I − M (s) Δ) for the M (s)-Δ model, and the celebrated zero exclusion principle is directly extended to the value set plots of det (I − M (s) Δ), which reveals the robust stability of the power system with rational fractional uncertainty. The performance of the proposed method is tested on several systems with integrated wind powers including a classic four-generator 11-bus test power system and a 547-machine 8647-bus model of the actual North China system. [ABSTRACT FROM AUTHOR]

The control of reactive power exchange between grids of different voltage levels has always been a concern for system operators. With production moving from the transmission to the distribution level, its importance increases. This paper proposes a novel approach to estimate reactive power capability of the grid as a whole. A linearized analytical model for an estimation of available reactive power exchange at the interface between two grids has been developed. The maximum estimation error for the scenarios we tested was only 2%. The model gives the relation between important grid parameters and the supported reactive power. The conclusions drawn from the model are confirmed on typical Swedish distribution network with scattered wind power and small industry consumers. Common scenarios in development of distribution grids are applied to show relevant parameters influence. One studied scenario is replacement of overhead lines with cables. It is shown that this particular change enhances the reactive power capability of the grid which is directly seen from the analytical analysis without running any optimal power flow. The analytical model proposed in this paper gives fundamental understanding of the reactive power capability of radial distribution grids. [ABSTRACT FROM AUTHOR]

The ever-growing insertion of intermittent energy sources requires to account for this uncertainty by precise models. Probabilistic constraints are an interesting technique to deal with the high fluctuations of such energy resources, while accounting for power flow and unit generating constraints. In the context of hydrothermal unit commitment problems, with a significant share of wind power generation, this paper suggests a novel mixed integer optimization model with joint probability constraints and continuous distributions that handles the probability constraints exactly. Thus, extending classical cutting plane methods, the approach provides at each iteration a feasible solution and a certificate on its gap to optimality. The efficiency of the algorithm is tested by numerical experiments when compared to two popular probability constrained approaches: individual and a sample-based method. A set of out-of-sample Monte-Carlo evaluations confirms that the solutions provided by the algorithm do indeed satisfy the a priori defined probability level with optimal costs, contrary to the solution given by individual probability constraints. The simulations also intend to highlight the advantages of the proposed optimization model since it does not depend on the set of sampled scenarios. The proposed method is also evaluated on a 46-bus based system, showing its capabilities on larger systems. [ABSTRACT FROM AUTHOR]

The inherent uncertainty in predicting wind power generation makes the operation and control of power systems very challenging. Probabilistic measurement of wind power uncertainty in the form of a reliable and sharp interval is of utmost importance, but construction of such high-quality prediction intervals (PIs) is difficult because wind power time series are nonstationary. In this paper, a framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator (DiE) is utilized to achieve high-quality PIs for nonstationary wind power time series. Moreover, to adaptively capture the uncertainties of both the prediction model and wind power time series in different seasons, the DiE is equipped with a fuzzy inference system and a tri-level adaptation function. The proposed framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the efficiency of the proposed framework compared to well-known conventional benchmarks using real wind power datasets from Canada and Spain. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for estimating the optimal amount of automatic frequency restoration reserve provided by an aggregation of renewable power plants. The increasing penetration of distributed weather-dependent renewable generation presents a challenge to grid operators. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently hinders their integration into reserve mechanisms. In the methodology developed a quantile regression forest model is used to forecast the aggregated production and a copula-based approach integrates the dependence between prices and renewable production. We then propose and compare three strategies to derive an optimal quantile of the combined production forecasts that can be used as basis to provide a reliable ancillary service to the System Operator. The methodology is evaluated using historical prices for energy and automatic frequency restoration reserve along with production measurements from the several renewable power plants. [ABSTRACT FROM AUTHOR]

The development of medium-/long-term studies for power-system operation and planning under the uncertainty of renewable generation is a key challenge faced by power-system agents worldwide. There is a vast literature on stochastic optimization models devoted to addressing the relevant issues on both operation and planning applications. Notwithstanding, few papers focus on addressing the gaps within the subject of joint scenario generation despite the high sensibility of stochastic optimization models with regard to their input scenarios. Characterizing wind power generation (WPG) stochastic processes to devise time- and spatial-dependent scenarios, based on simulation procedures, for time horizons of one to a few years is a difficult task. Multiple regimes and non-Gaussian distributions are two of the main issues that significantly change the risk described through generated scenarios. In this paper, a new methodology to simulate long-term joint scenarios for multivariate WPG time series is presented. The proposed framework, known as generalized auto regressive score models, is derived based on a new class of time-series model with time-varying parameters and an arbitrary non-Gaussian distribution. Our case study shows, based on real data from the Brazilian power system, that the proposed methodology is capable of producing scenarios with coherent temporal and spatial dependence that are needed in power system studies. [ABSTRACT FROM AUTHOR]

Uncertainty of the wind power output is the main influence factor for wind power utilization in the bulk power system. Because of the large capacity and the load characteristic, energy-intensive load holds great potentials to share the burden of wind power fluctuation smoothing. In this paper, a direct control scheme of the energy-intensive load power is proposed to smooth the wind power fluctuation. Based on the characteristic of the energy-intensive load production technology, the feasibility of energy-intensive load control and its influence on the production are discussed. A feedback control is designed to regulate the saturable reactor and transformer tap changer when the power support from the bulk power system is limited. Regulation capacity is analyzed quantitatively based on the energy-intensive load equivalent circuit model. A grid-connected energy-intensive enterprise driven by self-generation wind power plants and thermal power plants is studied as an example case. By the proposed energy-intensive load control scheme, power fluctuation of the wind farms can be smoothed with the minimum control cost. Besides, the regulation requirement of the bulk power system is eased correspondingly so that the utilization capacity of the power system can be improved significantly. Simulation is done on the real-time digital simulator to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability distribution of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimization is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then, two methods are employed to solve the decomposed problem, namely, the subgradient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case studies were conducted to verify the efficiency of the dual decomposition and L-BFGS-B method for solving the stochastic ED problem. [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]

Frequency control of isolated power systems with high penetration of wind power is a challenging task for system operators. The difficulty results from the limited frequency response capability of thermal power units to keep up with the fluctuations of wind power. This paper develops a field verification architecture to leverage aluminum smelting loads (ASLs) to participate in primary frequency control. A new method is proposed to coordinate ASLs with thermal power units to quickly stabilize the isolated power system with significant wind power fluctuations or curtailments. Moreover, a closed-loop control system for the ASLs controller is developed and tested on a real-world isolated power system in Inner Mongolia, China. Hardware-in-the-loop simulations, as well as field tests, are conducted under various scenarios of wind power fluctuations or curtailments to verify the proposed architecture for frequency control. The highly consistent simulation results and field tests demonstrate the good frequency control capability of the ASL. The regulated power from the ASL is demonstrated to be able to reach 6% of its rated power within 4 s. Working jointly with thermal power units, the ASL helps maintain the stable operation of the isolated power system, significantly improving the frequency response characteristic. [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]

Large-scale and ubiquitous penetration of wind power generation to power systems necessitates more conservative provision of system reliability by ensuring adequately committed reserve and observance of transmission constraints. In addition, wind power curtailment due to the technical limitations of system operations, such as transmission congestion, should be efficiently mitigated. To this aim, this paper presents a congestion risk-aware unit commitment formulation in a two-settlement market environment. The uncertainty impact of multicorrelated wind power and contingencies on the risk of transmission congestion for each line, called the Line Transfer Margins (LTM), is incorporated using basic statistical data on the nodal wind power forecast and probability of credible line-outages across the system. The LTMs, formulated free of any distributional assumptions, collectively provide a measure for transmission reserves, which effectively mitigate the likelihood of transmission congestion, reserve undeliverability, and wind power curtailment in the real-time economic dispatch. The effectiveness of the proposed approach is verified through comparative case studies on IEEE RTS-96 for various wind power and LTM scenarios. [ABSTRACT FROM AUTHOR]

We build an advanced offer curve in the day-ahead electricity market for wind power producers based on the multivariate (bivariate or two-dimensional) distribution of the real-time (RT) price and wind power forecasting errors. The bivariate distribution results in more profitable offers based on the conditional probability of wind power forecasting errors with respect to the RT price forecasting errors. The standard deviation of the bivariate distribution is reduced through a novel probabilistic wind power forecasting algorithm based on a parametric approach to further increase the profitability of the offer curve. In conclusion, the test of the advanced offer curve on the data sampled from the Iberian peninsula shows that the offer curve of the bivariate distribution has a higher profitability than offer curves of the marginal distribution. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources and electrical vehicles in utility distribution feeders, it is imperative to study the impacts of the resulting increasing uncertainty on the delivery capability of a distribution network. In this paper, probabilistic available delivery capability (ADC) is formulated for a general distribution network integrating various renewable energy sources (RES) and load variations. To reduce the computational efforts by using conventional Monte Carlo simulations, we develop and employ a computationally efficient method to assess the probabilistic ADC, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. Particularly, the proposed method is able to handle a large number of correlated random inputs with different marginal distributions. Numerical examples in the IEEE 13 and IEEE 123 node test feeders are presented, showing that the proposed method can achieve accuracy and efficiency simultaneously. Numerical results also demonstrate that the randomness brought about by the RES and loads indeed leads to a reduction in the delivery capability of a distribution network. [ABSTRACT FROM AUTHOR]

Using off-site predictors to capture spatio-temporal correlations among geographically distributed wind farms is seen as one solution to improve the forecast accuracy of wind power generation. However, in practice, wind farm operators are usually unwilling to share their private data with each other because of competitive reasons and security considerations. To address this issue, this paper presents how wind power probabilistic forecasting using off-site information could be achieved in a privacy-preserving and distributed fashion. Wind power probabilistic forecasts are created by means of multiple quantile regression. The original large-scale forecasting problem is first decomposed into a large number of small-scale subproblems. The subproblem can be computed locally on each farm. Then, the closed-form solution to the subproblem is derived exactly for achieving high computational efficiency. The proposed approach offers a flexible framework for using off-site information, but without having to exchange commercially sensitive data among all participants. It relies on the alternating direction method of multipliers algorithm to achieve the cooperation among all participants and finally converges to the optimal solution. Case studies with real-world data validate improvements in the forecast accuracy when considering spatio-temporal correlations. Distributed approaches also show higher computational efficiency than traditional centralized approaches. [ABSTRACT FROM AUTHOR]

The level of uncertainty, which the power system operators should account for, increases as more renewables are integrated in the system. Additional operating reserves needs to be scheduled within security constrained economic dispatch to ensure reliability. Existing practices rely on conventional generators for providing operational flexibility required to mitigate the uncertainty introduced by renewables. However, with the increased share of renewables in the generation mix, such intermittent resources would be expected to take part in the balancing tasks, too. Providing a reserve margin allows wind generators to hedge against uncertainty. This paper examines the market implications of wind reserve margin policies used to mitigate uncertainty from wind resources. The market implications of the recently proposed optimized reserve margin policy factors are studied. Analysis on the Reliability Test System 1996 shows that reserve margin policy factors along with the proposed compensation scheme help in allocating payments toward renewable resources that present a good quality of service. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Transmission network expansion planning (TNEP) is expected to cope with the power transmission inadequacy problem resulting from rapid growth of wind generation in power systems. However, due to significant uncertainties on the generation side, TNEP based solely on one snapshot or a limited number of scenarios cannot capture the time-variant characteristics of wind power and may be suboptimal. In this paper, a new heuristic methodology for TNEP based on chronological evaluation is presented. First, to assess the transmission adequacy, the multiperiod DC optimal power flow considering the generators’ ramping limits is employed for a system production simulation covering an entire year. Second, two sensitivity factors derived from Lagrange multipliers, i.e., the sensitivity factors of the branch capacity and the branch admittance, are applied to determine the initial set of the transmission network expansion plan (I-STNEP). Finally, the cost-benefit incremental relationship index and a degressive method are used to identify the best subset of the transmission network expansion plan within the I-STNEP. Simulation results based on a 3000-bus real-world network in northern China validate the effectiveness of the proposed TNEP method. [ABSTRACT FROM AUTHOR]

We present a hybrid metaheuristic optimization algorithm for solving economic dispatch problems in power systems. The proposed algorithm, based on bat algorithm, combines chaotic map and random black hole model together. Chaotic map is used to prevent premature convergence, and the random black hole model is helpful not only in avoiding premature convergence, but also in increasing the global search ability, enlarging exploitation area and accelerating convergence speed. The pseudocode and related parameters of the proposed algorithm are also given in this paper. Different from other related works, the costs of conventional thermal generators and random wind power are both included in the cost function because of the increasing penetration of wind power. The proposed algorithm has no requirement on the convexity or continuous differentiability of the cost function, although the effect on fuel cost, caused by the underestimation and overestimation of wind power, is included. This makes it feasible to take more practical nonlinear constraints into account, such as prohibited operating zones and ramp rate limits. Three test cases are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, a novel approach for look-ahead dispatch (LAD) considering large-scale wind power integration is proposed. An index designated the conditional value-at-risk of wind power (CVaR-WP) is introduced to evaluate the risk of wind power accommodation. A flexible LAD model is developed to balance the operational costs and the CVaR-WP based on robust optimization (RO). According to the proposed model, the base points, participation factors, and flexible capacity of automatic generation control units are co-optimized. In addition, a reasonable admissible region of wind power on each node can be obtained correspondingly. An efficient algorithm based on the big-M method and the decomposition method is presented to solve the resulting bilinear programing model. The proposed approach combines the advantages of RO and stochastic programing, which can avoid the overconservativeness of RO while maintaining its high computing efficiency. Test results illustrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The second part of this two-paper series presents the extended formulation for the lifted multiyear multistage robust transmission expansion planning model introduced in the first part. The proposed planning approach is successfully tested on the Garver 6-bus and on a 118-bus+118-bus test system with different input settings. [ABSTRACT FROM AUTHOR]

Frequency deviations of power systems caused by grid-connected wind power fluctuations is one of the key factors which restrains the increase of wind penetration level. This paper examines a combined wind and hybrid energy storage system (HESS, supercapacitor, and battery) to smooth wind power fluctuations. A fuzzy-based wind-HESS system (FWHS) controller is proposed to suppress the wind power fluctuations. The proposed controller takes full advantage of the complimentary characteristics of the supercapacitor and battery with the supercapacitor and battery in charge of high and middle frequency components of wind fluctuations, respectively. A differential evolution (DE)-based optimal sizing method for HESS systems is introduced to evaluate the minimum capacity of HESS as being limited by grid frequency deviation. The efficiency of the proposed scheme in the paper for wind-HESS system is evaluated by a real Chinese power system. [ABSTRACT FROM AUTHOR]

In the power system stochastic unit commitment (SUC) model, the uncertainty and variability of the renewable energy generation, such as wind power, are usually expressed by multiple scenarios. The performance of SUC is driven by how well the selected scenarios represent the stochastic nature, since too few scenarios may lose or distort the uncertainty or variability characteristics, while too many scenarios usually cause computational difficulty. This paper proposes a novel scenario representation method referred to as scenario mapping technology (SMT), which is able to compact large amount of scenarios while preserving the uncertainty and variability features of wind power as much as possible. Specifically, SMT proposes to combine similar wind power generation into a state at each period and contains the possible multiple transitions linking the states in adjacent periods. A novel two-stage SUC model is proposed based on SMT. The numerical results demonstrate a solid improvement of the performance for the SMT-based SUC model on both cost-efficiency and calculation efficiency, compared with traditional two-stage SUC model based on the scenario reduction technique. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) can locally provide energies in distribution network and thus help to reduce the negative impact caused by large-scale electric vehicle (EV) charging demand. This paper proposes a two-stage robust DG investment planning model capable of accommodating uncertainty of EV charging demand and renewable generations. The DG investment location and size are optimized in the first stage and the distribution network operation feasibility in the worst case scenario is checked in the second stage to avoid constraint violations. The EV charging demand uncertainty is modeled by sampling from real-life scenarios of travel behavior. Finally, the column-and-constraint generation algorithm is adopted to solve the proposed problem. Simulations on modified IEEE 123-node distribution network demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependencies between tens or hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a sparsity-controlled vector autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained mixed integer nonlinear programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a correlation-constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. The results obtained show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy, and efficiency. [ABSTRACT FROM AUTHOR]

Two desirable properties of electricity market mechanisms include: 1) revenue adequacy for the market, and 2) cost recovery for all generators. Previously proposed stochastic market-clearing mechanisms satisfy both properties in expectation only, or satisfy one property by scenario and another in expectation. Consequently, market parties may perceive significant risks to participating in the market since they may lose money in one or more scenarios, and therefore be discouraged from offering in the market or perhaps even from investing. We develop a stochastic two-stage market-clearing model including day-ahead and real-time settlements with an energy-only pricing scheme that ensures both properties by scenario. However, this approach is cost-inefficient in general and may sacrifice other desirable market attributes. Undesirable consequences include: One group of participants will have to pay more to ensure that all other participants have their costs covered, and thus their prices will not be equilibrium supporting; and day-ahead and real-time prices are not arbitraged in expectation, although this can be fixed by allowing virtual bidders to arbitrage but at the potential cost of increased market inefficiency. Considering these pros and cons, we propose our model as an appropriate tool for market analysis, and not for clearing actual markets. Numerical results from case studies illustrate the benefits and costs of the proposed stochastic market design. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method. [ABSTRACT FROM AUTHOR]

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on operational reliability studies of modern power systems taking into consideration the effects of energy storage systems (ESSs). The aim is to develop a new evaluation tool to assess the effects of different factors such as penetration rate, operational strategies, and capacities of the ESSs in determining the role of these systems as a source of operating reserve. In this regard, at first, some modifications are made to the Pennsylvania-New Jersey-Maryland (PJM) method aimed to precisely model the short-term variability in output generation of wind farms in reliability studies. Then, this algorithm is employed to examine the effects of ESSs operating strategies in operational reliability level of the system. Finally, a new formulation for ESSs scheduling problem is proposed to optimally gain the benefits of ESSs in improving the mid-term reliability level of the system. The usefulness of the proposed framework is illustrated using different case studies. [ABSTRACT FROM AUTHOR]

An improved single-machine equivalent method is proposed for wind power plants (WPPs) by calibrating the postfault power recovery behaviors. A real WPP is simulated with extensive wind scenarios to evaluate the traditional single-machine equivalent model, and it is found that its equivalent error is mainly resulted from the postfault recovery process. The simulation analysis further indicates that a wind turbine operating at different wind speeds restores to its prefault active power at a certain ramp rate after fault clearance, with different starting power and different recovery time. Taking a two-machine WPP as an example, the analytical expression for active power of the WPP is derived for the fault ride through process, and thus the equivalent error of traditional single-machine model is found to be resulted from two aspects. One is the mismatch of the starting power of the postfault recovery process, and another is the mismatch of the power recovery rates between the complete WPP and its equivalent model. The analytical expressions are extended to multimachine WPP to calibrate the starting power and the ramp rates of the postfault recovery process. Simulation results show that the proposed method has good performance for various voltage drops, wind scenarios, and grid characteristics. [ABSTRACT FROM AUTHOR]

There is growing concern regarding generation adequacy within the power system industry. The ever-increasing injection of intermittent renewable resources makes it harder than before to estimate the reliability of modern power systems using traditional approaches. This paper develops a framework for estimating the reliability of modern power systems that have considerable levels of wind power generation. Monte Carlo simulation is applied using a very efficient importance sampling technique based on the cross-entropy method as well as the Copula theory. Tailor-made importance sampling functions for conventional generation, load, and wind power generation drastically reduce the number of samples required to estimate reliability parameters of interest. The methodology enables simulation of multi-area power systems with considerable amount of correlated wind power generation in each of the different areas. Simulation results confirm the efficiency as well as the accuracy of the proposed method and show that it is orders of magnitude faster than crude Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. A novel approach is to model input power noise with time-correlated stochastic fluctuations and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses. [ABSTRACT FROM AUTHOR]

Uncertainties associated with wind power integration challenge the operational adequacy of conventional power systems. A set of wind power ramp limits (WPRLs) is proposed in this paper to evaluate the operational adequacy of power systems with high wind power penetration and to provide operating references to wind farms in the form of a ramp power limit (RPL) and ramp rate limit (RRL). The RPL is used to evaluate the minimum and maximum allowable generation of a wind farm by considering the power reserve capacities of generators and power flow constraints of transmission lines. A robust second-order cone programming RPLs formulation with AC power flow constraints and a column-and-constraint generation based solution method are proposed to maximize the total operating range of all wind farms. Meanwhile, a Pareto optimality based RPLs evaluation approach is proposed to handle the coupled relationship among the operating ranges of the wind farms to achieve a balanced RPLs solution for each wind farm. The RRL is used to evaluate the most rapid wind power ramp behavior that can be handled by system frequency regulation without exceeding the designated frequency range. A comprehensive criterion is proposed to evaluate the RRLs by considering primary and secondary frequency regulation. Finally, the effectiveness of the proposed evaluation approach is verified through case studies. [ABSTRACT FROM PUBLISHER]

The papers in this special section examine the enabling of very high penetration renewable energy integration into future power systems. Renewable energy is regarded as one of the main solutions to achieve power system sustainability. In recent years, renewable energy has been experiencing fast development. In 2016, 24.5% of the total power demand in the world was supplied by renewable energy, where 5.5% was produced by wind power and PV [item 1) in the Appendix]. It is estimated that by the end of 2017 the global wind power and PV capacity had reached about 565 GW and 335 GW respectively. Some power systems have already entered the era of high penetration of intermittent renewable energy integration. [ABSTRACT FROM PUBLISHER]