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This paper proposes a method for studying the reliability implications of line switching operations in power systems. Two case studies are conducted on RTS and IEEE 118-bus system to illustrate this method. This method is designed to explore previously overlooked areas in reliability evaluation of line switching operations. Line removal test is proposed to obtain simulation data of the system, and then with risk analysis and impact analysis, six reliability indices are used to evaluate reliability performance of each transmission line in the system. Instead of the traditionally used mean value, this method introduces variance into analysis. Weibull distribution is used to reconstruct distributions of reliability indices which provide worst case scenario comparisons in reliability evaluation. Eventually, with results obtained from the proposed reliability evaluation method, categorization for line switching operations is introduced to classify all transmission lines based on their reliability performance. The categories provide reliability implications of line switching operations and can be used for guidance in actual operations. [ABSTRACT FROM PUBLISHER]

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented. [ABSTRACT FROM PUBLISHER]

This paper proposes a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks. It aims to utilize multiple devices to counteract uncertain voltage fluctuation and deviation. In the hourly timescale (first stage), capacitor banks and transformer tap changers are scheduled before stochastic renewable output and load variations are realized. In the 15-min timescale (second stage), inverters that interface the renewable energy resources provide var support to supplement the first-stage decision after uncertainty is observed. The coordination is model as a two-stage stochastic programming problem with scenario reduction. It is then converted to a deterministic mixed-integer quadratic programming equivalence model and solved by commercial solvers combined. Compared with existing methods, the proposed volt/var control can achieve lower expected energy loss and can sustain a secure voltage level under random load demand and renewable power injection. The proposed method is verified on the IEEE 33-bus distribution network and compared with existing practices. [ABSTRACT FROM PUBLISHER]

To coordinate electric vehicle (EV) charging, the EV aggregator (EVA) is usually assumed to obtain the privilege from EV owners (EVOs) to determine the EV charging profile, and complex communication between EVA and EVOs is demanded, which poses difficulties for practical applications. In contrast, this paper proposes the concept of an EV chargeable region to evaluate the distribution network (DN) EV hosting capacity, i.e., how much EV charging demand can be accommodated in a DN, within which the technical constraints of DN (e.g., voltage deviation) are guaranteed and EVOs’ charging requests are maximally ensured. The optimization of the EV chargeable region is formulated as a two-stage robust optimization model with adjustable uncertainty set. The EV chargeable region and DN decision variables are optimized in the first stage and the feasibility in the real-time worst-case scenario is checked in the second stage, considering the uncertainty of EV charging demand and DN active and reactive power. A modified column and constraint generation and outer approximation method is adopted to address the proposed problem. Simulations on an IEEE 123-node DN demonstrate the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

Implementing automation system in distribution networks needs a huge investment that usually cannot be funded entirely in a short period of time. So distribution companies (DISCOs) should develop long-term plans to equip their network with automation technologies as far as economically justifiable. Aiming to provide a methodology for development such plans, this paper presents a novel multistage planning model for implementing distribution automation system considering all main hardware and software infrastructures. The proposed method optimally determines the type, location, and time in which an equipment or infrastructure must be added to network considering the network capacity expansion plan. The objective function of the planning problem is to maximize the net present value of the DISCO's profit in the planning time horizon. In this method, the optimization problem is solved using the genetic algorithm approach as a promising technique in solving the mixed integer nonlinear programming problems. The proposed method is evaluated on an illustrative test network and the obtained results are presented and discussed. [ABSTRACT FROM PUBLISHER]

Widespread three-phase imbalance causes inefficient uses of low voltage (LV) network assets, leading to additional reinforcement costs (ARCs). Previous work that assumed balanced three phases underestimated the reinforcement cost throughout the whole utility by more than 50%. Previous work that quantified the ARCs was limited to individual network components, relying on full sensory data. This paper proposes a novel methodology that will scale the ARC estimation at a utility level, when the data concerning the imbalance of circuits or transformers are scarce. A novel statistical method is developed to estimate the volume of assets that need to be invested by identifying the relationship between the triangular distribution of circuit imbalance and that of circuit utilization. When there are more data available in future, accurate probability distributions can be constructed to reflect the network condition across the whole system. In light of this, two novel generalized ARC estimation formulas are developed that account for generic probability distributions. The developed methodology is applied to a real utility system in the UK, showing that: 1) three-phase imbalance leads to ARCs that are even greater than the reinforcement costs in the balanced case; 2) a 1% increase in the demand growth rate, the maximum degree of imbalance (DIB) and the maximum nominal utilization rate leads to over 10%, approximately 1% and 2% increases in the ARCs, respectively; and 3) the ARC is not sensitive to the minimum DIB values and the minimum nominal utilization rates. [ABSTRACT FROM PUBLISHER]

The interactions between distribution and transmission systems have increased significantly in recent years. However, in traditional power system simulation tools, transmission and distribution systems are separately modeled and analyzed. Hence, it is difficult to analyze the impacts of distribution systems on transmission systems and their interactions in detail. To facilitate the analysis of integrated transmission and distribution (T&D) systems, a novel modeling framework is proposed in this paper, where the transmission system is modeled as one subsystem in three-sequence detail, while each distribution system connected to it is represented as a subsystem and modeled in three-phase detail. With this modeling approach, unbalanced conditions at the boundary between T&D systems and within the transmission system can be represented. The integrated T&D power flow is solved by iteratively solving a three-sequence power flow for the transmission system and a three-phase power flow for each distribution system. Furthermore, a new dynamic simulation algorithm for integrated T&D system is proposed. The main challenge in developing this integrated T&D dynamic simulation is associated with different network representations in the transmission and distribution systems. With a partitioned solution approach adopted for dynamic simulation, the multi-area Thévenin equivalent approach is utilized in the network solution step to address this challenge. The proposed algorithms have been tested against an all electromagnetic transient simulation. [ABSTRACT FROM PUBLISHER]

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed. [ABSTRACT FROM PUBLISHER]

This paper presents a method for real-time active network management (ANM) control to maximize network-wide energy yield in constrained networks. Coordinated scheduling of renewable distributed generation (DG) and distribution network control assets can limit DG curtailment and significantly increase energy yield and economic performance of DG. Here, an optimal power flow approach has been developed for real-time online scheduling of network control settings to better integrate high levels of temporally and spatially variable DG from renewable energy resources. Results show that the real-time prescription of ANM control settings provides a feasible alternative to network reinforcement under the existing passive management philosophy. [ABSTRACT FROM PUBLISHER]

Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply–demand balance. In this paper, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge, while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues. The proposed coordination is performed to capture both inter- and intra-hour net load variabilities. Numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

This paper proposes a data-driven method based on distributionally robust optimization to determine the maximum penetration level of distributed generation (DG) for active distribution networks. In our method, the uncertain DG outputs and load demands are formulated as stochastic variables following some ambiguous distributions. In addition to the given expectations and variances, the polyhedral uncertainty intervals are employed for the construction of the probability distribution set to restrict possible distributions. Then, we decide the optimal sizes and locations of DG to maximize the total DG hosting capacity under the worst-case probability distributions among this set. Since more information is utilized, our proposed model is expected to be less conservative than the robust optimization method and the traditional distributionally robust method. Using the CVaR (Conditional Value at Risk) reformulation technique and strong duality, we transform the proposed model into an equivalent bilinear matrix inequality problem, and a sequential convex optimization algorithm is applied for solution. Our model guarantees that the probability of security constraints being violated will not exceed a given risk threshold. Besides, the predefined risk level can be tuned to control the conservativeness of our model in a physically meaningful way. The effectiveness and robustness of this proposed method are demonstrated numerically on the two modified IEEE test systems. [ABSTRACT FROM PUBLISHER]

As a critical data input of security constraint unit commitment, bus load forecasting is currently conducted on a bus-by-bus fashion to improve the forecasting accuracy in most provinces in China. On such substation level forecasting, data quality is much worse than the aggregated power demand for the whole system, and identifying and restoring the inaccurate measurement and abnormal disturbance to retrieve the historical trend of load is urged to improve the forecasting accuracy. In this paper, a two-stage identification and restoration method is presented. The typical patterns of inaccurate measurement and abnormal disturbance are detected in the first stage based on statistical criteria independent with normal distribution. Historical trend is further retrieved in the second stage using frequency domain decomposition, and a typical daily curve is generated to compare with the data measurements. The deviations of the data measurements from the typical daily curve obey normal distribution and are used as criteria in the second stage. The effectiveness of the proposed methodology has been confirmed by examples in real bus load forecasting systems in this paper. [ABSTRACT FROM PUBLISHER]

Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies. [ABSTRACT FROM PUBLISHER]

This paper proposes a decision tree (DT)-based systematic approach for cooperative online power system dynamic security assessment (DSA) and preventive control. This approach adopts a new methodology that trains two contingency-oriented DTs on a daily basis by the databases generated from power system simulations. Fed with real-time wide-area measurements, one DT of measurable variables is employed for online DSA to identify potential security issues, and the other DT of controllable variables provides online decision support on preventive control strategies against those issues. A cost-effective algorithm is adopted in this proposed approach to optimize the trajectory of preventive control. The paper also proposes an importance sampling algorithm on database preparation for efficient DT training for power systems with high penetration of wind power and distributed generation. The performance of the approach is demonstrated on a 400-bus, 200-line operational model of western Danish power system. [ABSTRACT FROM AUTHOR]

Environmental factors, such as weather, trees, and animals, are major causes of power outages in electric utility distribution systems. Of these factors, wind and lightning have the most significant impacts. The objective of this paper is to investigate models to estimate wind and lighting related outages. Such estimation models hold the potential for lowering operational costs and reducing customer downtime. This paper proposes an ensemble learning approach based on a boosting algorithm, AdaBoost^+, for estimation of weather-caused power outages. Effectiveness of the model is evaluated using actual data, which comprised of weather data and recorded outages for four cities of different sizes in Kansas. The proposed ensemble model is compared with previously presented regression, neural network, and mixture of experts models. The results clearly show that AdaBoost^+ estimates outages with greater accuracy than the other models for all four data sets. [ABSTRACT FROM PUBLISHER]

This paper develops a generalized admittance matrix approach in Fortescue coordinate system to solve unbalanced/unsymmetrical distribution networks including different number of phases. This generalized Fortescue \pi equivalent is defined in this paper for solving the heterogeneous phase, and thus Fortescue, network model. The performance of the approach is demonstrated in different model networks with number of nodes ranging between 168 and 14200. It is found that the current iteration method exploiting the decoupling in admittance matrix in Fortescue coordinate is substantially faster than the typical unbalanced three-phase solution in phase domain. The method has a significant potential for application in real time active power network management. [ABSTRACT FROM PUBLISHER]

This paper proposes a distributed control approach to coordinate multiple energy storage units (ESUs) to avoid violation of voltage and thermal constraints, which are some of the main power quality challenges for future distribution networks. ESUs usually are connected to a network through voltage source converters. In this paper, both ESU converters active and reactive power are used to deal with the above mentioned power quality issues. ESUs' reactive power is proposed to be used for voltage support, while the active power is to be utilized in managing network loading. Two typical distribution networks are used to apply the proposed method, and the simulated results are illustrated in this paper to show the effectiveness of this approach. [ABSTRACT FROM PUBLISHER]

Security-constrained optimal power flow (SCOPF) problems are essential tools to transmission system operators for long-term and operational planning and real-time operation. The general goal of SCOPF problems is to optimize electricity network operation while ensuring that operational and planning decisions are consistent with technical limits under both pre- and post-contingency states. The solution of SCOPF problems is challenging because of the inherent size and scope of modern grids. As empirical evidence and longstanding operator experience show, relatively few of the constraints of SCOPF problems actually serve to enclose their feasible region. Hence, all those constraints not contributing directly to set up the SCOPF feasible space are superfluous and could be discarded. In light of this observation, this paper proposes an optimization-based approach for identifying so-called umbrella constraints in SCOPF problems where the network operation is approximated by the dc power flow. Umbrella constraints are constraints which are necessary and sufficient to the description of the feasible set of an SCOPF problem. The resulting umbrella constraint discovery problem (UCD) is a convex optimization problem with a linear objective function. For SCOPF problems of practical importance, the UCD is also quite large and requires the use of a decomposition technique. In this paper, we concentrate on an SCOPF formulation for preventive security generation dispatch. We show that by removing superfluous constraints, the resulting sizes of SCOPF problems are much smaller and can be solved significantly faster. [ABSTRACT FROM PUBLISHER]

Distribution system reconfiguration can be achieved by changing the statuses of switches. By altering the topology of a distribution system, the real power loss can be reduced. This paper proposes a novel charged system search (CSS) algorithm, the particle moving evaluation mechanism CSS (PMEM-CSS), for determining the switching strategy in order to solve the distribution system loss minimization problems. In CSS, the position of each charged particle is a solution that represents a specific topology of a distribution system. During the problem-solving process, new solutions are generated in CSS. In order to avoid generating illegal solutions, a novel decoding scheme is proposed in this paper. The proposed decoding scheme can also improve the search performance when PMEM-CSS seeks the best solution. Three different distribution systems, a 33-bus, a 66-bus, and a TPC 248-bus system are carried out in this paper. The performance of different algorithms (GA, PSO, CSS, and PMEM-CSS) is compared. The simulation results show that not only the efficiency of the proposed method is the best among these algorithms bust also the quality of the solution is superior to other algorithms. [ABSTRACT FROM PUBLISHER]

Conventional power system graphic applications use absolute coordinates approach, in which the objects are positioned with respect to a common origin. This can result in problems of re-adjustment and re-alignment in the network and substation diagrams when editing or merging the graphics. This paper introduces the notion of layouts and proposes a graphics model to facilitate positioning of graphical elements relative to each other in a hierarchy of layouts, thereby making the network diagrams flexible, sharable and extensible. This proposed graphics model, expressed in unified modeling language (UML), is shown to be compatible with the existing common information model (CIM) graphics model IEC 61970-453. Consequently, the need for extension of CIM graphics model is justified and an extension is proposed. Another key feature of the proposed model is configurability. Since the necessary parameters are explicitly modeled, the user only needs to provide configuration information describing the hierarchical connectivity of the substation or the network. The absolute coordinates can be calculated from the configuration and the power system diagrams can be auto-generated. This paper demonstrates the above benefits highlighting the technologies for implementation. Case study on 400-kV 20-substation diagrams of Maharashtra system in India is presented. [ABSTRACT FROM PUBLISHER]

The main goal of this paper is to provide an analytical study on the statistical behaviors of the electricity price at various conditions of the load and the network. In order to achieve this goal, a novel two-step modeling is proposed for the electricity price. At the first step, it is proved that there is a linear relationship between the locational marginal prices (LMP) and the strategies of generating companies (GenCos). In this linear relationship, the LMP is decomposed to the weighted summation of the strategies of marginal units plus constant value. At the second step, the LMP is modeled by classic central limit theorem (CLT). Using this model an analytical relationship is derived between the price discovery mechanism and characteristics of its probability density function (pdf). As a result, this paper shows analytically that increasing system load causes the price pdf deviates from the normal distribution. On the other hand, by decreasing system load, the electricity price distribution tends toward the normal distribution. The statistical studies on the real power markets data confirm these analytical results. Furthermore, the simulation results of the IEEE 300-bus test system illustrate the efficiency of the proposed model. [ABSTRACT FROM PUBLISHER]

The lifetime of transmission and distribution power systems is long and thus, long-term plans are needed for their successful development. In generating long-term scenarios, the starting point is the analysis of the present electricity consumption. The data of electricity consumption will become more exact by the end of 2013, when hourly based automated meter reading (AMR) consumption data will be received from each customer in Finland. The amount of data is huge and powerful analysis methods are needed. This paper presents a method for clustering the electricity consumptions using principal component analysis (PCA) and K-means clustering. AMR data of 18 098 customers from two city districts of Helsinki, Finland is applied for a case study reported in this paper. A multiple regression analysis is also carried out on the two largest clusters to find the most important explanatory factors for the load modeling. The interpretations of the clusters and the plausibility of the regression coefficients are considered very important. Five distinct and meaningful clusters are found. The regression models give interesting insights into the explanatory factors behind electricity consumption. The models of the main customer groups assist the distribution system operator (DSO) in the long-term development of the power system. [ABSTRACT FROM PUBLISHER]

Analysis of power systems is typically made using the method of either phase coordinates or symmetrical components depending on the type of network (un/symmetrical). The use of the symmetrical components method, a three-phase specific application of the Fortescue transformation, has been limited to pure three-phase symmetrical networks. Therefore phase coordinates has been the method used most for the analysis of distribution networks. This paper is extending the use of the symmetrical components method to distribution networks combining one-phase, two-phase, and three-phase symmetrical network parts. This paper derives a new integrated approach for the description of such networks based on Fortescue transformation. This approach is demonstrated using the forward-backward sweeping algorithm (FBS). [ABSTRACT FROM PUBLISHER]

This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming. [ABSTRACT FROM PUBLISHER]

Society's increased concern over green house gas emission and the reduced cost of electric vehicle technologies has increased the number of electric vehicles (EV) and plug-in hybrid vehicles on the road. Previous studies into the effects of electric vehicles on the electric system have focused on transmission, generation, and the loss of life of distribution transformers. This paper focuses specifically on identifying distribution transformers that are most susceptible to excessive loading due to the implementation of electric vehicles. The authors use a binomial probability model to calculate the probability that a specific distribution transformer will experience excessive loading. Variables to the function include the existing peak transformer demand, number of customers connected to the transformer, and the most common EV charger demand. Also included in the paper is an optimization approach that utilizes the results from the binomial function to determine the optimal replacement strategy to minimize replacement costs. An extension of the approach is also utilized to explore the effectiveness of EV targeted demand side management programs. The authors apply the described algorithms to 75 000 distributions transformers within a distribution system located in Denver, Colorado, USA. [ABSTRACT FROM PUBLISHER]

Wind generator (WG) output is a function of wind speed and three-phase terminal voltage. Distribution systems are predominantly unbalanced. A WG model that is purely a function of wind speed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate. These errors add up and become pronounced when a single three-phase feeder connects several WGs. Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-fed induction generator and type-4 permanent magnet synchronous generator. The proposed approach can be readily applied to any other type of WGs. The main advantages of these ANN models are their mathematical simplicity, high accuracy with unbalanced systems and computational speed. These models were tested with the IEEE 37-bus test system. The results show that the ANN WG models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of the proposed ANN WG models allow easy integration into commercial software packages such as PSS®E and PSS®SINCAL and implementations are also shown in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, an ultra-fast algorithm for the computation of multiphase short-circuit currents at all buses is proposed by addressing the concept of selected inversion. The short circuit summary is achieved through the computation of multiphase Thevenin equivalent at each bus by using the augmented nodal matrix. This typically requires the sequential solution of linear system of equations, i.e., direct inversion which can be demanding in terms of CPU time. In this paper, it is shown that dramatic gains in computational time can be obtained when the selected inversion (SelInv) technique is performed where only a subset of entries of the inverse of augmented nodal matrix is computed. These entries include the phase domain Thevenin impedances seen from network buses which are used in the computation of short-circuit currents with the predefined boundary conditions specific to the fault type. The performance of the method is demonstrated with very large scale and realistic distribution systems. [ABSTRACT FROM PUBLISHER]

This paper presents an improved way of applying Monte Carlo simulation using the crossentropy method to calculate the risk of capacity deficit of a composite power system. By applying importance sampling for load states in addition to the generation and transmission states in a systematic manner, the proposed method is many orders of magnitude more efficient than the crude Monte Carlo simulation and considerably more efficient than other crossentropy-based algorithms that apply other ways of estimating the importance sampling distributions. An effective performance metric of system states is applied in order to find optimal importance sampling distributions during presimulation that significantly reduces the required computational effort. Simulations, using well-known IEEE reliability test systems, show that even problems that are nearly intractable using crude Monte Carlo simulation become very manageable using the proposed method. [ABSTRACT FROM AUTHOR]

Inverter interfaced distributed generators (DGs) in microgrids have different characteristics and models that are not available in the existing conventional power flow analysis tools. This paper presents a static modeling approach for inverter interfaced DGs that can be applied for time spread load flow analysis and fault analysis of microgrids, including droop-based voltage controlled DGs. The static models have been derived from the common control schemes applied to inverter interfaced DGs, including the constraints emerging from droop control and reflect steady-state behaviors of inverters accurately. In addition to simplification of analysis procedure, the static models can provide a base for the analysis of microgrids with conventional numeric analysis tools. The presented static modeling approach has been validated comparatively with the dynamic modeling results of a test microgrid. [ABSTRACT FROM AUTHOR]

In this paper, the sensitivity of look-ahead economic dispatch in real-time power markets with respect to data perturbation is studied. In the look-ahead dispatch optimization problem, a small change in the data that are used for setting the interspatial and temporal equality/inequality constraints and the objective function may negatively affect normal operations, such as the calculation of real-time wholesale electricity prices and operating costs. This could lead to more distorted prices and larger operating costs with the look-ahead dispatch than with a static dispatch that use data for a single future time. We perturb Karush–Kuhn–Tucker conditions of the look-ahead dispatch optimization formulation and then, using them, derive a linear sensitivity matrix that assesses the impact of data corruption on look-ahead dispatch. This matrix illustrates the changing optimal solution of look-ahead dispatch subject to potential corruption in various types of spatial and temporal data—generator's bidding cost coefficients, capacity limits for generators and transmission lines, ramp rates and the estimate of initial generation output with ramp constraints, and multiple-time series of the forecast load for the look-ahead horizon. The results of the simulation are illustrated with numerical examples in the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

This paper discusses new criterion for the evaluation of load frequency control (LFC) performance. The proposed LFC performance evaluation is based on a standard deviational ellipse that characterizes a bivariate relationship between the frequency deviation and the area's interchange power variation. The standard deviational ellipse is evaluated using Tyler's maximum-likelihood estimator, which is distribution-free within the family of elliptical distributions. In addition, a target standard deviational ellipse is proposed, using predefined values for a target frequency bound and the area's frequency bias. The presented approach is based on the accordance of the estimated ellipse with the target ellipse, resulting in several grades for the LFC performance. A comparison of the proposed criterion is made with other LFC performance criteria known from the literature. Extensive measurements were applied, and the obtained results show the correctness of the proposed approach. [ABSTRACT FROM AUTHOR]

Buildings are candidates for providing flexible demand due to their high consumption and inherent thermal inertia. In the future, flexible demand side reserves may also help to relax the expected higher reserve requirements of the grid due to the presence of renewables. However, this flexible demand might be vulnerable to price signals, as the simultaneous increase in consumption by multiple buildings due to low (high) energy (reserves) price periods might cause congestion in distribution grids. In order to integrate congestion-free energy and reserve provision from buildings, this paper presents two benchmark pricing methodologies: (1 ) distribution locational marginal prices (DLMP), and (2) iterative DLMP (iDLMP). Both methods deploy convex optimization to obtain an optimal solution of the original problem. Using dual decomposition, a settlement scheme, which efficiently distributes the congestion cost among involved participants, is also presented. Case studies are performed on a benchmark distribution system along with the National Energy Market Singapore's price framework. The results prove that both methods optimally remove congestion from distribution grids and have potential to be integrated into the theoretical framework of liberalized markets. Furthermore, as a comparison, it is shown that the DLMP-based prices outperforms existing pricing structures of the distribution grid. Hence, using this scheme, the distribution system operator can evaluate existing tariffs and introduce incentives for price responsive demands. However, to support these methods, the high requirement for information sharing in the DLMP method and/or communication technology infrastructure for calculating iDLMPs must exist in the future grid. [ABSTRACT FROM AUTHOR]

The deepening penetration of renewables in power systems has contributed to the increasing need for generation flexibility. Specifically, for short-term operations, flexibility here indicates that sufficient ramp capacity is available to respond to the varying load and intermittent generation. To address the growing needs for ramp capacity, markets for ramp products have been launched in practice such as those in California ISO and Midcontinent ISO. Sometimes, expensive fast-start units must be committed in real time to guarantee sufficient ramp capacity. Occasionally even worse, inadequate ramp capacity from the given generation portfolio might lead to curtailment of renewable generation or load shedding. Therefore, more supplies of ramp capacity are required. In fact, wind power producers (WPPs) are physically capable of offering ramp services, which gives us a potential option. In this paper, we attempt to explore the mechanism and economic impacts of including WPPs as ramp capacity providers. To conduct the analyses, a two-stage stochastic real-time unit commitment model considering ramp capacity adequacy is formulated. Case studies indicate that both the system and the WPPs can benefit in the proposed framework. [ABSTRACT FROM AUTHOR]

This paper proposes an improved analytical model for the study of steady-state performance of droop-controlled VSC-MTDC systems. When power disturbances occur, such as those imposed by normal changes in power settings and converter outages, the powers of the converters with droop control may hit their limits and hence such converters no longer contribute to power sharing, which should be paid special attention when analyzing the impacts of power disturbances on the performance of multi-terminal HVDC (MTDC) systems. Based on the bisection algorithm and superposition principle, the proposed analytical model provides an easy way to estimate the results of power distributions, dc voltage deviations, and power loss variations of the MTDC system taking converter outage and converter overload into account. Simulations of a modified New England 39-bus system incorporating a four-terminal MTDC system are carried out for validating the proposed analytical model. [ABSTRACT FROM AUTHOR]

This paper proposes a flow-based approach for the transmission tariff allocation problem based on the notions of long-run marginal cost (LRMC) and nodal exchange factors (NEFs). A worst-case power flow, expressed in terms of NEFs, is formulated to characterize the system state. Additionally, the NEFs optimal values are determined considering a specific criterion to compute tariffs. The resulting problem is formulated as a bilevel program. The upper level is a worst-case power flow that maximizes line flows taking into account the transmission tariffs computed in the lower level. The criterion considered in the lower level aims to minimize the variation range of transmission tariffs, which is particularly desirable to provide incentives for renewable sources installed far away from load centers. The resulting bilevel problem may have multiple solutions. Here, the uniqueness in tariffs is preserved by solving an alternative problem once both the generation dispatch and the variation range of transmission tariffs are known. Numerical results are provided for a 6-bus system and the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

The current practice of discrete-time electricity pricing starts to fall short in providing an accurate economic signal reflecting the continuous-time variations of load and generation schedule in power systems. This paper introduces the fundamental mathematical theory of continuous-time marginal electricity pricing. We first formulate the continuous-time unit commitment problem as a constrained variational problem, and subsequently define the continuous-time economic dispatch (ED) problem where the binary commitment variables are fixed to their optimal values. We then prove that the continuous-time marginal electricity price equals to the Lagrange multiplier of the variational power balance constraint in the continuous-time ED problem. The proposed continuous-time marginal price is not only dependent to the incremental generation cost rate, but also to the incremental ramping cost rate of the units, thus embedding the ramping costs in calculation of the marginal electricity price. The numerical results demonstrate that the continuous-time marginal price manifests the behavior of the constantly varying load and generation schedule in power systems. [ABSTRACT FROM AUTHOR]

The deep interdependence between electrical and gas systems entails a potential threat to the security (or reliability) of both systems. It is imperative to investigate the impacts of massive uncertainties on the overall secure and economical operation of both systems. In this paper, a probabilistic energy flow framework of integrated electrical and gas systems is initially proposed considering correlated varying energy demands and wind power. Three aspects of couplings between electrical and gas systems are considered: gas-fired generators, electric-driven compressors, and energy hubs integrated with power to gas (P2G) units. Furthermore, a multilinear method is specially designed to produce a deterministic energy flow solution for each sample generated by Monte Carlo simulation (MCS). Finally, test results have verified that the proposed multilinear MCS method prevails over the nonlinear MCS. In addition, P2G effectively benefits the operation of both electrical and gas networks. [ABSTRACT FROM AUTHOR]

The number and locations of transmission lines that are likely to get simultaneously congested are useful information for analysis of power market. In this paper, we present a study that demonstrates the number of simultaneously congested transmission lines is very limited, as well as an algorithm for identifying a small set of transmission lines that may become simultaneously congested based on the current states of the system and market. To this end, we first investigate the maximum number of simultaneously congested lines. Then, a novel algorithm is developed to identify a smaller set using two scientific criteria – one is concerned with the absolute impact of generation dispatch decisions, and the other, the relative sensitivities of line flows with respect to the changes of generation outputs. The effectiveness of the proposed algorithm is demonstrated through two simulation studies, one based on IEEE 39-bus for illustration of concepts, and the other based on a large-scale, real-world power system for showing the practical application and significance of results. [ABSTRACT FROM AUTHOR]

This paper proposes a necessary condition for power flow insolvability in power distribution systems with distributed generators (DGs). We show that the proposed necessary condition indicates the impending singularity of the Jacobian matrix and the onset of voltage instability. We consider different operation modes of DG inverters, e.g., constant-power and constant-current operations, in the proposed method. A new index based on the presented necessary condition is developed to indicate the distance between the current operating point and the power flow solvability boundary. Compared to existing methods, the operating condition-dependent critical loading factor provided by the proposed condition is less conservative and is closer to the actual power flow solution space boundary. The proposed method only requires the present snapshots of voltage phasors to monitor the power flow insolvability and voltage stability. Hence, it is computationally efficient and suitable to be applied to a power distribution system with volatile DG outputs. The accuracy of the proposed necessary condition and the index is validated by simulations on a distribution test system with different DG penetration levels. [ABSTRACT FROM AUTHOR]

In some rural and sub-urban areas, the hosting capacity (HC) of low voltage networks is restricted by voltage limits. With local voltage control, photovoltaic generators can mitigate the voltage rise partly and, therefore, increase the HC. This paper investigates the effectiveness and general performance of different reactive and active power control concepts. It presents the findings of an extensive simulation-based investigation into the effectiveness of voltage rise mitigation, additional reactive power flows, network losses, and power curtailment. The two most common implementations of reactive power control have a similar effectiveness. The voltage rise can be compensated for by up to 25% and more than 60% for typical cable and overhead (OH) feeders, respectively. By additionally using active power curtailment of up to 3% of the annual yield, the HC can be increased by about 50% and 90% for the considered cable and OH feeder, respectively (purely rural feeders). [ABSTRACT FROM AUTHOR]

Estimating technical losses is fundamental to the planning and economics of electric power networks. This paper surveys the evolution of the ideas behind energy loss estimation and focuses on the development of the concepts of the loss factor and equivalent hours. The paper next identifies difficulties in using maximum demands and the loss factor to estimate energy losses. Based on this analysis, this study proposes an alternative loss estimation approach that relies on the “loss coefficient” as the fundamental parameter for describing load variations in loss estimation. A large load-curve data bank from Brazilian utilities is used to characterize load-curve parameters and provide perspective on the old and new concepts. Practical applications put the proposed ideas into perspective, showing how the use of average demands and loss coefficient can help to make better cable choices, increase accuracy in loss estimation for distribution transformers, and enhance the quality of information in loss estimation analysis. [ABSTRACT FROM PUBLISHER]

Power flow analysis is one of the tools that is required in most of the distribution system studies. An important characteristic of distribution systems is the load unbalance in the phases and a three-phase power flow analysis is needed. In this paper, a three-phase linear power flow (3LPF) formulation is derived based on the fact that in a typical distribution system, voltage angles and magnitudes vary within relatively narrow boundaries. The accuracy of the proposed 3LPF is verified using several test cases. Potential applications of the proposed method are in distribution systems state estimation and volt-VAR optimization. [ABSTRACT FROM PUBLISHER]

This paper presents analytical closed-form expressions that uncover the contributions of nodal active- and reactive-power injections to the active- and reactive-power flows on transmission lines in an AC electrical network. Paying due homage to current- and voltage-divider laws that are similar in spirit, we baptize these as the power divider laws. Derived from a circuit-theoretic examination of AC power-flow expressions, the constitution of the power divider laws reflects the topology and voltage profile of the network. We demonstrate the utility of the power divider laws to the analysis of power networks by highlighting applications to transmission-network allocation, transmission-loss allocation, and identifying feasible injections while respecting line active-power flow set points. [ABSTRACT FROM PUBLISHER]

State estimation (SE) in distribution networks is not as accurate as that in transmission networks. Traditionally, distribution networks (DNs) are lack of direct measurements due to the limitations of investments and the difficulties of maintenance. Therefore, it is critical to improve the accuracy of SE in distribution networks by placing additional physical meters. For state-of-the-art SE models, it is difficult to clearly quantify measurements' influences on SE errors, so the problems of optimal meter placement for reducing SE errors are mostly solved by heuristic or suboptimal algorithms. Under this background, this paper proposes a circuit representation model to represent SE errors. Based on the matrix formulation of the circuit representation model, the problem of optimal meter placement can be transformed to a mixed integer linear programming problem (MILP) via the disjunctive model. Comprehensive numerical studies show the effectiveness and efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

The discrete coordinate-descent algorithm is a practical approach that is currently used in centralized Volt/VAr Control (VVC) implementations, mainly due to its good performance and speed for real-time applications. Its viability is however challenged by the increasing number of distributed generation that contribute to the VVC solution, in addition to the conventional transformer taps and switched capacitors. This paper presents the exact computation of sensitivity factors that speed up the discrete coordinate-descent implementation, by significantly reducing the number of forward/backward substitutions in the current injection power flow method; the speed up is achieved without affecting the control setting quality of the original implementation. The optimality of the discrete coordinate-descent solutions is investigated by computing the gaps relative to mixed-integer linear programming set-points, derived from a polyhedral reformulation of the VVC problem. The sensitivity-based discrete coordinate-descent algorithm is tested starting from two initial points, the default one given by the current control set-points, and a continuous solution obtained from a linear approximation of the VVC problem. Numerical results on networks with up to 3145 nodes show that the sensitivity-based approach significantly improves the runtime of the discrete coordinate-descent algorithm, and that the linear programming initialization leads to VVC solutions with gaps relative to the mixed-integer set-points that are less than 0.5%. [ABSTRACT FROM PUBLISHER]

This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns. [ABSTRACT FROM PUBLISHER]

For the operation of distribution systems, loading balance of distribution feeders is important for reducing power loss and mitigating power flow overloading. In this paper, a loop power controller (LPC) is applied for the control of real power and reactive power flows by adjusting voltage ratio and phase shift so that the loading balance of distribution feeders can be obtained. To incorporate photovoltaic (PV) power generation in feeder loading balance, a Taipower distribution feeder with large PV installation is selected for computer simulation. Daily loading unbalance is determined by analyzing PV power generation recorded by the SCADA system and by constructing daily power load profiles based on distribution automation system (DAS) data. The load transfer required to achieve loading balance and the line impedance of distribution feeders are used to derive the voltage ratio and phase shift of the LPC. Computer simulations indicated that loading balance can be achieved in distribution feeders with large PV system installation by using loop power controllers according to the variation of solar energy and power loading of study feeders. The system power loss reduction resulting from feeder loading balance by LPC is also investigated in this paper. [ABSTRACT FROM PUBLISHER]

This paper proposes an effective approach based on the particle swarm optimization with integer coded to determine the switch operation schemes for feeder reconfiguration. The proposed approach, enhanced integer coded particle swarm optimization (EICPSO), is able to improve the search efficiency for feeder reconfiguration problems by considering the historical local optimal solutions when generating new particles. The local optimal solutions found during the evolution process are used when new particle are generated. Three different distribution systems are used in this paper to verify and validate the effectiveness of the proposed method. Simulation results show that the proposed method can find the solutions for feeder reconfiguration problems faster than other approaches such as discrete particle swarm optimization, modified binary particle swarm optimization, and genetic algorithm. [ABSTRACT FROM PUBLISHER]

This paper deals with radial distribution network reconfiguration for loss and switching mitigation. Its main contribution is the presentation of a genetic algorithm (GA) with two network encodings, capable of representing only radial connected solutions without demanding a planar topology or any specific genetic operator. The code was named sequential because the evaluation of the ith gene depends on the information forthcoming from all the previous genes. In addition to a full description of proposed techniques with examples, the paper presents a survey of existing network codifications including their search space and chromosomes for a 33-bus example system. In order to validate the proposed algorithms, these and other two techniques in the state of the art are applied to reconfigure three sample systems which have been broadly published in the technical literature. Performance is assessed by using the results of implemented representations and the reconfiguration literature. [ABSTRACT FROM PUBLISHER]