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This paper proposes a new scheme for bulk system restoration considering the available black-start resources (BSRs) in the distribution system (DS). The DS assisted generator start-up sequence (D-GSS) scheme is realized in a decentralized way, so that it does not only benefit the bulk system GSS by utilizing available BSRs in the DS, but also respects the independent operation of the transmission system operator (TSO) and distribution system operators (DSOs). First, the decentralized D-GSS scheme is presented including the interaction of the TSO and DSOs and the corresponding compact models. The models of the decentralized D-GSS scheme are built with the bulk system GSS modeled as a mixed-integer quadratic program (MIQP), the DS multi-step operation modeled as mixed-integer second-order conic programs (MISOCPs) and reliable power supply assessments of renewable energy sources as linear programs (LPs). Finally, a projection function based analytical target cascading (P-ATC) method is developed to iteratively solve the decentralized models with model complexity reduction. The P-ATC algorithm can handle integer variables and improve the computational efficiency of the iterative calculation process. The effectiveness of the proposed method is validated using the small-scale T6D2 system and large-scale T118D5 system, showing good GSS performance and computation efficiency. [ABSTRACT FROM AUTHOR]

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

For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work. [ABSTRACT FROM PUBLISHER]

This paper presents a new non-deterministic approach for multiyear distribution network reinforcement planning (DNRP) considering the uncertainty sources pertaining to loads, electricity prices, investment costs, and operation costs. Accordingly, the underlying idea of the information-gap decision theory (IGDT) is used to obtain a robust solution protected against different realizations of each uncertain variable lying in its robust region. The proposed model is capable of adjusting the robustness of the optimal solution in terms of a specific parameter designated as the budget of uncertainty. As the uncertain loads, investment and operation costs competitively tend to maximize their robust regions for a particular value of the budget of uncertainty, the normalized normal constraint (NNC) method as a proficient multi-objective optimization method is exploited in this paper to solve the proposed multiobjective IGDT-based DNRP (IGDT-DNRP) model. Mainly, the NNC method presents a set of Pareto optimal solutions rather than a single optimal solution. Accordingly, a posteriori out-of-sample analysis is introduced in this paper to find the best solution among the set of Pareto optimal solutions. The proposed IGDT-DNRP model is implemented on the IEEE 33-bus distribution network under different circumstances. Simulation results illustrate the effectiveness of the proposed nondeterministic approach. [ABSTRACT FROM AUTHOR]

This paper summarizes simulation based technical feasibility and cost benefit analyses of a range of off-the-shelf electrical losses reduction intervention solutions in the Isle of Wight (IoW) 11 kV distribution network in the United Kingdom (UK), assessed over a period of 45 years. Based on presented results, the paper provides a brief commentary on the merits, drawbacks, risks and recommendations associated with each considered network intervention. Among considered network interventions, the transformer auto stop-start scheme, where one of the transformers among a pair at a primary substation would be switched depending on the substation's varying load, was found to be the optimal solution, i.e., in reducing technical losses significantly and may also have a reasonable Net Present Value (NPV) with a good rate of Return on Investment (RoI) when fully implemented. [ABSTRACT FROM PUBLISHER]

Demand-response aggregators are faced with the challenge of how to best manage numerous and heterogeneous distributed energy resources (DERs). This paper proposes a decentralized methodology for optimal coordination of DERs. The proposed approach is based on Dantzig–Wolfe decomposition and column generation, thus allowing to integrate any type of resource whose operation can be formulated within a mixed-integer linear program. We show that the proposed framework offers the same guarantees of optimality as a centralized formulation, with the added benefits of distributed computation, enhanced privacy, and higher robustness to changes in the problem data. The practical efficiency of the algorithm is demonstrated through extensive computational experiments, on a set of instances generated using data from Ontario energy markets. The proposed approach was able to solve all test instances to proven optimality, while achieving significant speed-ups over a centralized formulation solved by state-of-the-art optimization software. [ABSTRACT FROM AUTHOR]

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

The existing microgrid-based distribution system restoration methods follow traditional restoration criteria, which achieve a maximum coverage of distribution systems. In extreme conditions (e.g., natural disasters and physical and cyber attacks), the traditional restoration plans could pose additional financial and security risks to local utility customers. That is, restored services could be further interrupted by subsequent outages in the extended event. To address such resilience challenges, this paper proposes the formation of adaptive multimicrogrids as part of the critical service restoration strategy. The proposed strategy comprises microgrid formation (MF) and load switching sequence (LSS) steps. The MF forms multiple microgrids with minimum overall scale and radial or looped topologies and includes optimally positioned mobile emergency resources to address potential risks in extreme conditions. The LSS determines a proper load switching sequence for ensuring the system dynamic performance in the restoration process. The effectiveness of the proposed strategy is verified by a case study on the IEEE 132-node test feeder system and the time-domain simulation in MATLAB Simulink. [ABSTRACT FROM AUTHOR]

Extreme weather events have a significant impact on the aging and outdated power distribution infrastructures. These high-impact low-probability events often result in extended outages and loss of critical services, thus, severely affecting customers’ safety. This calls for the need to ensure the resilience in distribution networks by quickly restoring the critical services during a disaster. This paper presents an advanced feeder restoration method to restore critical loads using distributed energy resources (DERs). A resilient restoration approach is proposed that jointly maximizes the amount of restored critical loads and optimizes the restoration times by optimally allocating grids available DER resources. The restoration problem is modeled as a mixed-integer linear program with the objective of maximizing the resilience to postrestoration failures while simultaneously satisfying grids critical connectivity and operational constraints and ensuring a radial operation for a given open-loop feeder configuration. Simulations are performed to demonstrate the effectiveness of the proposed approach using the IEEE 123-node feeder with five DERs supplying 11 critical loads and the IEEE 906-bus feeder with three DERs supplying 17 critical loads. The impacts of DER availability and fuel reserve on restored networks are assessed and it is shown that the proposed approach is successfully able to restore a maximum number of critical loads using available DERs. [ABSTRACT FROM AUTHOR]

Residential energy management (REM) systems facilitate the application of residential demand response programs to manage the increasing demand of electricity in power systems. The load curtailment at high price periods and the load shift from high-price to low-price periods are the main results of REM implementation, which may improve the technical and economical operation of the distribution systems. Customers’ satisfaction should be considered in the REM procedure to motivate the customers to respond to the received price signals. This paper proposes an optimization-based REM to minimize not only the customer's energy cost but also customer dissatisfaction, which is modeled as a function of load curtailment. In addition, the impact of broad application of REM in bus 6 of RBTS is studied on the reliability of a distribution system. To this end, two different methods for reliability evaluation are used to show the effect of load shift and load curtailment on the energy not supplied by the distribution system. The results verify the validity and effectiveness of the proposed REM approach and show the impact of application of REM programs with different penetration levels on the reliability of the distribution system. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic control strategy, namely the unsynchronized Addictive Increase Multiplicative Decrease (AIMD) algorithm, to manage the power flow of interconnected microgrids (MGs). The proposed control aims at achieving a tradeoff between the individual utility function of each MG while ensuring the stability of the grid. Both centralized and decentralized AIMD approaches are considered and compared. Extensive Monte Carlo simulations are performed on the IEEE 39-bus system, and show that the proposed control strategy is able to provide the sought tradeoff. [ABSTRACT FROM AUTHOR]

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

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

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

The aim of this paper is to define and develop an optimal hierarchical demand-side power-system primary-frequency control structure in order to provide a reliable complement to generator inertia and governor response while improving inter-area oscillation damping. This resilient optimal load control strategy utilizes both wide-area and local signals to guarantee that the system remains stable in the case of communication failures. In the device layer, we deal with demand-response and frequency regulation by minimizing the total disutility of consumers subject to power balance and frequency regulation over the network. In the supervisory layer, we handle the power system's electromechanical oscillations using an linear-quadratic regulator (LQR)-based state feedback scheme with sparsity promotion to achieve the necessary tradeoff between complexity and performance. Since the wide-area feedback loops are built on top of an existing decentralized load control strategy, this design approach creates a decentralized/hierarchical demand-side load-control structure with significant advantages in terms of reliability and operational flexibility. Our study clearly shows by simulation that a demand-side hierarchical load-control strategy has a significant potential for improving the dynamic performance of power systems, even when implemented with a limited number of measurement units and controlling less than $1\%$ of the total base load. [ABSTRACT FROM AUTHOR]

This two-part paper considers the coordination of a population of thermostatically controlled loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak energy constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data and price data, and is compared with other approaches in terms of aggregated load response. Simulation results indicate that our coordination framework can effectively improve the operational efficiency of the distribution system and reduce power congestion at key times. [ABSTRACT FROM AUTHOR]

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

Risk of blackouts is more than expected and must be considered in the power system studies. The interest of this paper is to minimize the blackouts risk during minimizing the costs in probabilistic transmission expansion planning (TEP). In this paper, a long-term reliability analysis, which was developed by self-organized criticality theory, is used in a multi-objective optimization to include risk of blackouts in TEP. The objective functions are costs and expected energy not supplied. Also, a new formulation for calculating transmission operating cost as a part of cost function is proposed. The problem is solved by a multi-objective particle swarm optimization to find an optimal Pareto set from which the decision-maker can choose the most desirable one, regarding existing budget. The proposed method is applied to the IEEE 118-bus test system to investigate its applicability and scalability. The results are compared with security-constrained TEP, which uses famous deterministic criterion known as N-1. The proposed approach produces more efficient plans for suppressing cascading blackouts with less network investment. Moreover, the results provide motivation for considering the risk of cascading blackouts in TEP, which afford substantial saving to society. [ABSTRACT FROM AUTHOR]

The recent deployment of distributed generation has led to a revolution in the use of distribution systems and the emergence of “smart grid” concepts. Smart grids are intended primarily as a means of facilitating the integration of renewable energy sources and of achieving greater system reliability and efficiency. Energy storage systems (ESSs) offer a number of benefits that can help utilities move toward those goals. One of those benefits is the capacity to improve the utilization of network infrastructure by means of proper load management. This paper proposes a methodology for allocating ESSs in distribution systems in order to defer system upgrades, minimize system losses, and take advantage of the arbitrage benefit. The cost and arbitrage benefit of energy storage installation are optimized with respect to system upgrade and energy losses costs. The primary goal of this research is to determine the optimal size and location of storage units to be installed, in addition to their optimal operation, so that total system costs are minimized, while system benefits are maximized. In this paper, a probabilistic load model is adopted instead of utilizing time-series based models, which provide an optimal solution that is valid only for the time-series pattern that is applied. [ABSTRACT FROM PUBLISHER]

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

Remote-controlled switches (RCSs) play an important role in prompt service restoration of distribution systems (DSs). However, the cost of RCSs and the vast footprint of DSs limit widespread utilization of RCSs. In this paper, we present a new approach to RCS allocation for improving the performance of restoration and optimizing reliability benefits with reasonable RCS cost. Specifically, the optimal number and locations of to-be-upgraded switches can be determined with different objectives: maximizing the reduction of customer interruption cost; maximizing the reduction of system average interruption duration index; or maximizing the amount of loads that can be restored using the upgraded RCSs. We show that these models can actually be formulated as mixed-integer convex programming problems. We further introduce a novel method to equivalently transform and efficiently solve each of them. The global optimum can thus be computed within a reasonable amount of time. The IEEE 33-node and 123-node test systems are used to demonstrate the proposed models and algorithms. [ABSTRACT FROM PUBLISHER]

Quasi-static time-series (QSTS) simulation is used to simulate the behavior of distribution systems over long periods of time (typically hours to years). The technique involves repeatedly solving the load-flow problem for a distribution system model and is useful for distributed energy resource planning. When a QSTS simulation has a small time step and a long duration, the computational burden of the simulation can be a barrier to integration into utility workflows. One way to relieve the computational burden is to simplify the system model. The segment substitution method of simplifying distribution system models introduced in this paper offers model bus reduction of up to 98% with a simplification error as low as 0.2% (0.002 p.u. voltage). In contrast to existing methods of distribution system model simplification, which rely on topological inspection and linearization, the segment substitution method uses black-box segment data and an assumed simplified topology. [ABSTRACT FROM PUBLISHER]

Quasi-static time-series simulations (QSTS) of distribution feeders are a critical element of distributed solar PV integration studies. QSTS are typically carried out through computer simulation tools, such as OpenDSS. Since a typical feeder contains thousands of buses, for long investigation periods or at fine time scales such simulations are computationally costly. Simulation times are reduced in this paper through a reduction of the number of buses in the model. The feeder reduction algorithm considers p-phase distribution feeders with unbalanced loads and generation, unbalanced wire impedance, and mutual coupling, while preserving the spatial variation of load and generation. An extensive Monte Carlo sensitivity analysis was performed on a real feeder from a California utility. All bus voltage differences are found to be less than 1.13% with a root mean square error of 0.21%. Simulation time savings were up to 96% when only one bus is selected to remain in the model. Example applications of the proposed algorithm are interconnection studies of utility-scale photovoltaic system to the distribution grid, siting analyses of other distributed energy resources and dynamic behavior of devices in large systems, such as smart inverters on distribution grids. [ABSTRACT FROM PUBLISHER]

This paper derives a set of sufficient conditions guaranteeing that the load-flow problem in unbalanced three-phase distribution networks with wye and delta constant-power, constant-current, and constant-impedance loads (ZIP loads) has a unique solution over a region that can be explicitly calculated from the network parameters. It is also proved that the well-known Z-Bus iterative method is a contraction over the defined region, and hence converges to the unique solution. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

Conventional measurement and verification for non-interval metered, direct load control (DLC) programs bidding into wholesale electricity markets usually requires a PURPA-compliant load research study. This requirement con- tributes to high operating costs and has been a factor in recent reductions in the amount of emergency demand response available to regional transmission system operators. This paper describes. a collaborative effort by PJM members to determine whether regional modeling using pooled historical data could satisfy DLC measurement and verification (M&V) requirements without sacrificing estimation accuracy. This paper also describes how PJM incorporated the regional "deemed savings estimates" into its Load Data Systems Manual. This paper concludes that regional approaches to MଦV could yield large cost savings for DLC resource providers around the country. [ABSTRACT FROM AUTHOR]

The accuracy of the load model has great effects on power system stability analysis and control. Based on our practice in China on modeling load from field measurements, this paper systematically develops a measurement-based composite load model. Principles guiding the load modeling practice are discussed based on detailed analysis on stochastic characteristics of the modeling procedure. The structure of the measurement-based composite load model is presented. A multicurve identification technique is described to derive parameters. The generalization capability of this built load model is also investigated in this paper. Two cases are studied to illustrate the accuracy of the developed load model on describing the load dynamic characteristics in the actual power system. [ABSTRACT FROM AUTHOR]

Increasing penetration of variable loads and renewable generation will cause the conventional operation strategy of the distribution system to become less certain and effective. This paper presents a method of determining the minimum loss network configuration of a distribution system with uncertain load and renewable generation. A robust optimization model is proposed wherein the optimal decisions on network configurations and losses are derived in two subsequent steps. The first step enforces the radiality constraint before knowing the actual system loads and output level of renewable generation. Power flows are computed next to achieve minimum network losses considering the worst operating conditions over the uncertainty sets. A mixed-integer two-stage robust optimization formulation and a decomposition algorithm in a master-slave structure are proposed to solve the problem. Results of an illustrative example and two test systems are presented to demonstrate the proposed method. [ABSTRACT FROM PUBLISHER]

Cascading outages in power systems can lead to major power disruptions and blackouts and involve a large number of different mechanisms. The typical development of a cascading outage can be split in two phases with different dominant cascading mechanisms. As a power system is usually operated in N-1 security, an initiating contingency cannot entail a fast collapse of the grid. However, it can trigger a thermal transient, increasing significantly the likelihood of additional contingencies, in a “slow cascade.” The loss of additional elements can then trigger an electrical instability. This is the origin of the subsequent “fast cascade,” where a rapid succession of events can lead to a major power disruption. Several models of probabilistic simulations exist, but they tend to focus either on the slow cascade or on the fast cascade, according to mechanisms considered, and rarely on both. We propose in this paper a decomposition of the analysis in two levels, able to combine probabilistic simulations for the slow and the fast cascades. These two levels correspond to these two typical phases of a cascading outage. Models are developed for each of these phases. A simplification of the overall methodology is applied to two test systems to illustrate the concept. [ABSTRACT FROM AUTHOR]

In this paper, a new procedure based on a backward/forward sweep (BFS) algorithm for solving power flows in weakly meshed dc traction networks is presented. The proposed technique is able to consider the trains as nonlinear and nonsmooth (nondifferentiable) voltage-dependent loads or generators. This feature permits the inclusion of the trains’ overcurrent protection and the squeeze control. With the use of the mentioned controls, the conventional power flow problem becomes a voltage constrained power flow problem, and the interaction between the trains and the network can be accurately modeled. However, the train control induces a highly nonsmooth voltage-dependent load characteristic, causing convergence problems in most of the derivative-based algorithms. The proposed algorithm is faster, more robust, and more stable than the derivative-based ones. In addition, the authors present all of the formulation in a compact matrix-based form by means of the graph theory application and the node incidence matrix. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive mathematical model to solve the restoration problem in balanced radial distribution systems. The restoration problem, originally modeled as mixed integer nonlinear programming, is transformed into a mixed integer second-order cone programming problem, which can be solved efficiently using several commercial solvers based on the efficient optimization technique family branch and bound. The proposed mathematical model considers several objectives in a single objective function, using parameters to preserve the hierarchy of the different objectives: 1) maximizing the satisfaction of the demand, 2) minimizing the number of switch operations, 3) prioritizing the automatic switch operation rather than a manual one, and 4) prioritizing especial loads. General and specialized tests were carried out on a 53-node test system, and the results were compared with other previously proposed algorithms. Results show that the mathematical model is robust, efficient, flexible, and presents excellent performance in finding optimal solutions. [ABSTRACT FROM PUBLISHER]

A model predictive controller (MPC) for load frequency control (LFC) of an interconnected power system is investigated. The MPC is based on a simplified system model of the Nordic power system, and it takes into account limitations on tie-line power flow, generation capacity, and generation rate of change. The participation factors for each generator are optimization variables, and suggestions are made as to how one can ensure tie-line power transfer margins through slack-variables, and pricing information through the objective function. The solution of MPC for LFC is completed by including a Kalman filter for state estimation. The presented MPC is compared against a conventional LFC/AGC scheme with proportional integral (PI) controllers. Simulations show that the MPC gives better frequency response while using cheaper resources. This paper illustrates that MPC could be a realistic solution to some of the LFC problems power systems are facing today. [ABSTRACT FROM AUTHOR]

This paper proposes a computationally efficient technique for estimating the composite load model parameters based on analytical similarity of parameter sensitivity. When the model parameters are updated in the optimization procedure to best fit the actual load dynamics, i.e., measurements, parameters of similar sensitivity representation in the given mathematical model structure are updated in the same manner at every iterative step. This research allows for practically reducing the number of load model parameters to be identified in the estimation process and the overall computational cost while preserving the desired complexity and accuracy of the original model. This approach consequently facilitates the parameter estimation in the optimization process and helps manage increased number of parameters often criticized for adopting the dynamic composite load model via measurement-based approach. Case studies for the real power system demonstrate the computational efficiency and intact accuracy of the proposed method with reference to the existing methods of estimating all the parameters of the given composite load model independently. [ABSTRACT FROM AUTHOR]

In this paper, a new scenario-based stochastic optimization framework is proposed for price-maker economic bidding in day-ahead and real-time markets. The presented methodology is general and can be applied to both demand and supply bids. That is, no restrictive assumptions are made on the characteristics of the pool and its agents. However, our focus is on the operation of time-shiftable loads with deadlines, because they play a central role in creating load flexibility and enhancing demand response and peak-load shaving programs. Both basic and complex time-shiftable load types are addressed, where the latter includes time-shiftable loads that are uninterruptible, have per-time-slot consumption limits or ramp constraints, or comprise several smaller time-shiftable subloads. Four innovative analytical steps are presented in order to transform the originally nonlinear and hard-to-solve price-maker economic bidding optimization problem into a tractable mixed-integer linear program. Accordingly, the global optimal solutions are found for the price and energy bids within a relatively short amount of computational time. A detailed illustrative case study along with multiple case studies based on the California energy market data are presented. It is observed that the proposed optimal price-maker economic bidding approach outperforms optimal price-maker self-scheduling as well as even-load-distribution. [ABSTRACT FROM AUTHOR]