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Soft open points (SOPs) are power electronic devices which provide interconnection between two feeders in place of normally open points in electricity distribution networks. SOPs can continuously control active power flow between feeders and inject reactive power controllably at both nodes, which can be used to provide substantial capacity support to the system. This paper provides a reliability-based method to quantify the capacity value (the additional load which can be accommodated without reducing reliability) of SOPs using the Effective Load Carrying Capability method within a Monte Carlo Simulation (MCS). Optimization of post-fault active/reactive power injections by SOPs to minimize energy not supplied is formulated (directly in matrix form) as a second-order cone programming problem. This results in very low computational times which enables embedding of the optimization problem within the MCS. The proposed methodology is applied to a modified real-world distribution network considering three different SOP sizes (5 SOPs totalling 2.5, 5, 10 MVA) across three redundancy levels (N-1, N-0.75, N-0.5), and on an unbalanced network with distributed generation. Results demonstrate capacity values ranging from 2.4–12.84 MVA. When operating under a relaxed redundancy level, the capacity value of a given SOP capacity can more than double relative to N-1. [ABSTRACT FROM AUTHOR]

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]

This paper is motivated by the need to ensure fast microgrid stability. Modeling for purposes of establishing stability criterion and possible implementations are described. In particular, this paper proposes that highly heterogeneous microgrids comprising both conventional equipment and equipment based on rapidly emerging new technologies can be modeled as purely electric networks in order to provide intuitive insight into the issues of network stability. It is shown that the proposed model is valid for representing fast primary dynamics of diverse components (gensets, loads, and PVs), assuming that slower variables are regulated by the higher level controllers. Based on this modeling approach, an intuitively appealing criterion is introduced requiring that components or their combined representations must behave as closed-loop passive electrical circuits. Implementing this criterion is illustrated using a typical commercial feeder microgrid. Notably, these set the basis for standards that should be required for groups of components (subgrids) to ensure no fast instabilities in complex microgrids. Building the need for incrementally passive and monotonic characteristics into standards for network components may clarify the system level analysis and integration of microgrids. [ABSTRACT FROM AUTHOR]

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

The unit commitment is still a widely studied problem, especially when more renewable energy of stochastic character is being added to power systems. This paper proposes a model for weekly planning of systems involving hydro, wind, and thermal energy under a stochastic perspective. The proposed model follows the endogenous reserve determination criterion to achieve a simultaneous optimization of energy and reserves considering wind uncertainty, forced outages of equipment, a dc Flow model of the network, and cascaded head sensitive hydro systems, among others. The paper also develops a practical solution methodology based on outer approximation and benders decomposition. Testing has been conducted over four systems, and computational results demonstrate that the proposed model and solution are useful and effective. [ABSTRACT FROM AUTHOR]

This paper analyzes the robust stability of a mixed source microgrid comprised of a synchronous generator-based distributed energy resource (DER) and an inverter-based DER. Few studies conducted recently indicated that power system stabilizers (PSSs) are advantageous for damping local oscillations of synchronous generator-based DERs. However, stability issues may arise due to interaction of the parameters of PSS and system. This paper proposes a method to determine the stability region for the system when any two parameters are considered uncertain based on robust control theory. A sufficient condition for robust stability is presented using bialternate sum matrix of the system with uncertain parameters. The bialternate sum matrix approach is compared with a branch and bound approach presented in earlier studies. These two methods are shown to yield identical results for a single machine infinite bus system. However, the proposed bialternate sum matrix approach requires far less computational effort. Therefore, it is applied to determine the stability region for the operation of mixed source microgrid in both grid-connected and islanded modes. [ABSTRACT FROM AUTHOR]

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

This paper proposes a fundamental model for continuous-time scheduling and marginal pricing of energy generation and storage in day-ahead power systems operation. The paper begins with formulating the economic operation problem of power systems with generating units and energy storage (ES) devices as a continuous-time optimal control problem, where the Lagrange multiplier trajectory associated with the continuous-time power balance constraint is proven to be the marginal price of energy generation and storage. The marginal price is calculated in closed-form, which reveals that in addition to the incremental cost rates of generating units, the marginal price embeds the financial ES charging offers and discharging bids that are defined as incremental charging utility and incremental discharging cost rates. This paper shows that the adjoint function associated with the ES state equation establishes a temporal dependence between the marginal prices during the ES charge and discharge states. A function space-based method is developed to solve the proposed model, which converts the continuous-time problem into a mixed-integer linear programming problem with finite dimensional decision space. The features of the proposed scheduling and pricing models are demonstrated using numerical studies conducted on the IEEE Reliability Test System. [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]

The uncertain nature of wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple wind farms are few. Although the traditional methods for the single wind farm can still be used, they have the limitations in capturing the spatial correlations among wind farms, and they are less robust when multivariate observations are not so complete. To improve the forecast quality in this case, we combine the multivariate distribution modeling and probabilistic forecasts in this paper. An advanced model—the regular vine copula, which can describe the wind farms’ dependence structure precisely and flexibly with various bivariate copulas as blocks, is used in this paper. Enough simulation data can be generated from the model, which can be easily used to form the conditional forecast distributions under multiple forecast conditions. A case of 10 wind farms in East China has been used to compare the proposed method with its competitors. The results showed the method's advantages of providing reliable and sharp forecast intervals, especially in the case with limited observations available. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for generating restoration sequences in distribution systems and microgrids with emphasis on deriving optimal switching times for remote control switches. Microgrid architecture considered are assumed to be operating in islanded single master mode. Control methods for handling the unbalance inherent in distribution systems using single-phase controlled PQ inverters were developed and incorporated in the proposed method. The problem of considering simultaneously both energy optimization and dynamic stability of the system, which is lacking in existing methods, was addressed by studying the transient behavior of microgrids based on extended electromagnetic transients program (EMTP) simulations and then approximate and incorporate these behaviors into the optimization formulation for restoration. The approach is based on multi-objective optimization of final restored load and total restoration time. The methodology was studied on a modified IEEE 123 node test feeder. An EMTP simulation in PSCAD of the formed microgrids was used to validate the accuracy of the dynamic and steady-state characteristics of the resulting systems. By considering the transient behavior of distributed generators (DGs) during restoration, a better restoration sequence that does not trigger protection relays could be realized. [ABSTRACT FROM AUTHOR]

This paper discusses a market-based pool strategy for a microgrid (MG) to optimally trade electric power in the distribution electricity market (DEM). The increasing penetration levels of distributed energy resources and MGs in distribution system (DS) stress distribution system operator and require higher levels of coordinated control strategies. The distribution system operator has limited visibility and control over such distributed resources. To reduce the complexity of the system and improve the efficiency of the electricity market operation, we propose a decentralized pool strategy for an MG to integrate with a distribution system through a market mechanism. A market-based interactions procedure between MGs and DS is developed for MGs as price makers to find an optimal bidding/offering strategy efficiently. To achieve a market equilibrium among all entities, we initially cast this problem as a bi-level programming problem, in which the upper level is an MG optimal scheduling problem and the lower level presents a DEM clearing mechanism. The proposed bi-level model is converted to a single mix-integer model, which is easier to solve. Uncertainties associated with MG's rivals’ offers and demands’ bids are considered in this problem. The solution results from a modified IEEE 33-Bus distribution system are presented and discussed. Finally, some conclusions are drawn and examined. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a mixed-integer linear formulation to incorporate reliability-oriented costs into the expansion planning model of electricity distribution networks. In this respect, revenue lost associated with the undelivered energy caused by network interruptions as well as costs incurred by the widely used reward–penalty regulations is considered as the major reliability-related costs from distribution companies point of view. A set of mixed-integer linear equations is proposed to calculate the most common distribution system reliability indices, i.e., expected energy not served, system average interruption frequency index, and system average interruption duration index. It is found that these equations can also facilitate the formulation of radiality constraint in the presence of distributed generation units. Moreover, application of the proposed method is investigated through various case studies performed on two test distribution networks with 24 and 54 nodes. [ABSTRACT FROM AUTHOR]

Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence of uncertainties. We adopt a chance constraint approach that accounts for arbitrary correlations between renewable resources at each of the buses. We show how the problem can be solved efficiently using historical samples analogously to the stochastic quasi-gradient methods. We also show that this optimization problem is convex for a wide variety of probabilistic distributions. Compared to conventional per-bus chance constraints, our formulation is more robust to uncertainty and more computationally tractable. We illustrate the results using standard IEEE distribution test feeders. [ABSTRACT FROM AUTHOR]

Present-day radial electric power distribution circuit faces multiple challenges due to the increased photovoltaic (PV) generation. This paper aims to examine and design modern distribution circuit topologies for accommodating high PV penetration, while addressing power quality concerns. The central hypothesis of the proposed design approach is that by decreasing the Thevenin impedance at the buses where PVs are connected, the impacts on feeder voltages due to PV generation become less pronounced thereby additional PV capacity can be integrated at the corresponding buses. A novel two-stage optimization framework is proposed. The first stage is a mixed-integer linear programing based formulation to design new optimal configuration for any given distribution circuit. The formulation allows the possibility that the circuit can be operated in either a radial or loop configuration. The second stage, a nonlinear programing based formulation, then finds PV hosting capacity for the identified optimal configuration. The proposed two-stage framework is evaluated for IEEE 123-bus test feeder. It is demonstrated that the PV hosting capacity of the feeder can be increased by 53% by optimally adding two new distribution lines with tie-switches. [ABSTRACT FROM AUTHOR]

Convexification of an optimal scheduling algorithm for distributed energy storage (DES) in radial distribution systems with high penetration of photovoltaic resources is studied. The AC power flow equalities are taken into account as constraints in the optimization model. Different from the typical optimal power flow problem, the objective function of a DES optimal scheduling (DESOS) problem varies with changing operational requirements. In this paper, three frequently-used objective functions are considered for the DESOS problem. Two of them are monotonic over the feasible set while the third is not. An illustrative example elucidates that the descent direction of a chosen objective function significantly impacts the efficiency of the second-order cone programming (SOCP) relaxation for the DESOS problem. To obtain tighter semidefinite programming (SDP) relaxations for the DESOS cases where the SOCP relaxation is not exact, this paper looks for computationally efficient convex constraints that can approximate the rank-1 constraint in the non-iterative framework. The designed non-iterative enhanced SDP relaxations are compared in terms of tightness of convexification for the DESOS problems considering the three objective functions independently. The comparison is performed on several radial IEEE test systems and a real world distribution feeder. [ABSTRACT FROM AUTHOR]

Enhanced utilization of the existing transmission network is a cheaper and paramount alternative to building new transmission lines. Flexible ac transmission system (FACTS) devices are advanced technologies that offer transfer capability improvements via power flow control. Although many FACTS devices exist in power systems, their set points are not frequently changed for power flow control purposes, which is mainly due to the computational complexity of incorporating FACTS flexibility within the market problem. This paper proposes a computationally efficient method for adjustment of variable impedance-based FACTS set points, which is also compatible with existing market solvers. Thus, the method can be employed by the existing solvers with minimal modification efforts. This paper models FACTS reactance control as injections to keep the initial shift factors unchanged. Next, the paper formulates a dc optimal power flow that co-optimizes FACTS set points alongside generation dispatch. The resulting problem, which is in a nonlinear program, is then reformulated to a mixed-integer linear program. Finally, an engineering insight is leveraged to further reduce the computational complexity to a linear program. Simulation studies on IEEE 118-bus and Polish 2383-bus test cases show that the method is extremely effective in finding quality solutions and being very fast. [ABSTRACT FROM AUTHOR]

This paper presents a new method for studying electromechanical transients in power systems using three phase, combined transmission and distribution models (hybrid models). The methodology models individual phases of an electric network and associated unbalance in load and generation. Therefore, the impacts of load unbalance, single phase distributed generation and line impedance unbalance on electromechanical transients can be studied without using electromagnetic transient simulation (EMTP) programs. The implementation of this methodology in software is called the Three Phase Dynamics Analyzer (TPDA). Case studies included in the paper demonstrate the accuracy of TPDA and its ability to simulate electromechanical transients in hybrid models. TPDA has the potential for providing electric utilities and power system planners with more accurate assessment of system stability than traditional dynamic simulation software that assume balanced network topology. [ABSTRACT FROM PUBLISHER]

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]

This paper proposes a hybrid combined-cycle gas turbine (CCGT) model for day-ahead market clearing, in order to enhance the operation flexibility of CCGTs in practice. The proposed hybrid model, by taking benefits of combined offers from market participants on both configurations and individual physical turbines, can more accurately reflect physical operation features of CCGTs than existing CCGT models. A comprehensive review on existing CCGT models in academia and industry practice with their advantages and shortcomings is conducted. By taking benefits of the two most investigated models, i.e., configuration-based model and component-based model, the mapping relationship between these two models is revealed for deriving the proposed hybrid model. Tight formulations are further discussed for achieving the better computational performance. The proposed hybrid model is tested and compared with other CCGT models via the modified IEEE 118-bus system and the midcontinent independent system operator system. Results show notable benefits in maintaining operation flexibility and enhancing social welfare. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study. [ABSTRACT FROM AUTHOR]

Realistic representations of contingencies in AC optimal power flow (ACOPF) often challenge traditional bus-branch network models. Derived from nodal analysis, such approaches represent network constraints in terms of the familiar bus admittance matrix, $Y_{\text{bus}}$. A fixed $Y_{\text{bus}}$ is unable to represent common circuit breaker actions such as bus splitting. Work-arounds for $Y_{\text{bus}}$ -based analysis typically rely on topology processing, switching between different $Y_{\text{bus}}$ matrices depending on breaker settings. In this paper, we propose a very general node-breaker approach, employing multi-port element models and using a sparse tableau formulation (STF) for network constraints. Instead of treating breaker action as altering network topology, and hence changing the structure of Kirchhoff's voltage law (KVL) and Kirchhoff's current law (KCL) equations, this approach represents a breaker's position as impacting only constraints associated with a single component, thereby maintaining fixed structure in the KVL and KCL constraints. While larger numbers of variables are required, STF proves sparser than $Y_{\text{bus}}$ formulations. Numerical case studies herein demonstrate that STF provides computational efficiency comparable to a $Y_{\text{bus}}$ -based ACOPF at the scale of several hundred buses, and lower computational cost in an example of over one thousand buses. [ABSTRACT FROM AUTHOR]

Smart inverters provide additional control capability to help optimize the operation of distribution systems. This paper proposes a framework for exact optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and voltage regulators in large multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV active power curtailment, and operations of capacitors and voltage regulators, in addition to elimination of voltage violations and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem. Effective computational strategies are proposed to allow the application of predictor-corrector primal-dual interior point method to solve optimization problems in real-time with a large number of constraints and variables, including discrete variables corresponding to switched capacitors and voltage regulators. The accuracy of the numerical solution and the ability to implement the proposed framework are validated using the unbalanced multi-phase IEEE 34-bus and EPRI 2,998-bus distribution systems with 15-minute load and PV data. The results show a significant loss reduction and elimination of both voltage violations and reverse power flow. [ABSTRACT FROM AUTHOR]

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]

This paper discusses the application of the recently proposed augmented Lagrangian alternating direction inexact Newton (aladin) method to non-convex ac optimal power flow problems (ac-opf) in a distributed fashion. In contrast to the often used alternating direction of multipliers method (admm), aladin guarantees locally quadratic convergence for ac-opf. Numerical results for 5–300 bus test cases indicate that aladin is able to outperform the admm and to reduce the number of iterations by about one order of magnitude. We compare aladin to numerical results for the admm documented in the literature. The improved convergence speed comes at the cost of increasing the communication effort per iteration. Therefore, we propose a variant of aladin that uses inexact Hessians to reduce communication. Additionally, we provide a detailed comparison of these aladin variants to the admm from an algorithmic and communication perspective. Moreover, we prove that aladin converges locally at a quadratic rate even for the relevant case of suboptimally solved local nonlinear programs. [ABSTRACT FROM AUTHOR]

The proliferation of electric vehicles (EVs) calls for an effective planning of charging stations by coupling the transportation network (TN) with the power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations in a coupled traffic-electric network. A comprehensive planning model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes, and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model. To retrieve a global optimal solution, an equivalent mixed-integer linear programming model with UTAM constraints is formulated for the coordinated planning model. Numerical experiments on coupled traffic-electric networks are conducted to validate the effectiveness of the proposed model and the solution method. [ABSTRACT FROM AUTHOR]

We present a novel distributed Gauss–Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP). The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss–Newton method. The resulting iterative Gauss–Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss–Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation. [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]

This paper addresses the problem of how best to coordinate, or “stack,” energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted ${\ell _{1}\text{-norm}}$ objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The large-scale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations. [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]

Volt/var control (VVC) is one of the primary functions of the distribution management system aiming at optimum operation of power distribution networks while respecting all of their operational and security constraints. However, the recent huge integration of highly stochastic distributed generation (DG) sources with the grid presents a significant challenge to the traditional VVC schemes, which assume the future to be perfectly known. This paper presents a two-stage chance constrained optimization scheme to handle these nodal power uncertainties and guarantees that the operational and security constraints are respected for almost all realizations of the uncertainty. The chance constrained model is solved by collecting enough randomly chosen samples from the probability spaces of the uncertain parameters so that the class of the problem, a mixed integer second-order cone program, is not elevated. The algorithm not only dispatches the optimum schedule for discrete controlling devices like transformers and shunt capacitors but also optimizes the predefined decision rules for reactive power control of DG sources, thus falling in line with the requirement laid down in the revised IEEE 1547 Standard. Numerical simulations on three different test systems show the superiority of the proposed algorithm over the traditional deterministic methods. [ABSTRACT FROM AUTHOR]

The growing adoption of residential photovoltaic (PV) systems around the world is presenting distribution network operators (DNOs) with technical challenges, particularly on low voltage (LV) networks. The need to mitigate these issues with simple yet effective measures in countries with high PV penetrations is likely to drive the adoption of limits on the very exports that affect this infrastructure. Defining the most adequate limit, however, requires understanding the tradeoffs between the technical benefits and the effects on PV owners. This paper proposes two methodologies: an optimal power flow (OPF) based technique to define the export limit that solves technical problems with minimal curtailment, and a Monte Carlo based analysis to investigate the spectrum of such tradeoffs considering different PV penetrations and export limits. A real U.K. residential LV network with 180 customers is analyzed using realistic 1-min resolution daily load and PV generation profiles across seasons. Results demonstrate that, for DNOs, the OPF-based approach is effective in determining the most technically adequate export limit. However, for policy makers, the spectrum of tradeoffs provided by the Monte Carlo approach can help defining export limits that reduce curtailment at the expense of partially mitigating technical issues. [ABSTRACT FROM AUTHOR]

This paper presents a risk-based procedure for obtaining the best network automation scenario in the presence of uncertainty in distribution networks where performance-based regulation of continuity of supply is used. The proposed approach takes into account the uncertainty in loads, failure rates, and interruption duration. It employs fuzzy sets, fuzzy mixed integer linear programming algorithm, and a tool for managing risk to determine the optimal number, type and location of different types of automation devices in the presence of the aforementioned uncertainty. The presented approach considers performance-based (reward/penalty) schemes for an extended group of network reliability indices (SAIFI, SAIDI, MAIFI, and ASIDI) along with the cost of new automation devices and cost of relocation of the existing devices. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on multiobjective optimization techniques to perform an optimized, coordinated, and selective allocation of control and protection devices in distribution networks with distributed generation (DG). The proposed mathematical model consists of two objective functions that consider economic issues and the network continuity index. Physical and operational constraints are taken into account, with emphasis on the set of constraints based on practical rules of distribution companies and international technical standards, which require the specification, coordination, and selectivity of the protection devices installed in the network. The possibility of load transfer from neighboring feeders and islanded DG operation is also considered. The proposed model is a mixed integer nonlinear programming, and we use NSGA-II (Nondominated Sorting Genetic Algorithm) to solve it. The proposed methodology is applied in a real 135-bus system found in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a new analytical probabilistic power flow (PPF) approach for power systems with high penetration of distributed energy resources. The approach solves probability distributions of system variables about operating conditions. Unlike existing analytical PPF algorithms in literature, this new approach preserves nonlinearities of ac power flow equations and retain more accurate tail effects of the probability distributions. The approach first employs a multidimensional holomorphic embedding method to obtain an analytical nonlinear ac power flow solution for concerned outputs such as bus voltages and line flows. The embedded symbolic variables in the analytical solution are the inputs such as power injections. Then, the approach derives cumulants of the outputs by a generalized cumulant method, and recovers their distributions by Gram–Charlier expansions. This PPF approach can accept both parametric and nonparametric distributions of random inputs and their covariances. Case studies on the IEEE 30-bus system validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the coordinated investment in transmission and storage systems under uncertainty is addressed from the perspective of a central planner. The problem is formulated with the objective of achieving an effective expansion of both transmission and storage that minimizes the investment cost, while achieving an effective operation of a power system with high penetration of renewable power. The problem is stated within an adaptive robust optimization framework that incorporates stochastic modeling. Future available generation capacity and peak load in the target year (long-term uncertainty) are modeled using a robust set, while daily operating conditions (short-term uncertainty) are represented with a scenario set. The expansion tradeoff between transmission and storage systems is analyzed using the IEEE RTS 24-bus and IEEE 118-bus networks. [ABSTRACT FROM AUTHOR]

As the rapid development of natural-gas fired units (NGUs), power systems begin to rely more on a natural gas system to supply the primary fuel. On the other hand, natural gas system contingency might cause the nonavailability of NGUs and inevitably jeopardize power system security. To address this issue, this paper studies security-constrained joint expansion planning problems for this combined energy system. We develop a computationally efficient mixed-integer linear programming (MILP) approach that simultaneously considers N-1 contingency in both natural gas system and electricity power system. To reduce the combinatorial search space of MILP models, an extension of a reduced disjunctive model is proposed to decrease the numbers of binary and continuous variables as well as constraints. The involving nonlinear terms in N-1 constraints are exactly linearized without sacrificing any optimality. Numerical results on two typical integrated energy systems demonstrate the necessity of extending N-1 criterion to the whole network of a combined energy system. Experimental results also show that compared with the conventional approach, our proposed MILP approach achieves a great computational performance improvement in solving security-constrained co-optimization expansion planning problems. [ABSTRACT FROM AUTHOR]

A novel method is proposed to locate the source of events in power distribution systems by using distribution-level phasor measurement units, a.k.a., micro-PMUs. An event in this paper is defined rather broadly to include any major change in any component across the distribution feeder. The goal is to enhance situational awareness in distribution grid by keeping track of the operation (or misoperation) of various grid equipment, assets, distribution energy resources, loads, etc. The proposed method is built upon the compensation theorem in circuit theory to generate an equivalent circuit to represent the event by using voltage and current synchrophasors that are captured by micro-PMUs. Importantly, this method makes critical use of not only magnitude but also synchronized phase angle measurements, thus, it justifies the need to use micro-PMUs, as opposed to ordinary RMS-based voltage and current sensors. The proposed method can work with data from as a few as only two micro-PMUs. The effectiveness of the developed method is demonstrated through computer simulations on the IEEE 123-bus test system, and also on micro-PMUs measurements from a real-life 12.47 kV test feeder in Riverside, CA. The results verify that the proposed method is accurate and robust in locating the source of different types of events on power distribution systems. [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 presents a wide area control (WAC) method to coordinate the local controllers of generators and renewable energy sources (RESs), for robust power oscillation damping. The WAC signals are synthesized by a two-level hierarchical controller, which utilizes wide area measurements from all the installed synchronous generators and RESs for maximizing the performance of their corresponding local controllers. In the resulting WAC scheme, the generators are “aware” of the RES oscillations and vice versa, the RESs are “aware” of the generator oscillations. Further, the WAC signals intended for the RESs are adjusted in order to require only slight modifications on the RES local controllers. This study illustrates that the correct coordination of generators and RESs, through the WAC, improves the damping performance of the entire system even under high penetration of renewables. The performance of the proposed scheme has been tested and validated in the IEEE 9-bus and 39-bus test systems. [ABSTRACT FROM AUTHOR]

At Washington State University, a modern cyber–physical system testbed has been implemented based on an industry-grade distribution management system (DMS) that is integrated with remote terminal units, smart meters, and a solar photovoltaic array. In addition, the real model from the Avista Utilities distribution system in Pullman, WA, is modeled in DMS. The proposed testbed environment allows students and instructors to utilize these facilities for innovations in learning and teaching. For power engineering education, this testbed helps students understand the interaction between a cyber system and a physical distribution system through industrial level visualization. The testbed provides a distribution system monitoring and control environment for students. Compared with a simulation-based approach, the testbed brings the students’ learning environment a step closer to the real world. The educational modules allow students to learn the concepts of a cyber–physical system and an electricity market through an integrated testbed. Furthermore, the testbed provides a platform in the study mode for students to practice working on a real distribution system model. This paper describes the new educational modules based on the testbed environment. Three modules are described together with the underlying educational principles and associated projects. [ABSTRACT FROM AUTHOR]

The demand response provides an opportunity for load serving entities (LSEs) that operate retail electricity markets (REMs) to strategically purchase energy and provide reserves in wholesale electricity markets (WEMs). This paper concerns with the problem of simultaneously determining the optimal energy bids and reserve offers an LSE submits to the WEM as well as the optimal energy and reserve prices it sets in the REM so as to maximize its profit. To this end, we explicitly model the trilayer market structure that consists of a WEM, a REM, and a set of end user customers, so as to capture the coupling between the bidding problem and the pricing problem. Based on the trilayer market model, we then formulate the joint bidding and pricing problem as a bilevel programming problem and further transform it into a single-level mixed integer linear programming problem, which can be solved efficiently. Numerical studies using the IEEE test cases are presented to illustrate the application of the proposed methodology as well as to reveal several interesting characteristics of the LSE's profit-seeking behavior. [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]

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

The current transition towards smart distribution systems necessitates the development of educational curricula that have the interdisciplinary breadth together with the technical depth to furnish the present and future generation of engineers with a new set of skills and tools to address the challenges associated with emerging smart grid technologies. This work presents a newly designed smart distribution course that is intended to meet the needs of the power distribution industry. The paper gives a high-level overview of the broad range of topics related to developments and trends in smart distribution technologies including automatic restoration, data management, cybersecurity, interoperability and standards, and future vision. It stresses the multidisciplinary nature of the course and the importance of the strong collaboration between academia and key stakeholders, and the support from utilities, industry, government, and consultants. The course has been developed and taught by distribution system experts who are currently implementing smart grid technologies along with smart grid pioneers who are known worldwide. Moreover, to measure the effectiveness of the instructional approach of the course, students learning outcomes were assessed and discussed. The lessons learned from the implementation of the course are also summarized. [ABSTRACT FROM AUTHOR]