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This paper presents an approach for incorporating actual voltage stability operating limits (VSOL) in the composite power system adequacy evaluation. The VSOL curves are fitted into simplex-type straight lines and added in the linear programming model for minimizing load curtailment as additional constraints. The incorporated voltage stability limits are those calculated for the operators in the power system control center. The introduction of the actual limits therefore leads to adequacy evaluation closer to the operation reality with very little overhead since the voltage stability operating limits are pre-calculated for system operation. The case studies for BC Hydro's 500/230 kV system are given and discussed.

This paper investigates the dynamic voltage support (DVS) of inverter-based resources (IBRs) under voltage sags to enhance the low-voltage ride-through performance. The DVS problem is formulated as a nonconvex optimization program that maximizes the positive-sequence voltage magnitude at the point of common coupling (PCC) subject to the current, active power, and synchronization stability constraints. Then, we perform the optimality analysis to explore the global optimum analytically. It is found that the unique global optimum has three scenarios/stages (S1S3), which depends on the specific relationship among grid voltage, grid strength, and physical limits of IBRs. The explicit closed-form solutions in S1 and S3 are derived, and the optimality conditions (an implicit solution) of S2 are provided, which guarantees the global optimality and enables the compatibility with fast real-time control. Finally, we implement the optimum with a grid-connected single-stage photovoltaic (PV) power plant by integrating a DVS controller. Dynamic simulations are carried out under different scenarios to test our proposal. The robustness against model errors is also discussed. Dynamic simulations are carried out under different scenarios to test our proposal and compare it with other existing methods.

Reliability studies of interconnected composite power systems are of increasing interest. The reliability evaluation of such ever larger systems with compatible computing time requires new techniques. The methodology for reducing the network size by introducing probabilistic equivalents is presented in this paper. Application examples to the IEEE-Reliability Test System (IEEE-RTS) and its proposed equivalent system are conducted. The results, produced by many reliability assessments to the IEEE-RTS and its equivalents, make the proposed method very promising.

Utilizing battery storage systems (BSSs) can reduce the intermittent output of PV generation systems (PVGSs) and make them dispatchable. The aim of this paper is to design an optimal charging/discharging scheduling for BSSs such that the line loss of distribution systems interconnected with sizeable PVGSs can be minimized. A mathematical model for BSSs which can be used to simulate the charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging procedures and the state of charge (SOC) is proposed first. The minimum line loss problem considering the intermittent output of PVGSs and the scheduling of BSSs is then formulated based on the BSS mathematical model. The optimal charging/discharging scheduling of BSSs can then be obtained by a genetic algorithm (GA) based method. Test results demonstrate the validity of the proposed mathematical model and optimal charging/discharging scheduling for BSSs.

In this paper, we make the case for an increased focus on teaching an understanding of societal context and the integral role it plays in energy infrastructure design. Power system design education is inadequate in terms of holistic understanding of the non-technical aspects of a client-society in the development of energy infrastructure solutions. These are reflected in many failed designs, primarily designed by student engineers as part of capstone projects, senior design projects, or extra-curricular work through humanitarian-oriented programs administered by NGOs. We are developing coursework that introduces students to the complexities involved in the practice of engineering in rural communities at the international level.

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.

In this paper an analytical method by which the transient stability of an induction machine is maintained regardless of the fault clearance times is introduced. The method can be applied in order to improve the transient stability of a large penetration of low-voltage (LV) connected microgeneration that can be directly interfaced by single-phase induction generators within domestic premises. The analysis investigates the effectiveness of using resistive-type superconducting fault current limiters (RSFCLs) as remedial measures to prevent the microgenerators from reaching their speed limits during remote faults, and hence improving their transient stability. This will prevent unnecessary disconnection of a large penetration of LV-connected microgeneration and thus avoiding the sudden appearance of hidden loads, and unbalanced voltage conditions. The minimum required value of a resistive element of RSFCL for mitigating the transient instability phenomena of LV-connected microgeneration based on the system and connected machine parameters is determined. The analytical method has been validated by conducting informative transient studies by using detailed models of a small microwind turbine with constant mechanical output interfaced directly within residential dwellings by a single-phase induction generator, a transient model of resistive superconducting fault current limiter (RSFCL), and a typical suburban distribution network with residential loads. All the models are developed in the time-domain PSCAD/EMTDC dynamic simulation.

This paper proposes a methodology for predicting online rotor angle stability in power system operation under significant contribution from wind generation. First, a novel algorithm is developed to extract a stability index ( SI ) that quantifies the margin of rotor angle stability of power systems reflecting the dynamics of wind power. An approach is proposed that takes advantage of the machine learning technique and the newly defined SI . In case of a contingency, the developed algorithm is employed in parallel to find SI s for all possible instability modes. The SI s are formed as a vector and then applied to a classifier algorithm for rotor angle stability prediction. Compared to other features used in state-of-the-art methods, SI vectors are highly recognizable and thus can lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to existing methods, followed by a discussion of results.

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.

This paper presents a new computationally efficient methodology for calculating the reliability indices of a bulk power system using the state enumeration approach. The proposed approach utilizes topological analysis to determine the contribution of each system state to the frequency and duration indices at both the system and the bus level. In this approach, there is no need to conduct additional adequacy evaluations or utilize an optimization technique to identify the boundary wall between success and failure states. The effectiveness of the proposed methodology is demonstrated by application to the IEEE Reliability Test System (IEEE-RTS) and a model of the existing network of SaskPower in Canada. The proposed approach can also be used for noncoherent systems.

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

This paper studies a unified steady-state power flow analysis considering electrical, natural gas, and district heating networks all together. The important reason of such a view is the increasing utilization of cogeneration plants which makes a strong coupling between these networks. Another reason is the increasing usage of district heating networks because they have a higher efficiency besides a lower carbon dioxide emission than localized boilers. In this paper, interdependencies of the mentioned infrastructures are considered in detail including a nonlinear part-load efficiency performance for units. An integrated framework based on the Newton–Raphson technique is presented to solve the combined power flow problem and several case studies are utilized to demonstrate the applicability of the proposed methodology.

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

This paper presents a test model for investigating how to coordinate a power grid and energy storage systems (ESSs) by wide-area monitoring (WAM). It consists of three parts: first, a model of a power grid containing different types of generators, loads, and transmission network; second, a model of lithium-ion battery ESSs; and third, a model of multivariate statistical analysis based WAM built to capture the grid information for guiding the operation of ESSs. Simulation studies using a reduced equivalent model specifically built for a UK power grid enhanced with lithium-ion battery ESSs and WAM illustrate the way in which WAM can coordinate a power grid and ESSs, and also demonstrate the benefit of ESSs on a power grid.

Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. Illustrative examples and case studies are used to show how even very accurate security rules can lead to prohibitively high risk exposure when used to identify optimal control actions. Subsequently, the inherent tradeoff between operating cost and security risk is explored in detail. To optimally navigate this tradeoff, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary. Bias in predictions is compensated by the Platt Calibration method. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules derived from supervised learning can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined balance between cost and risk. This is a fundamental step toward embedding data-driven models within classic optimisation approaches.

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

With the popularization of electric vehicles (EVs), the influence on power systems is receiving increasing attention. According to statistics, EVs are in a parking state for more than 90% of the time, thus, the electric energy of the EVs accumulated by aggregators can be used to support the operation of power systems. This paper takes reducing operation costs as the objective function and considers the constraints of EVs and power systems to establish the optimal charging/discharging control of electric vehicles, which meet the operational safety of power systems and convenient driving. The influence on operation costs, peak load, and loss for the proposed charging/discharging two-way control, charging-only control, and free charging modes of EVs are compared. The influence of battery charging and discharging efficiency on these results is also discussed in this paper.

Merchant transmission planning is considered as a further step towards the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash Equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare.

Uncertainty in power system planning problems can be categorized into two types: exogenous and endogenous (or decision-dependent) uncertainty. In the latter case, uncertainty resolution depends on a choice (the value of some decision variables), as opposed to the former case in which the uncertainty resolves automatically with the passage of time. In this paper, a novel stochastic multistage planning model is proposed that considers endogenous uncertainty around consumer participation in demand-side response (DSR) schemes. This uncertainty can resolve following DSR deployment in two possible ways: locally (at a single bus) and globally (across the entire system). The original formulation is decomposed with the use of Benders decomposition to improve computational performance. Two versions of Benders decomposition are applied: the classic version involving sequential implementation of all operational subproblems and a novel version, specific to problems with endogenous uncertainty, which allows for the parallel execution of only those operational subproblems that are guaranteed to have a unique contribution to the solution. Case studies on 11-bus and 123-bus systems illustrate the process of endogenous uncertainty resolution and underline the strategic importance of deploying DSR ahead of time.

More and more system operators are interested in calculating transfer capability in real time using real-time power flow models generated from the Energy Management System (EMS). However, in comparing these models with offline study models, the EMS models usually include only a limited portion of the interconnected system. It is not practical to extend the EMS model in order to capture the impact of the external systems and as such using an equivalent network becomes necessary for this purpose. Equivalent circuits derived from the offline models for certain predetermined operating conditions would suffer from invalid results under various real-time operating conditions. Hence, it is necessary to derive an equivalent circuit model that is adaptive to the real-time system conditions. This paper presents a general method of creating an equivalent circuit model based on the measured boundary station parameters such as voltages and power flows. Simulation studies show that such derived equivalent circuit models provide solutions with adequate accuracy.

Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies, such as energy storage, can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale mixed integer linear programming problems that contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this paper, we propose a novel, efficient, and highly generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of nonsequential investment state equations and subproblem nonconvexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty. The proposed method is shown to substantially outperform the current state of the art.

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit ( $\mu \text{PMU}$ ) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the $\mu \text{PMU}$ devices in our architecture, an optimal $\mu \text{PMU}$ placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real $\mu \text{PMU}$ data.

Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers' ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers' flexibility, as the latter mainly involves shifting of loads' operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers' market power, by employing relevant indexes from the literature.

This paper proposes a lifelong learning (LL) based complementary generation control (CGC) of interconnected power grids with high-penetration renewable energy sources and electric vehicles (EVs). The wind farms (WFs), photovoltaic stations (PVs), and EVs are aggregated as a wide-area virtual power plant (WVPP) for automatic generation control (AGC), which can significantly accelerate the system response and reduce the regulation costs of balancing unexpected power mismatches between generation side and demand side. Under such framework, CGC is decomposed into a multi-layer generation command dispatch to rapidly compute an optimal solution. LL is first employed for the primary layer CGC between conventional power plants and a WVPP. Then, the secondary layer is implemented according to the ascending order of regulation costs of all reserve sources. Finally, the tertiary layer is accomplished by a coordinated control in each WFs or PVs, whereas an online optimization of EVs is adopted by considering the charging demands. The imitation learning is introduced to improve the learning efficiency of agents with transfer learning, thus an online optimization of CGC can be satisfied. Case studies are carried out to evaluate the performance of LL for multi-layer CGC of AGC on a practical Hainan power grid of southern China.

In this paper, the traditional automatic generation control (AGC) two-area system is modified to take into account the effect of bilateral contracts on the dynamics. The concept of distribution companies (DISCO) participation matrix to simulate these bilateral contracts is introduced and reflected in the two-area block diagram. Trajectory sensitivities are used to obtain optimal parameters of the system using a gradient Newton algorithm.

This paper proposes a simplified sequential Monte Carlo simulation to enhance the efficiency of bulk power system reliability evaluation. The basic idea is to construct chronological system state sequence by using the chronological probability model of load supplying capability which reflects the joint chronological variation pattern of system state intrinsic properties including load supplying capability ( C ), system state average duration ( D ), and splitting percentage ( P ). The chronological probability model can simplify sequential simulation procedure no matter how system load pattern changes, and avoid the time-consuming optimal load curtailment model calculations by using the above-mentioned system state intrinsic properties. Thus, the proposed method can considerably accelerate simulation, especially when evaluating system reliability for different system load conditions. Besides, as the proposed method retains the chronological property of system state transition, it can not only obtain the probability distribution of reliability indices but also incorporate time-dependent renewable generations. RBTS, RTS79, and modified RTS79 with wind farm are utilized to verify the effectiveness of the proposed method.

This paper develops a general framework for reliability assessment of multi-microgrid (MMG) distribution systems. It also investigates reliability impacts of coordinated outage management strategies in a MMG distribution network. According to the proposed reliability evaluation framework, which is based on sequential Monte Carlo simulation method, distribution system is divided into smaller sections/microgrids based on protection system configuration and operating measures are efficiently simulated considering different operation modes. In order to demonstrate the role of outage management strategy in reliability performance of MMG distribution systems, at first, the required features of an outage management strategy are identified. Then, suitable centralized and hierarchical schemes are introduced for operation of such systems during outage events. The proposed schemes, which are based on model predictive control approach, minimize total load curtailments in the system. Moreover, they are flexible and can effectively deal with multiple contingencies as well as uncertainties of outage duration. The developed reliability assessment framework is applied to a test system and performance of the presented outage management schemes are explored through extensive case studies. Obtained results suggest that implementation of an appropriate coordinated scheme is crucial to reliable operation of MMG distribution systems.

This paper addresses how to improve the computational efficiency and estimation reliability in cascading outage analysis. Under mild assumptions, we first formulate a cascading outage as a Markov model with specific state space and transition probability by leveraging the Markov property of cascading outages. Such a model provides a rigorous formulation that allows analytic investigation on cascading outages in the framework of standard mathematical statistics. Then, we derive a sequential importance sampling (SIS)-based strategy for cascading outage simulations and blackout risk analysis with solid theoretical foundation. Numerical experiments manifest that the proposed SIS strategy can significantly enhance the efficiency of simulations and reduce the estimation variance of blackout probability/risk compared with the traditional Monte Carlo simulation strategy.

In this paper, a new network structure is proposed to improve reliability and reduce losses using superconducting fault current limiters (SFCLs) for primary distribution systems. SFCLs are used to tie two (or more) radial primary feeders to form loops. SFCLs diminish the short-circuit current and the looped operation significantly reduces the active power loss while simultaneously increasing reliability. With this technique, there is no need to upgrade the upstream circuit breakers and settings of protection devices when operating in a loop. It is demonstrated that there is a tradeoff between benefits (reliability improvement and loss reduction) and acquisition cost of SFCLs. The technical feasibility and benefits are discussed. A comprehensive economic analysis based on optimization methods is conducted to determine the number and location of SFCLs in a typical system considering costs and benefits. Two case studies are selected: The reliability standard system known as “Bus 4 of RBTS” and the 84-bus distribution network from Taiwan Power Company. The results are compared with conventional distribution system reconfiguration. The comparison makes evident the superiority of the new method.

The recent rapid development of distributed generation (DG) necessitates the consideration of transmission–distribution interactions in voltage stability assessment (VSA), and how to perform a transmission–distribution-coupled static VSA (TDVSA) has become a pressing problem. To resolve this urgent issue, this paper first theoretically analyzes the impacts of the voltage-maintaining capacity and low-voltage tripping of DG units on the static voltage stability of an integrated transmission–distribution system and then proposes a distributed TDVSA method that fully considers these impacts when assessing the voltage stability of the integrated system. As the core of the method, a distributed continuation power flow algorithm is designed that includes a distribution-equivalencing-based predictor, a distributed transmission–distribution corrector, and a step-length regulator and requires only a limited amount of data to be exchanged between the transmission system operator (TSO) and the distribution system operator (DSO). Numerical simulations show that even in the case of low DG penetration, a cascading low-voltage tripping event can significantly degrade an integrated system's voltage stability, and only the proposed distributed TDVSA method can produce an accurate assessment. In addition, the number of iterations between the TSO and DSO is shown to be limited, indicating the potential of applying the proposed method to future grids.

Microgrid formation is a potential solution in postdisaster electric grid recovery efforts. Recent works propose a distribution level microgrid formation model that applies to radial distribution systems characterized by directed power flows. However, with high renewable penetration levels in future power systems, the flows are expected to be undirected even in distribution grids. In this paper, we develop a model to deal with the restoration process of future power systems, embedding some of the characteristics these systems are likely to have. More specifically, our formulation can deal with radial and meshed topologies, and it requires little preprocessing of the input data. Additionally, we extend the model to allow for possible mobile and fixed distributed generation technologies and distributed energy resources, and explicitly include demand responsive loads with a minimum satisfiability constraint. Thus, this extended formulation can be used as an operation and a short term planning tool for the DG scenario-based location problem. We compare our extended formulation results to those obtained for the radial cases found in the literature. Also, we apply the proposed approach to other instances of the problem based on the IEEE 37-bus, 30-bus, and 118-bus systems.

With the rapid growth of wind power penetration, system operators are required to enforce grid codes that implement frequency regulation of wind farms. This paper presents a distributed cooperation framework for a doubly fed induction generator-based wind farm to participate in primary frequency regulation, including imitated inertia and droop characteristics similar to conventional plants. Compared with centralized schemes, the control efficiency is significantly improved. To realize fast distributed coordination and optimal power distribution among wind turbines (WTs), a distributed Newton method is developed, which only requires WTs to exchange limited information with their neighbors over a sparse communication network, and has a super-linear convergence rate. By introducing an index of state of energy, this method can adequately exploit the kinetic energy of all WTs without loss of security. The simulation results indicate that the method exhibits satisfying dynamic performance and reliability, and the system frequency can be stabilized faster when the wind farm controlled by the proposed method participates in frequency control.

In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to $4.2\%$ of the long-term average cost and $80.59\%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51\%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches.

In this paper, we address several growing concerns of wind power integration from the perspective of power system dynamics and stability. We propose a new control design technique called retrofit control, by which one can control the rotor voltages of doubly fed induction generators to suppress the oscillations in the tie-line power flows caused by a disturbance inside the wind farm. The controller can be designed in a modular way, and also implemented in a completely decentralized fashion using only local feedback from the wind generator states and the voltage at the point of common coupling without depending on the states of any of the synchronous machines in the rest of the system. We show the effectiveness of the design using simulations of the IEEE 68- bus, 16-machine power system model with two wind farms.

In this paper, a new device of FACTS family named as amalgam power flow controller (APFC) is proposed. APFC is the amalgamation of electromagnetic and voltage source based converter devices. The key feature of the model is that it inherits the characteristics of both devices. APFC is an economical FACTS device, which not only controls power flow in a network but also increases the reliability and security of power systems. This paper also proposes a new hybrid approach based modeling technique to develop a mathematical model of APFC. Converter losses of APFC are also incorporated in mathematical modeling. The feasibility and convergence characteristics of the proposed APFC are investigated using benchmark IEEE 30-bus and 118-bus test systems.

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today's power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parameters with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.

Low-voltage, low-power solar photovoltaic (PV) based dc microgrids are becoming very popular in nonelectrified regions of developing countries due to lower upfront costs compared to utility grid alternatives and limited power needs of rural occupants. The optimal planning of distribution architecture along with sizing of various system components such as solar panels, batteries, and distribution conductors is essential for minimizing the system cost and enhance its utilization. In this paper, we develop a framework for optimal planning and design of low-power low-voltage dc microgrids for minimum upfront cost. The analysis is based on region-specific irradiance and temperature profiles; constraints in storage and distributions; distribution loss analysis; and optimum component sizing (storage, conductor, and PV panel) requirements based upon an energy balance model for a 24-h operation. We further analyze the merits of tailoring distribution architecture for maximizing the system utility in the planning of future microgrid deployments.

All-electric ships commissioned for military use must survive any physical attack resulting in short-circuit faults that lead to service interruption. Unlike previous works on improving resiliency, this paper seeks to design new topologies by understanding the relationship between the resiliency and the structural topology of a shipboard power system (SPS). Resiliency is evaluated on the basis of the total load served in a network following an attack, which causes maximum disruption in the network. The worst-case attack is identified using a bilevel and a mixed-integer linear programming framework. This method is employed to analyze the resiliency of similarly sized nominal SPS topologies (in terms of load and generator ratings). Breaker-and-a-half and ring bus topologies are determined to be more resilient, as they can survive up to $\text{eight}$ simultaneous worst-case attacks. Based on the study of structural dependence of resiliency, two new topologies are designed that can survive up to $\text{14}$ worst-case attacks each. It is to be noted that the new topologies are designed with the same number of circuit breakers, DC buses, and lines as the nominal topologies.

Recently, an emerging subsynchronous resonance (SSR) issue was observed in series-compensated systems comprising multiple wind farms. However, only simplified single-machine-infinite-bus system models were adopted to investigate the characteristics of this issue in the previous work. As a result, it is unable to reflect the impact on SSR from some critical factors, such as the network topology, the spatial distribution of wind farms, the diversity of wind turbine generators, and the distributed wind speed. To fill in this gap, this paper proposes an impedance network model (INM) based SSR stability analysis method. The impedance models of each wind farm and transmission line are established, and then interconnected according to system topology to form the whole INM. Such INM is further aggregated into lumped impedance. By analyzing the impedance-frequency features of the latter, a new stability criterion is developed to quantify the stability of SSR. The application of the criterion is demonstrated on a practical power system containing multiple wind farms. Both field measurements and time-domain simulations have been presented to validate its effectiveness and accuracy. The proposed method is of great potential in analyzing the SSR issue for very large-scale wind power systems.

This paper examines the impact of dynamic interactions between the voltage source converters (VSCs) on the small-signal stability of an AC/MTDC (multiterminal dc) power system. The examination is carried out based on a closed-loop interconnected model consisted of an open-loop VSC subsystem and an open-loop subsystem of the rest of MTDC network (RDCN). Open-loop modal coupling is the modal condition that an oscillation mode of open-loop VSC subsystem is close to an oscillation mode of open-loop subsystem of the RDCN on the complex plane. Analysis indicates that the open-loop modal coupling may very likely degrade the small-signal stability of the AC/MTDC power system. If a VSC uses the active power control, the open-loop VSC subsystem is of no oscillation mode. Hence, open-loop modal coupling between the VSCs cannot occur in the MTDC network implementing the master–slave control. Whilst the open-loop modal coupling may introduce instability risk to the MTDC network using the dc voltage droop control. Therefore, the paper reveals the mechanism about how and why the dynamic interactions between the VSCs may degrade the small-signal stability of the AC/MTDC power system from the perspective of the system modal condition—the open-loop modal coupling.

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.

The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa.

One distinctive feature of renewable energy resources is that they contribute little inertia to power systems. With less system inertia, power grid is less capable of resisting frequency deviation from its nominal value in the first few seconds after disturbances. However, fast-responding storage devices can mimic inertial responses through some specified control algorithm. Thus, this paper focuses on the economic aspect of resource inertia as a service to the grid operation and aims to find a minimum-cost commitment of fast-acting storage devices for the enhancement of primary frequency responses. Costs are assigned to those storage resources and a commitment cost minimization problem is formulated with nonlinear primary frequency response constraints. A tractable iterative solution approach is developed and a sensitivity-based method through a rapidly solvable linear approximation of the nonlinear constraints is proposed to reduce the computational burdens of solving the optimization problem for large-scale systems. The effectiveness of the proposed methods is demonstrated using simulation results obtained using the 118-bus IEEE reliability test system.

This paper proposes an adaptive droop control (ADC) strategy for the voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) system, which enables the VSC-station to provide frequency regulation for onshore ac grids. A V-I-f characteristic is derived to establish the relationship between the frequency deviation and dc voltage. A VSC-station working with the V-I-f characteristic is able to adjust its dc voltage reference autonomously according to the grid frequency deviation. Thus, the power flow of the VSC-MTDC system will be redistributed and more power is going to be injected into the adjacent grid of which the frequency deviation exceeds the threshold value. The ADC strategy inherits the advantage of the traditional droop control (TDC) that there is no need for communication system. An ac/dc hybrid system that includes VSC-MTDC system, onshore grids, and offshore wind farms is built in DIgSILENT/Powerfactory. Modal analysis and nonlinear simulations are performed to obtain the optimal control parameters and demonstrate the performance of the ADC strategy.

This paper addresses the transmission expansion planning problem under long- and short-term uncertainty. Long-term uncertainty pertains to changes across years (demand growth and future increase in production capacity), whereas short-term uncertainty pertains to changes within a year (demand and wind/solar power production variability, and equipment availability). This expansion problem is formulated as an adaptive robust optimization problem that provides protection against long-term uncertainty while carefully representing short-term uncertainty via scenarios. The problem is solved via a tailored implementation of the primal Benders’ decomposition algorithm that focuses on an efficient solution of the subproblem. The effectiveness of the proposed algorithm to identify robust expansion plans and its computational efficiency are illustrated through a realistic case study.

Motivated by the fact that the location(s) and structural properties of the pinning node(s) affect the algebraic connectivity of a network with respect to the reference value and thereby, its dynamic performance, this paper studies the application of intelligent single and multiple pinning of distributed cooperative secondary control of distributed generators (DGs) in islanded microgrid operation. It is shown that the intelligent selection of a pinning set based on the degree of connectivity and distance of leader DG(s) from the rest of the network improves the transient performance for microgrid voltage and frequency regulation. The efficacy of the distributed control strategy based on the proposed algorithms is illustrated via numerical results simulating typical scenarios for a variety of microgrid configurations., Comment: 8 pages, 11 figures, submitted to Trans. on Power Systems

This paper describes the demonstration and evaluation of stacking wind power time-shifting and regulation by a 1 MW/2 MWh battery energy storage system (BESS) within a 10 MW wind park. The BESS time-shifted 15.8 MWh of wind power to peak periods while responding to the automatic generation control (AGC) signals that provide regulation over the 24 day test period. Its fast response time and accurate output allowed the BESS to provide both services simultaneously. Limiting the charging to periods when wind power was available lowered the amount of time-shifting and AGC that could be provided. Some form of AGC was delivered from the BESS for 78.5% of the test period and, if it were remunerated at the PJM rate when the test was performed, it would have earned 8627 USD, with 97% from regulation and the rest from time-shifting wind power. Using PJM's performance template, the BESS averaged a performance score of 70.5% during hours when regulation was performed. This work adds to the growing empirical data that is informing models of and policy for grid storage.

In this paper, we consider a voltage control problem in power distribution grids. The specific goal is that of keeping the voltages within preassigned operating limits by commanding the reactive power output of the microgenerators connected to the grid. We propose three strategies. The first two strategies are purely local, meaning that each microgenerator updates the amount of reactive power to be injected based only on local measurements of the voltages’ magnitude. Instead the third one is distributed, namely, the microgenerators, to perform the updating steps, require some additional information coming from the neighboring agents. The local strategies are simpler to be implemented, but they might fail in solving the voltage control problem. Instead, the distributed one requires the microgenerators to be endowed with communication capabilities, but it is effective in driving the voltages within the admissible intervals and, additionally, it exploits the cooperation among the agents to reach also a power losses minimization objective. Theoretical analysis and extensive numerical simulations are provided to confirm the arguments aforementioned.