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Optimal Design of Islanded Microgrids Considering Distributed Dynamic State Estimation
Abstract
This paper proposes an optimal zone clustering algorithm of islanded microgrids (IMG) based on supply adequacy taking into account the dynamic performance of distributed state estimation units. The IMG is partitioned into several localized, yet coupled zones, where each zone is responsible for its local state estimate and performs data fusion to reach consensus for shared state variables between zones. The technique proposes a novel algorithm to optimally define the placement of zones virtual boundaries by minimizing potential power transfer between adjacent zones. The proposed algorithm adopts the distributed particle filter (DPF) technique for the state estimation process. The proposed algorithm has the ability to come up with one optimal configuration considering different IMG events and scenarios. Monte Carlo simulations demonstrate the proposed technique efficiency in the presence of severely corrupted measurements and state values, as well as displaying tolerance to major load changes within the IMG. The DPF shows similar performance when compared to its centralized implementation, however it provides computational savings by a factor of number of zones.
... Hence, the authors in [115] used a social-based metaheuristic technique to achieve optimal stability by means of network reconfiguration, while [116] relied on a physics-inspired optimization method to obtain the same outcome. Furthermore, reducing the coupling of power to divide multiple microgrids into clusters was another target for DCIMG reconfiguration [99,119], as were the distribution line losses' reduction [109,110,118] as well as maximizing the number of loads served [114,117,120]. ...
... The uncertainty importance for DCIMG operation and planning studies was apparent as over 30 references accounted for uncertainty mainly in generation and demand. Moreover, it was deduced that the most prevailing uncertainty consideration method in DCIMG studies was scenario-based modeling [39,55,83,84,88,91,94,118,119,127,165,171] followed by the MCS method [57,92,103,136,166]. However, the computation burden of real-time problems that involves uncertainties is large. ...
... Conversely, power-sharing optimization studies have also included active powersharing error in multi-microgrids. As introduced in [113,119], the best cut-off branches were determined so that power line transmission index (PLTI) [113] and potential power transfer error (PPTE) [119] between clustered microgrids were reduced. Likewise, different control problem studies in DCIMG went on to minimize both active and reactive power-sharing error in their PI controller design, where the integral absolute error (IAE) [59], integral squared error (ISE) [58], and ITAE [169,170] for power-sharing errors were minimized accordingly. ...
This review paper provides a critical interpretation and analysis of almost 150 dedicated optimization research papers in the field of droop-controlled islanded microgrids. The significance of optimal microgrid allocation and operation studies comes from their importance for further deployment of renewable energy, reliable and stable autonomous operation on a larger scale, and the electrification of rural and isolated communities. Additionally, a comprehensive overview of islanded microgrids in terms of structure, type, and hierarchical control strategy was presented. Furthermore, a larger emphasis was given to the main optimization problems faced by droop-controlled islanded microgrids such as allocation, scheduling and dispatch, reconfiguration, control, and energy management systems. The main outcome of this review in relation to optimization problem components is the classification of objective functions, constraints, and decision variables into 10, 9 and 6 distinctive categories, respectively, taking into consideration the multi-criteria decision problems as well as the optimization with uncertainty problems in the classification criterion. Additionally, the optimization techniques used were investigated and identified as classical and artificial intelligence algorithms with the latter gaining popularity in recent years. Lastly, some future trends for research were put forward and explained based on the critical analysis of the selected papers.
... While addressing the issue of individual deception, the strategy incorporates a correction vector to counteract the impact of deceptive information, yielding positive outcomes but exhibiting high dependence on the virtual power plant. In [14], system power gaps were identified using system frequency to eliminate the leader node. However, dynamic regulation may fail when load power variations in the system are approximately balanced. ...
... The above parameters are taken as consistent variables to realize coordinated optimal dispatching. According to Eq (14), when the consistency variable of each node meets the following conditions, the system reaches the optimal state, and the total power generation cost is the minimum: ...
With the high penetration of renewable energy, the addition of a large number of energy storage units, and flexible loads, the source-load-storage structure of active distribution networks is becoming increasingly complex, making optimization and scheduling more challenging. In response to issues as difficult global information acquisition, less consideration of flexible load and energy storage unit access, individual deception, and insufficient security in the optimization scheduling process of active distribution networks, this paper constructed a distribution network optimization scheduling model that includes sources, loads, and storage. It proposed a distributed optimization scheduling strategy for source-load-storage distribution networks, combined with alliance chains. This strategy is based on the FISCO BCOS consortium chain platform, with blockchain multi-agent nodes forming a consortium chain network. The consistency variables are the incremental cost of distributed power generation and the incremental benefits of flexible loads. Distributed scheduling calculations were carried out using a consensus algorithm that includes leadership nodes. By combining the data storage mechanism and consensus algorithm advantages of the consortium chain, the centrality of leadership nodes is eliminated, achieving optimal power allocation in the distribution network at a minimum economic cost. The simulation results show that the distributed optimization scheduling strategy proposed in this paper can achieve optimal allocation of minimum cost in the distribution network and converge quickly in various scenarios such as non-flexible load fluctuations, leader node switching, node joining or leaving, and changes in power exchange instruction in the distribution network. It demonstrates good robustness and stability.
... An optimal design of islanded microgrids considering distributed dynamic state estimation is proposed in [18], and the use of PMUs is addressed in [19]. ...
... For the set of buses in which DG is installed ( g ), the derivatives are equal to (18) representing the Q-V droop characteristic. ...
A novel state estimation methodology is proposed in this paper for microgrids monitoring using synchronized and non-synchronized measurements. A Kalman filter model is proposed to track both system states and frequency along the time considering the measurements gathered from the grid by PMUs and smart meters allocated in the system. An augmented measurement Jacobian matrix is formed considering system states and frequency as variables to be estimated along the time. Distributed generation is connected to the grid by voltage source inverters (VSI) considering frequency and voltage droop control characteristics. A radial 18-bus test system is used for the computational simulations in order to prove the efficiency of the proposed methodology. The algorithm is tested considering microgrid in grid-connected and islanded operation modes providing satisfactory results with reduced estimation errors when compared to the RSCAD (Real-Time Simulation Software Package) software. The main contributions of this work include: (i) the explicit model of frequency and voltage droop control of VSI in a Kalman filter model; (ii) the capability of tracking microgrids frequency and system states along the time; (iii) the explicit model of derivatives of phasor measurement units and smart meters measurements with respect to the system states and frequency.
... Distributed computing applications are decomposed into multiple small parts and distributed to multiple computers for processing, which allows for reducing running time and sharing resource. Therefore, state estimation based on distributed computing has become a popular research topic [1]- [3]. ...
This paper focuses on discrete-time wireless sensor networks with privacy-preservation. In practical applications, information exchange between sensors is subject to attacks. For the information leakage caused by the attack during the information transmission process, privacy-preservation is introduced for system states. To make communication resources more effectively utilized, a dynamic event-triggered set-membership estimator is designed. Moreover, the privacy of the system is analyzed to ensure the security of the real data. As a result, the set-membership estimator with differential privacy is analyzed using recursive convex optimization. Then the steady-state performance of the system is studied. Finally, one example is presented to demonstrate the feasibility of the proposed distributed filter containing privacy-preserving analysis.
This paper proposes a bi-level control framework for dynamic microgrid clusters in a distribution network with distributed photovoltaic and battery storage systems. The proposed bi-level control framework comprises interactive secondary and tertiary level control systems. A distributed event-triggered mechanism is proposed for the secondary level control of each dynamic microgrid cluster to achieve frequency and voltage regulation and balancing of the state of charge of battery storage systems within the cluster. At the tertiary level, a receding horizon model predictive control is implemented to minimize transmission power losses and battery storage systems losses by providing optimal battery storage systems output powers and the voltage source converters output voltages, with a one-minute interval. The secondary level control is modified to implement the optimal solutions provided by the tertiary level model predictive control. Furthermore, the secondary level distributed event-triggered control minimizes unnecessary data transmission by introducing a time delay, resulting in a reduced communication burden. The proposed bi-level control framework is validated in real-time on a modified IEEE 13-node test feeder using RTDS with the tertiary level model predictive control solved on a computer via the hardware-in-loop method
This paper investigates the anomaly-resistant decentralized state estimation (SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements (i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points (MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach; 2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement; and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.
This work proposes a fully distributed moving horizon estimation method with an event-triggered communication strategy over wireless sensor networks. The proposed method calculates a local state estimation of each sensor by minimizing a quadratic objective function, which involves a fused arrival cost that is computed in a distributed manner. This approach adjusts data transmission rate by selectively transmitting local information and allows the incorporation of constraints on the noise and state variables. In addition, the estimation error is proved to be uniformly bounded in the mean square sense under the condition that the network topology is strongly connected and the system is collectively observable. Finally, a target tracking example is presented to demonstrate the validity of the proposed approach.
The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research.
This study addresses the problem of the estimation of state when heterogeneous multiagent systems are affected by homologous unknown inputs (UIs). Homologous UIs refer to identical UIs affecting different agents. An improved semidistributed filter based on previous research is proposed. The improved filter uses neighbors’ information for UI estimation but not state estimation. A necessary and sufficient condition for the proposed filter to achieve minimum-variance unbiased estimation is presented and proven. Moreover, the asymptotic stability of the filter is analyzed. A sufficient condition of the asymptotic stability is presented and proven. The theoretical and numerical analyses indicate that the proposed filter has less communication pressure, fewer calculation requirements, and better estimation performance compared with the existing solutions.
Note to Practitioners
—In the industry, homologous unknown inputs (UIs) exist in many different systems. For example, the same ambient temperature affects the performance of every battery in a battery pack. Similarly, the same wind power can affect different aircrafts flying in the same region. Temperature and wind power can be considered the homologous UIs of a multiagent system. Estimation of homologous UIs is important because of the latter’s massive impact on the system. In this study, data transmission delay and packet loss are ignored. Hence, the study is limited to low-rate systems. Moreover, nonlinear filters must be studied further in future work.
This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of pm 5\% recommended by the standard ANSI C84.1-1989.
This paper presents a cost-effective system-level restoration scheme to improve power grids resilience by efficient response to the damages due to natural or manmade disasters. A post-disaster decision making model is developed to find the optimal repair schedule, unit commitment solution, and system configuration in restoration of the damaged power grid. The physical constraints of the power grid, associated with the unit commitment and restoration, are considered in the proposed model. The value of lost load is used as a viable measure to represent the criticality of each load in the power grid. The model is formulated as a mixed-integer program and, then, is decomposed into an integer master problem and a dual linear subproblem to be solved using Benders decomposition algorithm. Different scenarios are developed to analyze the proposed model on the standard IEEE 118-bus test system. This paper provides a prototype and a proof of concept for utility companies to consider economics of disaster and include unit commitment model into the post-disaster restoration process.
Microgrids have gained a lot of interest under the smart grid vision demanding systematic procedures for their optimal construction and performance enhancement. By defining a new probabilistic index, this paper presents approaches for clustering active distribution systems into a set of microgrids with optimized reliability and supply-adequacy indices. After proposing two different methods for optimally designing the microgrids, as two corrective actions to improve the defined index, the advantages of installing distributed reactive sources (DRSs) and distributed energy storage resources (DESRs) are investigated. The problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case and through several sensitivity studies, the robustness of the design and the effect of interconnecting different amounts of DRSs' and DESRs' capacities on the defined index are investigated. It is found that by properly designing the system and integrating the DRSs and DESRs, the index can be improved from 23% in the base system without distributed generators (DGs) to 77% in the basic microgrid design with distributed generators and to 93% considering all resources.
The paper proposes a near-optimal, distributed implementation of the particle filter for large scale dynamical systems with sparse measurements, as in wireless sensor networks (WSN) and power distribution systems. Compared to the centralized approach, the distributed implementation of the particle filter is computationally efficient and provides considerable transmission bandwidth savings due to a significant decrease in information exchange between the neighbouring nodes. In our test simulations, the performance of the distributed implementation is almost indistinguishable from the centralized particle filter.
Motivated by non-linear, non-Gaussian, distributed multi-sensor/agent
navigation and tracking applications, we propose a multi-rate consensus/fusion
based framework for distributed implementation of the particle filter (CF/DPF).
The CF/DPF framework is based on running localized particle filters to estimate
the overall state vector at each observation node. Separate fusion filters are
designed to consistently assimilate the local filtering distributions into the
global posterior by compensating for the common past information between
neighbouring nodes. The CF/DPF offers two distinct advantages over its
counterparts. First, the CF/DPF framework is suitable for scenarios where
network connectivity is intermittent and consensus can not be reached between
two consecutive observations. Second, the CF/DPF is not limited to the Gaussian
approximation for the global posterior density. A third contribution of the
paper is the derivation of the exact expression for computing the posterior
Cramer-Rao lower bound (PCRLB) for the distributed architecture based on a
recursive procedure involving the local Fisher information matrices (FIM) of
the distributed estimators. The performance of the CF/DPF algorithm closely
follows the centralized particle filter approaching the PCRLB at the signal to
noise ratios that we tested.
In this paper, the computational complexity of the marginalized particle filter is analyzed and a general method to perform this analysis is given. The key is the introduction of the equivalent flop measure. In an extensive Monte Carlo simulation, different computational aspects are studied and compared with the derived theoretical results.
The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed "modes") indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model
A key challenge in designing distributed particle filters is to minimize the communication overhead without compromising tracking performance. In this paper we present two distributed particle filters that achieve robust performance with low communication overhead. The two filters construct a graph of the particles and exploit the graph Laplacian matrix in different manners to encode the particle log-likelihoods using a minimum number of coefficients. We validate their performance via simulations with very low communication overhead and provide a theoretical error bound for the presented filters.
This paper proposes an optimal distributed control strategy for the coordination of multiple distributed generators in an islanded microgrid. A finite-time secondary frequency control approach is developed to eliminate the frequency deviation and maintain accurate active power sharing in a finite-time manner. It is demonstrated that the traditional distributed control approach with asymptotical convergence is just a special case of the proposed finite-time control strategy under the specific control parameter settings. Then, a secondary voltage control approach is presented to regulate the average voltage magnitude of all distributed generators to the desired value and achieve accurate reactive power sharing. The implementation of the proposed distributed control strategy only requires information exchange among neighboring local controllers through a sparse communication network. Simulations with an islanded microgrid testbed built in MATLAB/Simulink are conducted to validate the effectiveness of the proposed distributed control strategy.
In contrast to the traditional centralised estimation methods, this paper proposes a distributed dynamic state estimation for microgrids incorporating distributed energy resources. Specifically, the distributed filter structure is designed in an interconnected way where the packet loss obviously occurs between them. Then the error function is written in a compact form using the matrix property of the Kronecker product. Afterwards, it can be transformed into the linear matrix inequality. Finally, the local and neighboring gains for the distributed estimator are effectively computed after solving the convex optimization problem. Simulation result shows that the proposed method can well estimate the system state within 10 iterations.
The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.
In this paper a novel distributed Dynamic State Estimation (DSE) method for real-time monitoring of power systems is proposed. In modern large-scale power grids, the number of deployed meters and the frequency of collecting data has remarkably increased. Such a growth in the spatio-temporal size of collected data overwhelms the existing monitoring system with a centralized star structure. Streaming of data from all meters of the network to the central control center also increases communication latency. To overcome these challenges, the power system is partitioned into subsystems with local estimators and DSE process is distributed amongst local estimators. The proposed distributed DSE is hosted at regional control centers and utilizes distributed Extended Kalman Filtering (EKF) based on internodal transformation theory to estimate the dynamic states of power systems. The local estimators only require data within their own subsystem and information is exchanged only between neighboring subsystems. The proposed distributed DSE is implemented on 68-bus test system and its accuracy is demonstrated.
In this paper, an optimization problem is formulated for the automatic back-feed service restoration in smart distribution grids. The formulated problem relies on the structure of smart distribution grids, clustered into multi-microgrids, capable of operating in both grid-connected and islanded modes of operation. To that end, three types of power transfer between the neighboring microgrids, during the restoration processes are introduced: load transfer, distributed generation (DG) transfer, and combined load–DG transfer. The formulated optimization problem takes into account the ability of forming new, not predefined islanded microgrids, in the post-restoration configuration, to maximize service restoration. To obviate the need for a central unit, the optimization problem is reformulated, in this work, as a distributed constraint optimization problem, in which the variables and constraints are distributed among automated agents. To reduce the problem complexity, the restoration problem is decomposed into two sequential and interdependent distributed sub-problems: supply adequacy, and optimal reconfiguration. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search to solve distributed sub-problems in a multi-agent environment. Several case studies have been carried out to evaluate the effectiveness and robustness of the proposed algorithm.
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 (WSCC) 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 (EnKF) algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad
parameters of a realistic hydro power plant against multiple system events.
Accurate real-time state estimation plays an important role in distributed generation (DG) networks. In order
to establish a reliable communication link for estimation, an information-centric networking (ICN) was employed with the development of smart grids technology. However, the widespread adoption of DG network boosts the amount of data transmitted through ICN, which is now the real-time performance bottleneck of estimation. By fully considering the nonlinearity of DG system, this paper develops an event-trigger particle filter (ET-PF) to
relieve the communication burden and achieve an appropriate estimation accuracy. An arrival rate guaranteed event-trigger strategy is established for saving the bandwidth at first, which selects only the observations containing innovational information for the purpose of filtering. When the observation is not triggered,
an ET-PF filtering algorithm is further proposed, making full use of the information from the prior event-trigger strategy, to enhance the performance of estimation. The design-oriented guaranteed arrival rate, specifically designed by ET-PF in statistical sense, is beneficial to design ICN infrastructure. Finally, its feasibility and performance is demonstrated using the standard IEEE 39-bus system with phasor measurement units (PMUs).
In this paper, a new frequency and phasor estimators are presented. These estimators are based on the maximum likelihood technique and exploit the multidimensional nature of electrical signals. To minimize the likelihood function, we present an optimization algorithm based on the Newton-Raphson technique. While the performance of Fourier-based estimators significantly degrades when the window length is not equal to a multiple of the fundamental half-period, the proposed estimators perform well regardless of the window length. Simulation results show that the proposed estimators clearly outperform the discrete time Fourier transform in terms of Total Vector Error (TVE) and Frequency Error (FE) whatever the signal to noise ratio, harmonic and interharmonic distortion, and off-nominal frequency deviation are. The benefits of the proposed estimators are also illustrated with real power system data obtained from the DOE/EPRI National Database of Power System Events.
Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of electric distribution grids. Microgrids with various types of distributed generators (DGs) have the potential to enhance the electricity supply continuity and thus facilitate resilient distribution grids under natural disasters. In this paper, a novel load restoration optimization model is proposed to coordinate topology reconfiguration and microgrid formation while satisfying a variety of operational constraints. The proposed method exploits benefits of operational flexibility provided by grid modernization to enable more critical load pickup. Specifically, a mixed-integer second order cone programming is employed to reduce the computational complexity of the proposed optimization with optimality guaranteed. Finally, the effectiveness of the proposed method has been verified on an IEEE 33-bus test case and a modified 615-bus test system.
This paper enumerates three commonly used filtering algorithms and shows the detailed steps of their incorporation with general nonlinear systems for dynamic state estimation. The mathematical model of a stand-alone solid oxide fuel cell (SOFC) is briefly discussed and derived, which is then mathematically connected to a multiarea, diverse-generator, interconnected complex test system. The mathematical representation of the entire power system is tailored into a certain compact form to provide suitability for the implementation of filtering algorithms for the design of dynamic state estimators. With the utilization of phasor measurement units, the state estimators are able to work in a decentralized manner with the mere knowledge of local noisy voltage and current measurements. Successfully estimating the internal dynamic states of SOFC connected to complex power systems offers a novel methodology for the acquisition of the internal unmeasurable states of SOFC, which will facilitate future controller designs that may require the otherwise inaccessible states.
This paper proposes a nonlinear particle filter (PF) method for dynamic state estimation in droop-controlled islanded microgrids (IMGs). The PFs are normally applied to systems that (1) have highly nonlinear system dynamics, and (2) do not require the additive process or observation noise to be Gaussian. This flexibility allows the PFs to handle noisy measurements from a range of varied distributions, thereby increasing its robustness. To that end, a nonlinear dynamic state model has been developed in this work for droop-controlled IMGs. Additive noise has been incorporated into the state model to account for the error in its accuracy. Monte Carlo simulations have been conducted to verify that the PF accurately tracks the IMG state variables in spite of using significantly corrupted state and observation values. A comparison between the PF and unscented Kalman filter (UKF) has been carried out to test the effectiveness and robustness of the proposed methodology.
This paper proposes an effective scheme for real-time operation and protection of microgrids based on the distributed dynamic state estimation (DDSE) that is applied to a single renewable distributed energy resource (DER) or other components. First, the DDSE can be used for setting-less component protection that applies dynamic state estimation on a component under protection with real-time measurements and dynamic models of the component. Based on the results, the well-known chi-square test yields the confidence level that quantifies the goodness of fit of models to measurements, indicating the health status of the component. With this approach, renewable DERs in microgrids can be protected on an autonomous and adaptive basis. Meanwhile, the estimated state variables of each component are converted to phasor data with time tags, and then collected to the distributed energy resources management (DERMS) of microgrids. These aggregated phasor data that are once filtered by the DDSE are input to the static state estimation in the DERMS along with unfiltered data sent from conventional meters, relays, and digital fault recorders, ultimately generating real-time operating conditions of microgrids. This paper also provides numerical simulations for comparing the DDSE-based approach with the conventional centralized state estimation in terms of data accuracy and computational speeds.
This paper proposes a combined distributed/centralized architecture for the control and monitoring of a hybrid dc/ac microgrid (MG) with energy storage capabilities. The monitoring system is based on an own developed C++ framework for the measurement and real-time state estimation of the MG. Calculations running at sampled values frequency (
) are implemented into a set of distributed measurement and processing units, which have a TCP/IP communication link with a central server running the power flow algorithm. The architecture is fully scalable, with the only restrictions of the signal processing capabilities of the distributed units, LAN bandwidth, and central server calculation capabilities. The proposed system includes the hardware and software architecture for the monitoring of the MG, the communications scheme, the implementation of real-time algorithms for grid state estimation, a graphical user interface including different visualization alternatives, and data storing/retrieving capabilities.
This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable - stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design.
Traditional state estimation (SE) methods are usually implemented in a centralized way. The increased size, complexity of power systems, and the deregulation of the power industries result in the inefficiency of the conventional SE methods. Thus, improved SE solutions in terms of computational speed, easy implementation, and flexibility are urgently needed for future power systems. In this paper, a fully distributed integrated solution is proposed for multiarea topology identification (TI) and SE problems of power systems. Both problems are formulated as a weighted least square problem and solved with a distributed subgradient algorithm via multiagent systems. By applying statistical tests, the TI can identify network topology change accurately. The SE estimates the actual states based on the identified network topology. The proposed distributed solution can be implemented in a fully distributed way based on the flexible decomposition of power systems, and is able to obtain comparable estimated states as centralized methods. Simulation results with IEEE 14-bus and large-scale power systems demonstrate its effectiveness.
Successful development of smart grid demands strengthened system security and reliability, which requires effective security analysis in conducting system operation and expansion planning. Classical N - 1 criterion has been widely used to examine every creditable contingency through detailed computations in the past. The adequacy of such approach becomes doubtful in many recent blackouts where cascading outages are usually involved. This may be attributed to the increased complexities and nonlinearities involved in operating conditions and network structures in context of smart grid development. To address security threats, particularly from cascading outages, a new and efficient security analysis approach is proposed, which comprises cascading failure simulation module (CFSM) for postcontingency analysis and risk evaluation module (REM) based on a decorrelated neural network ensembles (DNNE) algorithm. This approach overcomes the drawbacks of high computational cost in classical N-k-induced cascading contingency analysis. Case studies on two different IEEE test systems and a practical transmission system - Polish 2383-bus system have been conducted to demonstrate the effectiveness of the proposed approach for risk evaluation of cascading contingency.
This paper introduces a novel framework of coordinated voltage and frequency control strategy for islanded Microgrid (MG) operation. The proposed control schemes rely on local measurements as communication-free control approach. Therefore, the distributed controllers of the MG components have been deployed based on their slow, medium and fast dynamic responses to maintain the voltage and frequency in adherence to IEEE Standards 1547 and 929. The various voltage and frequency control responses associated with reactive power management scheme are efficiently utilized based on well-defined states of operation and transient management scheme. In each state, the roles of each device for voltage and frequency regulations are defined with its regulation capability and response time based on its local measurements. Consequently, the fast reactive power compensation and rapid frequency regulation are ensured based on the inverter based devices at challenging operating conditions. As a result, the proposed control strategy improves the voltage and frequency regulation, transient response and MG stability. A comprehensive simulation study has verified the superior performance of the communication-free approach during steady state and in response to severe disturbances.
Recent developments in microgrids place strict constraints on the underlying state estimation technology, including the need for a dynamic and distributed approach. Since the problem is reminiscent of classical information fusion [2], the paper explores the application of a fusion-based reduced order, distributed unscented particle filter (FR/DUPF) for dynamic state estimation in microgrids. By partitioning the nonlinear microgrid into a network of nsub localized and dynamically coupled systems, the FR/DUPF provides computational savings of a factor of nsub over its centralized version. Monte Carlo simulations verify its accuracy by confirming that estimates from the FR/DUPF and centralized filter evolve close to the ground truth.
Intensive research is being directed at microgrids because of their numerous benefits, such as their ability to enhance the reliability of a power system and reduce its environmental impact. Past research has focused on microgrids that have predefined boundaries. However, a recently suggested methodology enables the determination of fictitious boundaries that divide existing bulky grids into smaller microgrids, thereby facilitating the use of a smart grid paradigm in large-scale systems. These boundaries are fixed and do not change with the power system operating conditions. In this paper, we propose a new microgrid concept that incorporates flexible fictitious boundaries: “dynamic microgrids.” The proposed method is based on the allocation and coordination of agents in order to achieve boundary mobility. The stochastic behavior of loads and renewable-based generators are considered, and a novel model that represents wind, solar, and load power based on historical data has been developed. The PG&E 69-bus system has been used for testing and validating the proposed concept. Compared with the fixed boundary microgrids, our results show the superior effectiveness of the dynamic microgrid concept for addressing the self-adequacy of microgrids in the presence of stochastically varying loads and generation.
Particle filters provide a general framework for dynamic state estimation (DSE) in nonlinear systems. The scope for particle filter-based DSE can be significantly enhanced by exploiting data from phasor measurement units (PMUs) when available at higher sampling frequencies. In this paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The filter relies on typical output measurements assumed available from PMUs stationed at generator buses. The performance of the proposed filter is illustrated with dynamic simulations on IEEE 14-bus system including: 1) generators models with subtransient dynamics, 2) excitation units (IEEE DC1A, DC2A, AC5A), and 3) turbine-governor models (steam and hydro). The estimation accuracy of the proposed filter is assessed for three classes of disturbances assuming noisy PMUs' measurements and comparative results are presented with the unscented Kalman filter (UKF). The accuracy-computational burden trade-off is also analyzed and the results strengthen the feasibility of using particle filters for dynamic state estimation.
This paper reviews signal processing research for applications in the future electric power grid, commonly referred to as smart grid. Generally, it is expected that the grid of the future would differ from the current system by the increased integration of distributed generation, distributed storage, demand response, power electronics, and communications and sensing technologies. The consequence is that the physical structure of the system becomes significantly more distributed. The existing centralized control structure is not suitable any more to operate such a highly distributed system. Hence, in this paper, we overview distributed approaches, all based on consensus +innovations, for three common energy management functions: state estimation, economic dispatch, and optimal power flow. We survey the pertinent literature and summarize our work. Simulation results illustrate tradeoffs and the performance of consensus +innovations for these three applications.
An optimized communication and control infrastructure, based on the microgrid building block concept, for active distribution systems is essential to facilitate powerful control framework under the smart grid paradigm. In this paper, a novel methodology for designing a communication and control infrastructure is presented. The new design takes into account both communication system and distribution system-related aspects. The proposed design facilitates systematic and optimized clustering of the distribution system into a set of virtual microgrids with optimized communication requirements while considering the power quality aspects, characteristics of distributed generation units, distributed energy storage units, and distributed reactive sources. The new design facilitates robust infrastructure for smart distribution systems operation and control, e.g., self-healing control and optimized system-level operation, by using virtual microgrids as building blocks in future distribution systems. The motivations, conceptual design, problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case and through several sensitivity studies, the effect of optimization coefficients on the design, and the robustness of the algorithm are investigated. Finally, the algorithm is tested on the IEEE 123-bus distribution system.
This paper proposes Extended Kalman Filter (EKF) based dynamic state estimator for power systems using phasor measurement unit (PMU) data. Dynamic state estimation in power systems provides synchronized wide area system history of the dynamic events which is key in the analysis and understanding of the system performance, behavior, and the types of control decisions to be made for large scale power system contingencies. In this paper, 2-axis-fourth-order state space modeling and validation of the synchronous machine is explained in detail. The model is then used for dynamic state estimation using EKF in IEEE 3-Generator-9-Bus Test System. The simulation results show that the model and estimation approach are capable to provide accurate information about the states of the machine and eliminate the noise effects on the measurement signal. The main challenges of dynamic estimation in large power systems are also addressed in this paper.
State estimation (SE) plays an essential role in the monitoring and supervision of power systems. In today's power systems, SE is typically done in a centralized or in a hierarchical way, but as power systems will be increasingly interconnected in the future smart grid, distributed SE will become an important alternative to centralized and hierarchical solutions. As the future smart grid may rely on distributed SE, it is essential to understand the potential vulnerabilities that distributed SE may have. In this paper, we show that an attacker that compromises the communication infrastructure of a single control center in an interconnected power system can successfully perform a denial-of-service attack against state-of-the-art distributed SE, and consequently, it can blind the system operators of every region. As a solution to mitigate such a denial-of-service attack, we propose a fully distributed algorithm for attack detection. Furthermore, we propose a fully distributed algorithm that identifies the most likely attack location based on the individual regions' beliefs about the attack location, isolates the identified region, and then reruns the distributed SE. We validate the proposed algorithms on the IEEE 118 bus benchmark power system.
This paper presents an online optimal energy/power control method for the operation of energy storage in grid-connected electricity microgrids. The approach is based on a mixed-integer-linear-program optimization formulated over a rolling horizon window, considering predicted future electricity usage and renewable energy generation. Performance objectives include electricity usage cost, battery operation costs, and utility oriented goals related to the peak demand and load smoothing. A robust counterpart formulation of the optimization problem is also proposed to handle uncertainty in energy demand/generation prediction in a computationally efficient way. Further reduction in the computations is achieved by employing variable time steps and relaxing binary constraints. A series of simulations demonstrate the effectiveness of various features of the proposed energy/power management methodology in different scenarios.
The huge computational complexities involved with particle filters is known to be one of the major constraint to their widespread use for different real time applications. This paper analyzes the computational complexities of the sampling, importance weight and resampling steps for the two most popular types of particle filters; generic particle filter and regularized particle filter (RPF). In the resampling step, different types of resampling methods are considered. From experiments, the importance weight step is found to be the most computational intensive part and RPF showed a relatively higher complexity to the generic particle filter. Among the resampling methods considered, Independent Metropolis Hastings Algorithm (IMHA) resampling resulted in the lowest computational complexity followed by systematic, stratified, residual and multinomial resampling algorithms. In addition to the computational complexities, the performance of the algorithms is also considered by using the most common root mean squared error (RMSE) metrics. The results obtained are of importance in the study of accelerating the algorithm in a hardware based platform and to be applied in real time problems.
This paper presents a fully distributed state estimation algorithm for wide-area monitoring in power systems. Through iterative information exchange with designated neighboring control areas, all the balancing authorities (control areas) can achieve an unbiased estimate of the entire power system's state. In comparison with existing hierarchical or distributed state estimation methods, the novelty of the proposed approach lies in that: 1) the assumption of local observability of all the control areas is no longer needed; 2) the communication topology can be different than the physical topology of the power interconnection; and 3) for DC state estimation, no coordinator is required for each local control area to achieve provable convergence of the entire power system's states to those of the centralized estimation. The performance of both DC and AC state estimation using the proposed algorithm is illustrated in the IEEE 14-bus and 118-bus systems.
It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.
In this paper, a novel cooperative protocol has been proposed to provide a proper voltage control for multiple feeders having a transformer tap-changer (LTC), unbalanced load diversity (station with different feeder loads) and multiple distributed generation (DG) units in each feeder. The proposed cooperative protocol has been defined according to the distributed control technology, where LTC and DG units are considered as control agents. Two conflicting objectives have been defined for each control agent. The first objective aims to achieve the system requirements by minimizing the voltage deviation and the second objective aims to achieve the device requirements by reducing the tap operation and maximizing the energy capture for LTC and DG units, respectively. The interior structures of the control agents and the communication acts between them have been designed to achieve the best compromise between the two objectives of each control agent. The effectiveness of the proposed cooperative scheme has been verified via different case studies.
The MicroGrid concept assumes a cluster of loads and microsources (<100 kW) operating as a single controllable system that provides both power and heat to its local area. This concept provides a new paradigm for defining the operation of distributed generation. To the utility the MicroGrid can be thought of as a controlled cell of the power system. For example this cell could be controlled as a single dispatchable load, which can respond in seconds to meet the needs of the transmission system. To the customer the MicroGrid can be designed to meet their special needs; such as, enhance local reliability, reduce feeder losses, support local voltages, provide increased efficiency through use waste heat, voltage sag correction or provide uninterruptible power supply functions. This paper provides an overview of the MicroGrid paradigm. This includes the basic architecture, control and protection and energy management.
This paper presents a generalized approach to the design of independent local Kalman filters (KFs) without commu- nication to be used for state estimation in distributed generation- based power systems. The design procedure is based on an improved model of the virtual disturbance concept proposed in a previous work. The local KFs are then synthesized based only on local models of the power network and on the characteristics of the associated virtual disturbance. The proposed solution is applied to an interconnected power network. By choosing appropriate models for the virtual disturbance, the local KFs can be suited for both dc and ac distribution systems. It is shown for both cases that the local KF can infer the local states of the network, including the aggregated branch currents coming from the other buses. Simu- lation results show improved results with respect to the previous proposed modeling approach even when the subsystems present widely different dynamics. The herein presented approach is well suited for the agent-based decentralized control of microgrids.
Nonlinear, reduced order, distributed state estimation in microgrids
- S Saxena
- A Asif
- H Farag
Implementation of a hybrid distributed/centralized real-time monitoring system for a dc/ac microgrid with energy storage capabilities
- P Garca
- P Arboleya
- B Mohamed
- A A C Vega