scispace - formally typeset
Search or ask a question

Showing papers in "IEEE Transactions on Smart Grid in 2016"


Journal ArticleDOI
TL;DR: This paper reviews and categorizes various approaches of power sharing control principles, and compares in terms of their respective advantages and disadvantages.
Abstract: Microgrid is a new concept for future energy distribution system that enables renewable energy integration. It generally consists of multiple distributed generators that are usually interfaced to the grid through power inverters. For the islanding operation of ac microgrids, two important tasks are to share the load demand among multiple parallel connected inverters proportionately, and maintain the voltage and frequency stabilities. This paper reviews and categorizes various approaches of power sharing control principles. Simultaneously, the control schemes are graphically illustrated. Moreover, various control approaches are compared in terms of their respective advantages and disadvantages. Finally, this paper presents the future trends.

751 citations


Journal ArticleDOI
TL;DR: A novel distribution system operational approach by forming multiple microgrids energized by DG from the radial distribution system in real-time operations to restore critical loads from the power outage to maximize the critical loads to be picked up.
Abstract: Microgrids with distributed generation (DG) provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by DG from the radial distribution system in real-time operations to restore critical loads from the power outage. Specifically, a mixed-integer linear program is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem by controlling the ON/OFF status of the remotely controlled switch devices and DG. A distributed multiagent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.

678 citations


Journal ArticleDOI
TL;DR: A novel consumption pattern-based energy theft detector, which leverages the predictability property of customers' normal and malicious consumption patterns, and provides a high and adjustable performance with a low-sampling rate.
Abstract: As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers’ normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers’ privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.

512 citations


Journal ArticleDOI
TL;DR: Computational studies on the IEEE distribution test systems validate the effectiveness of the RDNP and reveal that distributed generation is critical in increasing the resilience of a distribution system against natural disasters in the form of microgrids.
Abstract: Natural disasters such as Hurricane Sandy can seriously disrupt the power grids. To increase the resilience of an electric distribution system against natural disasters, this paper proposes a resilient distribution network planning problem (RDNP) to coordinate the hardening and distributed generation resource allocation with the objective of minimizing the system damage. The problem is formulated as a two-stage robust optimization model. Hardening and distributed generation resource placement are considered in the distribution network planning. A multi-stage and multi-zone based uncertainty set is designed to capture the spatial and temporal dynamics of an uncertain natural disaster as an extension to the traditional ${N}$ - ${K}$ contingency approach. The optimal solution of the RDNP yields a resilient distribution system against natural disasters. Our computational studies on the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of a distribution system against natural disasters in the form of microgrids.

414 citations


Journal ArticleDOI
TL;DR: Numerical simulations on a microgrid consisting of a wind turbine, a photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization.
Abstract: This paper proposes an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG, and price responsive loads. The microgrid coordinates the energy consumption or production of its components, and trades electricity in both day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, and day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed-integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization.

364 citations


Journal ArticleDOI
TL;DR: The conventional DR programs in smart grid are modified to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smartgrid, namely a smart energy (S. E.) hub, formulated for the electricity and natural gas networks.
Abstract: The proliferation of technologies such as combine heat and power systems has accelerated the integration of energy resources in energy hubs. Besides, the advances in smart grid technologies motivate the electricity utility companies toward developing demand response (DR) programs to influence the electricity usage behavior of the customers. In this paper, we modify the conventional DR programs in smart grid to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smart grid, namely a smart energy (S. E.) hub. In our model, the IDR program is formulated for the electricity and natural gas networks. The interaction among the S. E. hubs is modeled as an ordinal potential game with unique Nash equilibrium. Besides, a distributed algorithm is developed to determine the equilibrium. Simulation results show that in addition to load shifting, the customers in the S. E. hubs can participate in the IDR program by switching the energy resources (e.g., from the electricity to the natural gas) during the peak hours. Moreover, the IDR program can increase the S. E. hubs’ daily payoff and the utility companies’ daily profit.

358 citations


Journal ArticleDOI
TL;DR: A novel bidding model is incorporated into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets and a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model.
Abstract: Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery’s fast ramping capability. However, battery life might be decreased by frequent charge–discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage’s economic viability. The validity of the proposed model is proven by case study results.

352 citations


Journal ArticleDOI
TL;DR: A new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference.
Abstract: Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.

322 citations


Journal ArticleDOI
TL;DR: A decentralized bi-level algorithm is applied to solve the problem with the first level to conduct negotiations among all entities and the second level to update the non-converging penalties in both grid-connected and islanded modes.
Abstract: This paper proposes a decentralized energy management system for the coordinated operation of networked microgirds (MGs) in a distribution system. In the grid-connected mode, the distribution network operator and each MG are considered as distinct entities with individual objectives to minimize their own operation costs. It is assumed that both dispatchable and renewable energy source-based distributed generators (DGs) exist in the distribution network and the networked MGs. In order to coordinate the operation of all entities, we apply a decentralized bi-level algorithm to solve the problem with the first level to conduct negotiations among all entities and the second level to update the non-converging penalties. In the islanded mode, the objective of each MG is to maintain a reliable power supply to its customers. In order to take into account the uncertainties of DG outputs and load consumption, we formulate the problems as two-stage stochastic programs. The first stage is to determine base generation setpoints based on the forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. Case studies of a distribution system with networked MGs demonstrate the effectiveness of the proposed methodology in both grid-connected and islanded modes.

318 citations


Journal ArticleDOI
TL;DR: It is shown that the $ {\mu) -synthesis approach due to considering structured/parametric uncertainties provides better performance than the ${H} _{ {\infty }}$ control method.
Abstract: This paper addresses robust frequency control in an islanded ac microgrid (MG). In an islanded MG with renewable sources, load change, wind power fluctuation, and sun irradiation power disturbance as well as dynamical perturbation, such as damping coefficient and inertia constants, can significantly influence the system frequency, and hence the MG frequency control problem faces some new challenges. In response to these challenges, in this paper, ${H} _{ {\infty }}$ and $ {\mu }$ -synthesis robust control techniques are used to develop the secondary frequency control loop. In the proposed control scheme, some microsources (diesel engine generator, micro turbine, and fuel cell) are assumed to be responsible for balancing the load and power in the MG system. The synthesized ${H} _{ {\infty }}$ and $ {\mu }$ -controllers are examined on an MG test platform, and the controllers’ robustness and performance are evaluated in the presence of various disturbances and parametric uncertainties. The results are compared with an optimal control design. It is shown that the $ {\mu }$ -synthesis approach due to considering structured/parametric uncertainties provides better performance than the ${H} _{ {\infty }}$ control method.

309 citations


Journal ArticleDOI
TL;DR: An appropriate framework is devised and the roles and tasks of different management entities in a multi-microgrids system are introduced and the effectiveness in confronting with different outage events is demonstrated through realistic case studies.
Abstract: This paper proposes a hierarchical outage management scheme to enhance the resilience of a smart distribution system comprised of multi-microgrids against unexpected disaster events. In this regard, after identifying the main features and requirements for a resilient outage management scheme, an appropriate framework is devised and the roles and tasks of different management entities in a multi-microgrids system are introduced. Based on this framework, the microgrids schedule their available resources in the first stage using a novel model predictive control-based algorithm. In the second stage, distribution system operator coordinates the possible power transfers among the microgrids and utilizes the unused capacities of microgrids’ resources for feeding the unserved loads in stage I. The general optimization model that needs to be run is formulated as a mixed integer linear programming problem and a novel index is presented to quantify the performance of the proposed method. The developed scheme is implemented on a test system and its effectiveness in confronting with different outage events is demonstrated through realistic case studies.

Journal ArticleDOI
TL;DR: A resilience-oriented service restoration method using microgrids to restore critical load after natural disasters is proposed in this paper, and the impacts of fault locations, available generation resources, and load priority on the restoration strategy are discussed.
Abstract: A resilience-oriented service restoration method using microgrids to restore critical load after natural disasters is proposed in this paper. Considering the scarcity of power generation resources, the concept of continuous operating time (COT) is introduced to determine the availability of microgrids for critical load restoration and to assess the service time. Uncertainties induced by intermittent energy sources and load are also taken into account. The critical load restoration problem is modeled as a chance-constrained stochastic program. A Markov chain-based operation model is designed to describe the stochastic energy variations within microgrids, based on which the COT is assessed. A two-stage heuristic is developed for the critical load restoration problem. First, a strategy table containing the information of all feasible restoration paths is established. Then the critical load restoration strategy is obtained by solving a linear integer program. Numerical simulations are performed on the IEEE 123-node feeder system under several scenarios to demonstrate the effectiveness of the proposed method. The impacts of fault locations, available generation resources, and load priority on the restoration strategy are discussed.

Journal ArticleDOI
TL;DR: This paper presents a novel model based on mixed integer linear programming for the optimization of a hybrid renewable energy system with a battery energy storage system in residential microgrids in Okinawa in which the demand response of available controllable appliances is coherently considered in the proposed optimization problem.
Abstract: Accelerated development of eco-friendly technologies such as renewable energy, smart grids, and electric transportation will shape the future of electric power generation and supply. Accordingly, the power consumption characteristics of modern power systems are designed to be more flexible, which impact the system sizing. However, integrating these considerations into the design stage can be complex. Under these terms, this paper presents a novel model based on mixed integer linear programming for the optimization of a hybrid renewable energy system with a battery energy storage system in residential microgrids in which the demand response of available controllable appliances is coherently considered in the proposed optimization problem with reduced calculation burdens. The model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models are considered. This paper investigates the effect of load flexibility on the component sizing of the system for a residential microgrid in Okinawa. Also under consideration are different operation scenarios emulating technical limitations and several uncertainty levels.

Journal ArticleDOI
TL;DR: A transformative architecture for the normal operation and self-healing of networked microgrids (MGs) is proposed and a consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way.
Abstract: This paper proposes a transformative architecture for the normal operation and self-healing of networked microgrids (MGs). MGs can support and interchange electricity with each other in the proposed infrastructure. The networked MGs are connected by a physical common bus and a designed two-layer cyber communication network. The lower layer is within each MG where the energy management system (EMS) schedules the MG operation; the upper layer links a number of EMSs for global optimization and communication. In the normal operation mode, the objective is to schedule dispatchable distributed generators (DGs), energy storage systems (ESs), and controllable loads to minimize the operation costs and maximize the supply adequacy of each MG. When a generation deficiency or fault happens in an MG, the model switches to the self-healing mode and the local generation capacities of other MGs can be used to support the on-emergency portion of the system. A consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way. The allocated portion corresponds to each MG’s local power exchange target, which is used by its EMS to perform the optimal schedule. The resultant aggregated power output of networked MGs will be used to provide the requested power support. Test cases demonstrate the effectiveness of the proposed methodology.

Journal ArticleDOI
TL;DR: In-depth analysis of customer smart meter data is presented to better understand the peak demand and major sources of variability in their behavior, and the first time in the power systems literature that the sample robustness of the clustering has been tested.
Abstract: Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and the improvement of energy profile modeling. However, due to the high stochasticity and irregularity of household-level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper, we present in-depth analysis of customer smart meter data to better understand the peak demand and major sources of variability in their behavior. We find four key time periods, in which the data should be analyzed, and use this to form relevant attributes for our clustering. We present a finite mixture model-based clustering, where we discover ten distinct behavior groups describing customers based on their demand and their variability. Finally, using an existing bootstrap technique, we show that the clustering is reliable. To the authors’ knowledge, this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Journal ArticleDOI
TL;DR: Simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden.
Abstract: This paper proposes a real-time price (RTP)-based demand-response (DR) algorithm for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the energy management center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A one-leader, ${N}$ -follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help in selecting the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden.

Journal ArticleDOI
TL;DR: A novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods is proposed.
Abstract: In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers’ electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.

Journal ArticleDOI
TL;DR: Two new SCSs control schemes are discussed to deal with droop control: a model predictive controller (MPC) and a Smith predictor-based controller, which is concluded that in terms of robustness, the MPC has better performance.
Abstract: One of the well-known methods to share active and reactive power in microgrids (MGs) is droop control. A disadvantage of this method is that in steady state the frequency of the MG deviates from the nominal value and has to be restored using a secondary control system (SCS). The signal obtained at the output of the SCS is transmitted using a communication channel to the generation sources in the MG, correcting the frequency. However, communication channels are prone to time delays, which should be considered in the design of the SCS; otherwise, the operation of the MG could be compromised. In this paper, two new SCSs control schemes are discussed to deal with this issue: 1) a model predictive controller (MPC); and 2) a Smith predictor-based controller. The performance of both control methodologies are compared with that obtained using a conventional proportional integral-based SCS using simulation work. Stability analysis based on small signal models and participation factors is also realized. It is concluded that in terms of robustness, the MPC has better performance.

Journal ArticleDOI
TL;DR: A unified resilience evaluation and operational enhancement approach, which includes a procedure for assessing the impact of severe weather on power systems and a novel risk-based defensive islanding algorithm, which aims to mitigate the cascading effects that may occur during weather emergencies.
Abstract: Several catastrophic experiences of extreme weather events show that boosting the power grid resilience is becoming increasingly critical. This paper discusses a unified resilience evaluation and operational enhancement approach, which includes a procedure for assessing the impact of severe weather on power systems and a novel risk-based defensive islanding algorithm. This adaptive islanding algorithm aims to mitigate the cascading effects that may occur during weather emergencies. This goes beyond the infrastructure-based measures that are traditionally used as a defense to severe weather. The resilience assessment procedure relies on the concept of fragility curves, which express the weather-dependent failure probabilities of the components. A severity risk index is used to determine the application of defensive islanding, which considers the current network topology and the branches that are at higher risk of tripping due to the weather event. This preventive measure boosts the system resilience by splitting the network into stable and self-adequate islands in order to isolate the components with higher failure probability, whose tripping would trigger cascading events. The proposed approach is illustrated using a simplified version of the Great Britain transmission network, with focus on assessing and improving its resilience to severe windstorms.

Journal ArticleDOI
TL;DR: This paper addresses the optimal bidding strategy problem of a commercial virtual power plant (CVPP), which comprises of distributed energy resources (DERs), battery storage systems (BSS), and electricity consumers, and participates in the day-ahead electricity market.
Abstract: This paper addresses the optimal bidding strategy problem of a commercial virtual power plant (CVPP), which comprises of distributed energy resources (DERs), battery storage systems (BSS), and electricity consumers, and participates in the day-ahead (DA) electricity market. The ultimate goal of the CVPP is the maximization of the DA profit in conjunction with the minimization of the anticipated real-time production and the consumption of imbalance charges. A three-stage stochastic bi-level optimization model is formulated, where the uncertainty lies in the DA CVPP DER production and load consumption, as well as in the rivals’ offer curves and real-time balancing prices. Demand response schemes are also incorporated into the virtual power plant (VPP) portfolio. The proposed bi-level model consists of an upper level that represents the VPP profit maximization problem and a lower level that represents the independent system operator (ISO) DA market-clearing problem. This bi-level optimization problem is converted into a mixed-integer linear programing model using the Karush–Kuhn–Tucker optimality conditions and the strong duality theory. Finally, the risk associated with the VPP profit variability is explicitly taken into account through the incorporation of the conditional value-at-risk metric. Simulations on the Greek power system demonstrate the applicability and effectiveness of the proposed model.

Journal ArticleDOI
TL;DR: A droop-based distributed cooperative control scheme for microgrids under a switching communication network with non-uniform time-varying delays that guarantees the stability and reliability of the microgrid.
Abstract: This paper develops a droop-based distributed cooperative control scheme for microgrids under a switching communication network with non-uniform time-varying delays. We first design a pinning-based frequency/voltage controller containing a distributed voltage observer and then design a consensus-based active/reactive power controller, which are employed into the secondary control stage to generate the nominal set points used in the primary control stage for different distributed generators (DGs). By this approach, the frequencies and the weighted average value of all DGs’ voltages can be pinned to the desired values while maintaining the precise active and reactive power sharing. With the proposed scheme, each DG only needs to communicate with its neighbors intermittently, even if their communication networks are local and time-varying, and their variant delays may be non-uniform. Sufficient conditions on the requirements for the network connectivity and the delay upper bound that guarantee the stability and reliability of the microgrid are presented. The effectiveness of the proposed control scheme is verified by the simulation of a microgrid test system.

Journal ArticleDOI
TL;DR: A scenario-based robust energy management method accounting for the worst-case amount of renewable generation and load, which is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification is developed.
Abstract: A scenario-based robust energy management method accounting for the worst-case amount of renewable generation (RG) and load is developed in this paper. The economic and robust model is formulated to maximize the total exchange cost while getting the minimum social benefits cost at the same time. Uncertainty of RG and load is described as an uncertain set produced by interval prediction. Then, the Taguchi’s orthogonal array (OA) testing method is used to provide possible testing scenarios. A simple, but practical, search strategy based on OA is designed for solving the optimization problem. By optimizing the worst-case scenario, the energy management solution of the proposed model is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification. Numerical cases on the typical microgrid system show the effectiveness of the model and solution strategy. In addition, the influence of exchange electricity price and other parameters are also discussed in the cases.

Journal ArticleDOI
TL;DR: The proposed distributed scheme successfully mitigates overvoltage situations due to high PV penetration and performs almost as well as the OPF-based solution with significantly less information and communication requirements.
Abstract: In this paper, the overvoltage problems that might arise from the integration of photovoltaic (PV) panels into low-voltage (LV) distribution networks is addressed. A distributed scheme is proposed that adjusts the reactive and active power output of inverters to prevent or alleviate such problems. The proposed scheme is model-free and makes use of limited communication between the controllers in the form of a distress signal only during emergency conditions. It prioritizes the use of reactive power, while active power curtailment is performed only as a last resort. The behavior of the scheme is studied using dynamic simulations on a single LV feeder and on a larger network composed of 14 LV feeders. Its performance is compared with a centralized scheme based on the solution of an optimal power flow (OPF) problem, whose objective function is to minimize the active power curtailment. The proposed scheme successfully mitigates overvoltage situations due to high PV penetration and performs almost as well as the OPF-based solution with significantly less information and communication requirements.

Journal ArticleDOI
TL;DR: Test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.
Abstract: This paper presents an intelligent protection scheme for microgrid using combined wavelet transform and decision tree. The process starts at retrieving current signals at the relaying point and preprocessing through wavelet transform to derive effective features such as change in energy, entropy, and standard deviation using wavelet coefficients. Once the features are extracted against faulted and unfaulted situations for each-phase, the data set is built to train the decision tree (DT), which is validated on the unseen data set for fault detection in the microgrid. Further, the fault classification task is carried out by including the wavelet based features derived from sequence components along with the features derived from the current signals. The new data set is used to build the DT for fault detection and classification. Both the DTs are extensively tested on a large data set of 3860 samples and the test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.

Journal ArticleDOI
Weiye Zheng1, Wenchuan Wu1, Boming Zhang1, Hongbin Sun1, Liu Yibing1 
TL;DR: In this paper, a fully distributed reactive power optimization algorithm that can obtain the global optimum solution of nonconvex problems for distribution networks (DNs) without requiring a central coordinator is presented.
Abstract: This paper presents a fully distributed reactive power optimization algorithm that can obtain the global optimum solution of nonconvex problems for distribution networks (DNs) without requiring a central coordinator. Second-order conic relaxation is used to achieve exact convexification. A fully distributed second-order cone programming solver (D-SOCP) is formulated corresponding to the given division of areas based on the alternating direction method of multipliers (ADMM) algorithm, which is greatly simplified by exploiting the structure of active DNs. The problem is solved for each area with very little interchange of boundary information between neighboring areas. D-SOCP is extended by using a varying penalty parameter to improve convergence. A proof of its convergence is also given. The effectiveness of the method is demonstrated via numerical simulations using the IEEE 69-bus, 123-bus DNs, and a real 1066-bus distribution system.

Journal ArticleDOI
TL;DR: This paper presents a two-stage stochastic programming approach to the optimal scheduling of a resilient MG, linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation.
Abstract: In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

Journal ArticleDOI
TL;DR: This paper presents a distributed economic dispatch strategy based on projected gradient and finite-time average consensus algorithms for smart grid systems that not only handles quadratic, but also nonquadratic convex cost functions with arbitrary initial values.
Abstract: In this paper, we present a distributed economic dispatch (ED) strategy based on projected gradient and finite-time average consensus algorithms for smart grid systems. Both conventional thermal generators and wind turbines are taken into account in the ED model. By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner with limited communication among neighbors. It is theoretically shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. This scheme also brings some advantages, such as plug-and-play property. Different from most existing distributed methods, the private confidential information, such as gradient or incremental cost of each generator, is not required for the information exchange, which makes more sense in real applications. Besides, the proposed method not only handles quadratic, but also nonquadratic convex cost functions with arbitrary initial values. Several case studies implemented on six-bus power system, as well as the IEEE 30-bus power system, are discussed and tested to validate the proposed method.

Journal ArticleDOI
TL;DR: Reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.
Abstract: In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.

Journal ArticleDOI
TL;DR: A spatio-temporal method for producing very-short-term parametric probabilistic wind power forecasts at a large number of locations is presented and demonstrates numerical advantages over conventional vector autoregressive models.
Abstract: A spatio-temporal method for producing very-short-term parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here, we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modeled as a vector-valued spatio-temporal process and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 min mean wind power generation at 22 wind farms in Australia. Five-min ahead forecasts are produced and evaluated in terms of point, and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks.

Journal ArticleDOI
TL;DR: An identity-based signature scheme and an identity- based encryption scheme are used to propose a new anonymous key distribution scheme for smart grid environments that allows a smart meter to anonymously access services provided by service providers using one private key without the help of the trusted anchor during authentication.
Abstract: To fully support information management among various stakeholders in smart grid domains, how to establish secure communication sessions has become an important issue for smart grid environments. In order to support secure communications between smart meters and service providers, key management for authentication becomes a crucial security topic. Recently, several key distribution schemes have been proposed to provide secure communications for smart grid. However, these schemes do not support smart meter anonymity and possess security weaknesses. This paper utilizes an identity-based signature scheme and an identity-based encryption scheme to propose a new anonymous key distribution scheme for smart grid environments. In the proposed scheme, a smart meter can anonymously access services provided by service providers using one private key without the help of the trusted anchor during authentication. In addition, the proposed scheme requires only a few of computation operations at the smart meter side. Security analysis is conducted to prove the proposed scheme is secure under random oracle model.