Power systems are evolving from centralized power grid structures to networks of intelligent microgrids (MGs) that can share power more independently. The interconnection between these MGs, forming the networked MGs (NMGs), will increase the power system’s stability and expand its capabilities. However, a critical concern that necessitates sophisticated management strategies for voltage stabilization across the microgrids is the optimal and effective power sharing between renewable resources and energy storage devices. Effective power sharing between AC and DC sides is critical, necessitating robust control mechanisms for interlink converters (ILC). This paper presents a coordinated energy management strategy for hybrid networked MGs (NMGs) to enhance stability during contingencies and fault instances. The proposed Energy Management System (EMS) operates in two distinct modes: grid-connected and islanded. The system takes specific actions to maintain stability depending on the current mode. In grid-connected mode, the grid supplies the load demand while one of the microgrids (MGs) serves as a backup to ensure system stability. Several scenarios were created using MATLAB/Simulink to test and validate the EMS. These scenarios included varying load demands, pulsed power loads, uncertainties in photovoltaic (PV) system generation, disconnection from the AC side, and fault conditions. The proposed EMS has proven its efficiency while maintaining the system stability within the standard limits and fulfilling the load demands.

This paper introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model was presented for the developed converter and its control system. The system under study was a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter was connected to the AC grid through an R-L filter. The classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, machine-learned controllers were used to dealing with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral (PI) was introduced for both outer and inner loops PI-PI controller. A Data-Driven Online Learning (DDOL) controller was then proposed. This controller was a Proportional Integral Neural Network (PI-NN) that enhanced the system performance in terms of dynamic and steady-state responses. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones was made under different operating scenarios. The converter control was tested under various operational conditions, and its dynamic and steady-state behavior was analyzed. The model was done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. Then, the system was designed, fabricated, and implemented in a hardware environment in our testbed, and the test results were verified. The results show that the intelligent controller would achieve better performance in both dynamic and steady-state responses.

Smart grid data can be analyzed for detecting abnormalities in many different areas such as cybersecurity, fault detection, electricity theft, etc. There is a strong case for the use of machine learning in anomaly detection. The raw grid data requires feature extraction. Anomalies can be defined as instances or changes in the smart grid data that are out of character concerning the average trend. A typical grid architecture results can vary significantly, depending on trends or changes in power, voltage, current, or consumption. This paper develops an anomaly detection model for a real-world smart grid system implemented on a hardware-based testbed. By detecting abnormal activities, one can improve the system behavior in data communication flow. It will also identify if there are parameter changes that indicate the presence of cyber-attacks. Our proposed anomaly detection model is build based on Isolation Forest (IF) to isolate outliers from standard observations through multiple decision trees. The performance of the proposed detection method was verified using the simulation results on a hardware-based testbed. Feature selection was optimized by principal component analysis and the model was further analyzed for performance with dickey-fuller test.

The paper introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model is presented for a converter and its control. The system under study is a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter is connected to the AC grid through R-L filter. On the DC side an energy storage system ESS is connected to the DC bus. Classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, a machine learned controllers are used to deal with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral for both outer and inner loops PI-PI controller is introduced. Then, a Data Driven Online Learning (DDOL) controller is proposed. This controller is a Proportional Integral Neural Network (PI-NN) that is used to enhance the system performance in terms of dynamic and steady-state responses. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones is made under different operating scenarios. The converter control is tested under different operational conditions, and its dynamic and steady-state behavior is analyzed. The model is done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. The results are showing that the intelligent controller can achieve the set reference points with better performance in terms of both dynamic and steady-state responses.

This paper proposes a multiagent-based energy market for multimicrogrid systems using game-theoretic and hierarchical optimization approaches. The proposed method is tailored to achieve the optimal operation of smart microgrids in distribution systems. Because of rapid load variations in distribution systems, it is necessary to develop fast optimization algorithms which minimize the power mismatch in and among microgrids. In this paper, a three-level market framework is proposed. The first level comprises a game-theoretic double-auction mechanism for the day-ahead market while the next two levels are optimal rescheduling and intermicrogrid reverse auction model for the hour-ahead and real-time markets, respectively. Using the hierarchical optimization algorithm in a multiagent-based area, it is anticipated to not only minimize the optimization solution time, but also reduce the dependency on the network in grid-connected mode or load shedding in islanded mode. Using this approach, load demand response capabilities along with rescheduling of Distributed Energy Storage Systems and distributed generations could be utilized in all market levels, which will lead to optimal operation of multimicrogrid systems. Agents are developed in DIgSILENT PowerFactory and dynamic data exchange is activated for communication among agents communicating through a data distribution service which utilizes the real-time publish-subscribe communication protocol. The developed framework is applied to the modified 37-bus IEEE distribution test feeder system to validate the effectiveness of this market structure.

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the hierarchical distributed model predictive control (HDMPC) to solve the energy management problem in the multitime frame and multilayer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized supervisory management (SM) layer and another short time-horizon during high-power variability for a distributed coordination management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically and experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.

In this paper, a hierarchical power routing scheme for Interlinking Converters (ICs) in unbalanced hybrid AC-DC microgrids is proposed. The proposed scheme aims to minimize power losses in the microgrid and increase the system loadability at the point of common coupling (PCC). Control variables of this optimization problem are active and reactive power bias factors of all three-phase ICs. At first, the optimization problem minimizes the power losses in the microgrid, afterward a supervisory control scheme is applied to maximize the loadability at the PCC by assigning the three phase power mismatches at the PCC to ICs. The obtained results from implementing the proposed strategy on the modified IEEE 13 bus system are compared with other strategies which are aimed to just maximize loadability or minimize power losses to show the validity and advantages of proposed method.

The robustness of modern power systems depends on the level of cyber security against cyber-attacks. Since energy transactions could significantly affect the power system operation, these transactions should be evaluated through a secure system to enhance the power system reliability. In this paper, a blockchain-based energy transaction framework is introduced and the level of security for different energy transaction frameworks are evaluated by calculating the overall probability of successful attacks (PSA)to each one. PSA is defined as a successful attack to the system which can lead to the system collapse. The numerical results will demonstrate that the blockchain based energy transaction framework is the most reliable system against cyber-attacks comparing with traditional centralized and modified decentralized energy transaction frameworks.

This paper presents a cloud-based and hybrid wireless mesh communication framework for bilevel, nested, distributed optimization of networked clusters of microgrids. The proposed optimization framework implements a diffusion-based, fully distributed algorithm on local wireless network and a quasi-distributed approach on wide-area internet-based cloud. The lower level of the bilevel optimization implements a distributed optimal economic dispatch solution for intramicrogrid among distributed energy resources, and the upper level implements a global optimal dispatch for intermicrogrid energy exchange. To demonstrate industrial applicability of the proposed framework, the IEC 61850 interoperability protocol is adopted to achieve a certain delay performance so that the distributed optimization convergence is guaranteed. First, hardware-based prototype intelligent electronic devices are developed using embedded systems. Then, the bilevel nested optimization algorithm is implemented for integration of networked microgrids. Finally, experimental results are demonstrated from a real-time smart grid testbed featuring realistic microgrids. The results demonstrate that the proposed framework meets communication requirements for distributed optimization of networked microgrids.

The smart grid is seen as a power system with real-time communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper, a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service Ethernet network with appropriate quality-of-service profiles. The developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida, and proved its effectiveness in reducing consumers' bills and achieving flat peak load profiles.

As intermittent generation profiles introduce more stress due to the sudden imbalance in supply and demand, power systems are becoming more dependent on online aggregated support of networked utility-independent private microgrids. During the aggregation process, microgrid operators’ preferences and bidding behaviors are priority. New aggregation approaches are required considering the individual behaviors of the networked and geographically dispersed independent microgrids. In this paper, we present a novel bidding behavior modeling and an auction architecture consisting of a central aggregator and networked microgrid agents. The bidding behavioral states of the microgrid agents are formalized as partially observable Markov processes for the belief updates and short-term policy determination in order to maximize the individual profit. A reverse auction model is adopted to enable competitive negotiations between the central aggregator and networked microgrid agents. The auction and aggregation processes were implemented in a power system control area along with an automatic generation control (AGC) scheme to contribute frequency control. The proposed AGC and auction mechanism were verified with an industrial multi-agent framework in a laboratory-based real-time application.

Emerging Software Defined Networking (SDN) technology provides excellent flexibility to large-scale networks in terms of control, management, security, and maintenance. In this paper, we propose an SDN-based communication infrastructure for Smart Grid distribution networks among substations. A Smart Grid communication infrastructure consists of a large number of heterogenous devices that exchange real-time information for monitoring the status of the grid. We then investigate how SDN-enabled Smart Grid infrastructure can provide resilience to active distribution substations with self-recovery. Specifically, by introducing redundant and wireless communication links that can be used during the emergencies, we show that SDN controllers can be effective for restoring the communication while providing a lot of flexibility. Furthermore, to be able to effectively evaluate the performance of the proposed work in terms of various fine-grained network metrics, we developed a Mininet-based testing framework and integrated it with ns-3 network simulator. Finally, we conducted experiments by using actual Smart Grid communication data to assess the recovery performance of the proposed SDN-based system. The results show that SDN is a viable technology for the Smart Grid communications with almost negligible delays in switching to backup wireless links during the times of link failures in reliable fashion.

This paper presents a simple state-space representation of induction machine (IM) transients under stator winding inter-turn short-circuit fault. The mathematical development of this dynamic model is presented in detail and the differential equations are transformed into space-vector form for reduction reasons. The simulations of this transient model are conducted in MatLab script using ordinary differential equation (ODE) solver. For verification purposes, the results of this simulation are compared with the ones obtained for the symmetrical IM. A turn fault signature study is performed for fault detection and identification using the electromagnetic torque ripple and the symmetric components transformation of the stator currents.

DC Microgrid power systems are becoming increasingly common in many applications. In this work, the comparative analysis of different energy control techniques for a dc microgrid with pulsed power loads is considered. A laboratory scale hybrid dc microgrid is developed to evaluate the performance of the energy control methods. In the proposed dc microgrid, a supercapacitor bank, acting as power buffer, and a pulse load are directly connected to a common coupling dc bus while the injected power to the grid is regulated through power converters. This microgrid is connected to an AC grid test-bed to further analyze the consequence of the pulse loads. The test results show that the energy control methods are different in terms of their maximum power requirement, their performance on the dc microgrid, and their effects on the ac grid.

This paper presents a comparative study among different Maximum Power Point Trackers for Photovoltaic Water Pumping load. Comprehensive analysis and simulation of KC-120-1 PV module (source), Kyocera SD 12-30 solar pump (load) and storage tank (storage) were conducted. Multi-objective optimization based on Genetic Algorithms were performed for two MPPT techniques: perturb and observe (P&O) and fuzzy technique. A GA cost function is developed and explained for optimization purposes. The fitness function considers the irradiance variations of two climates condition (sunny and cloudy days), however the algorithm can be easily changed for considering more cases. A comparative analysis of both techniques was perform before and after optimization based on the system energy error and the water flow rate. It is demonstrated that GA-optimized Fuzzy algorithm presents a more appropriate behavior under the different climatic conditions.

In grid-connected wind energy conversion systems (WECSs), phase locked loop (PLL) technique became widely used to enhance the stability and power quality. However, the accuracy of PLL is one of the major aspects that influence the system performance. Conventional synchronous-reference frame PLL (SRF-PLL) techniques have difficulty of low accuracy, frequency fluctuation, power oscillation and poor power quality. To improve these drawbacks, this paper proposes a new improved PLL technique. A comparative study for the conventional techniques and the developed technique is introduced. Two types of current controllers (hysteresis and vector oriented control) are compared. A reconfigurable inverter controller is also proposed here as an effective method that supports both grid-connected and stand-alone operation modes. This provides stable operation under various grid conditions and maintains stable frequency reference during islanding mode. More advantages include voltage unbalance operation capability and robustness under fault conditions. Simulation results are carried out to validate the proposed solution. The results have demonstrated that the proposed technique is more efficient than other conventional techniques to achieve better performance, improved power quality, and enhanced stability under various conditions.

This paper presents a modified synchronous reference frame for Active Power Filter control (APF) to compensate for harmonics and reactive power for both balanced and unbalanced non-linear loads. The proposed controller autonomously detects different power quality problems, filters out load created harmonics and compensates for unbalances and/or DC offsets resulting from non-linear loads connected at the point of common coupling (PCC). The main objective of the controller is to force the AC grid currents at the PCC to be balanced three-phase currents with minor harmonics regardless of the characteristics of the local AC load. In addition, the developed controller uses only current measurements which reduces the cost and complexity of system implementation. The performance of the developed controller was examined under several extreme cases, including: non-linear loads, distorted load currents, unbalanced loads, loss of one supply phase and existence of DC current component conditions. The results show that the developed controller succeeds to improve power quality at the PCC under various loading conditions.

In this paper, a real-time energy management algorithm for charging a plug-in electric vehicles (PEVs) network in a large urban area with renewable energy resources is proposed. In this system, the PEVs charging rates are controlled by a central aggregator through wireless communication. A statistical forecasting model of the energy requirement of the PEVs network at different times during the day is developed based on statistical US drivers' driving habits. With historical solar irradiance, and wind speed in this area, genetic algorithm (GA) is used to find the optimal scale of the renewable farm that can feed proper power for the PEVs network. Meanwhile, this urban area has a certain amount of local load that follows a daily pattern. Through vehicle to grid (V2G) and vehicle to vehicle (V2V) services, the proposed power optimization algorithm based on fuzzy logic control is used to minimize the impact of charging PEVs to the power grid, maximize the utilization of renewable energy and help the power grid to regulate the utility frequency, which will benefit the utility AC grid, PEVs network and its customers. The simulation results based on a large PEVs network demonstrate that the proposed smart charging algorithm can effectively limit the PEVs charging impact and help the grid regulate the frequency.

In this paper, some of the aspects related to the design, control, operation and protection of photovoltaic (PV) systems are presented and investigated. The photovoltaic systems under study are integrating their power to the common DC bus of a hybrid AC/DC microgird in a smart grid infrastructure, where a communication layer is allowing wide area monitoring, control and protection of the whole system. These PV systems can be mainly operated either in a voltage control mode, when the DC side of the system is disconnected from the main AC grid, or in a maximum power point tracking (MPPT) mode when connected to the DC bus. The control of the power conditioning units interfacing the PV panels to the rest of the systems in these two modes of operation is investigated. Furthermore, the protection of these converters against different types of faults will be also investigated. Simulation and experimental results are included in the paper to justify the validity of the ideas and concepts discussed.