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The paper proposes a control scheme to improve the dynamic response of power systems through the automatic regulators of converter-based Distributed Energy Resources (DERs). In this scheme, both active and reactive power control of DERs are varied to regulate both frequency and voltage, as opposed to current practice where frequency and voltage controllers are decoupled. To assess the proposed control against the current state-of-art, the paper also defines a metric that captures the combined effect of frequency/voltage response at any given bus of the network. Results indicate that the proposed control strategy leads to a significant improvement in the stability and performance of the overall power system. These results are based on a comprehensive case study carried out by employing a modified version of the IEEE 39-bus benchmark system, where a portion of the synchronous machines is substituted by converter-interfaced DERs. The impact on the proposed control of load models, the $R/X$ ratio of network lines, as well as the level of DER penetration to the grid, are properly evaluated and conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users’ characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Power converters are one of the failure sources in modern power systems, and hence driver of maintenance and downtime costs, which should be reduced by reliable design, control, and operation of converters. This paper proposes a power sharing control strategy for evenly distributing the thermal stresses among dc converters in dc microgrids, and consequently enhancing the overall system reliability. The aim of this paper is to extend the aging process of failure prone converters by adjusting their loadings. The proposed approach employs the prior experienced thermal damages on the converter's fragile components in order to adjust its contribution on demand supply. According to the proposed strategy, the higher the thermal stress on a converter is, the lower the power it will supply. As a result, the overall system reliability will be improved. A numerical case study on a dc microgrid is presented to illustrate the effectiveness of the proposed power sharing strategy. Moreover, experimental tests are provided to demonstrate the applicability of the reliability-oriented power sharing method. [ABSTRACT FROM AUTHOR]

Behind-the-meter battery energy storage is used to mitigate non-dispatchability of customer-owned renewable sources. While serving their owners’ objectives, their underutilized capacities can be used to provide grid services to justify their cost. However, the integration of these resources extends the size and complexity of the network operation problem, imposing a processing burden on the central control system. In this paper, an agent-based distributed control framework is proposed that enables utilizing of the underutilized behind-the-meter capacities. In this framework, while the prosumer agents initially schedule their resources for daily cost management, a local customer aggregator agent performs day-ahead active and reactive power control on the underutilized behind-the-meter batteries to provide multiple grid services. A novel grouping algorithm is developed based on voltage sensitivity and a modified $k$ -means clustering method to assign customer groups to the local agents and establish the network subsystems. Mixed-integer linear programming is used to model and solve multiple optimization problems. The proposed framework is applied to the 123-bus distribution network. The simulation results show its effectiveness in terms of complexity and optimality, compared to the central control approach in allocating the unused behind-the-meter resources for two common industry needs: network bottleneck congestion relief and grid-edge voltage support. [ABSTRACT FROM AUTHOR]

Due to the increasing integration of distributed energy resources (DERs) into the national grid, research is needed to understand high DER penetration in relation to grid reliability and stability. Emulation-based research is a viable option to consider hardware intensive investigations. Indeed, DER emulation-based research has advantages over DER software-based simulation and real DER implementation research. Some of these advantages are more realistic performance and boundaries and considerably less expense, higher safety, and modularity. While the downside of DER emulation-based research is the complexity of modeling large scale grid systems. This paper provides a rationalization for the selection of an emulation-based DER laboratory and description of the DER laboratory’s capabilities. Additionally, guidelines for running experiments are discussed and two approaches, power scaling using rescaling coefficients and system sizing using Multi-Port Thevenin Equivalence (MPTE), are proposed to emulate realistic complex interconnected DER grid connected systems. [ABSTRACT FROM AUTHOR]

With the proliferation of distributed generation in distribution networks, quickly and smoothly converting to a state of regional autonomy in the case of distribution network failure is one of the main challenges for the stable operation of a multimicrogrid (MMG). To address the aforementioned problem, this paper proposes coordinated control of the passive transition from grid-connected to islanded operation for a three/single-phase hybrid MMG. Under the premise of fully considering the constraints on the tie-line transfer power undertaken by the master power supply (MPS), the following two scenarios regarding safety and emergency are formulated: 1) when the output power of the MPS is in the safe range, the proposed strategy coordinates the energy storage (ES) in each submicrogrid (SMG) to achieve power balance; 2) when the output power is in the emergency range, this paper proposes a three-phase balance method based on a bubble sort to realize the combination optimization of the single-phase source-load-storage (SLS) and a fast power regulation method based on implicit enumeration for the SLS to reduce the tie-line transfer power, which could prevent failure of the mode transition from power overload. The test results verify the effectiveness and feasibility of the proposed coordination strategy. [ABSTRACT FROM AUTHOR]

In this paper, a robust neighbor-based distributed cooperative control strategy is proposed for dc cyber-physical microgrids, considering communication delays and slow switching topologies. The proposed robust control strategy can synchronize the voltages of a dc microgrid to the desired value while achieving the optimal load sharing for minimizing distributed energy resources’ (DERs) generation cost to achieve their economic operation at the same layer via a sparse communication network considering communication delays and slow switching topologies synchronously. The continuous interaction of physical–electrical and cyber networks generally exacerbates the occurrence of communication delays. Moreover, the arbitrary switching topologies could destroy the system’s transient characteristics at the switching time instants. To further quantify these impacts on the system stability, the communication delay and average switching dwell-time-dependent control conditions for the proposed control strategy are proved based on the Lyapunov–Krasovskii theory. Some sufficient conditions for the exponential stability of the cyber-physical delayed-switching system are developed, which guarantees the robustness of the proposed strategy against the communication delays and dynamically changing interaction topologies. The proposed control protocols are shown to be fully distributed and implemented through a sparse communication network. Finally, several cases on a modified IEEE 34-bus test network are investigated which demonstrate the effectiveness and performance of the results. [ABSTRACT FROM AUTHOR]

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents’ private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers’ coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply. [ABSTRACT FROM AUTHOR]

Solid state transformer (SST) integrated hybrid dc–ac microgrid (HMG) has wide operating capabilities in presence of dc–ac loads, renewable energy resources, and energy storage. This paper proposes control strategies for localized control of zonal hybrid microgrid, enabled by the SST and centralized energy storage devices (ESD) integrated using dual active bridge (DAB) converter. The control of DAB dc–dc converter is modified for zero circulating power flow (ZCPF) operation in both SST and ESD. Power management and coordination control used in this paper ensures the power supply reliability of the zonal HMG integrated through the SST. Microgrid system, along with its proposed controllers, provides a stabilized low-voltage dc and ac network ports, in both grid feeding and forming modes. The performance of the configured HMG system is evaluated under various power management scenarios using the MATLAB software platform. Further, the SST architecture, with its proposed controller, is tested on FPGA-based hardware platform. [ABSTRACT FROM AUTHOR]

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system. [ABSTRACT FROM AUTHOR]

This paper presents a novel load shedding scheme with consideration of the active power ramping capability of Distributed Energy Resources (DERs) to address the challenges due to low inertia and diverse types of DERs in microgrids. In the paper, it is demonstrated that due to the small inertia in microgrids, even with sufficient reserve power, the frequency could rapidly drop to a low level and trigger the DERs’ under frequency protection (thus the total system collapse), if the reserve active power is not ramped up at a sufficient rate. The proposed load shedding scheme addresses this challenge by considering not only the DERs’ reserve, but also their speed in injecting active power to the system to determine the amount of load should be shed, so that critical frequency thresholds are not violated. The proposed load shedding scheme is tested using a realistic real time hardware-in-the-loop arrangement. The results show that the proposed scheme can correctly detect the cases when the DERs’ responses are too slow and trigger the required load shedding actions, thus effectively containing the frequency above the critical threshold. [ABSTRACT FROM AUTHOR]

Multi-inverter microgrid systems, particularly those with loop topology, offer higher power supply reliability and robustness compared to conventional radial distribution systems. In meshed systems, communication-less protection schemes have proved to be ineffective for multi-inverter microgrids due to bidirectional power flow, and limited and controlled fault currents generated by the voltage source converters interfacing the energy source to the network. This makes communication-assisted line protection schemes preferable for such systems despite the necessity for communication means. While these protection schemes are effective, their reliability depends much on the communication availability. This requires a back-up communication path in case the main one fails bringing up the cost issue that hinders their uptake. This paper proposes a novel and low-cost line protection based on directional blocking strategy that can operate as a main as well as a back-up protection to any protection scheme using communication means between the line terminals. As the main, it requires low-bandwidth communication system. As a back-up, it would share the same communication means with the main one and use those of the healthy lines when the faulted line's fails. Thus saving the cost of back-up communication system. [ABSTRACT FROM AUTHOR]

In order to effectively make up for the defect of service life of re-tired power battery echelon used in microgrids (MGs) and improve the reliability of MGs system, an energy management method based on the energy hub of the electricity heat hydrogen multi-energy storage system (EHH-MESS) is proposed the in the paper. Firstly, considering the uncertain factors such as energy output, load fluctuation, retired power battery life defects and multi energy flow impact, MGs supply-demand balance model is built. Secondly, taking the economy of energy supply as the goal, a control method based on energy hub is proposed, which considers the weight of multi-objective optimization to control MGs. Finally, the effectiveness of the proposed energy management method is verified by simulation analysis, which improves the power supply reliability and economy of the system. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic optimization model for preparatory operation of distributed energy resources that enhance the resilience of critical loads against extreme weather events. Adopting hurricanes as a case study of extreme weather events, a high-precision spatio-temporal hurricane impact analysis model is first developed to enhance the situational awareness of automated distribution systems and generate a set of hurricane-induced outage scenarios. A stochastic mixed-integer conic programming model is then developed for co-optimizing the preparatory schedule of distributed energy storage and distributed generation ahead of hurricane along with post-event decisions of restoring services to customers, while minimizing the expected cost of energy not served as the index to measure the resilience. The proposed model prioritizes the service restoration of critical loads by integrating customer interruption cost function, which captures the customer type and outage costs as well as interruption duration in making restoration decisions. Additionally, a conic power flow model is adopted to accurately capture network operation constraints in pre- and post-outage scenarios. Numerical results conducted on the IEEE 33-bus system show that the proposed model can enhance the performance of automated distribution systems in responding to hurricane-induced outages by efficiently utilizing grid resources to improve the resilience of critical loads. [ABSTRACT FROM AUTHOR]

Multimicrogrid (MMG) systems have the potential to play an increasingly important role in the transformation of existing power grid to smart grid. However, the open and distributed connectivity of MMGs exposes the systems into various cyber-attacks, which may cause serious failures or physical damages, such as power supply interruption and human casualties. Therefore, ensuring the security of MMGs is of paramount importance. To address this issue, a new collaborative intrusion detection (CID) approach using blockchain is proposed in this paper for MMG systems in smart grid. Due to the consensus mechanism of blockchain, the approach is designed without the need of a trusted authority or central server while improving the accuracy of intrusion detection in a collaborative way. It is equipped with a proposal generation method that combines periodic and trigger patterns to generate the detection target of CID, i.e., a proposal. From the generated proposals together with the correlation model of MMGs, a CID is achieved by using the consensus mechanism. The final detection results of CID are stored on blockchain in sequence. The use of an incentive mechanism motivates a single microgrid to participate in consensus. The effectiveness of the presented approach is demonstrated through a case study on an MMG system. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear economic model predictive control based energy management of a multi-energy microgrid is presented. The microgrid is composed of the district heating system with a cogeneration power plant supplying both the heat and the electricity demand of residential buildings with photovoltaic sources. Furthermore, access to liberalized heat and electricity market is assumed. This kind of environment requires consideration of the economical aspects, the technical aspects, the safety/quality of supply, and consumer preferences. The major contributions introduced in the presented approach are the following. A nonlinear formulation is adopted to capture the nonlinear thermal models and the nonlinear algebraic model of the heat supply network. The second major contribution is the economic character of the objective following the assumption of a liberalized market environment. Such formulation accounts for the specifics of the energy market organization. Finally, the stochastic nature of the planning problem is introduced through a practical robust formulation. The results obtained on a representative case study clearly show the benefits of the proposed approach compared to the traditional energy management approach. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy resources in power systems, it is necessary to overcome the limitations of these resources in terms of the supply and demand balance in high-voltage power systems. A viable solution is employing zero net energy community microgrids, which can manage the demand locally and minimize the impact to the power grid. To this end, this paper proposes an optimal scheduling method for a zero net energy community microgrid with customer-owned energy storage systems (CES). It is assumed that the microgrid operator operates in the CES market based on a bilateral contract. The CES aggregators constitute the CES and participate in the market. The aggregated CES (ACES) is considered as a single energy storage system in the proposed scheduling method. Numerical examples are presented to demonstrate the effectiveness of the proposed method.  [ABSTRACT FROM AUTHOR]

Sectionalizing a distribution system with distributed energy resources (DERs) into multiple microgrids in the case of power outages has been recognized as a promising solution to restore critical loads. Nevertheless, most existing literatures focus on restoring loads by optimally dispatching distributed generators (DGs), where the controllability on the demand side is usually neglected. Moreover, unbalances in the restored microgrids should be limited in specified levels to prevent DGs from tripping to enhance the operational reliability. To address these issues, this paper proposes a novel method to restore a distribution system into multiple microgrids whilst taking the three-phase demand-side management (T-DSM) into account. The T-DSM exploits optimal phase positions of switchable loads to alleviate the unbalance issue from the demand side, which can be coordinated with network partition and DG dispatching to improve the controllability of microgrids during the restoration process. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) which enables applying the off-the-shelve solvers. To improve computational performance, a multi-timescale strategy is developed to enhance its practicality. Simulation results based on the IEEE 34- and 123-bus feeders demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on the latest research efforts in modelling, control and value proposition of distributed energy resources (DERs). Future power grids will face tremendous challenges in operation and planning as a massive number of distributed energy resources (DER) are integrated into the system. Due to their limited visibility and controllability, it is imperative to study the impacts of a high penetration of DERs over the power grid, and to analyze and evaluate their reliability and economic benefits especially when they are enabled to provide essential reliability services in a large scale. The lack of DER modelling method is another barrier to the efficient integration ofDERs into the existing operation, planning and market paradigm. This is also complicated by increasing complexities in the management systems developed to coordinate and control a large number of heterogeneous DERs. [ABSTRACT FROM AUTHOR]

Due to the higher proportion of non-synchronous power resources in microgrids, adequate frequency regulation supports are required to maintain acceptable system frequency stability. As one of the sources for frequency regulation support in microgrid operation, via proper droop controller operations, wind energy conversion systems (WECS) could deliver kinetic energy stored in their turbines to the grid when needed. However, as the system inertia becomes smaller, the droop controller's response to system disturbances could cause the WECS to resonate with the grid and lead to torsional stress and fatigue damage in the WECS drivetrain. To protect the WECS against such damage, a resonance damper based on pole-zero cancellation technique is proposed in this paper. Simulation results depict the possible resonance phenomenon, and the reduction of undesired fatigue damage by the proposed damper retrofitted to the droop controller while improving the overall frequency regulation. Sensitivity analysis results are also provided to verify its robustness for practical applications. [ABSTRACT FROM AUTHOR]

Extreme circumstances in which a local distribution system is electrically isolated from the main power supply may not always be avoidable. Efforts must be made to keep the lights on for such an isolated distribution system (IDS) until reconnection to the main power source. In this paper, we propose a strategy to enhance IDS survivability by utilizing the coordination of two flexible approaches, namely, separable mobile energy storage systems (SMESSs), which construct non-wires links for energy transmission between the IDS and the external live power sources, and demand response (DR), which adjusts the internal electrical demand of the IDS to provide effective operating stress alleviation. Considering the uncertainty of renewable energy generation (REG) and loads, a two-stage robust optimization (RO) model involving the joint scheduling of these two approaches is constructed. The objective is to minimize the fuel consumption and the decreased and unserved demand under the worst-case scenario to endow the IDS with extended survivability. Finally, test is conducted and the results demonstrate the effectiveness of the proposed method in enhancing the survivability of IDS. [ABSTRACT FROM AUTHOR]

Faults are extreme events that canadversely affect the voltages in islanded microgrids. This paper provides a new data-driven methodology for timely prediction of the post-fault voltage stability in hybrid AC/DC microgrids. The proposed method performs a binary classification with delay constraints by processing sequences of the short-time mean squared deviations using a deep learning system. The deep learning system consists of a bidirectional long short-term memory network whose output is a probabilistic voltage instability indicator. When the value of the indicator is non-zero, persistent voltage disturbances are most likely to occur even after the fault clearance. The proposed method enables the microgrid to carry out remedial or preventive actions, such as event-triggered protection and control of distributed energy resources (DERs), which are advantageous to the resilient operation of the microgrids. Extensive and detailed electromagnetic transient (EMT) simulations of a low-voltage hybrid AC/DC microgrid benchmark are analyzed, and the results confirm the effectiveness of the proposed method for online prediction and fast voltage regulation. [ABSTRACT FROM AUTHOR]

The small-signal stability of an interconnected source and load can be analyzed using the frequency-response methods of stability analysis. Existing frequency-response methods of stability analysis introduce artificial conservativeness and do not explicitly address stability robustness against load perturbations, that is, by how much the load can change such that the system will remain stable in the small-signal sense. This paper presents a frequency-response method of stability analysis which provides a less conservative stability condition compared to existing methods, in the sense of having less restriction on the control system; further, the proposed method establishes a robust stability margin in terms of perturbations in load parameters. The proposed method is based on μ analysis, and models the source-load dynamic interaction via a closed-loop system of impedances and admittances. The proposed method is used to examine small-signal stability of an islanded subnetwork extracted from a university campus microgrid, composed of a distributed energy resource (DER) unit feeding a load, providing a study with realistic parameter values. Two case studies are presented to show the features of the proposed method, namely, reduced conservativeness and the establishment of a robust stability margin. Another example is provided to show the application of the proposed stability analysis method in a multi-DER-unit network. The findings of the frequency-domain analysis are illustrated through time-domain simulations around the operating point on the system under study. [ABSTRACT FROM AUTHOR]

In this paper, a joint scheduling scheme for the electric supply and demand of Home Energy Management System (HEMS) and Building Energy Management System (BEMS) via energy management in houses and buildings is presented. To reflect ongoing policy, the Korea Electric Power COoperation (KEPCO) tariff is employed to formulate the optimization problem in terms of the sequential management procedure of prediction, supply control and demand control. As a result, an optimal electric supply pattern to produce the minimum energy cost can be obtained under a given curtailment. From simulation, the proposed scheduling scheme demonstrates greater reliability and adaptation for the interruptible load (IL) than the day-ahead method. [ABSTRACT FROM AUTHOR]

The networking of geographically-close microgrids (MGs) offers additional operational flexibility for realizing the benefits of various DERs. The failures of CMC (central master controller) or master controllers (MCs) in certain MGs due to natural or human-made disturbances might impede the performance of networked MGs and even compromise the system security. This paper proposes a unification scheme to restore the MG function in a networked MG system with failed CMC and certain MCs. In the unification scheme, failed CMC delegates its control authority to certain MCs and failed MCs relinquish their control responsibilities to designated MCs in adjacent MGs. The neighboring functional MCs are accordingly allowed to take the lead for managing the operations in malfunctioning MGs. The unification control signal provided by the designated MCs is imposed on pinned DERs and propagated among remaining DERs in the MGs with failed MCs, which reduces the communication complexity among MGs significantly. The proposed unification scheme is expected to achieve desired control performances even when the available communication resources are not sufficient to maintain continuous unification control signals in networked MG system. The effectiveness of the proposed unification scheme is validated by the time-domain PSCAD/EMTDC simulations of two networked MG systems. [ABSTRACT FROM AUTHOR]

The random failures in a distribution network lead to different reliability events, such as voltage sags, momentary interruptions, and sustained interruptions. Even momentary reliability events i.e., voltage sags and momentary interruptions cause significant financial losses to many customers. This paper develops a novel aggregated reliability event model for the reliability studies of distribution systems integrating the momentary reliability events in addition to sustained interruptions. The developed model incorporates the impacts of temporary and permanent failures on the customers considering different reliability events. A graph theory-based search algorithm is utilized to efficiently recognize different protection settings, alternate supplies, and presence of Distributed Energy Resources (DERs)/microgrids in the network. Furthermore, the proposed model incorporates the possible mitigation measures brought by the DERs/microgrids while quantifying the reliability profile of load points and the overall system. The case studies conducted on a practical test system show the effectiveness of the proposed model to evaluate the reliability and to carry out system upgrades in the context of active distribution systems. [ABSTRACT FROM AUTHOR]

In recent years, the increasing penetration of distributed generation in microgrids challenges the control and coordination of energy resources. Especially in microgrids with virtual synchronous generator (VSG)-controlled converters and conventional synchronous generators (SG), the inherent inertia difference (i.e., the VSG and SG) results in a poor transient performance when the VSG and/or loads are cut in/out. Thus, this paper explores the transient performance of microgrids with parallel VSG and SG systems. More importantly, a novel pre-synchronization control method is proposed to eliminate the phase jump while meeting the requirements in case of closures or re-closures of generation units. A small-signal dynamic model is presented, and accordingly, the VSG inertia and its damping can be designed considering the capacity ratio of VSG and SG units. In addition, with the power angle stability analysis, an active power provision strategy is introduced to suppress the transient power oscillation due to the inertia difference. Finally, the feasibility of the proposed methods is verified by simulations on a microgrid consisting of parallel VSG and SG units. [ABSTRACT FROM AUTHOR]

Droop-controlled distributed energy resource converters in dc microgrids usually show low output impedances. When coupled with ac systems, second-order harmonics typically appear on the dc-bus voltage, causing significant harmonic currents at the converters resource side. In this paper, we show how to reduce such undesired currents by means of notch filters and resonant regulators included in the converters control loops. The main characteristics of these techniques in terms of harmonic attenuation and stability are systematically investigated. In particular, it is shown that the voltage control-loop bandwidth is limited to be below twice the line frequency to avoid instability. Then, a modified notch filter and a modified resonant regulator are proposed, allowing to remove the constraint on the voltage loop bandwidth. The resulting methods (i.e., the notch filter, the resonant regulator, and their corresponding modified versions) are evaluated in terms of output impedance and stability. Experimental results from a dc microgrid prototype composed of three dc–dc converters and one dc–ac converter, all with a rated power of $\text{5 kW}$ , are reported. [ABSTRACT FROM AUTHOR]

Networking a series of autonomous microgrids (MGs) is a strategic effort toward the resilience enhancement in extreme conditions. In this paper, flexible division and unification control strategies are proposed to help networked MGs prepare adequately for extreme events and adapt comprehensively to subsequent changing conditions, which enhance the system resilience. According to the proposed strategies, networked MGs can switch between two distinct modes of division and unification by utilizing a sparse communication network without requiring any additional communication infrastructures or controllers. In division mode, each MG is regulated by its local master controllers (MCs) for active power sharing, which ensures that disruptions are handled effectively by local energy resources without utilizing those in adjacent MGs. Thus, any islanding or resynchronization of individual MGs would not introduce further disruptions to the remaining networked system. The proposed control strategies imply that the networked MGs system in division mode is managed in a proactive way to adequately prepare the networked system for extreme events. In the unification mode, the remaining networked MGs, which are still functional, use the proposed algorithm to share all available energy resources and adapt to continuously changing operating conditions in order to respond to extreme events. The proposed control algorithm for devising a flexible networked MGs system is a cost-effective scheme that can fully exploit the system operation flexibility corresponding to different operation stages for enhancing resilience. The proposed control strategies are applied to a networked AC MGs system and the performance is tested using time-domain PSCAD/EMTDC simulations. [ABSTRACT FROM AUTHOR]

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

Conventional energy generation sources mainly provide energy supply to remote areas nowadays. However, because of growing concerns over greenhouse gas emissions, the integration of renewable energy sources is mandatory to meet power demands and reduce climatic effects. The advancements in renewable generation sources and battery storage systems pave the way for microgrids (MGs). As a result, MGs are becoming a viable solution for power supply shortage problems in remote-area applications, such as oceanic islands. In this paper, an islanded MG, which consists of PV system, tidal turbine (TT), diesel generator (DG), and Li-ion battery, is considered for Ouessant island in Brittany region in France. The economic operation of the MG is achieved by including battery degradation cost, levelized costs of energy of the PV system and TT, operating and emission costs of DG, and network constraints. The developed model leads to a non-linear and non-convex problem, which unfortunately can converge to a local optimum solution. The problem has, therefore, been relaxed and converted to a convex second-order cone model to achieve an optimal decision strategy for islanded MG operations with a global or near-global solution. Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG. It is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost. [ABSTRACT FROM AUTHOR]

A resilient power system is capable of providing fast and efficient recovery from outages caused by natural or man-made attacks. After a major blackout, transmission, and distribution system operators (TSOs and DSOs) coordinate to restore the system in a timely and reliable manner. However, these two systems are usually operated separately without considering capabilities and limitations of each individual network, or their mutual impacts. This could prolong the restoration process and lead to unpractical outcomes. Therefore, a distributed strategy for integrated transmission and distribution systems restoration (ITDSR) has been developed in this paper. Based on the alternating direction method of multipliers (ADMM), the distributed algorithm is able to provide the coordinated restoration for both TSOs and DSOs by sharing limited information of boundary buses in an iterative procedure. The ADMM-ITDSR model includes convex ac power flow model and three-phase unbalanced branch flow model. The ITDSR strategy coordinates the operation of distributed energy resources in terms of serving high priority loads across different networks. The effectiveness and advantage of developed models and algorithms are validated and demonstrated through testing of the integrated IEEE test cases, such as 14-bus transmission with 13-node distribution systems, and 39-bus transmission with 123-node distribution systems. [ABSTRACT FROM AUTHOR]

The increase in distributed energy resources and flexible electricity consumers has turned TSO-DSO coordination strategies into a challenging problem. Existing decomposition/decentralized methods apply divide-and-conquer strategies to trim down the computational burden of this complex problem, but rely on access to proprietary information or fail-safe real-time communication infrastructures. To overcome these drawbacks, we propose in this paper a TSO-DSO coordination strategy that only needs a series of observations of the nodal price and the power intake at the substations connecting the transmission and distribution networks. Using this information, we learn the price response of active distribution networks (DN) using a decreasing step-wise function that can also adapt to some contextual information. The learning task can be carried out in a computationally efficient manner and the curve it produces can be interpreted as a market bid, thus averting the need to revise the current operational procedures for the transmission network. Inaccuracies derived from the learning task may lead to suboptimal decisions. However, results from a realistic case study show that the proposed methodology yields operating decisions very close to those obtained by a fully centralized coordination of transmission and distribution. [ABSTRACT FROM AUTHOR]

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. Note to Practitioners—This work is motivated by the emerging need for effective energy management approaches in multi-carrier microgrids. The inherent difficulty of scheduling simultaneously the operations of various energy infrastructures (e.g., electricity, natural gas) is exacerbated by the inevitable presence of uncertainties that affect the inter-dependent dynamics of different energy resources and equipment. The proposed robust MPC-based control strategy allows the energy manager to effectively determine an optimal energy scheduling of multi-faceted system components, making a tradeoff between performance and protection against data uncertainty. The presented strategy is comprehensive and generic, as it can be applied to different microgrid frameworks integrating various types of system components and sources of uncertainty, while at the same time being implementable in any energy management system. [ABSTRACT FROM AUTHOR]

This paper presents a distribution system operator (DSO) framework for wholesale and retail market participation of distributed energy resources (DERs) aggregators. The DSO coordinates aggregators’ energy and regulation offers as well as end-users’ energy consumption through the unbalanced retail market and submits balanced energy and regulation offers to the wholesale market on behalf of all the aggregators and end-users within its territory. Various kinds of DER aggregators including demand response aggregators (DRAGs), energy storage aggregators (ESAGs), electric vehicle (EV) charging stations (EVCSs), dispatchable distributed generation aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. To handle unbalanced distribution grids with single-phase aggregators, a linearized unbalanced power flow is tailored to model operating constraints of the distribution grid with various aggregators. A market settlement approach is proposed for the DSO, which coordinates with wholesale market clearing process and ensures the DSO’s non-profit characteristic. It is proved that at the wholesale-DSO coupling substation, the total payment received/compensated by the DSO under the wholesale price is identical to that under three single-phase retail prices for each phase at the substation. Case studies are performed on the modified IEEE 33-node and 240-node distribution test systems to investigate the market outcomes of the proposed DSO. [ABSTRACT FROM AUTHOR]

Mission profiles such as environmental and operational conditions together with the system structure including energy resources, grid and converter topologies induce stress on different converters and thereby play a significant role on power electronic systems reliability. Temperature swing and maximum temperature are two of the critical stressors on the most failure-prone components of converters, i.e., capacitors and power semiconductors. Temperature-related stressors generate electrothermal stress on these components ultimately triggering high potential failure mechanisms. Failure of any component may cause converter outage and system shutdown. This paper explores the reliability performance of different converters operating in a power system and indicates the failure-prone converters from wear out perspective. It provides a system-level reliability insight for design, control, and operation of multiconverter system by extending the mission-profile-based reliability estimation approach. The analysis is provided for a dc microgrid due to the increasing interest that dc systems have been gaining in recent years; however, it can be applied for reliability studies in any multiconverter system. The outcomes can be worthwhile for maintenance and risk management as well as security assessment in modern power systems. [ABSTRACT FROM AUTHOR]

The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process. [ABSTRACT FROM AUTHOR]

Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone toward fully transactive energy systems. In this paper, we evaluate such a fully integrated transactive system by, first, modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), second, modeling a wholesale market and a local market, and, third, coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates that how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition. [ABSTRACT FROM AUTHOR]

The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)—and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided. [ABSTRACT FROM AUTHOR]

Energy dispatch in multi-user networks with limited energy resources can lead to competitive markets where individuals would place bids with the goal of maximizing their own profits. Game theoretic techniques are elegant mathematical candidates to solve such problems. However, they cannot guarantee the existence of equilibrium points. Even if such points are somehow reached, Pareto optimality of the solution cannot be ensured. In this paper, an alternative approach based on multi-objective optimization is proposed in order to maximize the “collective benefits” of a group of players. Instead of allowing individuals to compete for resources directly, a neutral third party finds a solution that is Pareto optimal and meets all constraints associated with individual users. A multi-microgrid industrial park is considered here, where the park is assumed to be equipped with a central controller that purchases energy from the grid and dispatches it among individual microgrids. When this energy is limited due to capacity constraints, each microgrid would try to receive a larger portion so as to increase its profit level. Naturally, these individual objectives are contradictory. As such, the problem is formulated as multi-objective optimization and solved using a modified approach based on goal programming to ensure the Pareto optimality of the overall solution. This way, the limited energy resource is shared in a way that maximizes the collective profit of the group, instead of individuals. The problem formulation is, then, extended by developing its robust counterpart so that model and parameter uncertainties can also be taken into account. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources (DERs) and the increasing flexibility in active distribution networks (ADNs), the economic operation of ADN coordinating both generation and demand sides is required to be studied. In this paper, considering the electricity price response of DER, a coordinated operation strategy for ADN is presented based on a bi-level agent framework. The DER agent makes their own response based on the technical operability and economic consideration, while the ADN agent will finally coordinate each participant by using the interactive benefit prioritization (IBP) principle. The simulation results indicate that the proposed strategy cannot only reduce the power imbalance but also improve the economic operation of ADN. Moreover, consumption of renewable energy is ameliorated as well. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed control strategy for coordination of distributed energy resources (DERs) in low-voltage resistive microgrids. The proposed framework consists of two level structures; primary and secondary control. Unlike the existing distributed control solutions, the proposed method is based upon the practical assumption of network impedance being resistive. The primary control level consists of a V-I droop mechanism, where GPS timing is used to synchronize the control agents. A consensus-based distributed secondary control method is introduced to improve the voltage regulation and load sharing accuracy of the V-I droop method. In the proposed approach, the d-axis component of the voltage is altered so as to regulate the average microgrid voltage to the rated value while guarantying proper sharing of active power among the DERs. Additionally, the q-axis component of voltage is adjusted to perform proper current and, accordingly reactive power sharing. The proposed control methodology accounts for the distribution line impedances. It features a plug-and-play environment; prior system knowledge is not required, and an arbitrary DER can enter the microgrid without any need for additional synchronization. An ac microgrid is prototyped to experimentally demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Ultracapacitor (UC) based dc–dc power supplies are widely used for addressing surge power demands and to provide energy backup to critical loads. The conventional control techniques, such as unified control strategy, though ensure seamless mode transition, do not offer complete flexibility in charging and discharging controls which is crucial in UC-based backup systems. On the other hand, controls, such as independent switch control, though allow great flexibility in control, do not ensure seamless mode transition without appropriate mode-switch logic. This paper proposes a time-varying virtual resistance based mode transition control for independent switch control which not only ensures smooth, seamless transition between charging and discharging control modes but also ensures complete control over the mode transition durations. The proposed control is found to be robust to error mode identification as compared to introducing dead band between control modes. The proposed control is verified on a proof of concept experimental setup at a power level of $P_o=65$ W, voltage level of $V_g=26$ V with the dc–dc converter switching at $f_{\text{sw}}=100$ kHz. The performance comparison with PWM blocking method is also evaluated where the proposed control is found to work well. [ABSTRACT FROM AUTHOR]

Islanded microgrids provide unique challenges due to the lack of transmission support, requiring local energy supply and grid regulation architecture. This paper presents a unified probabilistic assessment of wind reserves for demand response (DR) and frequency regulation in islanded microgrids. A multivariate nonparametric kernel density estimation algorithm is used to generate the probabilistic models of the wind resource, electrical demand, and predicted performance of wind generation. These models are numerically combined to evaluate the capability of wind generation to act as a dynamic reserve and predict its performance for DR, secondary generation, and frequency regulation in an islanded system. The probabilistic model captures multivariate cross-correlation, nonstationary environmental and load behavior, as well as multimodality in their underlying probability distributions. A case study is conducted to validate the proposed model, which predicts wind generation effectiveness for varying load profiles, wind profiles, and generation capacities. PLEXIM simulation software is used to implement a model microgrid to demonstrate the integration of wind generation and its regulatory capabilities. The proposed algorithm has applications in power system planning and operation, and it provides a method using probabilistic data set for long term energy management and optimization of islanded microgrids. [ABSTRACT FROM AUTHOR]

Harnessing flexibility from distributed energy resources (DER) to participate in various markets while accounting for relevant technical and commercial constraints is essential for the development of low-carbon grids. However, there is no clear definition or even description of the salient features of aggregated DER flexibility, including its steady-state and dynamic features and how these are impacted by network constraints and market requirements. This paper proposes a comprehensive DER flexibility modelling and characterisation framework that is based on the concept of nodal operating envelope (NOE). In particular, capacity, ramp, duration and cost are identified as key flexibility metrics and associated with different but consistent NOEs describing capability, feasibility, ramp, duration, economic, technical and commercial flexibility features. These NOEs, which conceptually arise from a Venn diagram, can be built via optimal power flow (OPF) analysis, visualised in the active-reactive power space, and used by different stakeholders. Results from a canonical test system and a real distribution system illustrate the value and applicability of the proposed framework to model and characterise provision of flexibility and market services from DER for different use cases. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven secondary controller for regulating to some desired values several state variables of interest in an inverter-based power system, namely, electrical frequency, voltage magnitudes at critical buses, and active power flows through critical lines. The secondary controller is based on online feedback optimization, leveraging the learned sensitivities of changes in the state variables to changes in inverter active and reactive power setpoints. To learn the sensitivities accurately from data, the feedback optimization has a built-in mechanism for keeping the secondary control inputs persistently exciting without degrading its performance or compromising system operational reliability. To ensure safe and reliable operation, we present an approach based on Gaussian process regression that, by making an inference about the modeling uncertainties not accounted for in the sensitivity-based prediction model, allows the controller to correct the predictions and find safe control actions, for which the prediction errors are more likely to be small. The feedback optimization also utilizes the learned power-voltage characteristics of photovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow the PV arrays to track the power setpoints. To learn the power-voltage characteristics, we separately execute a data-driven approach that fits a concave polynomial to the collected power-voltage measurements by solving a sum-of-squares (SoS) optimization. We showcase the secondary controller using the modified IEEE-14 bus test system, in which conventional energy sources are replaced with inverter-interfaced DERs. [ABSTRACT FROM AUTHOR]

Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the coordination of distributed energy resources (DERs) and demand response (DR) for voltage and frequency support of islanded microgrids. The proposed method basically relies on extracting information from real and reactive power sensitivities at different buses for minimizing the voltage and frequency deviations of the islanded microgrid. To this aim, a new power flow procedure is adopted in which the frequency deviation appears as an additional state variable. This helps to calculate the required setpoints for the DERs as well as the amount of the demanded power curtailed through the controllable loads to meet the overall goal. The loads are classified based on their a-priori known controllability degree. To minimize the manipulated load, the most effective buses are selected based on their associated sensitivity values. In the grid-connected mode, the total operation cost is minimized, while the microgrid bus voltages are maintained within the pre-specified acceptable range. In both modes, the whole process is formulated as a multiobjective problem solved by the particle swarm optimization (PSO). The control procedure involves a series of commands for which the incident command system (ICS) is used as a secure communication structure. The performance of the proposed control framework is evaluated for the case of a typical MV microgrid in both grid-connected and islanded modes. [ABSTRACT FROM PUBLISHER]