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The growing penetration of fast charging stations (FCSs) to electric vehicles (EVs) and distributed energy resources (DERs) in the electrical power system brings technical issue changes in the voltage profile throughout grid nodes and feeder current overload. The provision of ancillary services by DERs and FCSs arises as an appealing solution to reduce these adverse effects, enhancing the grid hosting capacity. The control of microgrids is essential for the coordinated implementation of these services. Although microgrid control is widely applied to DERs, few studies address the coordinated control of DERs and FCSs to obtain benefits for the electrical power system. This paper proposes a coordinated and simultaneous control of DERs and FCSs based on the power-based control (PBC) strategy, efficiently exploiting FCSs in a microgrid model previously unaddressed in the literature. The results show that, with the coordinated control of DERs and FCSs, the control of the power flow in a minigrid (MG) is achieved both in moments of high generation and in moments of high load, even with the maximum operation of DERs. This method allows for the maintenance of voltage levels within values considered acceptable by technical standards (above 0.93 pu). The maintenance of voltage levels is derived from reducing the overload on the point of common coupling (PCC) of the minigrid by 28%, performing the peak shaving ancillary service. Furthermore, the method allows for the control of zero power flow in the PCC of the minigrid with the upstream electric grid in periods of high generation, performing the ancillary service of valley filling. The method performs this control without compromising vehicle recharging and power dispatch by DERs. [ABSTRACT FROM AUTHOR]

Community batteries (CBs) are emerging to support and even enable energy communities and generally help consumers, especially space-constrained ones, to access potential techno-economic benefits from storage and support local grid decarbonization. However, the economic viability of CB projects is often uncertain. In this regard, typical feasibility studies assess CB value for behind-the-meter (BTM) operation or whole-sale market participation, i.e., front-of-meter (FOM). This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits. A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.

We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies' Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks. [ABSTRACT FROM AUTHOR]

Due to the advantages of fewer energy conversion stages and a simple structure, direct current (DC) microgrids are being increasingly studied and applied. To minimize distribution loss in DC microgrids, a systematic optimal control framework is proposed in this paper. By considering conduction loss, switching loss, reverse recovery loss, and ohmic loss, the general loss model of a DC microgrid is formulated as a multi-variable convex function. To solve the objective function, a top-layer distributed integral convex optimization algorithm (DICOA) is designed to optimize the current-sharing coefficients by exchanging the gradients of loss functions. Then, the injection currents of distributed energy resources (DERs) are allocated by the distributed adaptive control in the secondary control layer and local voltage–current control in the primary layer. Based on the DICOA, a three-layer control strategy is constructed to achieve loss minimization. By adopting a peer-to-peer data-exchange strategy, the robustness and scalability of the proposed systematic control are enhanced. Finally, the proposed distribution current dispatch control is implemented and verified by simulations and experimental results under different operating scenarios, including power limitation, communication failure, and plug-in-and-out of DERs. [ABSTRACT FROM AUTHOR]

In September 2020, the Federal Energy Regulatory Commission (FERC) released Order 2222, which opens wholesale markets to small-capacity distributed energy resources (DERs), recognizing their potential in improving operational efficiency by providing bulk grid services. Therefore, a co-simulation capability that can connect transmission and distribution (T&D) simulations and evaluate the impacts of DER provision of bulk grid services is needed. In this paper, we present a new integrated T&D co-simulation platform that incorporates T&D system simulators, DER aggregator/group strategies, and a co-simulation coordinator. Industry-standard communication protocols are employed to mimic real-world conditions. Secondary frequency regulation is selected as the representative bulk grid service, and we simulate the responses of DERs to the frequency regulation signals. The simulation results for a solar-rich distribution feeder in Colorado, USA, demonstrate how the T&D co-simulation setup is used to evaluate the contributions of DERs to minimize the bulk grid frequency deviation.

The aggregation of distributed energy resources (DERs) enables them to provide various grid services as a virtual power plant (VPP). Utilities use enterprise control solutions, such as advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS), to efficiently integrate DERs and realize the benefits of a VPP. These control solutions can complement each other to offer additional benefits. This paper evaluates the coordinated operation of an ADMS and a DERMS that collectively implements a VPP to provide peak demand reduction and voltage regulation through the simulation of an actual distribution feeder. A commercial ADMS reduces the peak demand through conservation voltage reduction (CVR). A prototype DERMS dispatches residential battery energy storage systems (BESS) based on real-time optimal power flow to provide additional peak demand reduction. The DERMS also maintains voltage regulation across the feeder by controlling both residential batteries and rooftop PV systems. The results from the controller-hardware-in-the-loop (CHIL) real-time simulations conducted in a realistic laboratory environment show that the coordinated operation of the ADMS and the DERMS effectively achieves peak demand reduction while enforcing voltage regulation across the feeder. Specifically, the ADMS dynamic voltage regulation (DVR) application and DERMS working together achieved a peak demand reduction of nearly 500 kW, whereas the ADMS DVR application alone obtained a reduction of approximately 100 kW. The DERMS VPP control in this work relies on the residential BESS for the demand reduction; the demand reduction accomplished depends on the BESS capacity available in the distribution system.

This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency (RoCoF) protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.

Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control set- points of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regression-based controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

The growing penetration of fast charging stations (FCSs) to electric vehicles (EVs) and distributed energy resources (DERs) in the electrical power system brings technical issue changes in the voltage profile throughout grid nodes and feeder current overload. The provision of ancillary services by DERs and FCSs arises as an appealing solution to reduce these adverse effects, enhancing the grid hosting capacity. The control of microgrids is essential for the coordinated implementation of these services. Although microgrid control is widely applied to DERs, few studies address the coordinated control of DERs and FCSs to obtain benefits for the electrical power system. This paper proposes a coordinated and simultaneous control of DERs and FCSs based on the power-based control (PBC) strategy, efficiently exploiting FCSs in a microgrid model previously unaddressed in the literature. The results show that, with the coordinated control of DERs and FCSs, the control of the power flow in a minigrid (MG) is achieved both in moments of high generation and in moments of high load, even with the maximum operation of DERs. This method allows for the maintenance of voltage levels within values considered acceptable by technical standards (above 0.93 pu). The maintenance of voltage levels is derived from reducing the overload on the point of common coupling (PCC) of the minigrid by 28%, performing the peak shaving ancillary service. Furthermore, the method allows for the control of zero power flow in the PCC of the minigrid with the upstream electric grid in periods of high generation, performing the ancillary service of valley filling. The method performs this control without compromising vehicle recharging and power dispatch by DERs.

In this article, a multi-period optimal scheduling framework considering load priorities is proposed to optimize the operation of an islanded smart distribution network. The main idea of this work is to provide sufficient power supply to high priority loads even during limited generation periods while also assuring network security and achieving optimal generation schedules for distributed energy resources and optimal charging/discharging schedules for battery bank (BB). The proposed scheduling framework is formulated as a multi-objective optimization problem in four different operating cases, over multiple time periods considering conventional generation sources, renewable energy sources, battery bank (BB), and various loads. Loads comprise hospitals, public consumer loads, industries, education centers, and domestic loads. The usual objectives of optimal scheduling problems in the literature are minimizing network power loss, total cost of generation, and maximizing customer benefit; however, less attention has been paid to distribution network security. Therefore, in this article, voltage stability index is considered as one of the objective functions along with network power loss, total cost of generation, and total load curtailment. The proposed scheduling problem is solved using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and its performance is evaluated on the modified IEEE 34 bus system. The accuracy of results is verified by comparing with the results obtained by solving the proposed scheduling problem using Python optimization modeling objects (Pyomo) software with interior point optimizer as the solver. [ABSTRACT FROM AUTHOR]

By analyzing traditional distributed energy resource (DER) modeling methods, this paper proposes a feasible method of DER modeling based on static generator in the simulation software — DIgSILENT. Behavioral model is proposed in this paper to simplify the DER model, in which simple electronic circuits or mathematical functions are used to reflect the physical characteristics of a certain model. With the application of the modeling method, the battery energy storage system (BESS) are implemented in DIgSILENT simulation environment. In each simulation case, the behavioral model as well as the control strategy is illustrated and the simulation result is analyzed in the end.

Due to the advantages of fewer energy conversion stages and a simple structure, direct current (DC) microgrids are being increasingly studied and applied. To minimize distribution loss in DC microgrids, a systematic optimal control framework is proposed in this paper. By considering conduction loss, switching loss, reverse recovery loss, and ohmic loss, the general loss model of a DC microgrid is formulated as a multi-variable convex function. To solve the objective function, a top-layer distributed integral convex optimization algorithm (DICOA) is designed to optimize the current-sharing coefficients by exchanging the gradients of loss functions. Then, the injection currents of distributed energy resources (DERs) are allocated by the distributed adaptive control in the secondary control layer and local voltage–current control in the primary layer. Based on the DICOA, a three-layer control strategy is constructed to achieve loss minimization. By adopting a peer-to-peer data-exchange strategy, the robustness and scalability of the proposed systematic control are enhanced. Finally, the proposed distribution current dispatch control is implemented and verified by simulations and experimental results under different operating scenarios, including power limitation, communication failure, and plug-in-and-out of DERs.

This article proposes a new control strategy in stationary frame to improve the operational robustness of voltage source converters (VSCs) under weak grid conditions. First, dynamics of the phase-locked loop (PLL) are incorporated into the model of a grid-connected VSC in stationary reference frame. It is rigorously shown that the three-phase PLL introduces cross-coupling terms between the two control channels. Second, based on the augmented model, a multivariable controller is proposed which combines the current control feedback gains with PLL feedback gains. Third, the gains of the controller are designed using an optimal control theory approach. It is shown through theoretical analysis, computer simulations, and a laboratory experimental setup that the proposed controller improves the stability margins of the VSC during weak grid conditions. More specifically, under a given identical operating conditions, while the conventional controller’s responses become increasingly oscillatory for short-circuit ratio (SCR) below 1.95 and eventually becomes unstable at SCR=1.26, the proposed controller exhibits smooth and stable responses for SCR as low as 1. The proposed controller also produces cleaner sinusoidal currents during unbalanced grid voltages. [ABSTRACT FROM AUTHOR]

With the rapid integration of distributed energy resources (DERs), distribution utilities are faced with new and unprecedented issues. New challenges introduced by high penetration of DERs range from poor observability to overload and reverse power flow problems, under-over-voltages, maloperation of legacy protection systems, and requirements for new planning procedures. Distribution utility personnel are not adequately trained, and legacy control centers are not properly equipped to cope with these issues. Fortunately, distribution energy resource management systems (DERMSs) are emerging software technologies aimed to provide distribution system operators (DSOs) with a specialized set of tools to enable them to over-come the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources. However, as DERMS technology is still emerging, its definition is vague and can refer to very different levels of software hierarchies, spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems (called utility DERMS). Although they are all frequently simply called DERMS, these software technologies have different sets of tools and aim to provide different services to different stake-holders. This paper explores how these different software technologies can complement each other, and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control cen-ters of distribution utilities.

Virtual power plant (VPP) concept creates additional benefits for different stakeholders, such as system operators, distributed energy resources’ owners, and aggregators. This article presents an auction-based energy management solution from VPP aggregator point of view. To this end, a network-constrained economic dispatch has been developed taking into account a comprehensive resource portfolio, including the conventional and renewable distributed generations, energy storage units, and flexible loads. The VPP aggregator aims at minimizing its expected costs through optimal dispatch of its own resources as well as purchasing and selling electricity from/to upstream network. To this end, a stochastic programming approach has been employed to consider the generation uncertainty of renewable energy resources as well as day-ahead energy market prices using a set of plausible scenarios. The payments to the prosumers are according to an auction-based pricing method that could be an efficient strategy for the VPP aggregator in order to guarantee fairness among consumers and producers within the VPP. In the auction-based pricing scheme, the electricity price at each local node can be calculated according to the demand and generation in each time slot. The proposed framework is a mixed-integer linear programming problem and solved by the CPLEX solver in the GAMS software. It is noteworthy that the effectiveness of the model has been verified, considering several numerical studies. [ABSTRACT FROM AUTHOR]

With an increasing penetration of distributed energy resources (DERs), it has become a public interest regarding how to efficiently manage large-scale DERs into wholesale markets. The existing literature has focused on various coordination methods including iteration-based (e.g., Lagrangian relaxation) and projection-based (e.g., vertex enumeration) ones, which may not work for the cases with complicated constraints due to curse of dimensionality. In this article, we propose an efficient projection-based method that adapts to characterizing the feasible region of active distribution networks with temporal-coupled constraints. The problem of finding the temporal-coupled feasible region is formulated as a max–min optimization program, aiming to determine the largest set allowing zero-valued slack variables. Based on Duality Principle, the max–min model is transformed into a solvable mixed-integer linear programming that can be efficiently optimized by off-the-shelf solvers. In contrast to the existing methods searching for a vertex from interior points, we develop an outer progressive approximation algorithm that generates feasibility cuts for each iteration. Case studies based on a modified IEEE 33-bus feeder and Caracas 141-bus system demonstrate the effectiveness and efficiency of our proposed method in characterizing the feasible region with large-scale temporal-coupled constraints. [ABSTRACT FROM AUTHOR]

This article proposes a supervised learning approach for centralized localization of faults in microgrids with radial configuration that can operate in grid-connected or islanded mode. The key concept is to identify faults by learning and analyzing the features of voltage and current disturbances detected by a microgrid central protection unit. Two algorithms are developed that determine fault indices and transient features for simulation-based training, classification, and backup protection purposes. The proposed data-driven approach is effective under high penetration of distributed energy resources (DERs), and is robust under microgrid topology variations. Moreover, it requires few sensors (two for radial structure) and is easy to implement, yet provides high degree of reliability. The performance and accuracy of the developed approach are verified through extensive simulations of low-voltage microgrids that incorporate different types of DERs, including wind and solar photovoltaic systems. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed generation (DG) has changed the way modern power systems are planned and operated. However, new market mechanisms at the retail level are still needed to effectively integrate energy resources and emerging market participants in the distribution grid. In this article, a stochastic market clearing and settlement model with distributed renewable energy (DRE) utilization constraints is presented. The proposed model considers the uncertainties associated with the DRE productions in the distribution grid and ensures that a certain portion of the total available DRE at a specified time will be utilized with a high probability. The problem is formulated as a chance-constrained two-stage stochastic programming model that minimizes the expected distribution system operation cost and is solved through a sample average approximation algorithm. Case studies are conducted to verify the effectiveness of the proposed model for different DRE utilization requirements. [ABSTRACT FROM AUTHOR]

Renewable energy resources are gaining fast adoption in the power grid because of their relatively low cost and offered environmental benefits. However, the grid will experience decreased inertia as a result of these added resources. Numerous research studies have focused on improving the grid damping, including the lack of inertia, due to renewable resource integration. In this article, we present an adaptive damping controller to help mitigate oscillations during disturbances on the grid, caused by fault occurrences, sudden load changes, capacitor switching, or even the intermittent nature of the energy sources for most renewable systems, considering both integrated transmission and distribution systems. The test system is developed based on Kundur’s two-area system as the transmission system and the IEEE 123-bus system as the distribution system. Then, the novel proposed adaptive optimal damping control architecture is validated via simulation-based test cases conducted in MATLAB/Simulink using real-life grid data. It is observed that the proposed control architecture not only damps the oscillations more than 10% as compared with the state-of-the-art methods but can also control multiple generators. [ABSTRACT FROM AUTHOR]

Customer baseline load (CBL) reconstruction is a critical problem in residential demand response. The difficulty of residential CBL lies in the variability of both irregular consumption and on-site distributed energy resources. Targeting the CBL reconstruction of residential prosumers, a regression-based estimation scheme is proposed using stacked autoencoders (SAEs) under the federated learning (FL) framework. In the FL framework, each residential unit (RU) stores and trains data locally without sharing them with neighboring RUs or the independent third party (ITP) responsible for CBL reconstruction. Local updates containing no load information are exchanged with the ITP (server) for the training improvement. The FL framework can, thus, protect the privacy of customers. Experimental results show that the proposed FL-based cascaded SAE outperforms the baseline on all tests and achieves up to 62.5% improvement in reducing reconstruction error. Moreover, it has enhanced privacy-preserving knowledge-sharing ability, higher efficiency, and better stability. [ABSTRACT FROM AUTHOR]

Abstract Distributed generators (D.G.’s) enable us to generate, supply and be self-reliant on power while also allows us to supply power to meet the demand through virtual power plants. The virtual power plants also help us analyse, control, optimise, and help bridge the gap of demand and supply in these vast energy requirements. With this also comes challenges associated with securing physical systems, data protection and information privacy. Recent technological advancements have aided cybercriminals to disrupt operations by carrying out deliberate attacks on the energy sector. Though security researchers have tried to mitigate the risks, vulnerabilities, and it remains a challenge. This paper aims to present a comprehensive Edge-based security architecture to help reduce the risks and help secure the physical systems and ensure privacy and data protection.

Given the historically static nature of low-voltage networks, distribution network companies do not possess the tools for dealing with an increasingly variable demand due to the high penetration of distributed energy resources (DERs). Within this context, this paper proposes a probabilistic framework for tariff design that minimises the impact of DER on network performance, stabilises the revenue of network company, and improves the equity of network cost allocation. To deal with the lack of customers' response, we also show how DER-specific tariffs can be complemented with an automated home energy management system (HEMS) that reduces peak demand while retaining the desired comfort level. The proposed framework comprises a nonparametric Bayesian model which statistically generates synthetic load and PV traces, a hot-water-use statistical model, a novel HEMS to schedule customers' controllable devices, and a probabilistic power flow model. Test cases using both energy- and demand-based network tariffs show that flat tariffs with a peak demand component reduce the customers' cost, and alleviate network constraints. This demonstrates, firstly, the efficacy of the proposed tool for the development of tariffs that are beneficial for the networks with a high penetration of DERs, and secondly, how customers' HEM systems can be part of the solution.

Microgrids provide multiple benefits to end-use customers and electric utilities, including enhanced reliability and resilience, reduced operational costs, streamlined renewable generation integration, and improved energy efficiency. However, the microgrid technology’s large capital cost remains a major barrier to establishing its economic viability. This paper addresses this challenge by proposing a practical methodology for microgrid generation sizing. The proposed methodology uses the concept of robust optimization and a multi-criteria decision-making process, taking overall cost, emission reduction, and demand response into account as important factors in optimal generation sizing. The objective is to minimize the supply gap throughout the year, which is defined as the unmet load or required load curtailment under various load and solar generation scenarios. Numerical simulations on a real-world microgrid, ComEd’s Bronzeville Community Microgrid (BCM) on Chicago’s South Side, exhibit the practicality of the proposed method and its applications for electric utilities. The study proposes an optimal size of 4.8 MW considering the commercially available generator sizes for the BCM, which has a total peak load of 7 MW, 0.75 MW of PV and 0.5MW/2MWh of Battery energy storage installed.

A coupled load flow algorithm is developed to perform transmission and distribution (T&D) co-simulation. Subsequently, the effects of distributed energy resources (DERs) on the dynamics of power systems are studied by extending the T&D load flow algorithm to T&D dynamic co-simulation. The detailed distribution and transmission systems are modeled separately and are coupled at the substation nodes at each time step. Dynamics of the DER_A mechanisms are modeled separately downstream of the distribution substation using the North American Electric Reliability Corporation model (NERC-2018). The effects of the DER_A settings are examined on the dynamics of the transmission system and DER operation. In addition, lumped and distributed induction motor load models are compared at the distribution level. The results of the study show that under high DER penetration excessive operation may occur at the distribution load tap changers. Also, the effect of reactive power on voltage recovery is significant under DER high fault-ride-through capability and DER reactive current injections. Moreover, it is observed that DER frequency control may adversely affect the DER operation keeping the active and reactive currents at high levels due to frequency deviations. Finally, distributed induction motor loads stimulate more DER reactive power injection causing voltage overshoots. [ABSTRACT FROM AUTHOR]

A dual-source voltage inverter system is proposed to improve the power and consistency aspects of using a microgrid. Evaluation of the Proportional Integral (PI), the intelligent Fuzzy Logic Controller (FLC), and the developed control method is known as the Instantaneous Symmetrical Component Theory (ISCT). A Dual Voltage Source Inverter (DVSI) uses Distributed Energy Resources (DER) to exchange power and compensate for unbalanced and non-linear loads in the system. The direct-quadrature-zero transformation (Dq0) conversion is used to obtain the positive sequence voltage. To evaluate the system control method, an inverter connected to a three-phase, four-wire distribution combination is used. The proposed system is verified by MATLAB simulation methods using a PI controller and an intelligent FLC system. [ABSTRACT FROM AUTHOR]

The Alpine ski-resorts are conventionally powered by the electrical distribution network to perform the snow-making operations throughout the ski slopes. On one hand, these grids can present a low capacity, and on the other, the line lengths are not negligible due to natural obstacles and altitude changes. As a consequence, the voltage drops are remarkable when several power loads are fed by the low voltage section. Based on this aspect, a novel evolution of ski-resorts power grids toward microgrids is pursued to improve the power system quality of service and to foster the implementation of distributed energy resources (DERs). The article examines the ac electrical distribution system installed in the ski-resort of San Vito di Cadore, Italy. A preliminary power flow analysis identifies bottlenecks on voltage profiles and weights effective solutions to evolve the power grid. Four system configurations are contrasted in terms of voltage drop in order to establish the most convenient redesign. A proper voltage profile along the entire ski-resort is ensured by the last solution, which redistributes the loads powering among the five resort's substations. Finally, a techno-economical-environmental analysis explores the possibility of installing DER and storage to feed the resort loads. The study demonstrates the capability of hydropower plant in enhancing microgrid sustainability and environmental friendliness. [ABSTRACT FROM AUTHOR]

This article investigates the voltage monitoring and control feature for smart meters and identifies the impact of this feature on both power distribution and communication systems. Regarding the voltage monitoring, a cosimulation platform is developed using GridLAB-D and ns-3 to analyze the impact of adding voltage measurements to smart meter readings and assess the mitigation strategies for reducing timeout errors and packet drops of smart meter data. Regarding the voltage control, a new voltage stability control scheme is developed, which applies the voltage stability margin as the control objective, instead of the traditional voltage magnitude. The proposed control scheme makes use of existing advanced metering infrastructure and distributed energy resources (DERs), requiring small marginal costs. It is indicated that integrating the voltage monitoring and control feature, smart meters could enable the voltage stability issues being solved at end-user sides, i.e., the “last-mile” segment. It is also implied that the new feature could support the coordination of the local and system-level voltage controls using both customer-owned and utility-scale DERs. [ABSTRACT FROM AUTHOR]

Due to environmental concerns, renewable energy sources (RESs) gradually replace conventional power generations. Over the past decade, wind and solar electricity generation have increased from 55 to 275 million MW·h and from 2 to 96 million MW·h, respectively, in the U.S. Recently, FERC order 2222 allows distributed energy resources, including distributed renewable generation and energy storage, to participate in the electricity wholesale market through aggregations. Limited rotating inertia of renewable generation resources results in reduced system inertia, which will increase the rate of change of frequency due to sudden mismatches of generation and demand. During the high visibility national social event, the brightness of LED TV display put simultaneous demand change in power systems. With the development of slim, tightly packaged, and yearly update features of smart TV, the penetration of different LED displays climbed rapidly from 9% to 60% in the U.S. during 2012–2019. In this article, the impact of societal events on frequency stability caused by large viewership on LED TVs are discussed. Increasing RESs penetration and FERC order 2222 are considered in this study. The power demand data of a 55-inch LED TV are collected and analyzed to address the problem. This article discusses the frequency stability issue caused by LED TVs in a low inertia power system. It can help system operators to develop frequency control practices accordingly. [ABSTRACT FROM AUTHOR]

The effects of distribution-connected solar farms on the transmission and distribution systems are studied in this paper using a practical power system network. Both the transmission and distribution networks are modeled and co-simulated for intermittent effects of solar power and changing load profiles. These effects include flicker in both the transmission and the distribution systems, operation of the substation Load Tap Changer (LTC) and feeder regulators, and distribution feeder voltage profiles. The results of the study show a potential hunting effect causing excessive LTC operation especially during periods of high reverse power flow condition and a significant visible flicker in the distribution system but lower levels of flicker at the transmission level. It is shown that the hunting behavior of the LTC can be mitigated and downstream feeder voltage profiles can be improved via enhanced bandwidth, block operation mode, and adjusting time delay of the substation LTC controller. In addition, the inverter Volt-Var control can be used to effectively diminish excessive LTC operation based on IEEE Std 1547-2018 Volt-Var characteristics with modified settings. Finally, an enhanced load-flow algorithm with modified Jacobian is presented that incorporates the Distributed Energy Resource (DER) Volt-Var characteristics in the conventional Newton-Raphson method.

Low voltage ride through (LVRT) is one of the indispensable issues of recent decade in the context of grid codes. LVRT stands for the ability of a generation facility to stay connected during the voltage dip. Despite the numerous discussions in recent works, but they mostly concentrate on the LVRT-based control of distributed energy resources (DERs) integrated into a microgrid and its improvement. However, what has been hidden and not addressed any more yet is an index to measure the LVRT capability of a DER-penetrated distribution network (DPDN) under different voltage sags. This takes precedence when we want to evaluate the LVRT capability of DPDNs with consideration of various LVRT categories of DERs mandated in IEEE 1547 standard. This paper introduces a general framework for LVRT assessment of a DPDN by solving a system of differential algebraic equations (DAEs). Then expected LVRT capability of a DPDN is evaluated by a proposed LVRT index through the implementation of Monte Carlo simulation technique.

The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Power grids include entities such as home-microgrids (H-MGs), consumers, and retailers, each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs. Therefore, there is the need for a smart structure to handle the new situation. This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources (DERs) and energy storage in H-MGs by considering the demand response (DR). In general, the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs. The upper-level problem is decomposed into a set of low-level market clearing problems. Both electricity and heat markets are simultaneously modeled in this paper. DERs, including wind turbines (WTs), combined heat and power (CHP) systems, electric boilers (EBs), electric heat pumps (EHPs), and electric energy storage systems, participate in the electricity markets. In addition, CHP systems, gas boilers (GBs), EBs, EHPs, solar thermal panels, and thermal energy storage systems participate in the heat market. Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources, but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

This article presents a Lagrange multiplier-based adaptive droop control to mitigate distribution power loss of parallel-connected distributed energy resource (DER) systems in dc microgrids. The distribution power loss comprising line loss and converter loss can be modeled as a quadratic function of the output currents of the DER systems, which can be optimized by the tertiary-layer Lagrange multiplier method to obtain the optimal output current references for the secondary-layer adaptive droop control. The output currents are compensated by the adaptive droop control to provide output voltage references for the primary-layer local dual-loop control, which is a conventional local control scheme for the regulations of grid-connected dc–dc converters. Both simulation and experimental results validate that the proposed control strategy can reduce the distribution power loss of parallel-connected DER systems in 48 V dc microgrids as compared to the conventional control strategy by only optimizing the line loss in different cases. [ABSTRACT FROM AUTHOR]

The aim of this article is to explore various operating situations of a microgrid based on master–slave control and the self-healing of the microgrid after fault detection. It also finds a scheduling strategy for the energy-storage equipment through an artificial bee colony algorithm to meet the needs of the microgrid. Many operating modes will face multiple instances of connection and disconnection operations. The disturbance generated by distributed energy resources can be effectively reduced through the regulation of the voltage, frequency, and phase angle. Moreover, when the microgrid is disconnected, its power flow will reduce the disturbance. In addition, a fault point is detected when there is a fault. The recovery and isolation actions are automatically carried out, which can reduce the area affected by the fault and enable the nonfault area to quickly restore the power supply. Therefore, the self-healing function of the power grid is achieved. In this article, the bee colony algorithm is applied to the scheduling strategy for the operation of the energy-storage equipment. Multiple demands need to be satisfied by the energy-storage equipment. Multiple assessment indicators are set up as the basis for the algorithm to assess the merits and demerits of the scheduling strategy. The experimental results show that the control of the energy-storage equipment through the bee colony algorithm can satisfy the demands of the various characteristics of the microgrid. It can also reduce the peak load during critical periods and maintain the reliability of the system. [ABSTRACT FROM AUTHOR]

Since distributed energy resources (DERs) can provide capacity in distribution system, distribution network reinforcement interacts with DER planning (DERP) for meeting load growth. This article proposes the coordination of distribution network reinforcement and DER planning in competitive market. The model is formulated as a multi-stage programming. In first stage, distribution companies (DISCOs) and DER aggregators (DERAs) make bidding plans for network reinforcements and DERP. In second stage, DSO checks the network security. If the check fails, DSO sends capacity signals to DISCOs and DERAs for more capacities. In third stage, the distributed market is cleared by DSO’s optimal operation problem and distribution locational marginal price (DLMP) is returned to DERAs as price signal. An iterative algorithm is proposed to solve the multi-stage model. The algorithm embodies the process of information interaction among DISCOs, DERAs, and DSO, where DISCOs and DERAs compete in capacity market under DSO’s supervision. Numerical results on IEEE 33-system and an actual 141-bus system demonstrate that the proposed model coordinates distribution network reinforcement and DERP in competitive market. [ABSTRACT FROM AUTHOR]

• Proposing an outage management model based on a risk-averse strategy in an unbalanced MG, • Applying the proposed model to common contingencies in extreme weather events, • Convexifying non-convex proposed model via semi-definite programming, • Optimal scheduling of a low to moderate penetration level of dispatchable DERs, • Considering the demand response program as curtailed demands in the proposed model. During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model. [ABSTRACT FROM AUTHOR]

This paper deals with optimal scheduling of networked microgrids (NMGs) considering resilience constraints. The proposed scheme attempts to mitigate the damaging impacts of electricity interruptions by effectively exploiting NMG capabilities. A three-stage framework is proposed. In Stage 1, the optimal scheduling of NMGs is studied through determining the power transaction between the NMGs and upstream network, the output power of distributed energy resources (DERs), commitment status of conventional DERs as well as demand-side reserves. In Stage 2, the decisions made at Stage 1 are realized considering uncertainties pertaining to renewable generation, market price, power consumption of loads, and unintentional islanding of NMGs from the upstream network and resynchronization. Stage 3 deals with uncertainties of unintentional islanding of each MG from the rest of islanded NMGs and resynchronization. The problem is formulated as a mixed-integer linear programming problem and its effectiveness is assured by simulation studies.

Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)-interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-inter-faced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.

Distributed generators (D.G.'s) enable us to generate, supply and be self-reliant on power while also allows us to supply power to meet the demand through virtual power plants. The virtual power plants also help us analyse, control, optimise, and help bridge the gap of demand and supply in these vast energy requirements. With this also comes challenges associated with securing physical systems, data protection and information privacy. Recent technological advancements have aided cybercriminals to disrupt operations by carrying out deliberate attacks on the energy sector. Though security researchers have tried to mitigate the risks, vulnerabilities, and it remains a challenge. This paper aims to present a comprehensive Edge-based security architecture to help reduce the risks and help secure the physical systems and ensure privacy and data protection. [ABSTRACT FROM AUTHOR]

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Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This article proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system. [ABSTRACT FROM AUTHOR]

Abstract Integration of more renewable energy resources introduces a challenge in frequency control of future power systems. This paper reviews and evaluates the possible challenges and the new control methods of frequency in future power systems. Different types of loads and distributed energy resources (DERs) are reviewed. A model representation of a population of the water heater devices for the demand side frequency response is considered. A model representation of a population of battery energy storage system (BESS)-based DERs such as smart electric vehicles (EVs) charging, large-scale BESSs, and residential and non-residential BESSs, are highlighted. The simplified Great Britain power system and the 14-machine South-East Australian power system were used to demonstrate the effectiveness of the new methods in controlling power system frequency following a disturbance. These new methods are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems.

As more countries seek solutions to their de-carbonization targets using renewable energy (RE) technologies, interconnection standards and national grid codes for distributed energy resources (DER) are being updated to support higher penetrations of RE and improve grid stability. Common grid-code revisions mandate DER devices, such as solar inverters and energy storage systems, ride-through (RT) voltage and frequency disturbances. This is necessary because as the percentage of generation from DER increases, there is a greater risk power system faults will cause many or all DER to trip, triggering a substantial load-generation imbalance and possible cascading blackout. This paper demonstrates for the first time a methodology to verify commercial DER devices are compliant to new voltage, frequency, and rate of change of frequency (ROCOF) RT requirements established in IEEE Std. 1547-2018. The methodology incorporates a software automation tool, called the SunSpec System Validation Platform (SVP), in combination with a hardware-in-the-loop (HIL) system to execute the IEEE Std. 1547.1-2020 RT test protocols. In this paper, the approach is validated with two commercial photovoltaic inverters, the test results are analyzed for compliance, and improvements to the test procedure are suggested.

Distributed energy resources (DERs) often rely on renewable sources whose random power fluctuations bring about voltage disturbances in distribution networks. Under such circumstances, voltage regulation through centralized control of DERs requires reliable detection and coordination mechanisms. This article proposes a new data-driven approach for event-triggered and coordinated control of DERs based on features of voltage disturbances. Synchrophasor datasets are processed to construct disturbance matrices that quantify spatio-temporal features of voltage disturbances. The estimated features are employed in clustering and control of DERs to suppress incipient events before exceeding a critical time. The proposed approach is tested in the IEEE 123-bus network, which has 15 solar photovoltaic sources with battery energy storage systems. The simulation results validate reliability and efficiency of the proposed method, and confirm that feature extraction combined with coordination of DERs can improve reliable and economic operation of distribution grids with renewables. [ABSTRACT FROM AUTHOR]