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Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system's resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

Electricity, as a sustainable energy carrier, plays a central role in the transition scenarios for carbon neutralization of energy systems. Expanding the potential of electricity requires intelligent integration of electricity infrastructures and electricity markets with distributed energy resources (DERs) including roof‐top solar photovoltaics (PVs), controllable loads, and storage batteries. This integration will advance the management of power distribution grids, resulting in harmonization of the distribution grids and variable renewable generation. In the foreseeable future, the DERs will be actively used to perform advanced management of power distribution grids and compensate fluctuation in variable renewable power generation such as PVs and wind power. Market‐based coordination mechanisms are already practiced in large‐scale electricity production, electricity trading and transmission around the world. Using these mechanisms at the distribution grid level to integrate numerous flexible devices into the grids remains an important research topic. This paper reviews the role of DER integration toward carbon‐neutral power systems, the state‐of‐art and challenges associated with market‐based approaches for integrating flexible demand, distributed generation, and storage. Methods for assessing the value of DER aggregation such as virtual power plants and microgrids are also explored. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for jointly optimizing the sizing and operation of AC microgrids. The methodology is applied to the CAMPUSGRID microgrid, which is currently being developed at the State University of Campinas (UNICAMP) in Brazil. The distributed energy resources (DERs) considered in the study include a battery energy storage system (BESS), a dispatchable gas generator, and photovoltaic (PV) generation. The proposed energy management system (EMS) minimizes the costs associated with day-ahead operation of the DERs in an unbalanced three-phase AC microgrid. To develop the EMS as a mixed-integer linear programming (MILP) model, the MATLAB Link module of HOMER Pro and the AMPL API are used. Heuristic rules are also developed for contingency and post-contingency operations in case of unintentional disconnection from the main grid. The sizing simulations combine tariff regimes, contingencies, and dispatch strategies. The results demonstrate the influence of the tariff on the net present cost (NPC) and the importance of resilience in sizing DERs. Additionally, the proposed model is capable of assessing the impact of DERs on the electrical grid. Finally, the joint strategy proposed in this paper leads to a reduction in microgrid investment cost, improvement in voltage magnitude profile, and reduction in active power losses when compared to the default dispatch strategies of HOMER Pro. [ABSTRACT FROM AUTHOR]

To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized. [ABSTRACT FROM AUTHOR]

The increasing inclusion of electric vehicles (EVs) in distribution systems is a global trend due to their several advantages, such as increased autonomy and reduced price. However, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the demand. Thus, in this paper, an adequate planning of the EVCSs allocation and sizing is carried out to ensure better power quality indices, in addition to reducing costs related to EVCSs installation and EV users' recharging. Besides the optimal planning of EVCSs, this paper performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate the problem related to voltage levels and power losses. Additionally, a spatial distribution of EVs was performed for the 24 h, considering residential and commercial nodes of a distribution feeder test, from the closeness centrality of graph theory. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 34-node test feeder. Even with the integration of EV loads, the optimal allocation and sizing of EVCSs and DERs promoted a reduction in voltage deviation and power losses of 11.576% and 56.683%, respectively, in addition to a low variation in the energy costs of 4.447%. [ABSTRACT FROM AUTHOR]

The goal of the paper is twofold: a) following the identification of the merits of Distributed Energy Resources (DERs) by FERC 2222 for the flexibility they bring to the grid, we recognize water heaters and thermostats as mini-batteries and introduce volumetric/swing options that the Aggregator can sell by shifting consumers' consumption while rewarding them; b) We propose to address the pricing complexity of these volumetric options - which are in our setting very general in terms of number of exercises and quantities constraints - by a novel approach that combines Monte Carlo simulations and Recurrent Neural Networks (RNN). [ABSTRACT FROM AUTHOR]

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM. [ABSTRACT FROM AUTHOR]

This article discusses the key technologies of smart energy system optimization. It highlights the importance of constructing and optimizing a smart energy system that combines energy and information technology to meet the requirements of the rapidly changing energy field. The article presents four research articles that focus on topics such as economic energy management, load forecasting, time-of-sale price adjustment, and grid regulation. These research articles propose strategies and models to achieve efficient and economical operation, improve the robustness of the system, and optimize the control operation of the power grid. The article emphasizes the significance of the construction and optimization of smart energy systems for the long-term development of the energy field. [Extracted from the article]

This paper proposes a reliability-based optimal scheduling model of integrated microgrids. The proposed model is capable of delivering optimal scheduling of each individual microgrid and aggregated system. In addition, it is compatible with both grid-connected and islanded operation modes. The developed problem formulation of the proposed model takes into account the reliability indices SAIDI, SAIFI, and CAIDI to evaluate the reliability of the integrated system. Numerical simulations on a modified IEEE-33 bus test system, involving four integrated microgrids, were performed to investigate the advantages and the robustness of the developed model. Furthermore, the impact of merging Battery Energy Storage Systems (BESSs) on the optimization outcome is examined, which demonstrates its significant effectiveness in improving the power systems' reliability. [ABSTRACT FROM AUTHOR]

Penetration of Distributed Energy Resources (DER) causes overloading or overvoltage in the grid-edge. Traditional measure of DER curtailment could decrease the economy or operating rate. Centralized optimal control of the total grid-edge is usually too complex to solve and requires ubiquitous information system. So Distributed Energy Resource Management System with Distribution Grid Stabilization (DERMS-DGS) was proposed. It decomposes the optimization into one Distribution Grid Operator (DGO) and multiple Micro Grid Operators (MGOs), with an improved Generalized Benders Decomposition (GBD). DGO realizes the optimal power flow of the grid with demand response, and MGO realizes the optimal operation of adjacent DERs in the demand side. To guarantee the convergence of GBD, the feasible region of MGO was estimated by a dedicated optimization so that the feasibility cut was avoided. Typical seasonal distribution grid models for China in 2030 were formulated according to real data and trend. Simulation result showed that, compared with the baseline technology which is based on DER curtailment, DERMS-DGS resolved more overloading or overvoltage, made 3.4% less profit in urban and 90.8% more profit in suburban with DER penetration speed 100%, and 22.7% more profit in urban and 229.5% more profit in suburban with DER penetration speed 150%, and significantly reduced PV curtailment or EV waiting. [Display omitted] • Distributed Energy Resources (DER) damages the stability of the distribution grid. • Traditional method is complex centralized optimization or isolated device control. • Generalized Benders Decomposition realizes the distributed optimization of grid-edge. • Avoiding the feasibility cut ensures the convergence of the master problem. • Distributed optimal management brings more profit when DER penetration is higher. [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]

In microgrids, to have minimal operation cost, optimal power scheduling among distributed energy resources is an essential task. Owing to nonlinearity and complexity of power scheduling problem, it is vital to use a high-performance optimization technique to efficiently solve this problem. To achieve this goal, in this paper, crow search algorithm (CSA) is proposed and improved to optimally solve the power scheduling problem in a microgrid including photovoltaic modules, wind turbines and combined heat and power system. In the improved CSA (ICSA), there are three modifications: (1) oriented differential operator, (2) adaptive flight length and (3) repulsion factor. These modifications help the algorithm to provide a better balance between diversification and intensification. In order to validate the effectiveness of the proposed algorithm, the results obtained by ICSA are compared with the results found by the other algorithms. Over IEEE 37-node feeder, simulation results indicate that the proposed algorithm finds more accurate results than the other methodologies. In comparison with artificial fish swarm, additive increase multiplicative decrease, memory-based genetic algorithm, genetic algorithm, particle swarm optimization with inertia weight (PSOw) and particle swarm optimization with constriction factor (PSOcf), when ICSA is applied, operation cost decreases around 19, 19.7, 0.7, 36, 6.1 and 5%, respectively. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) is a cloud-based aggregator that combines heterogeneous distributed energy resources to give a reliable and friendly power supply. In this article, a VPP that consists of natural gas network, power to gas (P2G) equipment, flexible load, and other resources is introduced in the electricity market and natural gas market considering the uncertainties of renewable energy outputs and demand responses. Moreover, the optimal scheduling strategy of VPP includes power and gas dispatch considering bidirectional flows of power and gas, and the optimization model considering dual-energy markets is investigated by developing different scenarios to maximize the VPP's profit. Case studies have been carried out to analyze the economy and scheduling strategy of the VPP. Simulation results show that the VPP studied in this article can effectively reduce the proportion of renewable curtailment by selling electricity or P2G equipment. Besides, it is beneficial to maintain pressure stability and security operation of the natural gas network, and the VPP could decrease unbalanced penalty cost and reduce the impact from uncertainties in dual-energy markets. [ABSTRACT FROM AUTHOR]

This paper presents decentralized control of an islanding/grid-connected DC/AC hybrid microgrid (μG) using battery energy storage system (BESS) and distributed energy resource (DER). In DC/AC lG unified with different DERs and BESS, the intermittent output nature of DERs due to fluctuating input characteristics and strong interaction of sources, loads, and primary grid, causes variations in frequency and bus voltage. To solve these issues, the paper proposes a decentralized control method which incorporates active/reactive power (P/Q) control, DC virtual droop control, voltage control, frequency droop control, current control, maximum power point tracking, and a phase-locked loop (PLL). The coordination and integration of various control parameters will ensure a smooth transition as well as the overall stability of the system. The BESS and DER's interfacing converters are autonomously controlled in this control scheme without communication using the frequency droop and bus voltage control. Correspondingly, the control techniques comprehend different operating modes depending on the state of charge of BESS, distributed generation, and grid. During switching the two interlinking converters (IC), high-frequency harmonics are created, which are eventually mitigated by the inductor-capacitor-inductor (LCL) filters employed in the AC bus. Virtual droop control will ensure active damping of the harmonics in the LCL network. The maximum power transfer in interlinking converters (ICs) is improved using the proposed approach for input-source variations. Simulations were performed using MATLAB/Simulink and verified using the hardware in the loop (HIL) to prove the effectiveness of the control scheme proposed. [ABSTRACT FROM AUTHOR]

The design of accurate and robust protection scheme is one of the major challenges associated with microgrids. In microgrids, detecting faults during measurement noise and their discrimination from switching transients is challenging. To address these issues, this paper designs a protection scheme using the cumulative sum of differential negative-sequence power angle as a fault detection index. The simulation results, obtained using MATLAB/Simulink, show that the proposed scheme accurately detects all the internal faults and efficiently discriminates between internal fault events and other switching events. And it is found that all the faults are precisely detected within the minimum, maximum, and average fault detection time of 18.6, 28.63, and 22.74 msec, respectively. The scheme is further validated for real-time applications by developing an experimental setup using OPAL-RT real-time simulator. It is found that the proposed scheme is independent of the fault types, fault locations, fault inception angle, change in sampling frequency, simultaneous fault, and evolving fault. Furthermore, the proposed scheme is successfully compared with one of the existing schemes and found superior over the existing scheme. Based on the obtained results, it can be said that the scheme is reliable, accurate, discriminative, and consistent for all the considered events. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces an optimal voltage control (OVC) framework for islanded microgrids (MGs) as a unified hierarchical control scheme, started from bus voltage set‐points optimization at tertiary level, then effectively followed by secondary control, which controls the distributed energy resources via their primary level controllers. The distributed control structure is proposed here for both tertiary and secondary levels to benefit from higher reliability, avoidance of a single point of failure, and a relatively simple communication system. At the tertiary level, a new distributed voltage protocol, based on the Lyapunov theory, is proposed to optimize power losses and voltage profile deviations in the MG. The obtained optimal voltage references are passed to the secondary level, in which a modified distributed protocol (MDP) is utilized to ensure the OVC and dynamic stability. In addition, the proposed MDP can maintain the critical bus voltage against load changes until the next optimal set‐point updates. The performance of the MDP is examined for different scenarios such as load changes and DGs plug and play operation in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

A novel hierarchical Energy Management System (EMS) framework is proposed for the incorporation of Thermostatically Controlled Loads (TCLs) in the provision of ancillary services, namely frequency and voltage control of a microgrid. The TCL population is aggregated as a function of the ambient temperature, voltage and frequency using a neural network-based approach. The proposed EMS comprises primary and secondary control levels. TCLs’ participation in the primary control is selected to be semi-autonomous in order to obtain a fast response with low communication burden. The secondary control is centralized and aims to find the optimal dispatch of regulating resources including TCL clusters in order to minimize frequency and voltage deviations as well as grid operation cost. The secondary control relies on a Trust-Region Power Flow (PF)-based multi-objective optimization. Simulation results demonstrate that the participation of TCLs in the proposed EMS does not compromise customer comfort while short cycling and the number of switching events are minimized. [ABSTRACT FROM AUTHOR]

The integration of highly intermittent and unpredictable distributed energy resources increases the complexities in the energy management system (EMS). Therefore, the conventional control used in microgrid EMS is no more effective and requires alternative control strategies that can cope up with the new dynamics in the system. Adopted from computer science, a distributed artificial intelligence technique called multi agent system (MAS) can serve this purpose well due to its inherent autonomous nature, scalability, flexibility, and adaptability. Consequently, in this paper, a new multi agent based architecture for microgrid energy management system has been proposed. The proposed model considers different forecasting agents and a real time correction agent for microgrid energy management. The performance of proposed MAS based microgrid EMS modeled in a new platform called Stateflow has been evaluated by developing different scenarios considering resource fluctuations and load variations. The simulation results demonstrate the effectiveness of the proposed architecture in outperforming the conventional microgrid energy management system and the effective usage of each component of the microgrid. [ABSTRACT FROM AUTHOR]

The electrification of rural or isolated areas coupled with increasing environmental concerns have promoted the emergence of Distributed Energy Resources (DER) and the operation by isolated microgrids. However, the integration of such resources involves technical issues related to the reliability and continuity of the electricity supply. Indeed, the uncertainty of renewable generation sources and the reduced inertia of isolated microgrids are challenges for the operation of these distribution systems. One way to address them is by providing ancillary services through all the resources involved in the system’s operation (generation assets, demand share, and storage systems). Accordingly, this paper first presents a literature review of the challenges and potential benefits of integrating DERs into the operation of a distribution system. It also includes some common strategies to mitigate the vulnerability of the introduction of these technologies in microgrids. Afterwards, the current state of each type of resource in Colombia is assessed. Finally, some basic strategies that enhance the benefits of DER integration are outlined along with the overcoming of challenges of microgrid operation in said country. To that end, we consider isolated Colombian regions to be natural laboratories where the effects of DER integration and the requirements for the operation by local production units can be analyzed.

The limited and bidirectional fault current of inverter interfaced islanded microgrids make the fault detection and faulty feeder identification the major challenges for conventional overcurrent-based protection systems. To address the first challenge, this article proposes a fault detection strategy based on the modified short-time correlation transform (MSTCT) of feeder current. Using the superimposed component of MSTCT, a new directional element is defined to address the second challenge. The developed strategy relies on an intelligent electronic device in each feeder, making it a cost-effective scheme. The proposed scheme is effective for all types of fault and can differentiate normal switching events from the fault conditions. Also, it properly operates in the presence of nonlinear loads and measurement noises. The reliable performance of the proposed protection scheme is assessed on the MATLAB/Simulink simulation model of the CIGRE benchmark low-voltage microgrid network. [ABSTRACT FROM AUTHOR]

This paper presents a robust H∞ control technique for an islanded microgrid in the presence of sudden changes in load conditions. The proposed microgrid scheme consists of a parallel connected inverter with distributed generations. When the load is suddenly changed the frequency deviates from its nominal value. The objective is to design a robust frequency droop controller in order to achieve the frequency at nominal values without using any secondary controller and communication systems while improving power sharing accuracy. Small signal modeling of the power system is designed for the formulation of the problem and the H∞ optimal linear matrix inequality technique is applied in order to achieve the objectives. The proposed controller has been tested with the MATLAB/ SimPowerSytem toolbox. [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]

A two-layer optimal consensus-based distributed control strategy is proposed for the coordinated operation of networked microgrids (MGs). Several networked MG operation objectives are optimized using the proposed control strategy, including frequency/voltage regulation, proportional active power sharing, and smooth MG islanding/reconnection operation. The global objective function of networked MGs is decomposed into a series of local objectives assigned to participating distributed energy resources (DERs). In the decomposed optimization, each control layer is realized in a distributed manner in which DER state variables reach consensus along with optimizing their assigned local objective functions. Each control layer in the two-layer control strategy is formulated as an optimal consensus problem in which the optimality and asymptotical stability of the system equilibrium point for each control layer are demonstrated. The effectiveness of the proposed control strategy is validated in a modified IEEE 33-bus distribution system using the PSCAD/EMTDC platform. [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]

• Methodology for assessing the operational flexibility of Virtual Power Plants. • Methodology to facilitate the integration of renewable energies into the power system. • Graphical representation of operational flexibility of distributed energy resources. • Assessment of the available flexibility to facilitate decision-making under uncertainty. • Participation of Virtual Power Plants in the ancillary services and power reserve offer. Distributed energy resources (DERs) are elements that actively participate in the supply of renewable energy and contribute to the decarbonization of the power system. However, they lack two factors necessary to take advantage of their operational flexibility: observability and controllability. In this sense, Virtual Power Plants (VPPs) are a feasible alternative to provide the necessary requirements for the optimal management of a set of distributed units. Therefore, knowledge of the technical and energy characteristics of each unit that makes up the VPP is a necessary condition for the effective integration of DERs into the power system. This paper proposes a methodology to graphically represent, quantify and exploit the aggregate operational flexibility of a set of units. The proposed methodology is based on five metrics related to active and reactive power, which serve as a tool to facilitate the VPP Operator's decision-making under uncertainty. Consequently, achieving the coordinated operation of several distributed units makes it possible to achieve common objectives. For instance, frequency and voltage regulation, compliance with a planned power curve, or dealing with the variability of renewable energies. The proposal is applied to a theoretical case study and through real operational tests between a hydroelectric unit and a photovoltaic plant. Finally, it is shown that the results obtained are a useful tool in real-time. [ABSTRACT FROM AUTHOR]

An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model. [ABSTRACT FROM AUTHOR]

When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program (SDP). The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and other operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few SDPs in most cases. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a state feedback primary control strategy for microgrids with multiple distributed energy resource units, improving their transient behavior in both islanded and grid-connected modes of operation. To that end, the interaction of each distributed energy resource unit within the microgrid is modeled as a lumped dynamic system, which results to be nonlinear and multivariable. For the closed-loop control of such multivariable systems, the full state feedback formulation is preferred which requires a suitable state observer. For the design of the observer and feedback gains, the solution of the linear-quadratic estimation and regulation problems is considered. For simplicity, an approximate linear model at a representative operating point is derived. The linear quadratic Gaussian/loop-transfer recovery is adopted as the design procedure to optimize the trajectory of the state variables subject to a desirable actuation effort, in this case of the voltage amplitude and frequency, yielding a solution that is robust to model uncertainties. The effectiveness of the strategy is assessed through time-domain simulation on the CIGRE benchmark medium-voltage distribution network with three distributed energy resource units. These results are compared to those obtained with conventional static droop gains and with a state-of-the-art technique from the literature. [ABSTRACT FROM AUTHOR]

Highlights • A scenario-based investment planning model is proposed for the isolated microgrids. • The supply and demand of the electrical, heating and cooling energy are considered. • The heuristic moment matching method is employed to generate the scenarios. • The solution minimizes the overall microgrid costs and Carbon dioxide emissions. Abstract Multi-energy microgrids provide a flexible solution for the utilization of the distributed energy resources in order to meet the electrical, heating and cooling energy demands in the off-grid communities. However, the planning of the multi-energy microgrids is a non-trivial problem due to the complex energy flows between the sources and the loads pertaining to the electrical, heating and cooling energy, along with the intermittency of the renewable distributed generation. This work proposes a scenario-based stochastic multi-energy microgrid investment planning model that aims to minimize the investment and operation costs as well as the Carbon dioxide emissions by determining the optimal distributed energy resource mix, siting and sizing in the isolated microgrids. The proposed planning model employs the power flow and heat transfer equations to explicitly model the energy flows between electrical, heating and cooling energy sources and loads. Moreover, an uncertainty matrix is employed to tackle the operational uncertainties associated with the wind and photovoltaic generation, and the electrical, heating and cooling loads. The uncertainty matrix is modeled using the heuristic moment matching method that effectively captures the stochastic moments and correlation among the historical scenarios. The numerical results obtained from the case-study in the 19-bus microgrid test system confirm that the proposed methodology provides significant reductions in the investment and operation costs as well as the Carbon dioxide emissions. Finally, the superiority of the proposed planning solution is also validated using the deterministic planning solution as the comparison benchmark. [ABSTRACT FROM AUTHOR]

In this paper, a distributed online prediction optimization algorithm is proposed to optimize the operation of distributed energy resources (DERs) considering multi-period constraints. First, we build a time-varying operation optimization model of DERs in multi-area distribution networks considering the operation constraints among different periods (e.g., the relationship of DERs energy within a certain amount of time). Second, we design an online prediction algorithm to solve the time-varying model in a distributed way. Specifically, it decouples the sensitivities of power flow states among different distribution areas. Based on this, a model is set up to characterize the mapping relationship among power flow states and the increments of DER output powers among different periods, and to predict the power flow states in the future only depending on the local and some aggregated information of each area. Thus, the solution of the time-varying optimization problem is decomposed into solving several subproblems in a distributed way. Finally, the proposed algorithm is verified in a 502-node distribution system. It achieves the optimal operation of DERs with satisfying the constraints of the energy plan formulated to reserve enough energy for the regulation in future periods. Compared with the centralized method, the proposed algorithm achieves similar optimization results with significant reduction of calculation time. • The sensitivities of power flow states are decoupled based on the influence of injection power changes on power flow states. • A distributed online prediction optimization algorithm considering the multi-periods constraints is proposed. • The algorithm only depends on local measurements and boundary information for each distribution area. • Case study on a 502-node distribution test system, indicates that the calculation time of the proposed algorithm is only 2.34% of the value of the centralized online prediction optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper explores a information model for a small electrical network (Microgid) that integrates renewable energies based in standard protocols. The information model of the micro network will be made up of a solar energy source, a wind energy source, an energy storage element, a non-renewable source such as a diesel generator. Each of the renewable sources is modelling based on the logical nodes of the IEC 61850-4-2, IEC 61850-7-420, IEC 61400-25-2 standards. The objective of this definition is select: logical nodes, variables and commands, required to achieve coordinated control. This interchange of information is required to develop more complex algorithm of distributed control resources. A control architecture based and structured in these standards will to allow interoperability between the controllers of the power sources of the network, regardless of who develops and manufactures the device. This work is intended to ensure that information can be exchanged to control the load flows in the electric network that integrates renewable energies. All communications between controllers will be based on these protocols that allow the exchange of information over ethernet networks for critical process. [ABSTRACT FROM AUTHOR]

Microgrids expansion problems with battery energy storage (BES) have gained great attention in recent years. To ensure reliable, resilient, and cost-effective operation of microgrids, the installed BES must be optimally sized. However, critical factors that have a great impact on the accuracy and practicality of the BES sizing results are normally overlooked. These factors include the wide range of characteristics for different technologies, the distributed deployment, the impact of depth of discharge and the number of charging/discharging cycles on the BES degradation, and the coordination of microgrid operation modes. Thus, this paper proposes a comprehensive BES sizing model for microgrid applications, which takes these critical factors into account when solving the microgrid expansion problem and accordingly returns the optimal BES size, technology, number, and maximum depth of discharge. The microgrid expansion problem is formulated using mixed integer linear programming. The nonlinear relationship between the BES depth of discharge and lifecycle is linearized using piecewise linearization technique and implemented to model the BES degradation. The proposed model is validated using a test microgrid. The conducted numerical simulation shows that the proposed model is able to determine the optimal BES size, technology, number, and maximum depth of discharge and further enhances the accuracy and practicality of the BES sizing solutions. [ABSTRACT FROM AUTHOR]

This paper presents a method that combines fuzzy inference system and Integral-Proportional named Fuzzy-IP controller to regulate an independent microgrid voltage with a distributed energy resource unit. The unit employed photovoltaic (PV) array with DC voltage converted to three-phase AC voltage. The control design was carried out through modeling and simulation using Matlab software environment. Transfer function with the 2×2 structure using system identification has estimated the non-linear plant model. Two controllers transformed the ab-c to the d-q axis coordinates of voltage to simplify linear control design. An oscillator is applied to set frequency according to the recommendation. The results show that the compensated casestudy system using Fuzzy-IP with disturbance tracked the setpoint excellently and it regulated the voltage properly including frequency control using internal oscillator. The paper presents performance superiority of the proposed method over PI control by comparing the transient response, the mean squared error, and the root mean squared error. [ABSTRACT FROM AUTHOR]

Area coverage is a critical issue which will have a major impact on the sensing quality over targeted regions in wireless sensor networks. This paper studies the area coverage problem (ACP) with non-penetrable obstacles in microgrids, where a sensor cannot be deployed or sensing signals cannot pass through. The target coverage region is discretized in a deployment map representation of non-penetrable obstacles. A probability detection model is utilized to represent sensors with a variable distance coverage. The ACP is formulated as a mixed integer nonlinear programming problem and an iterative process is developed to solve the problem. The microgrid at Illinois Institute of Technology (IL Tech) is used as a practical example. Both the normal and the N-1 contingency deployments of sensors are simulated for optimizing the ACP at the IL Tech microgrid. [ABSTRACT FROM PUBLISHER]

This paper presents a new droop-based control strategy for hybrid microgrids (HMG) with improved power sharing. When ac microgrids (AC-MG) and dc microgrids (DC-MG) are present in a distribution grid, there is an opportunity to interconnect them via an interlinking converter (IC) and form a HMG. Power sharing and adequate voltage/frequency control are the main functions of AC-MG and DC-MG control systems in standalone mode. When it comes to a HMG, active power sharing throughout the whole system must also be properly achieved by controlling the IC active power throughput. Furthermore, the possibility of participation of IC in AC-MG reactive power adds some complexity to a HMG control system. In this paper, a new decentralized control strategy is presented for a HMG which relies on regulating the voltage magnitude of a common bus in each microgrid. In this regard, new droop characteristics for sources across both microgrids as well as IC are proposed. The proposed droop characteristics result in better active/reactive power sharing across both microgrids and at the same time results in better voltage regulation. The derivation of new droop characteristics is thoroughly discussed in this paper. Simulation results are used to show the improvement achieved. [ABSTRACT FROM AUTHOR]

Due to the unique structure and special characteristics of electric distribution networks, along with the increase in the number and severity of the natural disasters in recent years, presenting a proper framework and procedure for evaluating the resiliency of these systems is more essential. In addition, it is obligatory to model the different capabilities and features of the smart grids components in order to provide a better knowledge about their expected performance in such circumstances. In response to these challenges, this paper addresses the concept of resiliency and its dimensions in distribution networks. A new model based on mixed-integer linear programming is proposed to properly model and evaluate the resiliency of smart distribution systems. In the proposed model, optimal formation of dynamic microgrids (MGs), their service areas, and the optimal management of different technologies such as energy storage (ES) units, demand side management programs and distributed generations (DGs) units are investigated. In addition, employing a two-stage framework based on stochastic programming, the impact of increasing penetration level of the renewable energy resources and their related uncertainties on system resiliency is examined. The efficiency and applicability of the proposed integrated model is verified by performing multiple simulations on the modified 118-bus test system and a real distribution network. [ABSTRACT FROM AUTHOR]

Microgrid is universally accepted as a new approach to solve the global energy problem. In a microgrid, the optimal sizing of energy storage is necessary to ensure reliability and improve economic efficiency. Its sizing results are impacted by uncertainty on natural resources, energy storage as well as load, and it is hard to coordinate these factors. Therefore, microgrid needs more improved strategies for optimal sizing. In this paper, we present a power source sizing strategy with integrated consideration of characteristics of distributed generations, energy storage and loads. Distributed generations consist of wind turbine, photovoltaic panels, combined heat and power generation (CHP) as well as electric vehicles. A two-layer hybrid energy storage system with three storage types (i.e. super capacitor, li-ion battery, lead-acid battery) is constructed based on their power density, energy density, response speed and lifetime, as well as load classification. Power load differences among different time intervals which are supplied by different types of storage leads to allocation of energy storage. An objective function is established based on life cycle cost (LCC) theory, which includes construction cost, operation maintenance cost, recycling profit, environment cost, and energy shortage compensation. Three scenarios, in which particle swarm optimization (PSO) is used for the optimal sizing, modeling and results calculating. From the simulations results analysis, it is found that the proposed model and strategy are feasible and practical. [ABSTRACT FROM AUTHOR]

This paper presents a technique for the optimal planning and design of hybrid renewable energy systems for microgrid applications. The Distributed Energy Resources Customer Adoption Model (DER-CAM) is used to determine the optimal size and type of distributed energy resources (DERs) and their operating schedules for a sample utility distribution system. Using the DER-CAM results, an evaluation is performed to evaluate the electrical performance of the distribution circuit if the DERs selected by the DER-CAM optimization analyses are incorporated. Results of analyses regarding the economic benefits of utilizing the optimal locations for the selected DER within the system are also presented. The electrical network of the Brookhaven National Laboratory (BNL) campus is used to demonstrate the effectiveness of this approach. The results show that these technical and economic analyses of hybrid renewable energy systems are essential for the efficient utilization of renewable energy resources for microgrid applications. [ABSTRACT FROM AUTHOR]

This study develops a dominant-subordinate-interactive stochastic programming (DSISP) model for the wind-photovoltaic-based distributed energy resource systems in Lize Financial Business District of Beijing, China. During the synergistic optimization process with 24-h prediction outputs of solar and wind power as constraints, the dominant level puts more emphasis on renewable energy utilization, while the minimization both of pollutant emissions and system cost are modeled as a multi-objective programming (MOP) problem placed at the subordinate level. Results indicate that: (a) renewable energy technologies would exert an increasing paramount role in power systems, with an average share of 15.54% through a whole year; (b) a higher violation risk would lead to a decreased strictness of the constraints for facility capacity availabilities, and then to a higher system cost, more green electricity generation, and lower non-renewable electricity generation. As a consequence, thermal supplies especially in winner days would depend strongly on gas-fired boiler, which would result in more pollutant emissions as a violation risk increases. Moreover, the performance of the DSISP model is enhanced by comparing with three single-objective programs and the MOP approach. Results demonstrate that the DSISP model can provide much comprehensive and systematic strategies as considering the bi-level structure within the systems. [ABSTRACT FROM AUTHOR]

CRMs can effectively share distributed energy resources of multiple owners and enhance resilient electricity supply in communities during disruptions. However, in contrast to single-entity microgrids, CRMs present a unique structure and bring new challenges for operations and control, which can often be interrupted due to cascaded failures in interconnected electrical/communication components. An installation in Potsdam, New York, incorporates some control/communication solutions for resilient and economic operation of CRMs. [ABSTRACT FROM AUTHOR]

Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling. [ABSTRACT FROM AUTHOR]

By consulting various worldwide definitions of microgrids and distributed energy, this study presents a microgrid-structured multi-agent system and uses Matlab/Simulink to construct a circuit with microgrid features, which enables the changes in each electrical source and load in the microgrid to be monitored and controlled. This multi-agent system adheres to the Java Agent Development Framework (JADE) platform specifications of the Foundation for Intelligent Physical Agents (FIPA), facilitating communication, information transfers, and the receipt of real-time information regarding the microgrid and each component in the microgrid. Furthermore, the real-time state in the microgrid can be correspondingly controlled, achieving the most efficient real-time monitoring and control for electrical sources and load management in the microgrid.

Networked microgrids are emerging for coordinating distributed energy resources in distribution networks in the future Energy Internet, for which developing an efficient energy market model is crucial for facilitating multi-directional trading among microgrids. In this paper, a peer-to-peer energy trading mechanism is presented using non-cooperative bidding among microgrids. Multidimensional willingness, including time pressure and counter behavior for mimicking the personalized behaviors of microgrids, was taken into account in the design of the bidding strategy. Under a parallel trading framework based on a blockchain, the proposed multidimensional willingness bidding strategy turns out to be able to make rational decisions with sufficient flexibility in the bidding process. The simulation results of a realistic case of microgrids from Guizhou Province, China, validate that the proposed peer-to-peer energy trading mechanism is capable of raising the microgrids’ profits and renewable energy source utilization.

Providing a cost-efficient, eco-friendly and sustainable energy is one of the main issues in modern societies. In response to this demand, new features of microgrid technology have provided huge potentials while distributing electricity more effectively, economically and securely. Accordingly, this paper presents a new multi-objective generation management model for optimal day-ahead operational planning of medium voltage microgrids. The proposed model optimizes both pollutant emission and operating cost of a microgrid by using multi-objective optimization. Besides, a seamlessness-selective algorithm is integrated into the model, which can be adopted to achieve the desired self-sufficiency level for microgrids along a specified planning horizon. Furthermore, the model is characterized by a reserve-assessment strategy developed to handle the load and renewable energy resources estimation errors. The introduced model is solved using a combination of numerical and evolutionary methods of species-based quantum particle swarm optimization to find the optimal scheduling scheme and minos-based optimal power flow to optimize the operating cost and emission. In addition, the suggested solution approach also incorporates an efficient mechanism for considering energy storage systems and coding the candidate solutions in the evolutionary algorithm. The proposed model is implemented on a test microgrid and is investigated through simulations to study the different aspects of the problem. The results show significant improvements and benefits which are obtained by applying the proposed intelligent management. [ABSTRACT FROM AUTHOR]

Microgrids are presently receiving great attention and are considered the future trend for power distribution systems. This paper presents the control and management of a microgrid with distributed energy resources connected to a single bus. The microgrid management system uses a centralized and heuristic approach that considers the stochastic production of a photovoltaic power source, the use of a fuel cell, the state of charge of batteries, the variable load profile, and the flag-color of electricity tariff. The goal of the control and management system is the reliable and economic using of energy resources and their integration to power distribution systems while preserving the power quality. The responses regarding the control system of the power inverters connected to the distributed energy resources in the microgrid are evaluated during grid connection, standalone operation, and transition between the aforementioned operating modes. Simulation results prove that the control and management strategy is suitable in all operating conditions of the microgrid. The whole system composed of distributed energy resources, power electronic systems, and their respective control approaches that allow power management were developed in PSCAD/EMTDC simulation program. [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]

• A framework for DER aggregator to participate in day-ahead market considering security check is proposed. • A single-leader-multi-follower Stackelberg game model is established. • A linearized power flow model of DS with substation transformer tap is proposed. • An A-RBRD algorithm is proposed to solve the Stackelberg game model. • Performance of the model and algorithm are verified using a constructed and a realistic T&D system. With increasing distributed energy resources (DERs) integration, the strategic behavior of a DER aggregator in electricity markets will significantly affect the secure operation of the distribution system. In this paper, the interactions among the DER aggregator, energy and reserve markets, and distribution system are investigated through a single-leader-multi-follower Stackelberg game model with the DER aggregator as the leader and the independent system operator and distribution system operator as the followers. To guarantee the operation security of the distribution system, security check problems under three different scenarios are involved in the follower level, which is linearized using a mixed-integer linearized power flow model. Then, using the strong duality theorem, the proposed model is converted into a bi-level mixed-integer linear (BMILP) programming model with only mixed-integer linear follower-level problems. Next, an accelerated relaxation-based bi-level reformulation and decomposition algorithm is proposed to solve the BMILP problem. Finally, case studies are carried out on a constructed integrated transmission and distribution (T&D) system and a practical integrated T&D system to verify the effectiveness of the proposed model and algorithm. The simulation results indicate that the available downward reserve of the DER aggregator will decrease with the security limitation of the distribution system. [ABSTRACT FROM AUTHOR]

This paper presents a model for the microgrid planning problem with uncertain physical and financial information. The microgrid planning problem investigates the economic viability of microgrid deployment and determines the optimal generation mix of distributed energy resources (DERs) for installation. Net metering is considered for exchanging power with the main grid and lowering the cost of unserved energy and DER investments. A robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices. The microgrid islanding is further treated as a source of uncertainty. The microgrid planning problem is decomposed into an investment master problem and an operation subproblem. The optimal planning decisions determined in the master problem are employed in the subproblem to examine the optimality of the master solution by calculating the worst-case optimal operation under uncertain conditions. Optimality cuts sent to the master problem will govern subsequent iterations. Numerical simulations exhibit the effectiveness of the proposed model and further analyze the sensitivity of microgrid planning results on variety levels of uncertainty. [ABSTRACT FROM AUTHOR]