Showing papers on "Microgrid published in 2019"
TL;DR: The decomposable structure of the multiclass energy management problem is exploited to devise a distributed price-directed optimization mechanism, providing scalability and prosumer data privacy.
Abstract: This paper proposes a peer-to-peer energy market platform based on the new concept of multiclass energy management, to coordinate trading between prosumers with heterogeneous (i.e., beyond purely financial) preferences. Power networks are undergoing a fundamental transition, with traditionally passive distribution network consumers becoming “prosumers”; proactive consumers that actively manage their production and consumption of energy. The paper introduces the new concept of energy classes, allowing energy to be treated as a heterogeneous product, based on attributes of its source, which are perceived by prosumers to have value. Examples include generation technology, location in the network and owner's reputation. The proposed peer-to-peer energy market platform coordinates trading between subscribed prosumers and the wholesale electricity market, to minimize costs associated with losses and battery depreciation, while providing added value by accounting for the prosumers’ individual preferences for the source/destination of the energy they consume/produce. The decomposable structure of the multiclass energy management problem is exploited to devise a distributed price-directed optimization mechanism, providing scalability and prosumer data privacy. Receding horizon model predictive control allows the prosumers to adjust their planned power flows based on the wholesale energy price, and up-to-date renewable generation and load predictions.
352 citations
TL;DR: This paper comprehensively reviews the state of the art of HESSs system for MG applications and presents a general outlook of developing HESS industry.
Abstract: Energy storages introduce many advantages such as balancing generation and demand, power quality improvement, smoothing the renewable resource’s intermittency, and enabling ancillary services like frequency and voltage regulation in microgrid (MG) operation. Hybrid energy storage systems (HESSs) characterized by coupling of two or more energy storage technologies are emerged as a solution to achieve the desired performance by combining the appropriate features of different technologies. A single ESS technology cannot fulfill the desired operation due to its limited capability and potency in terms of lifespan, cost, energy and power density, and dynamic response. Hence, different configurations of HESSs considering storage type, interface, control method, and the provided service have been proposed in the literature. This paper comprehensively reviews the state of the art of HESSs system for MG applications and presents a general outlook of developing HESS industry. Important aspects of HESS utilization in MGs including capacity sizing methods, power converter topologies for HESS interface, architecture, controlling, and energy management of HESS in MGs are reviewed and classified. An economic analysis along with design methodology is also included to point out the HESS from investor and distribution systems engineers view. Regarding literature review and available shortcomings, future trends of HESS in MGs are proposed.
327 citations
TL;DR: The concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT and an application of the proposed infrastructure considering a Virtual Power Plant aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.
Abstract: The unpredictability and intermittency introduced by Renewable Energy Sources (RESs) in power systems may lead to unforeseen peaks of energy production, which might differ from energy demand. To manage these mismatches, a proper communication between prosumers (i.e., users with RESs that can either inject or absorb energy) and active users (i.e., users that agree to have their loads changed according to the system needs) is required. To achieve this goal, the centralized approach used in traditional power systems is no longer possible because both prosumers and active users would like to take part in energy transactions, and a decentralized approach based on transactive energy systems (TESs) and Peer-to-Peer (P2P) energy transactions should be adopted. In this context, the Distributed Ledger Technology (DLT), based on the blockchain concept arises as the most promising solution to enable smart contracts between prosumers and active users, which are safely guarded in blocks with cryptographic hashes. The aim of this paper is to provide a review about the deployment of decentralized TESs and to propose and discuss a transactive management infrastructure. In this context, the concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT. An application of the proposed infrastructure considering a Virtual Power Plant (VPP) aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.
285 citations
TL;DR: By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming.
Abstract: By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.
261 citations
TL;DR: In this article, a microgrid system that consists of photovoltaic, wind turbine generator, electric storage system and diesel generator is implemented to test their commercial prospects in rural communities that have no access to electricity due to economic and technical constraints.
260 citations
TL;DR: A latest nature-inspired metaheuristic optimization algorithm named Grasshopper Optimization Algorithm (GOA) is applied to an autonomous microgrid system in order to determine the optimal system configuration that will supply energy demand reliably based on the deficiency of power supply probability (DPSP) and cost of energy (COE).
258 citations
TL;DR: In this paper, the authors evaluate a fully integrated transactive system by modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation, and coupling these elements into an integrated trans-active energy simulation.
Abstract: Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone toward fully transactive energy systems. In this paper, we evaluate such a fully integrated transactive system by, first, modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), second, modeling a wholesale market and a local market, and, third, coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates that how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.
255 citations
TL;DR: A composite nonlinear controller is proposed for stabilizing dc/dc boost converter feeding CPLs by integrating a nonlinear disturbance observer (NDO)-based feedforward compensation with backstepping design algorithm with strictly guaranteed large signal stability.
Abstract: Transportation electrification involves the wide utilization of power electronics based dc distribution networks and the integration of a large amount of power electronic loads. These power electronic loads, when tightly controlled, behave as constant power loads (CPLs) and may cause system instability when interacting with their source converters. In this paper, a composite nonlinear controller is proposed for stabilizing dc/dc boost converter feeding CPLs by integrating a nonlinear disturbance observer (NDO)-based feedforward compensation with backstepping design algorithm. First, the model is transformed into the Brunovsky’s canonical form using the exact feedback linearization technique, to handle the nonlinearity introduced by the CPL. Second, the NDO technique is adopted to estimate the load power variation within a fast dynamic response, serving as a feedforward compensation to increase the accuracy of output voltage regulation. Then a nonlinear controller is developed by following the step-by-step backstepping algorithm with strictly guaranteed large signal stability. The proposed controller not only ensures global stability under large variation of the CPL but also features fast dynamic response with accurate tracking over wide operating range. Both simulations and experiments are conducted to verify the proposed strategy.
243 citations
TL;DR: An intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks that can provide significantly better fault type classification accuracy and can also detect the locations of faults, which are unavailable in previous work.
Abstract: Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
241 citations
TL;DR: A distributed secondary control scheme with a sampled-data-based event-triggered communication mechanism is proposed to achieve active power sharing and frequency regulation in a unified framework, where neighborhood sampled- data exchange occurs only when the predefined triggering condition is violated.
Abstract: This paper is concerned with active power sharing and frequency regulation in an islanded microgrid under event-triggered communication. A distributed secondary control scheme with a sampled-data-based event-triggered communication mechanism is proposed to achieve active power sharing and frequency regulation in a unified framework, where neighborhood sampled-data exchange occurs only when the predefined triggering condition is violated. Compared with traditional periodic communication mechanisms, the proposed event-triggered communication mechanism shows some prominent ability in reducing the number of communication among neighbors while guaranteeing the desired performance level of microgirds. By employing the Lyapunov–Kravovskii functional method, some sufficient conditions are derived to characterize the effects of control gains, system parameters, and sampling period on stability of microgrids. Finally, case studies on a modified IEEE 34-bus test system are conducted to evaluate the performance of the proposed distributed control scheme, showcasing its effectiveness, robustness against load changes, and plug-and-play ability.
224 citations
TL;DR: The voltage of the microgrid is controlled by using different controllers and their results are investigated, and the performance of controllers is investigated using MATLAB/Simulink SimPowerSystems.
Abstract: This paper describes the usefulness of renewable energy throughout the world to generate power. Renewable energy adds a remarkable scope in power system. Renewable energy sources act as the prime mover of a microgrid. The Microgrid is a small network of power system with distributed generation (DG) units connected in parallel. The integration challenges of renewable energy sources and the control of microgrid are described in this paper. The varied nature of DG system produces voltage and frequency deviation. The unknown nature of the load produces un-modeled dynamics. This un-modeled dynamic introduces measurable effects on the performance of the microgrid. This paper investigates the performance of the microgrid against different scenarios. The voltage of the microgrid is controlled by using different controllers and their results are also investigated. The performance of controllers is investigated using MATLAB/Simulink SimPowerSystems.
TL;DR: Comparisons with other state-of-the-art deep neural networks and traditional methods proves that the proposed method can overcome defects of traditional signal process and artificial feature selection.
TL;DR: The proposed ADPED algorithm can be adaptive to both day-ahead and intra-day operation under uncertainty and can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation.
Abstract: This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP-based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.
TL;DR: A near-optimal algorithm, named Energy Cost Optimization via Trade (ECO-Trade), is proposed, which coordinates P2P energy trading among the smart homes with a Demand Side Management (DSM) system and shows that cost savings do not always increase linearly with an increase in the renewables and storage penetration rate.
TL;DR: The challenges of DC microgrid protection are investigated from various aspects including, dc fault current characteristics, ground systems, fault detection methods, protective devices, and fault location methods.
Abstract: DC microgrids have attracted significant attention over the last decade in both academia and industry. DC microgrids have demonstrated superiority over AC microgrids with respect to reliability, efficiency, control simplicity, integration of renewable energy sources, and connection of dc loads. Despite these numerous advantages, designing and implementing an appropriate protection system for dc microgrids remains a significant challenge. The challenge stems from the rapid rise of dc fault current which must be extinguished in the absence of naturally occurring zero crossings, potentially leading to sustained arcs. In this paper, the challenges of DC microgrid protection are investigated from various aspects including, dc fault current characteristics, ground systems, fault detection methods, protective devices, and fault location methods. In each part, a comprehensive review has been carried out. Finally, future trends in the protection of DC microgrids are briefly discussed.
TL;DR: The optimal load dispatch of community microgrid with deep learning based solar power and load forecasting achieves total costs reduction and system reliability improvement.
TL;DR: Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.
TL;DR: A comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids is presented, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones.
TL;DR: In this paper, the minimum cost of energy (COE) for five different global locations (Squamish, Canada, Los Angeles and Golden, USA; and Brisbane and Adelaide, Australia) based on renewable energy systems was determined.
TL;DR: In this paper, a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids.
Abstract: Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.
TL;DR: A model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.
Abstract: In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc–dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.
TL;DR: A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real- time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price.
Abstract: This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.
TL;DR: An optimal distributed control strategy for the coordination of multiple distributed generators in an islanded microgrid (MG) is proposed and a secondary voltage control approach is presented to regulate the average voltage magnitude of all distributed generators to the desired value and achieve accurate reactive power sharing.
Abstract: This paper proposes an optimal distributed control strategy for the coordination of multiple distributed generators in an islanded microgrid (MG). A finite-time secondary frequency control approach is developed to eliminate the frequency deviation and maintain accurate active power sharing in a finite-time manner. It is demonstrated that the traditional distributed control approach with asymptotical convergence is just a special case of the proposed finite-time control strategy under the specific control parameter settings. Then, a secondary voltage control approach is presented to regulate the average voltage magnitude of all distributed generators to the desired value and achieve accurate reactive power sharing. The implementation of the proposed distributed control strategy only requires information exchange among neighboring local controllers through a sparse communication network. Simulations with an islanded MG testbed built in MATLAB/Simulink are conducted to validate the effectiveness of the proposed distributed control strategy.
TL;DR: Different energy management strategies have been presented as energy management plays very important role in optimizing the size and rating of energy storage system and their maximum utilization.
Abstract: Due to inherent advantages of DC system over AC system such as compatibility with renewable energy sources, storage devices and modern loads, Direct Current Microgrid (DCMG) has been one of the key research areas from last few years. The power and energy management in the DCMG system has been a challenge for the researchers. MG structure and control strategies are the integrated part of the power and energy management system. This paper covers all the aspects of the control of DCMG, whether it is DC bus voltage, power or energy related. Different MG Structures with their comparative analysis has been given in this paper. Various control schemes: Basic control schemes like centralized, decentralized and distributed control and multilevel control scheme such as hierarchal control has been discussed. The Power management in grid-connected, Islanded mode and transition mode has been presented. Different energy management strategies have been presented as energy management plays very important role in optimizing the size and rating of energy storage system and their maximum utilization. The energy management of a battery and super capacitor based HESS in all configurations has also been discussed and finally, future trends in further research are presented.
TL;DR: A novel distributed control algorithm for current sharing and voltage regulation in DC microgrids is proposed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weights of the voltage references.
Abstract: In this paper, a novel distributed control algorithm for current sharing and voltage regulation in DC microgrids is proposed. The DC microgrid is composed of several distributed generation units, including buck converters and current loads. The considered model permits an arbitrary network topology and is affected by an unknown load demand and modeling uncertainties. The proposed control strategy exploits a communication network to achieve proportional current sharing using a consensus-like algorithm. Voltage regulation is achieved by constraining the system to a suitable manifold. Two robust control strategies of sliding mode type are developed to reach the desired manifold in a finite time. The proposed control scheme is formally analyzed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weighted average of the voltage references.
TL;DR: In this article, the authors proposed a learning-based approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price, which is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost.
Abstract: Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.
TL;DR: This paper considers the distributed load sharing problem of the microgrids operating in autonomous mode under FDI, and constructs an FDI attack model, where the attacker is capable of injecting false data into the bus agents.
Abstract: In microgrids, distributed load sharing plays an important role in maintaining the supply–demand balance of power. Because false data injection (FDI) is one of the crucial threats faced by future microgrids, the study of the impact of FDI on distributed load sharing is both of theoretical merit and practical value. In this paper, we consider the distributed load sharing problem of the microgrids operating in autonomous mode under FDI. Each bus is assumed to be equipped with an agent. Under a well-developed distributed load sharing protocol based on multiagent systems, we first construct an FDI attack model, where the attacker is capable of injecting false data into the bus agents. Then, a utilization level is introduced for coordinating generators, and its variation is evaluated in the presence of FDI attacks with given injection strategies. The stable region of the microgrid is defined, and conditions are given to determine stability. Finally, theoretical results are validated on the Canadian urban distribution system.
TL;DR: The proposed framework takes into account a realistic formulation to minimize the total microgrid costs in both grid-connected and multiperiod islanded modes and a stochastic framework based on unscented transform to model the uncertainties associated with renewable energy sources output power, market energy price, and load demand.
Abstract: This paper addresses the optimal operation and scheduling of reconfigurable microgrids incorporating the dynamic line rating limitations during the islanded and grid-connected mode operations. The incorporation of the dynamic line rating of overhead feeders can potentially improve the system security when providing economical and technical benefits for the microgrid. The proposed framework takes into account a realistic formulation to minimize the total microgrid costs in both grid-connected and multiperiod islanded modes. Also, a stochastic framework based on unscented transform is proposed to model the uncertainties associated with renewable energy sources output power, market energy price, and load demand, as well as the weather uncertain parameters such as solar radiation, wind speed, and ambient temperature. Due to the high nonlinearity and complexity of the proposed problem, an efficient optimization algorithm based on the collective decision optimization algorithm is proposed. A new two-stage modification method is also developed to improve the algorithm search ability and avoid premature convergence. The proposed problem is examined on the IEEE 32-bus microgrid test system. The simulation results show the effectiveness of the proposed model and verify its economic and reliability merits.
TL;DR: A thorough review of the primary control of interfacing converters to integrate the power quality compensation are presented, with a focus on the hybrid AC/DC microgrid harmonics compensation and unbalance compensation.
Abstract: Today, conventional power systems are evolving to smart grids, which encompass clusters of AC/DC microgrids, interfaced through power electronics converters. In such systems, increasing penetration of the power electronics-based distributed generations, energy storages, and modern loads provide a great opportunity for power quality control. In this paper, an overview of the power quality control of smart hybrid AC/DC microgrids is presented. Different types of power quality issues are studied first, with consideration of real-world hybrid microgrid examples, including data centers, electric railway systems, and electric vehicles charging stations. It shows that compared to traditional centralized power quality compensations, smart interfacing power converters from distributed generations, energy storages, and loads, and the AC and DC subgrids interfacing converters are promising candidates for power quality control. To realize the smart interfacing converters' power quality control, both primary converters control and secondary system coordination are required. In this paper, a thorough review of the primary control of interfacing converters to integrate the power quality compensation are presented, with a focus on the hybrid AC/DC microgrid harmonics compensation and unbalance compensation. For multiple interfacing converters, the secondary control with system-level coordination and optimization for harmonics and unbalance compensation (considering both unbalance and harmonics in single-phase and three-phase systems) are also presented. Challenges like low switching frequency of interfacing converters, parallel interfacing converters operation, and interfacing converters communications are discussed, and typical solutions for primary and secondary controls to deal with them are presented. The paper also includes rich case study results.
TL;DR: An overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs is provided, specifically, inner loop and droop control approaches in primary control are reviewed.
Abstract: With the rapid development of power electronics technology, microgrid (MG) concept has been widely accepted in the field of electrical engineering. Due to the advantages of direct current (DC) distribution systems such as reduced losses and easy integration with energy storage resources, DC MGs have drawn increasing attentions nowadays. With the increase of distributed generation, a DC MG consisting of multiple sources is a hot research topic. The challenge in such a multi-source DC MG is to provide voltage support and good power sharing performance. As the control strategy plays an important role in ensuring MG’s power quality and efficiency, a comprehensive review of the state-of-art control approaches in DC MGs is necessary. This paper provides an overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs. Specifically, inner loop and droop control approaches in primary control are reviewed. Centralized, distributed, and decentralized approach based secondary control is discussed in details. Key findings and future trends are also presented at last.