Microgrid Power Research Articles

Cascading failures in networks of heterogeneous node behavior.

Variability in the dynamical function of nodes comprising a complex network impacts upon cascading failures that can compromise the network's ability to operate. Node types correspond to sources, sinks, or passive conduits of a current flow, applicable to renewable electrical power microgrids containing a variable number of intermittently operating generators and consumers of power. The resilience to cascading failures of ensembles of synthetic networks with different topology is examined as a function of the edge current carrying capacity and mix of node types, together with exemplar real-world networks. While a network with a homogeneous composition of node types can be resilient to failure, onewith an identical topology but with heterogeneous nodes can be strongly susceptible to failure. For networks with similar numbers of sources, sinks, and passive nodes the mean resilience decreases as networks become more disordered. Nevertheless all network topologies have enhanced regions of resilience, accessible by the manipulation of node composition and functionality.

Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

A Wasserstein based two-stage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties

Combined cooling, heating and power (CCHP) micro-grids are getting increasing attentions due to the realization of cleaner production and high energy efficiency. However, with the features of complex tri-generation structure and renewable power uncertainties, it is challenging to effectively optimize the operation of CCHP micro-grid. This paper proposed a novel Wasserstein based two-stage distributionally robust optimization (WTSDRO) model for the day-ahead optimal operation of CCHP micro-grid. The uncertainties of wind power (or other renewable energy sources with random power output) forecasting errors are modeled as an ambiguity set based on Wasserstein metric, which is assumed to contain all the possible probability distributions with a confidence level. In the first stage, CCHP micro-gird’s operation cost is minimized according to the forecast information. In the second stage, for hedging against the perturbation of random wind power outputs, flexible resources are adjusted under the worst-case distribution within the ambiguity set. Multiple demand response programs (DRPs) are integrated to make electrical, thermal and cooling loads controllable. Finally, a reformulation approach is proposed based on strong duality theory, which equivalently transforms the WTSDRO model into a tractable MILP framework. Simulations implemented on a typical-structure CCHP micro-grid are delivered to show that our proposed model: (1) is data-driven and keeps both of the conservativeness and computational time at relatively low levels, (2) reaches effective operation results in terms of cost optimization, wind power accommodation and waste heat utilization etc. Moreover, operation cost and CO2 emission can be further saved by integrating multiple DRPs.

Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia

Challenging frequency control issues, such as the reliability and security of the power system, arise when increasing penetration levels of inverter-interfaced generation are imposed. As a result of the displacement of convention generation in favour of renewable energy sources, the reduction of frequency response capabilities can be seen. A promising method of overcoming the aforementioned challenges is to utilise Battery Energy Storage Systems (BESS), which provides frequency support by injecting instantaneous power to the grid and back up the conventional generation systems. However, large battery systems increase the cost while inadequate battery capacities result in poor performance. This paper, therefore, proposes an approach for finding the optimum BESS size for an islanded microgrid power system. The determination of the optimum BESS size is based on an existing case study, under which the most severe contingencies of generation loss and load loss have been accounted for, as well as different levels of penetration of renewable energy sources. As a result of using meta-heuristic optimization (Grey Wolf Optimization), the constraint optimization problem has been identified as BESS sizing. Through the use of real-time simulation DIgSILENT PowerFactory software, estimated BESS size can be applied to a standalone microgrid to test the frequency of support capabilities. The simulation has made it apparent that through the selection of the optimum BESS size, the system frequency response is not only mitigated, but improved.

Multi-agent approach to modeling and simulation of microgrid operation with vehicle-to-grid system

This study presents the modeling and simulation of a vehicle-to-grid (V2G) system within a microgrid considering the requirements of various components of the microgrid system such as distributed renewable energy resources (DERs), plug-in electric vehicles (PEVs) and non-PEV loads. The system modeling is carried out using an agent-based methodology where components of the microgrid are modeled as agents. The use of PEV car batteries collectively as a virtual power plant (VPP) enables PEVs to not only act as loads but as energy sources alongside DERs such as wind and solar power generation. The smart control and scheduling of the charging and discharging of the PEVs by the charging station can be used to achieve sustainable integration of a high number of PEVs in the microgrid power system. In addition to simulating a microgrid operation, results of this study show how agents’ behavior change based on factors such as penetration of renewable energy, penetration of PEVs, travel pattern of PEV drivers and price of energy generation.

Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid

With the development and utilization of distributed energy and microgrid, distributed energy storage has become a new development trend. However, small pumped storage units have the advantages of flexible engineering location, low investment, quick effect, low requirements on transmission lines, and a better solution to the peak load demand of the system. Therefore, it is more and more used in the microgrid, and it conducts joint dispatching with wind power, photovoltaic, and other clean energies. To solve the capacity problem of small pumped storage units within the microgrid, a new control strategy is proposed in this paper. Two pumped storage units are used for joint operations. Taking the smoothed combined output power of wind power, photovoltaic power, and pumped storage power as the target, and considering the limitations of transmission lines, the constraints of wind power and photovoltaic power fields as well as the restrictions of pumped storage power units and corresponding reservoirs are taken into account. In this paper, social particle swarm optimization (SPSO) with improved weight is used to calculate and solve the model. The effectiveness of the new control strategy is verified.

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control.

Virtual Impedances Optimization to Enhance Microgrid Small-Signal Stability and Reactive Power Sharing

Microgrid instability poses critical issues to the power delivery following a load change or a tripping event. In island operating mode lack of grid intensifies this challenge. This study aims at controlling several converter-based distributed generations (DG) sharing the power in an island microgrid (MG). At first, the microgrid model including virtual impedances and phase-locked loop (PLL) is introduced. Afterwards a novel small-signal stability analysis for island microgrids is proposed. Finally, an optimization algorithm based on particle swarm optimization (PSO) is proposed to design the virtual impedances. The optimization algorithm analyzes all possible operating points and aims at maximizing the microgrid stability index while keeping the reactive power mismatches at minimum level. The fractional objective function facilitates reaching at these objectives simultaneously. The proposed optimization algorithm is implemented in two separate case-studies and the corresponding virtual impedances are drawn in any microgrid. On the other hand, The voltage drops are checked as a condition in the optimization process. The results drawn from two separate case-studies verify that the proposed algorithm effectively maximizes the microgrid stability index and minimizes the reactive power mismatches.

Bidirectional DC–AC Converter-Based Communication Solution for Microgrid

Abstract The communication system of a microgrid can transfer the information of electricity price, power consumption and so on between users and the control centre. This capability is of great significance to improve the efficiency and sustainability of power facilities. In this paper, a bidirectional DC–AC converter topology is proposed to achieve the composite transmission of power and signals in microgrids. Since the transmitted signals are modulated by power switches of converters and integrated into the currents, the cost of signal couplers can be saved and the circuit structure can be simplified. In order to verify the feasibility of the proposed method, a simulation model of the proposed converter is implemented in MATLAB/Simulink. With the power supply frequency of 50 Hz, when the converter operates in the inverter mode and rectifier mode, the data transmission rate can reach 120 bit/s and 48 bit/s, respectively.

Modified WCA-Based Adaptive Control Approach Using Balloon Effect: Electrical Systems Applications

This paper suggests an adaptive control approach using water cycle algorithm optimization method (WCA) modified by a new technique called “Balloon Effect”. The main concept of Balloon Effect is to increase the ability of the WCA to tune individually the gains of the controller online. The proposed technique has been used to tune the gains of PID controller and applied to control both of the load frequency control in micro-grid power system (which minimize the fundamental frequency variations) and the position of armature-controlled DC motor. Digital simulations have been carried out to prove the efficiency of the proposed adaptive controller-based WCA with Balloon Effect. The obtained results showed that the proposed adaptive controller maintains robust performance, and effectively reduces the impact of identified disturbances and uncertainties.

Decentralized Control of Voltage- and Current-Controlled Converters Based on AC Bus Signaling for Autonomous Microgrids

This article proposes a fully decentralized control approach, based on AC bus signaling, to integrate the operation of voltage- and current-controlled converters that exist in an isolated low-voltage microgrid, so they may be fully steered under grid-feeding, grid-supporting, and grid-forming control principles. The proposed strategy, devised by classic and modified droop-based controllers, allows control of the microgrid active power relating to the system frequency, while regulating the reactive power related to the voltage, dispensing any need for communication infrastructures. Beyond ensuring proper microgrid power balance at all times, the control strategy prioritizes energy extraction from non-dispatchable sources (i.e., photovoltaic-based systems), whereas it uses dispatchable sources (i.e., battery-based systems) to share active and reactive power proportionally to their capabilities. As a consequence of the proper and novel management of battery-based converters, battery overvoltage and overcurrent are avoided, supporting a prolonged lifespan. Simulation results considering an autonomous microgrid operating under several scenarios are presented, to demonstrate the capabilities of the proposed control scheme on steering the different topologies of converters.

Simultaneous Identification of Multiple Control Loops in DC Microgrid Power Converters

Power electronic converters used in a dc microgrid environment are usually equipped with several control loops. When many converters are connected to a common dc bus, the performance of some loops can be different from the behavior designed for the stand-alone converter, depending on the number, topology, and control of the interconnected converters. In order to establish the performance of each loop in real-time, it is important to continuously monitor the loop gains. This article presents the application of an online identification technique in rapid and simultaneous measurement of several control loops in dc microgrid power converters. To this end, multiple orthogonal pseudorandom binary sequences (PRBSs) are simultaneously injected into different loops of power converters. Since the frequency components excited by each PRBS are unique and different from any other, multiple PRBSs can be injected at the same time. This will allow to measure all the loop gains in only one measurement cycle. Hence, the operating condition of the system is kept unchanged during the measurement, which is an important factor to achieve accurate monitoring. The online identification method is applied to an experimental dc microgrid prototype, composed of three droop-controlled converters. After identifying different control loops, an adaptive tuning algorithm is implemented to adjust the digital compensator of each loop, in order to reach the desired dynamic performance. The reported experimental results confirm the accuracy and the applicability of this technique.

Frequency performance analysis of multi-gain droop controlled DFIG in an isolated microgrid using real-time digital simulator

With the increased share of wind power in Microgrid (MG), wind farm is expected to have significant contribution in overall grid frequency regulation. Wind farm impersonating synchronous generator with conventional droop control method may threaten the grid stability during low frequency oscillations. In this paper, synthetic inertia with a novel frequency dependent multi-gain droop control strategy is proposed to regulate wind farm for providing primary frequency response. The proposed multi-gain droop control is combined with a proportional and proportional-integral type pitch angle controller for comparative analysis. A 20% power reserve is conserved for primary frequency regulation. The influence of multi-gain parameters (power-frequency) and numerous power margin of wind farm is also examined under different contingency events. The relative performances of the proposed control scheme are investigated in a small isolated MG with 20.8% and 41%wind penetration in real-time. Simulation results show that it is possible to achieve optimum frequency control performance with the proposed multi-gain droop control scheme compared with the conventional droop control. It is also found that parameter selection for multi-gain droop control portrays significant domination on the overall frequency control outcome. Moreover, the proposed multi-gain droop method performs satisfactorily at different power margin of wind farm.

Battery Monitoring and Control System for Photovoltaic based DC Microgrid

Abstract This paper presents a battery control and monitoring strategy for a DC microgrid feed by a public utility (PU) photovoltaic (PV) including with multi-battery bank (BB). The BBs respond to the changes in a power imbalance between generation and demand within a DC micro-grid, to maintain the micro-grid voltage and reliability enhancement. The fuzzy model reference learning control (FMRLC) controller has been designed to power control of the battery bank. The proposed battery control and monitoring system (BCMS) strategy keep the battery charging and discharging power as per standard charging/discharging characteristics of the battery. The BCMS allows continuous monitoring and intelligent control of distribution system operations such as battery bank energy storage (BBES), the PV system and the electric load of the consumer. A power sharing between the sources has been controlled for smooth supply. A graphical user interface (GUI) in the MATLAB environment has been configured to demonstrate the control and monitoring of the DC micro-grid and the operation performance of the BCMS algorithm for various scenarios (i. e modes). The proposed BCMS shows better operational performance for various operating conditions in terms of monitoring and control of the batteries power in the grid-connected DC micro-grid.

Phase Balancing and Reactive Power Support Services for Microgrids

Alternating current (AC) microgrids are expected to operate as active components within smart distribution grids in the near future. The high penetration of intermittent renewable energy sources and the rapid electrification of the thermal and transportation sectors pose serious challenges that must be addressed by modern distribution system operators. Hence, new solutions should be developed to overcome these issues. Microgrids can be considered as a great candidate for the provision of ancillary services since they are more flexible to coordinate their distributed generation sources and their loads. This paper proposes a method for compensating microgrid power factor and loads asymmetries by utilizing advanced functionalities enabled by grid tied inverters of photovoltaics and energy storage systems. Further, a central controller has been developed for adaptively regulating the provision of both reactive power and phase balancing services according to the measured loading conditions at the microgrid’s point of common coupling. An experimental validation with a laboratory scale inverter and a real time hardware in the loop investigation demonstrates that the provision of such ancillary services by the microgrid can significantly improve the operation of distribution grids in terms of power quality, energy losses and utilization of available capacity.

Peer-to-Peer Energy Trading in DC Packetized Power Microgrids

As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assistance of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.

A decoupled extended power flow analysis based on Newton-Raphson method for islanded microgrids

Recently, microgrid (MG) power flow (PF) studies have gained a lot of attention due to the emergence of autonomous MGs which feature distributed generations (DGs). There are some inherent limitations in the islanded operation mode of MGs which cannot be addressed by conventional PF methods. In this regard, recent studies have proposed some updates to the conventional approaches by inclusion of network frequency as a variable in their modeling and omission of the slack bus from the grid. These considerations specifically in the Newton-Raphson (NR) method change the Jacobean matrix (JM) formulations. This paper concentrates on improving the previous NR methods for MGs which include droop controlled DGs. For this purpose, the partial derivatives of both calculated and scheduled powers are considered in the Taylor series expansion of bus power injections. Thus, the convergence and accuracy of the PF method are enhanced by adding these derivatives in modeling of generations, loads and losses. Moreover, these extended PF equations are decoupled by reformulating the JM based on the consideration that the lines in MGs are mostly resistive, which results in simplified JM calculations and improved convergence speed. The effectiveness of the proposed decoupled extended NR (DENR) method for MG PF analysis is illustrated in several case studies including 6-bus and 38-bus networks. Moreover, two convergence enhancement methods are also incorporated into the proposed approaches and their merits are investigated.

Distributed energy storage system‐based nonlinear control strategy for hybrid microgrid power management included wind/PV units in grid‐connected operation

Distributed energy storage system‐based nonlinear control strategy for hybrid microgrid power management included wind/PV units in grid‐connected operation

A Study on Optimal Design Feasibility of Microgrid Power System for Rural Electrification: Amhara Region in Ethiopia

Power system is an essential energy domain in recent years which helps to converts non-electrical energy resources, such as hydraulic, thermal, solar, wind and other natural resources to electrical energy. It conveys the generated power to the consumers via transmission and distribution networks. The conventional power system has many problems, which are significant power loss at the transmission and distribution networks, poor power quality and reliability, and ultimately it is not an environmental friendly. These problems are resolved by using a microgrid which will provide electricity to the consumer economically with improved power quality, reliability, and minimum loss by integrating and optimizing different renewable energy sources. The main objective of this research study is to enable the optimal power provision and feasibility to design a microgrid. Based on this objective of Micro grid power system, the study has extended to deliver electricity to satisfy the location of Ethiopia, Bahir Dar Town, specifically the rural electrification as a model for the interior village Abay Mado-Gedro kebele primary school, health post and local communities demand by enhancing the power quality, reliability and minimum transmission / distribution line losses. The microgrid consists of solar, wind and battery storage sources. It is designed to operate in stand-alone mode of operation. Optimum designing and sizing of different components of the microgrid is taken as major contributions of this research work to the study for the Village Gedro as a rural electrification model. Hence it is observed through the analysis and design using HOMER Optimization tool, the total power consumptions for the site of optimal power is 25 kWh / day and 6 kW peak with the consideration of various environmental parameters like solar radiation, temperature and wind speed. Based on the optimal power energy consumption resulted out that the required various power resources are 7 kW Photo-Voltaic (PV), 3 kW Wind turbine, 104 kWh storage battery and 6 kW converter with the total investment cost $ 75993.