Microgrid Power Research Articles

Advancing microgrid power quality: integration of GRU-based control in PV-UPQC systems

Advancing microgrid power quality: integration of GRU-based control in PV-UPQC systems

Ultra-short-term prediction of microgrid source load power considering weather characteristics and multivariate correlation

Multiple microgrids interconnect to form a microgrid cluster. To fully exploit the comprehensive benefits of the microgrid cluster, it is imperative to optimize dispatch based on the matching degree between the sources and loads of each microgrid. The power of distributed energy sources such as wind and photovoltaic systems and the sensitive loads in microgrids is related to the regional weather characteristics. Given the relatively small geographical scope of microgrid areas and the fact that distributed energy sources and loads within the grid share the same weather characteristics, simultaneous ultra-short-term forecasting of power for both sources and loads is essential in the same environmental context. Firstly, the introduction of the multi-variable uniform information coefficient (MV-UIC) is proposed for extracting the correlation between weather characteristics and the sequences of source and load power. Subsequently, the application of factor analysis (FA) is introduced to reduce the dimensionality of input feature variables. Drawing inspiration from the concept of combination forecasting models, a combined forecasting model based on Error Back Propagation Training (BP), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory Neural Network (BiLSTM) is constructed. This model is established on the MV-UIC-FA foundation for the joint ultra-short-term forecasting of source and load power in microgrids. Simulation is conducted using the DTU 7K 47-bus system as an example to analyze the accuracy, applicability, and effectiveness of the proposed joint forecasting method for sources and loads.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately, a hybrid power prediction method is presented in this paper. First, the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network (BiGRU) to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the hyperparameters of the combined prediction model. The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively. In addition, considering the coordinated utilization of various energy sources such as electricity, hydrogen, and renewable energy, a multi-objective optimization model that considers both economic and environmental costs was constructed. A two-stage adaptive multi- objective quantum particle swarm optimization algorithm aided by a Lévy flight, named MO-LQPSO, was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system. This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems. The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

Control of Power Converters in Micro Grids

Owing to several operational drawbacks in the existing power management schemes for interlinked AC-DC micro grids which are either concerned about only on sharing power or regulation of voltage but not on both, so as to overcome these issues this paper is proposed. This proposed autonomous power management scheme will consider the specific loading condition of the DC micro grid before importing power from the interlinked AC microgrid. This strategy not only enables voltage regulation in the DC micro grid but also reduces the number of converters in operation which will in turn reduces the power transfer losses. Theproposed scheme is fully self governed as it holds on the plug-nplay features for generators and tie-converters. The performance of the proposed control scheme has been validated under different operating scenarios. The results reveal the usefulness of the proposed scheme in managing the shortcoming of power in the DC microgrid efficiently and autonomously, on the other hand maintaining the better voltage regulation in the DC micro grid. In this project a DG interfacing network and its control also to be simulated to analyze the system stability The results are verified through MATLAB/SIMULINK environment.

Quantitative Representation of Disturbance Waveform for Microgrid Connected PCC Voltages Using Improved Atomic Decomposition

The fluctuation of microgrid power flow leads to serious voltage problems at the point of common coupling (PCC). The quantitative representation of the disturbance parameters of the voltage waveform at the PCC is necessary for evaluating and controlling the impact of distributed generation in the microgrid on the power system. An improved atomic decomposition (IAD) method is proposed to represent the disturbance parameters quantitatively and efficiently. Based on the disturbance characteristics of the PCC voltage, a coherent atom dictionary composed of four subdictionaries is constructed to improve the decomposition efficiency. To further improve the computational efficiency, an improved matching pursuits algorithm is proposed by alternating the search way to extract the disturbance components in the atomic decomposition. Meanwhile, simulation results show that the proposed IAD method has better antinoise and disturbance parameters quantization ability than wavelet transform.

A secure and highly efficient blockchain PBFT consensus algorithm for microgrid power trading

There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput.

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Design of daily load profiles in environmentally friendly commercial and industrial microgrids

The purpose of the study is to solve the problem of design of daily load profiles for optimal control of an environmentally friendly commercial and industrial microgrid (CIM) (power is generated only by renewable energy sources) connected to the power system by a power transmission line. This goal is achieved by adjusting the planned daily load profiles of consumers located on the territory of the CIM. The adjustment means shifting power consumption to another time of the day in question (power consumption is delayed). The problem of the delayed power allocation is represented as an optimization multiple knapsack problem that adapts to the problem-solving process. The proposed algorithm was tested on a 6-node system according to the scenario that involved adjustment of the load profile in the CIM to ensure that the power flow from the power system remains within specified limits. Compliance with the limits guarantees uninterrupted power supply from the power system, which is a fundamental requirement when developing load profiles. Experiments were carried out to evaluate the delivery of uninterrupted power supply to CIM consumers depending on the initial data. The findings indicate that the disruptions in power supply to CIM consumers are completely eliminated if the load can be divided to shift it to other hours of the day and when the load of 0.151 MW is disconnected in operating state 7. The total load should be divided into at least three parts. Disconnection of 0.151 MW is performed to prevent disconnection of the commercial and industrial microgrid from the power system, which would result in a power deficit of 4.652 MW.

Microgrid Power Quality Enhancement with Adaptive Control Strategies: A Literature Survey

Microgrid Power Quality Enhancement with Adaptive Control Strategies: A Literature Survey

Optimization and privacy protection of microgrid power trading system based on attribute encryption technology

This paper presents an effective technique and approach to deal with the dual challenges of performance optimization and privacy protection in microgrid power trading systems (MPTS) by utilizing attribute encryption technology. By embedding advanced cryptographic techniques into the operational substrate of microgrids, we introduce a novel approach to dramatically enhance the efficiency of energy distribution, while guaranteeing the privacy protection and integrity of participant data. The core objective of this technique is the application of attribute-based encryption (ABE), a method that offers fine-grained access control, ensuring sensitive information is made available only to eligible users based on their attributes, rather than their identities. In doing so, it meets the important requirement of securing data, without impairing the overall productivity of a power trading system. This paper presents a novel technique of ABE in the domain of MPTS, but also quantifies, through extensive theoretical analysis and simulations, how this integration leads to superior energy resource allocation and lower operational costs.

A Unified Data Profile for Microgrid Loads, Power Electronics, and Sustainable Energy Management with IoT

Controllable devices in a classical centralized grid work independently, providing desired functionalities for the owner only, making grid stability and efficient energy management challenging. Therefore, the dynamically developing communication infrastructure has been crucial in improving local energy management and stability, introducing the Internet of Things and, finally, creating micro- or smart grids. Communication technology already allows for exchanging data and information with high bandwidth in practical industrial and grid applications. However, considering the increasing number of electrical devices with different purposes, parameters, and possibilities to work as an energy source or storage, the challenge is device profile standardization, especially for power electronics devices. As many devices as possible should be able to exchange information with the grid operator or local area/home energy management device, like industrial agents, energy routers, or smart transformers. The number and types of parameters, outputs, and input signals available in the devices from the communication point of view are significantly different, making it extremely difficult to apply the advanced energy management algorithm. Therefore, the unified data profile for modern loads was developed and discussed. The proposed device model was standardized, including mandatory information about the device’s work and parameters, with the possibility of reading the control commands from the supervisor device. The proposed unified model was studied in simulation research, integrating self-balanced and self-control areas.

Optimal demand management in the electrical microgrid considering energy market prices uncertainty

The optimal management of electrical energy systems is a major concern for energy providers facing uncertainties. In this work, energy demand management and dispatch in the electricity microgrid are performed using an interval optimization strategy under conditions of electricity price uncertainty. Demand dispatch is implemented through the Demand Response Program (DRP) incentive modeling. As a solution, incentive modeling based on the price of the offer to reduce electricity demand during peak periods is proposed. In order to minimize the effects of electricity price uncertainty, the interval optimization approach is used. The main goal of the proposed approach is to minimize the operational cost, and the epsilon constraint-based fuzzy method was used to solve and handle the problem. Finally, the power microgrid was used in different case studies for the results of the numeric simulations and for the positive effects of the proposed modeling under conditions of uncertainty.

Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous studies, the disparity between predicted and actual generation remains a significant issue. This study utilizes historical power and atmospheric data from diverse plants, employing preprocessing techniques to enhance quality and reduce noise. K-Means clustering is applied to the dataset, optimizing deep learning training periods and increasing accuracy. A resilient hybrid deep learning model is proposed for microgrid (MG) power forecasting, encompassing preprocessing, model training, and assessment stages. Mathematical models for PV systems, battery storage, and wind systems, along with a K-means clustering algorithm, contribute to accurate forecasting. The recurrent neural network based on gated recurrent unit architecture outperforms traditional algorithms, demonstrating superior accuracy, and reduced errors in extensive experimental analyses. Pearson coefficients reveal associations between different power production forms, emphasizing the potential of hybrid renewable energy clusters to enhance forecasting. Case studies illustrate the partial controllability of concentrated solar power production, reducing overall renewable energy cluster unpredictability. The proposed method showcases the efficacy of the hybrid model in addressing challenges and improving accuracy in large-scale power forecasting.

Hybrid energy storage configuration method for wind power microgrid based on EMD decomposition and two-stage robust approach

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy.

DG Compensation Using Optimal Distribution in Microgrids with High DG Penetration

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considerssix various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effectivethan itspeers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.