Stable Operation Of Microgrid Research Articles

Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

ADPA Optimization for Real-Time Energy Management Using Deep Learning

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

A novel weighted exponential sliding mode controller with a modified reaching law for the frequency regulation of a renewable integrated isolated AC microgrid

This paper investigates the development and application of a novel hybrid controller through novel sliding surface and novel reaching law, Weighted Exponential Sliding Mode Control with Modified Reaching Law (WESMC-MRL), for frequency regulation in an isolated Microgrid (MG) with intermittent Renewable Energy Sources (RESs) such as solar and wind. Major contributions include the analysis of frequency regulation of isolated MG controlled by WESMC-MRL under various load perturbations. The performance of WESMC-MRL is compared with conventional Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Integral Sliding Mode Control (ISMC) controllers. Outcomes indicate that the WESMC-MRL effectively regulates the MG frequency with minimal chattering, and outperforms LQR SMC, and ISMC in terms of settling time, overshooting, and undershooting in frequency deviation. With the WESMC-MRL the maximum frequency deviation is found only 0.00724 Hz and undershoot is negligible. The proposed controller is also robust against RES integration variability, disturbances, and nonlinearities making it a promising solution for reliable and stable MG operation.

Energy Management Strategy for Wind Solar Storage Microgrid Based on Improved Ant Lion Optimizer

The energy management of microgrids involves optimizing the capacity configuration, which significantly impacts the economic and stable operation of microgrids. This paper presents a control strategy for microgrid operation that effectively manages distributed power sources and energy storage to optimize capacity configuration. A mathematical optimization model for microgrid energy management is established considering minimum annual cost and optimal scale constraints. The traditional Ant Lion Optimizer (ALO) is improved by using dynamic weight coefficients and chaotic mapping to enhance the diversity of the population and improve the convergence of the algorithm. This can effectively avoid local optimal solutions and premature problems, and improve the convergence speed and search ability of the ALO algorithm. Based on this control strategy and improved ALO algorithm, simulations and tests were conducted using actual data from an independent microgrid, resulting in the optimal solution for the microgrid capacity allocation model. The case study results validate the practicality of the proposed microgrid operation control strategy as well as the superiority of the improved ant colony optimization algorithm.

Microgrid energy management strategy based on LSTM-DDQN

Abstract This paper introduces a novel algorithm, LSTM-DDQN, designed to address the stochastic economic dispatch problem in microgrids. The inherent variability and randomness of photovoltaic (PV) energy pose challenges for accurate output prediction. Traditional approaches, including physical and statistical methods, often fall short in terms of accuracy. For microgrids with a high proportion of PV generation, we propose a new algorithm that combines Long Short-Term Memory (LSTM) neural networks with the Double Deep Q-Network (DDQN) algorithm. The algorithm leverages the LSTM model to capture the uncertainty of the learning environment and optimizes the Q-value iteration rules in the DDQN algorithm, thereby enhancing the training speed of the neural network. Experimental results demonstrate the algorithm’s remarkable dispatch capabilities in managing the interplay between PV generation, battery capacity, and overall load demand. This algorithm provides robust support for the stable operation of microgrids.

Bibliometric Analysis of Microgrid Control Strategy from 2007 to 2022 Based on CiteSpace

Efficient and reliable control strategy is the key to the stable operation of microgrids. Most studies on microgrid control strategy are from single perspectives, lacking integrated views. Thus, we use CiteSpace to process a multidimensional bibliometric analysis to depict the publication statistics, collaboration dynamics, research status, and development trends; we also construct a panorama knowledge framework and show future research features. We discover that microgrid control strategy is a hot domain with global cooperation. Besides, electrical and electronic engineering is the most distinctive discipline, and droop control is one of the most critical control methods. In addition, future studies may focus on engineering application and sustainability; some ongoing hotspots have generated new trends, such as battery, stability analysis, and renewable energy sources. This research is innovative; we provide comprehensive and integrated knowledge details, show the clear theoretical logical structure, and pinpoint future practical frontiers: the research is objective, quantitative, full-scale, multidimensional, and future-oriented, making up for the shortcomings of previous review studies. The results provide valuable theory and practice references for scholars new and interested in this field.

Machine-Learning-Based Adaptive Settings of Directional Overcurrent Relays With Double-Inverse Characteristics for Stable Operation of Microgrids

Machine-Learning-Based Adaptive Settings of Directional Overcurrent Relays With Double-Inverse Characteristics for Stable Operation of Microgrids

CuEMS: Deep reinforcement learning for community control of energy management systems in microgrids

Nowadays, energy management assumes a pivotal role in ensuring the stable operation of microgrids, facilitating enhanced monitoring, analysis, and optimization of energy utilization for end-users. The implementation of an energy management system (EMS) offers invaluable assistance to microgrid users, enabling them to cut electricity costs, ensure reliability, and provide an elevated level of comfort. However, as the scope of microgrid management proliferates, balancing between the comfort of community microgrid users with more devices and their profitability becomes increasingly challenging. To address this issue, this paper proposes a novel community control approach for EMS, called CuEMS, that utilizes deep reinforcement learning (DRL) to manage multiple appliances and energy storage systems (ESSs). The proposed approach optimizes energy profitability for operators while taking into account user comfort. It reformulates energy scheduling as a Markov decision process and learns the optimal scheduling strategy through deep Q-networks. The presented CuEMS approach is evaluated using real world data from Finland, and the results demonstrate that it can optimize the operation of multiple appliances and improve the profitability and user comfort compared to other methods.

Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition

Accurate photovoltaic power prediction is important to ensure stable and safe operation of microgrids. However, due to the high volatility of photovoltaic power data, the prediction accuracy of traditional prediction models is often unsatisfactory. To ensure stable operation of microgrids, this study proposes a combined improved extreme gradient boosting-kernel extreme learning machine short-term photovoltaic power prediction model consisting of multidimensional similar day clustering and dual decomposition. Initially, gray relation analysis, Pearson correlation coefficient, and Kmeans++ are used for clustering to obtain high-precision similar days. Subsequently, a dual signal decomposition model based on variational modal decomposition and complete ensemble empirical mode decomposition with adaptive noise is proposed. Finally, predictions are made using a combination of predictive models with complementary strengths and weaknesses, and the prediction results of each component are fitted with under three weather conditions, the average root mean square error is reduced by 78.02%,62.99%, and 62.48%, and the average mean absolute error is reduced by 82.55%, 71.13%, and 67.07% in comparison with the baseline model. The results show that the model is effective in improving the prediction accuracy in a variety of different environments.

A Centralized Stage-Wise Control Approach for Frequency and Voltage Regulation in PV–Wind–Storage Based Islanded Microgrid

In a microgrid, perturbations stemming from uncertain renewable sources and sudden load fluctuations can result in deviations in system voltage, frequency, and more, leading to instability. Thus, focusing on achieving stable microgrid operation, this paper proposes a novel stage-wise control approach for regulating the frequency and voltage of islanded microgrids. The approach employs an intelligent fuzzy logic controller with 64 rules. Specifically, the control approach is divided into three stages. In stage 1, the proposed approach manages the system under minor perturbations to regulate all system parameters within a feasible operating range, utilizing a battery as ancillary support. Furthermore, during significant perturbations, stages 2 and 3 of the proposed approach involve percentage-wise shedding non-critical loads to enhance stable microgrid operation. The load-shedding process continues until the system parameters stabilize within an acceptable operating range. The performance of the proposed approach is validated using MATLAB software and is compared against existing topologies. Notably, it has been observed that in the presence of significant small and large perturbations, the system demonstrates considerably lower deviations, following the recommended standards, i.e., 0.57 and 0.72% voltage deviation, including 0.14 and 0.17% frequency deviation. This observation emphasizes the superiority of the proposed control approach.

Nonfragile power and frequency control in islanded microgrids under abnormal asynchronous stochastic cyber attacks

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov–Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment.

Cascaded dual fuzzy logic controller for stable microgrid operation mitigating effects of natural uncertainty in solar and wind energy sources

In a microgrid system, the perturbations due to variable solar irradiance, fluctuating wind speed, and even the sudden fluctuation in load lead to the deviation in system voltage, frequency, DC-Link voltage, etc., causing instability. Thus, this paper proposes a robust and adaptive energy management system using the cascaded dual-fuzzy logic-based control approach to mitigate such issues. Besides, the system considers the auto-tuned PI-based voltage regulator with the reference voltage 415 V for inverter control and nine controllable/non-controllable system parameters for the proposed cascaded control approach. The proposed algorithm can control the battery operation, mode of microgrid, and load management to improve stability. It is analyzed for experimental conditions like reduced renewable output, microgrid transition, and unhealthy grid/microgrid conditions. In addition, the proposed method has been compared with the existing control topologies by assessing load voltage, frequency, and DC-Link voltage. The proposed approach has been validated for stable microgrid operation using MATLAB-Software. As per observations, it maintains the limits of 0.25% frequency deviation, 0.57% voltage deviation, and 0.877% DC link voltage deviation, which are acceptable as per IEEE 1547 standard, IEEE 2030 standard, and Indian standard CERC 2011 for the concerned system parameters.

Adaptive Virtual Inertia Control for Stable Microgrid Operation Including Ancillary Services Support

This article proposes an adaptive virtual inertia control system for stable operation of microgrids: it theoretically improves recent related results in the literature. The overall control system is conceived to provide ancillary services support (namely, enhanced frequency regulation with inertial response and voltage support) to an ac/dc microgrid that consists of a dc bus providing power to the ac one, being, in turn, composed of a diesel generator and loads. The ac voltages and currents are controlled via a nonlinear algorithm based on control-induced time-scale separation and singular perturbation analysis, whose use greatly simplifies the control design and the choice of the tuning gains, when compared with the classical linear controls. With the aim of reducing oscillations from the grid by assuring suitable short circuit ratio (SCR) and proper <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X/R$</tex-math> </inline-formula> characteristics, a virtual impedance is also implemented, along with frequency and voltage droops: they constitute high-level controllers that give references to the virtual inertia scheme and compose a comprehensive control. Detailed simulations illustrate, in different relevant settings, the performance of the proposed scheme.

Hierarchical Control for Voltage Unbalance Mitigation Considering Load Management in Stand-Alone Microgrid

Unevenly distributed loads at different phases may lead to stability issues and incur high losses in a low inertial microgrid with a rise in Voltage Unbalance (VU). Thus, a suitable control mechanism is mandatory for stable microgrid operation and mitigating VU under peak and off-peak loading conditions. This paper presents a hierarchical control mechanism that includes a primary Droop-based Positive-Negative Sequence Compensation (PNSC) approach and a secondary controlling technique implemented using Demand Side Management (DSM). Besides mitigating VU, DSM also performs effective energy management by manipulating Thermostatically Controlled Loads (TCLs) and Electric Vehicle Charging stations (EVCs), with specific emphasis on the Thermal Comfort (TC) of customers. The primary controller’s stability is also examined to test and validate its potential to mitigate VU during off-peak loading. Furthermore, a comprehensive analysis of the proposed hierarchical controller is performed with several dynamics taken into account. Moreover, the control technique is designed to mitigate generation and demand mismatch while adhering to IEC-61000-3-13. The effectiveness of the proposed control framework is tested using a modified IEEE-13 bus system in a MATLAB/ Simulink and Digsilent PowerFactory environment. Further the Hardware in Loop (HIL) is done in OPAL-RT to validate the real time performance of the system.

Study on Dynamic Interval Power Flow Calculation of Microgrid Based on Monte Carlo Algorithm

In order to effectively monitor the stability of the microgrid, based on the advantages of the Monte Carlo algorithm, a dynamic interval power flow calculation method for microgrid is designed. First, based on the multilayer complex structure of the microgrid, the hierarchical topology of its interval structure is analyzed. Then, the micropower flow model is designed, the affine algorithm is used to accurately describe the relationship between the variables in the microgrid structure, and the dynamic interval affine calculation is completed. Therefore, in the dynamic interval of the microgrid, the Monte Carlo probability method is applied to obtain more random data. Based on this, a model is established for the probability of wind power generation and photovoltaic power generation to simulate the output characteristics of the microgrid. Finally, the voltage variable, active power variable, and reactive power variable are calculated and embedded in the iterative algorithm to realize the stochastic power flow calculation of the microgrid. After experimental verification, the voltage amplitude calculated using the method proposed in this article has a small error value compared to the actual voltage, with a minimum error value of 0.2, close to 0. Under the condition of convergence accuracy of 10−6, the minimum convergence frequency is 3 times, and the power flow calculation process time does not exceed 38 seconds. This proves that the algorithm has high calculation accuracy, good convergence performance and timeliness, and can provide certain technical support for the stable operation of microgrids.

Distributed coordinated control of hybrid energy storage in offshore oil field microgrid in short time scale

The offshore oilfield microgrid can effectively integrate distributed power and hybrid energy storage, and its coordinated control can effectively ensure the safe and stable operation of the microgrid. In order to ensure the effect of coordinated control and improve the efficiency of coordinated control, a distributed coordinated control method for hybrid energy storage of offshore oil field microgrids in a short time scale is proposed. By setting two objective functions, namely, short time scale and optimal allocation of hybrid energy storage power of offshore oilfield microgrid, configuration capacity, and operation power constraints, a two-stage distributed coordinated control model for hybrid energy storage of offshore oilfield microgrid is constructed. The experimental results show that the proposed hybrid energy storage distributed coordination control method for offshore oilfield microgrids has a good effect and high efficiency, and can effectively ensure the stable operation of offshore oilfield microgrids. The experimental results show that the proposed distributed coordinated control method for hybrid energy storage of offshore oilfield microgrids has a good effect and high efficiency. When the number of iterations reaches 100, the coordinated control time of this method can reach a lower time. It effectively ensures the stable operation of the offshore oil field microgrid.

An Improved Control Strategy to Reduce Operating Hours of DG Genset in Solar PV-BES-DG Based AC Microgrid

The diesel-engine (DG) Genset-based microgrids are extensively used in remote areas. However, DG Genset 24 hours operation increases the overall cost of electricity and also pollutes the environment, diminishing the main purpose of renewable energy-based microgrids. However, the microgrid's dependency on the DG Genset cannot be eliminated, but it can be minimized by using a proper control strategy. By focusing on it, this work presents an improved control strategy to reduce the DG Genset's working hours in a solar photovoltaic (SPV) battery energy storage (BES) and DG Genset-based microgrid. The designed scheme is based on a set of rules, which are formulated by checking the status of the SPV array generation, BES, and load demand, and accordingly operates the microgrid with DG Genset or without DG Genset modes. Compared to the existing state-of-the-art scheme, the designed scheme operates the DG Genset during only an emergency, reducing its overall working hours. In addition, it provides a stable microgrid operation compared to the existing scheme, when the microgrid's operating mode is shifted from one to another. Besides, DG Genset is operated at its rated power with a unity power factor, giving maximum fuel efficiency. Its power quality performance is also monitored and ensured that the IEEE std. 519 does not defy in case of nonlinear/unbalanced loading. To improve power quality, the designed control scheme uses the cascaded generalized second-order integrator (C-SOGI) based phase-locked loop (PLL). A comprehensive simulation study is performed to show the advantages of the developed scheme over the existing schemes, which is supported by experimental results.

Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.

An Improved Equal Area Criterion for Transient Stability Analysis of Converter-Based Microgrid Considering Nonlinear Damping Effect

As a flexible and reliable way for distributed energy consumption and integration, the converters-dominated microgrid has attracted more and more attention recently. Owing to the low inertia and high nonlinearity of power converters, the islanded microgrid under large-signal disturbances is easily suffer to transient stability problems. To support the stable operation of microgrid, grid-forming converters are extensively investigated and applied. In this paper, an improved equal area criterion (IEAC) is proposed for the transient stability analysis of a simple microgrid equipped with both grid-following and grid-forming converters. Firstly, a simplified second-order model of the microgrid is established, which contains a nonlinear damping term relying on the power angle. Then, the limitation of conventional equal area criterion (EAC) for transient stability analysis is revealed, which completely ignores the unfavorable influence of negative damping and may lead to erroneous judgement. To fill this gap, an improved equal area criterion is proposed to derive the transient stability conditions for islanded microgrid, which could improve the estimation accuracy of stability boundaries. Moreover, impacts of system parameters and controller parameters on transient stability are studied, which provides useful guidelines for the parameter optimization of grid-forming and grid-following converters. Eventually, simulation and hardware-in-loop experiments are conducted to verify the effectiveness and superiority of the proposed method.

Multi-time scale energy management of microgrid considering the uncertainties in both supply and demand

The uncertainty of supply and demand have different characteristics, which can adversely affect the stable operation of microgrid. This paper presents a multi-time scale scheduling strategy, which mainly consists of day-ahead scheduling layer and intraday adjustment layer. In the day-ahead scheduling layer, the Monte Carlo method and demand response are used to deal with the uncertainty of both supply and demand, and a stochastic optimal model is established by considering the uncertainty in the microgrid. In the first stage, the unit commitment decisions are derived in a stochastic optimization framework to achieve minimum operating cost. In the second stage, the day-ahead economic power scheduling is achieved by using a deterministic optimization framework. In the intraday adjustment layer, a rolling optimization algorithm is applied to adjust the day-ahead economic power scheduling plan, which does not change the unit commitment decisions. The power fluctuations caused by inaccurate day-ahead forecast data are effectively smoothed with intraday rolling optimization. The simulation results show that the proposed model and strategy can effectively reduce the impact of uncertainty on the optimal scheduling of the microgrid. In addition, comparing the scheduling results for one month, it can be seen that the proposed strategy has better economy in the long-time operation of the microgrid.