Simulation Of Microgrid Research Articles

Microgrid control under uncertainty

Microgrids – decentralized electrical grids that can function both in conjunction with wide area macrogrids and without – are a powerful tool to address energy resiliency and climate change mitigation. Microgrid control, however, remains a challenge; their bespoke nature and the existence of multiple sources of uncertainty lead to a control problem that traditional grid modeling and control techniques are ill-suited to handle. We build a microgrid interface to simulate microgrids under uncertainty and devise off-policy reinforcement learning algorithms to control microgrids. Our algorithms, which incorporate domain randomization and random network distillation for exploration and computational efficiency, achieve performance better than model predictive control and rule based control benchmarks under battery model uncertainty on seven of ten tested scenarios. Our model code is available at https://github.com/ahalev/Microgrid-Control-Under-Uncertainty and our microgrid simulator is available at https://github.com/ahalev/python-microgrid.

Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

The integration of solar and wind energy sources in microgrids has witnessed significant growth, giving rise to distinct challenges due to their intermittent nature when it comes to achieving efficient microgrid control. However, estimating the parameters of the dynamic microgrid components facilitates capturing the complex and time-varying characteristics of renewable energy generation. This requires an accurate estimation of the parameters from the dynamic differential equations for effective modeling and control. In this research paper, we presented a novel methodology based on the integration of Bayesian inference and Neural ODEs. The Bayesian inference quantifies the uncertainty, and the Neural ODEs model the dynamic systems. By combining the strengths of both methods, we aimed to achieve a precise and robust parameter estimation of the dynamic microgrid components. The methodology is validated on a simulated microgrid that consists of a diesel generator, Solar PV array, double-fed induction generator, and a battery energy storage system. The results showed promised inferences estimation obtained from the parameter posterior distribution even in the presence of uncertainty. This can enhance our understanding of the dynamics of renewable energy systems and can contribute to the advancement of decision-making microgrid control strategies.

Research on virtual inertia of energy storage systems under different grid-forming control strategies

Abstract To reasonably evaluate the support capability of grid-forming energy storage in power systems characterized by “double high” characteristics, it is essential to investigate the factors influencing the virtual inertia of grid-forming energy storage. This paper delves into the relationship between two control strategies of grid-forming energy storage and virtual inertia from an energy perspective. Initially, the operational principle and technological advantages of grid-forming energy storage are analyzed. Subsequently, based on the principles of two grid-forming control strategies, mathematical models for both types of grid-forming energy storage are established, elucidating the physical significance of virtual inertia in grid-forming inverters and revealing the adjustability of virtual inertia. Finally, a microgrid simulation system is constructed in PSCAD to validate the theoretical analysis.

ОЦІНЮВАННЯ ВПЛИВУ РОБОТИ ДВОНАПРЯМЛЕНОГО НАПІВПРОВІДНИКОВОГО ПЕРЕТВОРЮВАЧА НА ПОКАЗНИКИ ЯКОСТІ ЕЛЕКТРОПОСТАЧАННЯ В MICROGRID

The publication considers the modeling of the operating modes of inverter equipment used in low-voltage Microgrids. Provided description of the developed Microgrid simulation model, one of the components of which is a bidirectional semiconductor converter with a control system based on the application of abc-dq0 transformation. Was determined the influence of the presence of inverter equipment operating in the mode of current generation and regulation of reactive power on the indicators of the quality of power supply in the Microgrid, taking into account the transient processes that occur when connecting powerful loads. References 10, figures 4.

A Virtual Resistance Optimization Method Based on Hybrid Index in Low-voltage Microgrid

Introduction: The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrid, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation at the same time in this paper. The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. The effects of switching load and micro-source on the optimization results are analyzed through the simulation of low-voltage microgrid, and the simulation results show that the virtual resistance optimization method can significantly suppress circulating current while improving power quality. When distributed generators are connected to utility grid through inverters and feeders, differences in feeder parameters and inverter control strategies easily cause circulating current and uneven power distribution among micro-sources. The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrids, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation. Methods: The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. Results: In the simulation scenario of two micro-sources and three micro-sources, the virtual resistance obtained by the method proposed in this paper has more obvious improvement on the system operation index, and is not affected by the load type. Conclusion: The method of optimizing virtual resistance based on the hybrid index can achieve the effect of restraining circulating current and improving power sharing degree on the premise of guaranteeing power quality and satisfying system stability. The optimization of virtual resistance is affected by the number of feeders. It is necessary to re-optimize the virtual resistance after changing the number of feeders, but the process of switching micro-source and adjusting load does not affect the optimized resistance value.

Model-Based Algorithm for Flexible Power Point Tracking for Photovoltaic Participation in Primary Frequency Regulation

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems.

Control Optimization Method for Ship Direct Current Microgrid Based on Impedance Reshaping

In response to the constant power negative impedance characteristics on the load side of a ship DC microgrid, leading to voltage oscillation issues in the DC bus, this paper proposes a control optimization method based on impedance reshaping using bus voltage feedback. First, a simplified small-signal diagram of a lithium battery energy storage system converter is analyzed. Combining active damping control technology, an impedance regulator is introduced, and its parameters are optimized to effectively reduce the output impedance magnitude on the power source side. Subsequently, a ship DC microgrid simulation model is constructed using MATLAB R2022a/Simulink for validation, and comparative analysis is conducted on the anti-interference ability of the DC bus voltage before and after impedance reshaping. Finally, a model is built on a semi-physical simulation platform to experimentally verify the proposed method. The research results indicate that the proposed control optimization method can effectively increase the system’s stability margin, suppress DC bus oscillations, and enhance the anti-interference ability of the ship DC microgrid’s bus voltage when facing significant power load variations.

Frequency regulation scheme for islanded microgrid

When the main grid of the power grid malfunctions undergoes emergency repairs, the microgrid is in off-grid operation. At this time, the microgrid in an isolated state must have self-frequency regulation capability. This article proposes a frequency control strategy for isolated microgrids, which can improve their ability to resist frequency interference. The proposed control strategy considers the different characteristics and capabilities of various micro-sources for frequency regulation. It classifies the frequency modulation micro-sources in the microgrid. Judgment conditions and parameters are set to determine whether each micro-source participates in frequency modulation and the participating components. The isolated microgrid has an economical and fast frequency modulation strategy. Finally, a microgrid simulation model is established using Matlab/Simulink simulation software to verify the effectiveness of the proposed frequency modulation strategy.

Design and simulation of a building-based off-grid photovoltaic microgrid using PVsyst

In the absence of a main or central grid, an off-grid renewable energy-based system could be a viable solution to address the electricity demand of a particular region by utilizing the available renewable energy sources (RES) of that area. This also leads society to a step toward sustainable energy development. A planned RES-integrated microgrid system not only fulfills the energy crisis and reduces electricity costs but also plays a significant role in the conservation of fossil fuels. Therefore, the design and simulation of RES integrated systems are indispensable as this type of analysis determines the vital parameters of the system e.g., the system performances including electricity output, energy conversion efficiency, yearly energy potential, and the system losses before being involved in practical implementation. Further, the cell temperature of the Photovoltaic (PV) panel based on regional weather conditions, the system life cycle based on the uncertainty factor of weather, total system installation cost with a full account of the annual savings and payback etc. can also be determined by designing and simulating such systems. This paper presents a design of a 40 kW off-grid photovoltaic (PV) microgrid system according to the load requirements at the Department of Electronics and Communication Engineering (ECE), Tezpur University, India using PVsyst software. The proposed design has been created to meet the daily peak load demand of 37 kW of the department. The energy available through the generating units of the proposed designed PV system is 45.46 MWh/Year and the system performance ratio in terms of efficiency over the year is 0.814. The overall performance analysis and simulation results would be helpful in determining the efficiency and viability of the proposed PV system while implementing it practically in the near future.

Simulation Technologies Applicable to Microgrids

The new paradigm of the electric sector, given the increased penetration of distributed generation, includes a growing number of microgrids. Therefore, the implementation of microgrids needs to be preceded by studies and simulations to evaluate the possible scenarios under which these systems will operate. This paper presents an overview of state-of-the-art technologies for microgrid simulation and describes an example implemented at the facility to which the first authors are affiliated.

A Simulation-Assisted Proactive Scheduling Method for Secure Microgrid Formation Under Static and Transient Islanding Constraints

Microgrids (MGs) provide a promising solution to ensure the constant power supply under extreme conditions due to their islanding ability. However, unintentional islanding caused by emergencies may trigger significant a power imbalance in MGs, leading to unsuccessful MG formation in the event that static or transient operational constraints are violated. To address this challenge, we propose a proactive distribution system scheduling method to continue serving critical loads in emergencies by forming reliable MGs. The power flow on the lines is restricted prior to interruption to mitigate the power imbalance in MGs when equipment failures occur. Both static and transient constraints are incorporated into the proposed model to ensure the adequacy of MGs in supplying demands and transient security during the islanding transition. Note that the inclusion of nonlinear transient constraints makes it difficult to solve the model directly. Thus, a two-level simulation-assisted solution approach is presented, where simulation level sends the maximum allowable power imbalance in the MGs to optimization level to determine optimal scheduling schemes. By interfacing with a transient simulation of MGs, the frequency dynamics are exactly incorporated into the constraints. Numerical results validate the effectiveness of the proposed method in secure islanding transition and MG survivability improvement.

Machine Learning Approaches for Fault Detection in Renewable Microgrids

This paper presents a novel use of machine learning techniques for identifying faults in renewable microgrids within the field of decentralized energy systems. The study investigates the effectiveness of machine learning models in identifying abnormalities in dynamic and variable microgrid environments. It utilizes a comprehensive dataset that includes parameters such as solar, wind, and hydro power generation, energy storage status, and fault indicators. The investigation demonstrates a notable 94% precision in identifying faults, highlighting the superiority of machine learning compared to conventional rule-based approaches, which attained an accuracy rate of 80%. The precision and recall measures emphasize the well-balanced performance of the machine learning models, reducing both false positives and false negatives, and guaranteeing precise problem detection. The effect of faults on microgrid efficiency is significantly reduced, with an only 2% decrease recorded under fault situations, demonstrating the models’ ability to maintain an efficient energy supply. A comparative study reveals a 14% improvement in accuracy when compared to conventional techniques, emphasizing the benefits of adaptive and data-driven approaches in identifying intricate fault patterns. The sensitivity study validates the resilience of the machine learning models, demonstrating their capacity to adjust to different settings. The practical application of the models is validated by real-world testing in a simulated microgrid environment, which leads to their repeated improvement and improved performance. Ethical concerns play a crucial role in assuring ethical data use during research, particularly in the implementation of machine learning, by upholding privacy and security requirements. The study results indicate significant implications for identifying faults in renewable microgrids, providing a potential opportunity for the progress of robust and sustainable decentralized energy networks. The effectiveness of machine learning models stimulates further study in expanding their deployment for varied microgrid situations, including more machine learning approaches, and resolving obstacles associated with real-time application in operational settings.

Imitation Learning-Based Energy Management Algorithm: Lille Catholic University Smart Grid Demonstrator Case Study

This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic an expert demonstration to solve a microgrid (MG) energy management problem. Those demonstrations are derived from solving a set of linear programming (LP) problems. Consequently, the imitation-Q-learning algorithm learns by interacting with the MG simulator and imitating the LP demonstrations to make decisions in real time that minimize the MG energy costs without prior knowledge of uncertainties related to photovoltaic (PV) production, load consumption, and electricity prices. A real-scale MG at the Lille Catholic University in France was used as a case study to conduct experiments. The proposed approach was compared to the expert performances, which are the LP algorithm and the conventional Q-learning algorithm in different test scenarios. It was approximately 80 times faster than conventional Q-learning and achieved the same performance as LP. In order to test the robustness of the proposed approach, a PV inverter crush and load shedding were also simulated. Preliminary results show the effectiveness of the proposed method.

Micro-Grid Line Protection Method Based on Current Frequency Variability

Due to the access of multiple distributed power sources (DG) in the Micro-Grid, the original radiation topology is changed, making the traditional distribution network line protection methods no longer applicable; and most of the existing Micro-Grid line protection methods discuss their grid-connected and Islanded operation separately, resulting in a complex and variable line protection configuration. By analysing the transient current characteristics provided by the DG side and the network side of the Micro-Grid during grid-connected operation and the transient current characteristics provided by different types and numbers of DG on both sides during Islanded operation, it is concluded that there are significant differences in the frequency characteristics of the first-mode components of the current provided on both sides of the protected section of the Micro-Grid line during a fault. As a result, a Micro-Grid line longitudinal differential protection method based on the frequency similarity of the current first-mode quantities is proposed to achieve the identification and removal of faults inside and outside the protected section. A 380 V Micro-Grid simulation model is set up in MATLAB software and the proposed method is tested in a large number of experiments to enable it to operate correctly under different operating modes, different fault locations, and different transition resistances of the Micro-Grid, and to adapt to different operating conditions.

Gain-Scheduled Structured Control in DC Microgrids

Power buffers are electronic converters that shield direct current (dc) microgrids from the effects of abrupt load changes. By promoting collective behaviors, distributed control solutions can improve the overall grid performance, as power buffers can collectively react to abrupt load changes. In this article, a novel gain-scheduled structured control scheme for power buffers is proposed. The control objective is to regulate both the stored energy and the input impedance of the power buffers in a distributed fashion, while guaranteeing, at the same time, the closed loop stability regardless of load changes occurring within predefined ranges. The proposed controller is expressed as a gain-scheduled state feedback law, whose coefficients depend affinely on the scheduling parameters. The structure of the controller gain is chosen to reflect the topology of the communication network implementing the distributed controller. In this article, the existing theory is extended to deal with such control problem, providing a novel strategy, which can be used to design gain-scheduled controllers for the considered class of structured feedback systems. Simulation results using a high-fidelity dc microgrid simulator verify the effectiveness of the proposed approach.

The Improved Coordinated Control Method of Islanding and Grid-Connected Operations for DC Microgrids

The DC microgrid plays an essential role in realizing smart power supply systems for the DC loads. It combines various distributed generations and transmits the power to the loads or the utility grid through the DC bus. It attaches great importance to energy management because of the load and photovoltaic power fluctuations. This paper proposes an improved coordinated control based on the characteristics of various distributed generations and utility grids. The proposed method can realize the stabilization of bus voltage when the system switches between multiple operating modes in grid-connected and island mode due to the change of energy. Compared with the present ways, the proposed method provides a further optimization on the design of charging for energy storage units in the grid-connected mode and the current sharing with the backup storage units in the islanded mode which is conducted by SoC balancing droop control. Finally, the DC microgrid simulation platform is built to verify the correctness and feasibility of the proposed method.

The grid simulation device for photovoltaic power generation

Solar photovoltaic power generation is the solar radiant energy directly into electricity method, namely the sun radiant energy by solar cells is converted into electrical energy, again through energy storage, control, and transformation, and into people’s dc or ac power that can be used directly. The so-called grid-connected network is simply said to be realized by using the principle of simultaneous operation of voltage, phase, phase sequence, and frequency. The simulation device of its inverter grid-connected is designed, the scheme of simulating grid-connected is given, the drive control system and filter system are designed, and the implementation scheme of the drive control system is introduced in detail. The design and parameter selection of the main circuit and the filter system are given in detail. The hardware and software implementation scheme and circuit structure of the system are given based on DSP. The entire system can meet the requirements of microgrid simulation operation.

The role of risk-based demand response in resource management of a grid-connected renewable-based large-scale microgrid with stationary and mobile energy storage systems and emission tax

Integration of energy storage systems with local electricity systems, like microgrids, is emerging due to their economical, reliability, and environmental benefits. These advantages can be achieved by load management subject to the related condition, such as the hour of the day, electricity price, etc. From an economic point of view, energy storage systems can reduce the share of costly power generators in supplying the peak demand. In other words, they can be charged in low-demand hours and discharged in peak-demand hours, when the electricity price is high. In this work, the impact of stationary battery storage and electric vehicles on the resource management of a large-scale microgrid is assessed through a stochastic model. The understudy microgrid includes also renewable energy sources (namely solar photovoltaic systems and wind turbines) as well as diesel generators. This study considers the uncertainty in the electricity price, where a scenario-based risk management approach is employed to control the risk associated with price uncertainty. The resulting model is implemented in the GAMS software package to be solved by a suitable solver. Numerical simulation of a large-scale microgrid is accomplished to validate the effectiveness of energy storage systems in the economic improvement of the understudy microgrid.

Optimal frequency support method for urban microgrids of building prosumers

High penetration of renewable energy sources (RES) is raising a number of technical challenges for the sustainable and reliable operation of electric power systems. To this end, the need for ancillary services provided by the building sector to the electric power system is becoming particularly significant; with frequency support being one among them. Urban microgrids and especially those of them hosting building prosumers will have a key role in frequency support as they comprise flexible components that can be exploited to this end. In this paper, detailed models for the accurate simulation of frequency support mode of operation are developed. Frequency support in microgrids of buildings is enhanced by properly utilizing the flexibility of building prosumers. Frequency is also controlled by optimally regulating building local generation units and their adjustable electrical loads in a flexibility-based way and taking into consideration gas emission limitation. Emphasis is placed on the enhancement of the computational efficiency of the proposed support method by suitably exploiting smart power dispatch techniques. The developed method is validated through the simulation of a realistic complex urban microgrid.

Demand response based microgrid's economic dispatch

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.