Control Method For Microgrid Research Articles

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

A novel stochastic power flow calculation and optimal control method for microgrid based on multivariate stochastic factors fusion – Sensitivity

The stochasticity of power flow of distributed generations (DGs) and load in the microgrid has great influence on power flow distribution and voltage quality of the distribution network. For improving the voltage quality of the distribution network, the questions need to be further studied, which include the description of the stochasticity of the power flow in the microgrid and the impact of the microgrid into the distribution network on the power flow. Therefore, a novel stochastic power flow calculation and optimal control method for the microgrid based on multivariate stochastic factors fusion-sensitivity (MSFF-sensitivity) is proposed in this paper. Firstly, the multivariate stochastic factors fusion (MSFF) function is developed by using the probability density function to extract the stochasticity and correlation of power flow among different stochastic factors in the microgrid, which are effectively unified. Furthermore, the fusion-sensitivity (F-sensitivity) of the power flow in the microgrid integrated into the distribution network is constructed to accurately characterize the influence degree of various stochastic factors in the microgrid on the power flow of the distribution network. Based on this, the output power of the stochastic factor is adjusted to optimally control the power flow of the distribution network. Finally, the algorithm verification suggests that, compared with the conventional power flow methods, the method proposed in this paper is more suitable for the microgrid. The influence of stochastic power flow on the distribution network can be effectively reduced and the voltage quality of the distribution network can be improved by optimizing control of the power flow in the microgrid integrated into the distribution network.

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

Power Electronics-Based Operation for Intelligent Energy Management in Microgrid

This article focuses on intelligent energy management in microgrid systems, providing a comprehensive control engineering perspective on power electronics-based operation. The proposed approach utilizes advanced control algorithms to optimize power flow, load balancing, and integration of renewable energy sources within the microgrid. This article proposes the integration of multiple control strategies to create a hybrid approach that overcomes their disadvantages and addresses the limitations of individual algorithms. We aim to achieve a more robust and adaptive microgrid control method by combining the strengths of different control techniques. This hybrid approach leverages the power electronics capabilities while mitigating their limitations.

A virtual synchronous generator control method in microgrid with vehicle-to-grid system

Abstract The inertia in power systems plays a very important role in stabilizing the power system. With the growth of distributed generation (DG)/renewable energy source (RES) penetration in the power grid, there has been a concern about the lack of inertia in the power system. One of the solutions to solve the problem is to use the Virtual Synchronous Generator (VSG) control method. For energy storage, the Vehicle-to-grid (V2G) storage system can replace the traditional hybrid energy storage which consists of batteries and supercapacitors. This application will be implemented in Microgrid. The article will focus on simulating the Microgrid with the V2G system and solar energy as a renewable energy source, the inverters are controlled by the method of VSG control. The models will be simulated and the results will be shown on MATLAB/SIMULINK software.

Research and application of intelligent information processing system in power hierarchical control

Abstract In this paper, we propose a distributed secondary FM control method for microgrids to address the problem that the operating cost of microgrids increases significantly under load dropout and use the consistency algorithm to make the system reasonably distribute the load power to re-achieve the stable state. In the actual microgrid, the communication delay discusses the influence of information transmission and system stability. Simulation experiments are used to verify and set different convergence coefficients to obtain a suitable range of convergence coefficients. Then, the microgrid economic power allocation algorithm was used to describe the operating cost of microsources and limit the generator output power of the microgrid system to obtain the microgrid economic power allocation values. The cost amount of MG1 is $1327.3 in the equal micro-increase rate consistency calculation. The control condition test reduced 73.14 kWh in the overall load outage of intelligent microgrid control. The power system’s efficiency can be improved through a control method using intelligent information processing.

Synchronous oscillation characteristic and finite-time function projection synchronization control method for microgrids

Due to the nonsynchronization of each microsource node in a microgrid, the physical parameters of each node are not uniform. Therefore, unstable oscillation will occur for the whole microgrid system. To solve the oscillation problem caused by the nonsynchronization of each microsource node in the microgrid, a hierarchical control structure microgrid model is researched in this paper. Based on the theory of chaotic synchronization and finite time, a new microgrid synchronous oscillation characteristic and finite-time function projection synchronization control method is proposed. First, combining complex networks and microgrids, a nonlinear dynamic model of microgrid hierarchical control is established, and the abilities of the small-world network model and the formal network model to adjust for asynchronous oscillations are verified by comparing the two models. Then, aiming at the unsynchronized oscillations caused by external system noise, a new finite-time function projection synchronization control method is proposed. According to the verification results, the method can control external disturbances, avoiding system oscillation.

Artificial Intelligence Assisted Energy Optimization and Control Method for Microgrids

<p>As the future direction of power development, microgrids are particularly important for rational and efficient energy management. This article establishes an optimization model with multiple uncertainties as parameters for the microgrid energy system, with the objective function of minimizing operating costs. Then, an improved harmony algorithm was used to solve for the optimal solution of the model parameters. Finally, a microgrid system consisting of wind and thermal power units established in a certain area of Hebei Province was used for solution analysis. After experimental verification, the proposed method in this paper achieved significant improvements in both operational cost reduction and microgrid efficiency.</p> <p> </p>

An Improvement of Power Control Method in Microgrid Based PV-Wind Integration of Renewable Energy Sources

Microgrids are quickly becoming a great success for the future of electricity. The notion of the microgrid combines several microsources without interfering with the functioning of the larger utility grid. The DC and AC networks of this hybrid Microgrid are powered by photovoltaic and wind generators. Both AC and DC Microgrids may couple with energy storage devices. A microgrid powered by a combination of renewable energy sources, such as wind and solar, is shown and controlled in this project. The wind energy conversion machine is a doubly fed induction generator (DFIG), and it is coupled to a battery bank through a DC bus. Solar power is efficiently converted utilising a DC-DC boost converter from a solar photovoltaic (PV) array and then evacuated at the common DC bus of DFIG. With the line side converter's droop characteristics implemented, voltage and frequency may be regulated using an indirect vector control. A battery's energy level is monitored, and the frequency set point is adjusted accordingly to prevent excessive charging or discharging. When wind power is not available, the system can still function. Maximum power point tracking (MPPT) is a feature of the control algorithm used by both wind and solar energy blocks. All conceivable operational scenarios have been accounted for in the system's design, making it fully autonomous. An external power supply is included into the system and may be used to charge the batteries whenever needed. The feasibility of wind and solar energy, imbalanced and nonlinear loads, and a depleted battery are only some of the scenarios simulated in this paper, along with the corresponding simulation findings.

Research on distributed energy storage pinning coordinated control method of microgrid clusters

In this paper, a pinning coordination secondary control method for microgrids (MGs) is proposed, which can realize the weak connection operation mode between the microgrid and the grid, that is, the operation mode where there is almost no interaction and dependence between the MGs and the grid. It is used when the MG cluster (MGC) composed of multiple MGs is separated by external interference, the MG and the power grid quickly separate from the whole system with less influence. The pinning coordination control strategy based on distributed droop theory is applied for the energy storage system (ESS) in MG, to reduce the required communication bandwidth and control cost, and improve the dynamic performance of the system. Finally, to verify the feasibility and correctness of the weak connection operation mode based on pinning secondary control, an MG cluster experimental platform based on StarSim HIL is built, and the experiments of the system are carried out in a variety of scenarios. The results show that when the MGC is in the weak connection operation mode, it can effectively reduce the impact of external interference on the power grid and MGs.

Robust Control Strategies for Microgrids: A Review

Microgrids consisting of photovoltaic (PV) power plants and wind farms have been widely accepted in power systems for reliability enhancement and power loss reduction. Microgrids are capable of providing voltage and frequency support, improving power quality, and achieving proper power-sharing. To achieve such goals and deal with the nonlinear behavior in such systems, appropriate robust control strategies are required to be adopted. This article presents a comprehensive review of robust control methods for microgrids, including AC, DC, and hybrid microgrids, with different topologies and different types of interconnection to conventional power systems based on recently published research studies. The main control objectives, along with proposed control methods, are comparatively discussed for different types of microgrids. Furthermore, several research gaps in this area related to the scalability, robustness assessment, and evaluation approach are discussed. Recommendations are made that can potentially open new research lines to enhance the effectiveness of robust controllers for AC, DC, and hybrid microgrids.

Adaptive Frequency Control of Microgrid Based on Fractional Order Control and a Data-Driven Control With Stability Analysis

In this paper, a new adaptive fractional frequency control method for microgrids(MG) and low inertia networks is presented. The newly proposed grid frequency control is based on the droop and inertia controller for doubly-fed induction generator (DFIG) wind turbine (WT) using a data-driven algorithm. Also, in this paper, to ensure the grid frequency stability, the stability region boundary has been calculated using the parameter space method for above and below synchronous speeds. In the first step in the proposed method, a data-driven control (DDC) is used to update the frequency control coefficients. In the second step, in order to deployment more frequency data and to use both frequency error concavity and slope, the utilization of fractional gradient descent (FGD) and fractional inertia control is proposed. In the third step to maintain frequency stability, the updated frequency controller coefficients are compared with the stability region boundary. If the updated frequency controller coefficients are in the authorized band, they are applied. To test the proposed idea is a simulated real grid in Ontario, Canada. The simulation results indicate the proper operation of the control system in transient and steady-state and under different conditions.

Data‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning

To reduce the total power generation cost and improve the frequency stability of an island microgrid integrating renewable energy generation sources, a data-driven cooperative load frequency control (DC-LFC) method is proposed for solving the coordination control problem occurring between the controller and power distributor of the system. A novel algorithm, termed the effective exploration-distributed multiagent twin-delayed deep deterministic policy gradient (EED-MATD3) algorithm, is further proposed, the design of which is structured based on the concepts of imitation learning, ensemble learning, and curriculum learning. The EED-MATD3 method employs various exploration strategies, and the controller and power distributor are treated as two agents. Through centralized training and decentralized execution, a robust cooperative control strategy is realized. The performance of the proposed algorithm is verified in an LFC model of Zhuhai Tandang Island, an island microgrid in the China Southern Power Grid.

Research on Control Method of Microgrid Based on Multi Distributed Generation

Reducing the dependence of microgrid upon the communication system and realizing the efficient control of multiple distributed generation of the microgrid are problems that need to be solved urgently. Through the research, based on multiple microgrid operation modes, the peer-to-peer control strategy in microgrid is investigated, and the peer-to-peer control strategy method of microgrid is given for a variety of complex control problems of distributed power According to the peer-to-peer control strategy method, distributed power supply adopts droop control in adjusting distributed power supply in output voltage and frequency; the droop controller has P-f and Q-U droop characteristics. This paper establishes a peer-to-peer control microgrid simulation model, adopts the droop controller designed in this paper to island mode and grid-connected mode, and investigates how the microgrid switches between the two modes. In accordance with Matlab/Simulink simulation outcomes, the research examines frequency, voltage and power changes in distributed generation in the microgrid, and verifies the validity and feasibility of microgrid peer-to-peer control strategy.

An enhanced second-order-consensus-based distributed secondary frequency controller of virtual synchronous generators for isolated AC microgrids

Virtual synchronous generator (VSG) control is a promising control method in microgrid (MG) for the inertia support characteristic. However, the VSG inherits the oscillation feature of synchronous generator (SG), thus the active power and frequency oscillation may be observed when VSG control is applied. In MG, the oscillation may cause the unreasonable dynamic active power sharing among VSGs. Subsequently, the VSGs are prone to overload and even unstable, and the existing works based on consensus control cannot suppress the oscillation effectively. In order to tackle this problem, this paper proposes an enhanced second-order-consensus-based distributed secondary frequency controller by introducing the active power second-order consensus control, and reveals the oscillation suppression capacity of the second-order consensus algorithm in depth. Since there is no specific control requirement for the second-order variables, i.e., the change rate of active power and frequency of MG in steady state, the proposed controller can be simplified by replacing the integral control of second-order variables with the proportional control of first-order variables, i.e., active power and frequency, thus the communication data volume will not increase comparing to the traditional first-order-consensus-based controller. This paper also provides a small-signal model of MG with the proposed controller to evaluate the system dynamic performance and design the parameters. Finally, the simulation results verify the effectiveness of the proposed controller.

Research on energy management and control method of microgrid considering health status of batteries in echelon utilization

With the large-scale application of electric vehicles, the use of retired power batteries in power system and other fields has become a trend in the future. How to quickly and accurately estimate the health status of power batteries and reasonably carry out energy management has become one of the main problems to be solved. This paper takes the application of retired power batteries in microgrid (MG) as an example. Firstly, considering the influence of the depth and times of batteries charge and discharge on the operation life, the life loss model of retired power batteries is established, and the coupling relationship between the power batteries life decay model and SOC, SOH is established. Secondly, according to the characteristics of SOC and SOH, K-means clustering method is used to cluster the batteries to construct the energy storage system (ESS). Finally, considering the characteristics of supply and demand balance, operation reliability and economy, the energy management and control of ESS in MG is carried out. Simulation results show that the proposed energy management and control method can effectively improve the economy, reliability and security of the ESS in the process of echelon utilization.

EMSx: a numerical benchmark for energy management systems

Inserting renewable energy in the electric grid in a decentralized manner is a key challenge of the energy transition. However, at local scale, both production and demand display erratic behavior, which makes it challenging to match them. It is the goal of Energy Management Systems (EMS) to achieve such balance at least cost. We present EMSx, a numerical benchmark for testing control algorithms for the management of electric microgrids equipped with a photovoltaic unit and an energy storage system. EMSx is made of three key components: the EMSx dataset, provided by Schneider Electric, contains a diverse pool of realistic microgrids with a rich collection of historical observations and forecasts; the EMSx mathematical framework is an explicit description of the assessment of electric microgrid control techniques and algorithms; the EMSx software EMSx.jl is a package, implemented in the Julia language, which enables to easily implement a microgrid controller and to test it. All components of the benchmark are publicly available, so that other researchers willing to test controllers on EMSx may reproduce experiments easily. Eventually, we showcase the results of standard microgrid control methods, including Model Predictive Control, Open Loop Feedback Control and Stochastic Dynamic Programming.

Research on frequency control method for micro-grid with a hybrid approach of FFR-OPPT and pitch angle of wind turbine

Micro-grid is the main application scenario of the renewable energy. When wind power serves as the dominant source of the micro-grid, the uncertainty of the wind energy caused by the climate makes the maintenance of the frequency stability of the micro-grid more complicated. Provided in this paper is a novel method to control the electrical output of WTG to maintain the frequency of the micro-grid close to its target. Firstly, a fast frequency response optimized power point tracking method (FFR-OPPT) is designed. The double excitation from the first-order and second-order derivative of the grid frequency can improve the speed of kinetic energy extraction of the wind turbine due to the increase of the virtual inertia time constant. Secondly, in order to enhance the service life of wind turbine, a hybrid control strategy of FFR-OPPT and pitch angle is proposed based on boundary analysis of the wind turbine. The control strategy can reduce the adjustment frequency and range of the pitch angle of wind turbine significantly. The proposed method is tested in a micro-grid combining by wind turbines and small thermal generations. The load frequency control in micro-grid is dominated by wind turbine and is assisted by thermal generation. The simulation results illustrate that the control strategy can control the frequency of micro-grid close to its schedule value effectively, accompanied by decrease of the frequency and range of pitch angle adjustment during control process.

Control and synchronization of a fixed-frequency control method in microgrid based on global positioning system

Control methods based on global positioning systems (GPS) are reported in multiple literatures recently, which achieve a fixed frequency operation of the microgrid and therefore has the tremendous benefit that any problems related to frequency instability are eliminated. However, the possible interruption of GPS timing signals may cause current circulating and finally leads to instability of the microgrid, yet it is largely neglected in literatures. This paper presents an angle synchronizing mechanism which utilizes the timing signal from GPS satellites and an auxiliary frequency droop loop to ensure synchronization during GPS offline events. Smooth transfer is guaranteed between primary and auxiliary control loops with discrete control architecture. Also, to allow microgrid using GPS-based control to work in tandem with the bulk power system or another frequency droop microgrid, an extra synchronization algorithm is proposed. The viability and performance of the proposed control structure is validated by case studies.

Distributed Resilient Control for Energy Storage Systems in Cyber–Physical Microgrids

As a cyber-physical system (CPS), the security of microgrids (MGs) is threatened by unknown faults and cyberattacks. Most existing distributed control methods for MGs are proposed based on the assumption that secondary controllers of distributed generation units operate in normal conditions. However, the faults and attacks of the distributed control system could lead to a significant impact and consequently influence the security and stability of MGs. In this article, a distributed resilient control strategy for multiple energy storage systems (ESSs) in islanded MGs is proposed to deal with these hidden but lethal issues. By introducing an adaptive technique, a distributed resilient control method is proposed for frequency/voltage restoration, fair real power sharing, and state-of-charge balancing in MGs with multiple ESSs in abnormal condition. The stability of the proposed method is rigorously proved by Lyapunov methods. The proposed method is validated on test systems developed in OPAL-RT simulator under various cases.