Microgrid Energy Research Articles

Optimum utilization of hub energy micro-grids with micro-networking strategy of local biogas productions

In this paper, the optimal utilization of local biogas production in multi-energy systems is investigated. The micro-grid policy of local biogas production, to supply the variable demand of natural gas and also to convert it into electrical energy required by an energy hub micro-grid, along with other sources of scattered energy production and national electricity and gas networks, is one of the main goals of this paper. The multi-factor intelligent optimization method is considered to investigate the non-linear and heterogeneous optimization problem in the IEEE standard 33-bus energy hub microgrid. Most of the main indicators of interest are economic savings and minimization of operating costs. The multi-objective optimization method has been chosen as a method to solve the aforementioned non-linear and heterogeneous problem. Simulation has been done in GAMS software and the results of optimal load distribution of electric energy and natural gas supply for selected energy microhub have been explained and compared. The final results show a significant reduction of 46 % (2.8 thousand dollars per day) in operating costs. Significant reduction of energy flow between local network microhubs by 39.4 % is also one of the optimization results studied. Also, the Island performance of the local energy microhub has been simulated with the desired strategy. The cost of local energy microhub energy supply in island mode has decreased by 34 % (1.4 thousand dollars per day) compared to normal mode.© 2022 Published by.

Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost.

Experimental and developed DC microgrid energy management integrated with battery energy storage based on multiple dynamic matrix model predictive control

This study presents the energy management and control strategy in the islanded DC microgrid structure in the presence of renewable energy sources (RES) and battery storage units (BU). The BU control structure is planned by considering the state of charge (SOC) indicator of each BU. The proposed model based on sequential distributed energy management and multiple dynamic matrix model predictive control algorithm (MDMMPC) is developed. The MDMMPC algorithm is implemented for power control and management by local controllers. The energy management strategy is formulated by considering generation prioritization and minimal communication based on primary and secondary control objectives. The simulation results have been analyzed in different scenarios such as power generation changes, load changes, disconnection between participating units in energy supply and battery discharge. Also, a hardware-in-the-loop (HIL) environment along with an experimental setup based on the Micro Lab box and dSPACE control desk (DS1202) is presented. In experimental environment, by creating suitable coordination between the converter's behavior and the ESS unit inertia, it not only reduces the undesirable converter's fluctuations but also the converter's behavior is associated with the least overshoot. Simplicity, rapidity, ease of operation, and distributed control scheme are the important features of the experimental structure.

Optimization of Switching Control and Microgrid Energy Management System with Alternate Arm Converter Based on Bacterial Foraging Algorithm

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand.

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

AbstractMicrogrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability.

On the control of microgrids against cyber-attacks: A review of methods and applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified.

Polymorphic Virtual Synchronous Generator: An Advanced Controller for Smart Inverters

Virtual synchronous generators (VSGs) are one of the most relevant solutions to integrate renewable energy in weak grids and microgrids. They indeed provide inverters characteristics of rotating machines (inertia for instance) that are useful for stabilizing the system, notably in the context of the high variability of the production. Thanks to the virtual characteristics of the VSG, the virtual parameters of the emulated synchronous machine can be optimally adapted online as a function of the electric environment of the inverter. We call that inverter’s control a polymorphic VSG. The online adaptation of the critical control parameters of the VSG helps reduce the risk of deterioration of the inverter’s constituents that might be induced by harsh events (frequent in weak grids) but, more importantly, improves the robustness of the system. In this paper, four implementations of a polymorphic VSG controller are compared on a simple microgrid study case to a complete VSG model. For the test, polymorphic VSGs have to minimize frequency and voltage oscillations while withstanding short circuits, which is typically a requirement for units in this context. One of the controls is based on recurrent optimization over a prediction time horizon, and two sub-optimal ones target practical implementation in industrial inverters with limited computational power. Results show a clear reduction in incidents in the microgrid thanks to the controllers. The error reduction with the complete polymorphic VSG is up to 100% for the voltage, 32% for the currents, and 79% for the duty ratio. Those values are decreased by 30 to 50% with the sub-optimal controllers but for a reduction in the computational burden of more than 97%. Recommendations are proposed for the development of an auto-adaptive polymorphic VSG from a high technology-readiness-level perspective, i.e., targeting a compromise between error reduction and computational burden.

Design and implementation of an off-grid hybrid microgrid for Chittagong and Faridpur

The challenge of rural electrification has become more challenging today than ever before. Grid-connected and off-grid microgrid systems are playing a very important role in this problem. Examining each component’s ideal size, facility system reactions, and other microgrid analyses, this paper proposes the design and implementation of an off-grid hybrid microgrid in Chittagong and Faridpur with various load dispatch strategies. The hybrid microgrids with a load of 23.31 kW and the following five dispatch algorithms have been optimized: (i) load following, (ii) HOMER predictive, (iii) combined dispatch, (iv) generator order, and (v) cycle charging dispatch approach. The proposed microgrids have been optimized to reduce the net present cost, CO2 emission, and levelized cost of energy. All five dispatch strategies for the two microgrids have been analyzed in HOMER Pro. Power system reactions and feasibility analyses of microgrids have been performed using ETAP simulation software. For both the considered locations, the results propound that load following is the outperforming approach having the lowest energy cost of $0.1728/kWh, operational cost of $2944.13, present cost of $127,528.10, and CO2 emission of 2746 kg/year for the Chittagong microgrid and lowest energy cost of $0.2030/kWh, operating cost of $3530.34, present cost of 149,287.30, and CO2 emission of 3256 kg/year for Faridpur microgrid with a steady reaction of the power system.

Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas

The microgrid enhances power grid reliability, resiliency, and sustainability, particularly in rural and islanded areas with limited manual network management. However, microgrid energy management systems (EMS), especially in islanded mode, require precise and reliable techniques to prevent severe blackouts/brownouts. This paper presents a novel deep deterministic policy gradient (DDPG) algorithm to schedule EMS for the autonomous microgrid in real-time. Our solution utilizes deep reinforcement learning (DRL) to converge model-free, sequential, random, and continuous characteristics of the microgrid. Additionally, we use reward shaping and transfer learning attachment to DDPG to support microgrid performance restrictions and minimize load shedding during peak hours. This solution offers an efficient training process comparable to other DRL techniques in simplicity, less computation, and supporting future system extension. Residential Gasa Island microgrid profile characteristics have been selected and tested to examine the proposed approach. Results demonstrate the high efficiency and accuracy of the proposed technique compared to existing methods.

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area.

A Micro Energy Grid Optimal Design and Economic Operation Using Genetic Algorithms

This paper presents an optimal design procedure and economic operational scheduling of micro energy grids (MEGs). The optimization objectives are to minimize cost, carbon dioxide emissions, and energy deficiency. The energy sources and conversion technologies included in this study are renewable-based sources (wind and photovoltaic), a furnace, an electrical heater, a main power grid, and a local power station. Two proposed control levels are applied to control the operation of the MEG. The supervisor control level selects the energy supplier based on price and/or availability. The inner control level dynamically matches the demand profile with the supply profile. The control loops guarantee dynamic matching between the demand profile and supply profile. Two scenarios are simulated, zero interest rates and 5.25% interest rates. The results showed renewables contribute with a significant share as an energy source, however, higher interest rates would negatively impact this contribution. It also confirms that carbon taxes can reduce the use of fossil fuels as an energy source.

An optimal multi-objective demand side management of a smart Microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

Incorporating renewable Distributed Energy Resources (DER) into the main grid is crucial for achieving a sustainable transition from fossil fuels. However, this generation system is complicated by the fluctuating behavior of renewable resources and the variable load demand, making it less reliable without a suitable energy storage system (ESS). This study proposes an Optimal Power Flow Management (OPFM) strategy for a grid-connected hybrid Micro Grid (MG) comprising a wind turbine (WT), a photovoltaic (PV) field, a storage battery, and a Micro Gas turbine (MGT). This proposed strategy includes (i) minimizing the MG’s daily energy cost, (ii) decreasing CO2 emissions by considering the variable load, weather forecast, and main grid fees to optimize the battery charging/discharging strategy, and (iii) optimizing the decision-making process for power purchase/sell from/to the main grid. The suggested OPFM approach is implemented using a Genetic Algorithm and compared with the Bellman Algorithm and a restricted management system via several simulations under the Matlab environment. Furthermore, the hybridization of the Bellman Algorithm and the Genetic Algorithm is proposed to enhance the OPFMC strategy’s efficiency by leveraging both algorithms’ strengths. The simulation results demonstrate the effectiveness of the proposed strategy in lowering energy costs and CO2 emissions and enhancing reliability. Additionally, the comparison of the hybridized GA algorithm reveals a cost 16% higher than the Bellman Algorithm; however, the use of the hybridized GA algorithm leads to a reduction in GHG emissions by 31.4%. These findings underscore the trade-off between cost and environmental impact in the context of algorithmic optimization for microgrid energy management.

Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm

The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO’s quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system.

Multi-objective energy management of island microgrids with D-FACTS devices considering clean energy, storage systems and electric vehicles

Abstract The development of microgrids is progressing due to intelligent load demands, clean energy, batteries and electric vehicles. The presence of such systems in microgrids causes power balance inconsistency, leading to increased power losses and deviation in voltage. In this paper, a mixed-integer non-linear programming model is proposed for modelling island microgrid energy management considering smart loads, clean energy resources, electric vehicles and batteries. Similarly, a flexible distributed AC transmission system device is proposed to prevent voltage deviation and reduce power losses. A scenario-based multi-objective function has been proposed to decrease energy losses and voltage deviations and energy outages of clean energy resources, reduce emissions from fossil-fired distributed generation and finally decrease load outages to reduce the vulnerability of the islanded microgrid. Regarding the proposed mixed-integer non-linear model and the high number of variables and constraints, a modified evolutionary algorithm based on particle swarm optimization has been proposed to solve the proposed model, which can be more efficient than other algorithms to achieve global optimal solutions. The model presented is implemented on a 33-node island microgrid and the results illustrate that the proposed algorithm and model are effective in reducing energy losses and voltage deviation, as well as reducing the vulnerability of the microgrid. The simulation results demonstrate that the proposed approach can lead to significant improvements in the performance of the microgrid. Specifically, the approach can result in a 27% reduction in losses, a 6% reduction in pollution and a 31% improvement in voltage. Additionally, the approach allows maximum utilization of renewable energy sources, making it a promising solution for sustainable energy management.

A Nash bargaining model for energy sharing between micro-energy grids and energy storage

Micro-energy grids (MEGs) play a crucial role in achieving diverse energy utilization and promoting low-carbon economies. However, the intermittent nature of distributed generation and the inter-coupling of multiple energy types pose challenges to efficient energy management in MEGs. In this paper, we propose a novel Nash bargaining game-based electricity-gas energy-sharing model for MEGs. Our model incorporates bus structure-based energy storage combined with power-to-gas technology, which enables MEGs to establish an integrated electricity-gas energy-sharing network. This network facilitates both direct sharing and buffered sharing of energy, addressing the limitations of intermittent generation and enhancing overall energy management. To address the issue of computational complexity, the proposed Nash bargaining game model is decomposed into two sub-problems: social energy cost minimization and benefits sharing. Through optimization, we obtain optimal energy-sharing profiles and energy-sharing payments. Moreover, we consider the investment payback period of energy storage and adjust the initial benefit-sharing results accordingly. Case studies demonstrate that our model significantly improves economic performance, increasing the self-sufficiency ratio of MEGs to 64% and energy storage efficiency to 63% compared to operating independently. Additionally, the multiplayer Nash bargaining game model facilitates Pareto optimal benefits for MEGs and energy storage by capturing their interactive dynamics.

Data-Based Optimal Microgrid Management for Energy Trading With Integral Q-Learning Scheme

This article proposes an integral Q-learning scheme to study the optimum management strategy of the battery for energy trading in the microgrid. The obtained optimal strategies are managed to minimize the cost of the microgrid, and simultaneously guarantee the better performance of the battery such that service life of battery is extended. First, the microgrid model is constructed, where renewable energy and profiled loads are considered. To satisfy the microgrid demand and the environmental energy consumption, an integral Q-learning scheme will be developed for the input policy of microgrid energy system such that the model dynamics are avoided. Moreover, the minimized cost and the property of the battery are considered in the performance function. Rather than the traditional Q-learning iterative scheme, this paper proposes a self-learning architecture for the integral-Q learning scheme with two neural networks. The first network can learn the optimal Q-function. Another network is used to learn the optimal action such that the Q-function is minimized and load demand can be satisfied. The network weights are updated with the gradient method and the stability analysis is presented. Finally, the experiment data is used to verify the proposed integral Q-learning scheme.

Optimizing Hybrid Photovoltaic/Battery/Diesel Microgrids in Distribution Networks Considering Uncertainty and Reliability

Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated.

Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions

Microgrid technology offers a new practical approach to harnessing the benefits of distributed energy resources in grid-connected and island environments. There are several significant advantages associated with this technology, including cost-effectiveness, reliability, safety, and improved energy efficiency. However, the adoption of renewable energy generation and electric vehicles in modern microgrids has led to issues related to stability, energy management, and protection. This paper aims to discuss and analyze the latest techniques developed to address these issues, with an emphasis on microgrid stability and energy management schemes based on both traditional and distinct approaches. A comprehensive analysis of various schemes, potential issues, and challenges is conducted, along with an identification of research gaps and suggestions for future microgrid development. This paper provides an overview of the current state of the field and proposes potential areas of future research.

Reconfigurable hybrid micro-grid with standardized power module for high performance energy conversion

This paper proposes a reconfigurable power module system for hybrid micro-grid to reduce the designing cost with standardized software and hardware components. The developed system is composed of three layers including application function layer, interconnection layer and physical layer with the capability to be interfaced with different types of energy resources for the hybrid micro-grid. By implementing the multi-layer power module system, the energy conversion designing procedures for different types of resources can be generalized and automated with less cost on the specified application scenarios. The control functions for different energy resources can be extracted from the application function layer. The desired number of standardized power modules can be integrated from the physical layer to form different power converter topologies. The coordination of the power converter, micro-controller, sampling, high level control is managed by the interconnection layer to construct the complete energy conversion system. The major advantageous results cover three aspects. Firstly, the proposed reconfigurable system generalizes the energy conversion of the hybrid micro-grid with different AC and DC resources. Secondly, the designing procedures of the hybrid micro-grid energy conversion system are simplified with less cost for various interfaced applications. Thirdly, the energy conversion performance and reliability of the micro-grid is improved with standardized power modules and self-restoration capability. The experimental test verified the proposed system.