Integration Of Microgrids Research Articles

Capacity allocations of SOPs considering distribution network resilience through elastic models

Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method.

Integration of Microgrids and Electric Vehicle Technologies in the National Grid as the Key Enabler to the Sustainable Development for Rwanda

Rwanda is an East African Community (EAC) nation with rapid and remarkable past development in different sectors and still with the ambitious targets and plans to be achieved in the coming years ahead. The government plans universal electricity access by 2024 with 52% grid connection and 48% off-grid connections. In the transport sector, the concept of electric vehicles has been initiated and started in order to contribute to the UN Paris agreement and decrease the reliance of the transport sector on gaseous fuels which are one source of air pollutants leading to climate change, premature deaths, and morbidity associated with poor air quality. With higher electricity demand than the generation of the Rwandan power grid, different energy strategies are being developed with the overall objective to achieve the targeted universal energy access. In order to overcome the aforementioned issue, this paper proposes an integration of solar PV microgrids for the satisfaction of electric vehicle (EV) technology in Rwanda. Using HOMER Grid software, a managed EV charging station is simulated to a grid connected solar PV microgrid with storage in order to assess the economic impact. The results show that the proposed technology can lower the levelized cost (LCOE) of electricity by 139.7%. This study can contribute to further research developments in either different perspectives related to the integration of distributed energy resources (DERs) with electric vehicles or studies related to affordable and environment-energy systems.

A Comprehensive Review on Integration Challenges, Optimization Techniques and Control Strategies of Hybrid AC/DC Microgrid

The depletion of natural resources and the intermittence of renewable energy resources have pressed the need for a hybrid microgrid, combining the benefits of both AC and DC microgrids, minimizing the overall deficiency shortcomings and increasing the reliability of the system. The hybrid microgrid also supports the decentralized grid control structure, aligning with the current scattered and concentrated load scenarios. Hence, there is an increasing need to explore and reveal the integration, optimization, and control strategies regarding the hybrid microgrid. A comprehensive study of hybrid microgrid’s performance parameters, efficiency, reliability, security, design flexibility, and cost-effectiveness is required. This paper discusses major issues regarding the hybrid microgrids, the integration of AC and DC microgrids, their security and reliability, the optimization of power generation and load management in different scenarios, the efficient management regarding uncertainty for renewable energy resources, the optimal placement of feeders, and the cost-effective control methodologies for the hybrid microgrid. The major research areas are briefly explained, aiming to find the research gap that can further improve the performance of the grid. In light of the recent trends in research, novel strategies are proposed that are found most effective and cost-friendly regarding the hybrid microgrid. This paper will serve as a baseline for future research, comparative analysis, and further development of novel techniques regarding hybrid microgrids.

Integration of DC Microgrid with Existing System

Modern trends of power circle are swiftly tending towards more sensitive form of energy generation to sustain the sensitive loads like electric-powered, commercial loads. By integrating the DC microgrid, we can increase the quality of power. DC microgrid has good use rather than AC microgrid. DC microgrid is the best solution to the raised problems like reliability, stability and efficiency of the existing system. That's why here we integrating DC micro grid for good power quality.

Power quality improvement in a photovoltaic based microgrid integrated network using multilevel inverter

Abstract The increasing use of power electronics devices as well as the integration of renewable source-based microgrids (MG) has seriously affects the power quality (PQ) of the three-phase power system. Therefore, for the improvement of PQ, it is required to reduce the total harmonics distortion (THD) in the utility network. In this work, the improvement of PQ is discussed in a photovoltaic (PV) based MG integrated three-phase system using a three-level H-bridge (3LHB) multilevel inverter (MI). The MI is used for compensating the source current harmonics and reducing the THD by meeting the IEEE standard guidelines. Besides, the proposed model helps manage the reactive power with control of DC link bus voltage through the PV system. The proposed model is helpful not only in reducing the harmonics but also in providing additional active power to the load if any electrical disturbances occur on the grid side. The maximum power point tracking (MPPT) technique employed in PV is of an improved form of Perturb and Observe (P&O). Further, the reference current generation is derived using the direct current control (DCC) and indirect current control (ICC) techniques. The MG integrated MI is investigated in both DCC and ICC method using three different DC bus voltage controllers; proportional-integral (PI), fuzzy logic controller (FLC), and fuzzy sliding mode control (FSMC). The proposed microgrid integrated system is analyzed with the MATLAB/Simulink tool.

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration: A comprehensive review

The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single-objective and multi-objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system.

Power Management of a Hybrid Micro-Grid with Photovoltaic Production and Hydrogen Storage

To deal with energy transition due to climate change and a rise in average global temperature, photovoltaic (PV) conversion appears to be a promising technology in sunny regions. However, PV production is directly linked with weather conditions and the day/night cycle, which makes it intermittent and random. Therefore, it makes sense to combine it with Energy Storage Systems (ESS) to ensure long-term energy availability for non-interconnected micro-grids. Among all technological solutions, electrolytic hydrogen produced by renewable energies seems an interesting candidate. In this context, this paper proposes a control strategy dedicated to hydrogen storage integration in micro-grids for a better use of PV production. The objective is to optimize the management of the micro-grid with proton exchange membrane Fuel Cell (FC), alkaline Electrolyzer (El), lithium-ion Batteries Energy Storage System (BESS) and PV, according to the system state and PV production intermittency. First, a control strategy based on a Distributed explicit Model Predictive Control (DeMPC) is developed to define current references for FCs, Els and batteries. Secondly, the performance of the control strategy is validated in simulation and confirmed on a Power-Hardware-in-the-Loop test bench.

Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented [formula omitted]-constraint method

The integration of microgrids and the combined cooling heating and power (CCHP) systems can foster a better utilization of energy. In order to achieve economic optimization and peak-load reduction of the CCHP microgrids model, this paper proposes a multi-objective optimal scheduling model for CCHP microgrids integrated with renewable energy, energy storage system and incentive based demand response. First, linearization methods are applied to change the original nonlinear optimization model into a mixed-integer linear programming (MILP) problem. Then, an augmented ε-constraint (AUGMECON) method is implemented to solve the multi-objective optimization problem (MOP). Finally, the final scheme is selected from the obtained Pareto optimal set by fuzzy clustering method according to the preference of decision maker. The results show that the CCHP microgrids is effective in reducing pollutant gas emissions and reducing the cost of treating them. And compared with the other four intelligent algorithms, the proposed MILP method has better accuracy and computational efficiency. In addition, with the inclusion of the peak-load shifting function, the interruptible load and the battery can effectively respond to peak load changes by shifting the peak of the exchange power curve in the point of common coupling (PCC) of the CCHP microgrids. In the end, the sensitivity analysis is carried out and the results present that electricity price, natural gas price, and the efficiency of PV have varying degrees of impact on model performance.

Energy sharing platform based on call auction method with the maximum transaction volume

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia’s electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high.

Sizing and Siting of Energy Storage Systems in a Military-Based Vehicle-to-Grid Microgrid

Due to the absence of utility power grid infrastructure in remote military bases, on-site diesel generators serve as the primary sources for power demands. Increasing efficiency and preventing frequent startup/shutdown operations of on-site diesel generators are therefore becoming a critical issue for reducing fuel cost. Application of vehicle-to-grid technology in a military-based microgrid embodies potential for significant fuel economy benefits since on-board vehicle generators and energy storage units can serve as mobile power sources that provide higher flexibility for supplying power demands. In addition, energy storage system integration is considered as an alternative solution for increasing on-site diesel generators efficiency and lessening their startup/shutdown operations. This article proposes a three-stage planning procedure for identifying the optimal locations and capacities of energy storage systems, considering multiple operating scenarios via stochastic programming. Note that on-site diesel generators and on-board vehicle generators support plug-and-play functionality, meaning their startup/shutdown operations can be decided in real time. Furthermore, network-constrained ac unit commitment model is used to optimize operation of microgrids. It is assumed that in the tested microgrid systems, several tactical military vehicles with on-board generators and energy storage units are deployed as alternative power sources. The economic merits of vehicle-to-grid implementation and energy storage system integration in a military-based microgrid are validated in the numerical studies.

A Research Survey on Microgrid Faults and Protection Approaches

The conventional electrical system, production of power is happened in several places also power transmitted into the grid and finally distribution takes place to the individual customers. In conventional powergrid suffers high capital cost, finite reliability, rising greenhouse gases emissions and growing power losses in the transmission line. The power company is choose an interconnecting diverse nonconventional form small generations close to user places, based on their demand also giving smart lever to the power grid. An extensive result, power generation from microgrid is recommended per scientists it could supplies secure, standard and effective supply of source to individuals. Microgrid integration is already available power delivery network forms more intricate to the radial power distribution network. It further lead to magnitude of fault current is vary aggressively based on operation methods (isolated or grid integrated), source, status, also total distributed production of power. In existing protecting control strategies are modelled in radial energy circulation and centralized production of power, it creates available protecting approach is fall in the microgrid. So, effort is carried out to re-examine the fundamental conceptions also importance, problems experienced by microgrid as theme of diverse protection approaches.

Improving the Power Quality of Smart Microgrid Based Solar Photovoltaic Systems

Microgrids are hybrid power systems that consist of several distributed generation resources and local loads that can supply electrical power to remote or specific areas. The integration of microgrids with the utility network is one of the most recent technologies developed in countries like Egypt. One area of study is how the integration of smart microgrids and utility systems can be used to solve power quality problems such as voltage sags, increased use of distributed generators, deep energy, and power loss. This paper is aimed at investigating a possible solution to some common and dangerous power quality issues associated with the integration of smart microgrids and utility systems such as voltage fluctuation and total harmonic distortion (THD) at different solar irradiance and load conditions. This study used a MATLAB and Simulink code developed to model and analyze smart microgrid and utility system integration and the power quality issue at different loads. This study focuses on five scenarios of voltage analysis and two scenarios of THD at different irradiance and load conditions. The results show that using an integrated smart microgrid and utility system will reduce the voltage drop percentage with high solar irradiance and will increase it with low solar irradiance at both high and low loads. Additionally, THD decreases with increasing solar irradiance and increases with decreasing solar irradiance at both high and low loads.

A centralized monitoring interface for microgrid using lab view

Micro-grids care the electric utility main grid through allowing the integration of emergent distributions of different energy resources. In addition, distributed energy resources (DERs) decrease energy losses and improving the efficiency of the electric power system. The DERs integrated micro grid structure will involuntarily require perfect, specific, as well as reactive power control to make sure well-organized power flow. It is important to cultivate a back-end system (user interface) to permit simple access to the whole power system development. This paper proposes a Lab VIEW based user-friendly front-end and back-end interfaces for PV system integration in micro grid. The proposed interface permits a control programming in the block end which offers the opportunities for deploying the comprehensive and inclusive control strategies. The system consists of load scheduling and PV generation during load dispatch epoch. The proposed LabVIEW control system also manage to pay for monitoring and tracking of maximum PV power point selections which allow insightful PV analysis forecast to establish the subsequent future exploit. The proposed PV interface used artificial neural network back propagation for the PV power Load Forecasting.

Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid

Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

Presently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.

Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network

Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.

Probabilistic Load Forecasting Based on Adaptive Online Learning

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.

Technological and Operational Aspects That Limit Small Wind Turbines Performance

Small Wind Turbines (SWTs) are promissory for distributed generation using renewable energy sources; however, their deployment in a broad sense requires to address topics related to their cost-efficiency. This paper aims to survey recent developments about SWTs holistically, focusing on multidisciplinary aspects such as wind resource assessment, rotor aerodynamics, rotor manufacturing, control systems, and hybrid micro-grid integration. Wind resource produces inputs for the rotor’s aerodynamic design that, in turn, defines a blade shape that needs to be achieved by a manufacturing technique while ensuring structural integrity. A control system may account for the rotor’s aerodynamic performance interacting with an ever-varying wind resource. At the end, the concept of integration with other renewable source is justified, according to the inherent variability of wind generation. Several commercially available SWTs are compared to study how some of the previously mentioned aspects impact performance and Cost of Electricity (CoE). Understanding these topics in the whole view may permit to identify both tendencies and unexplored topics to continue expanding SWTs market.

California's adoption of microgrids: A tale of symbiotic regimes and energy transitions

California is a pioneer in energy system innovations, and it is not surprising that the state is leading the nation in the adoption of microgrids. However, the inherent characteristics of microgrids make them challenging to integrate and operate within the current energy system. This is mainly because unlike other decentralized energy systems, microgrids often tend to interact with the centralized grid infrastructure as both a parallel and a competing distribution grid model at the micro scale. The adoption and integration of microgrids, therefore, represents a socio-technical transition in the electric power sector. In this context, this study relies on the Multi-Level Perspective framework to examine the drivers, contexts, processes, and policies that have enabled an ongoing transition in California's electricity system that has allowed adoption of microgrids. The analysis shows that even though several drivers carry weight, including resilience concerns and market factors, California's pathway to microgrids is largely driven by the legislative and regulatory push toward clean energy and a symbiotic relationship between powerful regime actors and a microgrid niche. This is enabling a transformation in the state's energy system, in which the entry of microgrids is subtly facilitated by the regime in a way that does not threaten the current utility-dominated electricity system. By providing a detailed account of how a decentralized energy system that challenges the centralized grid regime has made its place in the centralized grid regime in California, this paper identifies the key facets that affect the integration of microgrids in the broader context of energy transitions.

Adaptive overcurrent protection scheme suitable for station blackout power supply of nuclear power plant operated through an integrated microgrid

Inclusion of distributed energy resources based integrated microgrid (IMG) into emergency station blackout (SBO) (EM-SBO) power system of nuclear power plant (NPP) has complicated its existing protection scheme due to varying system fault level with bidirectional flow of power. During SBO in NPP, it is of topmost priority to safeguard the reactor through EM-SBO supply. However, if any severe fault occurs, it may lead to complete collapse of the safety power supply in the NPP. So, availability of this tiny power system is most important as compared to the tripping of reactor safety of the NPP. In this paper, a 460 MWe NPP station has been considered to implement IMG into its EM-SBO power system and focuses on its overcurrent protection coordination scheme as a case study. The combined fuse and numerical relays inter-linked with digital logic-based adaptive overcurrent protection (AOP) scheme has been proposed after load flow analysis and calculating short circuit fault level through ETAP software. The sensitivity, the selectivity and the speed of the protection scheme have been evaluated in different network configurations like on-grid, semi-islanding and islanding mode of operation of IMG. Finally, it is proposed that the protection schemes vary from case-to-case basis leading to necessity of communication among the relays. So, an advanced AOP scheme with digital logic linked through peer-to-peer Hi-speed communication (IEC 61,850 confirmed GOOSE technology) has been proposed to resolve the protection issue of the modern NPPs operated through IMG.