Microgrid Power Research Articles

A Novel Cyber-Attack Detection and Mitigation for Coupled Power and Information Networks in Microgrids Using Distributed Sliding Mode Unknown Input Observer

A Novel Cyber-Attack Detection and Mitigation for Coupled Power and Information Networks in Microgrids Using Distributed Sliding Mode Unknown Input Observer

A Critical Review on DC Microgrids Voltage Control and Power Management

A Critical Review on DC Microgrids Voltage Control and Power Management

Notice of Violation of IEEE Publication Principles: A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids

Notice of Violation of IEEE Publication Principles A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids, by H. R. Baghaee; M. Mirsalim; G. B. Gharehpetian; H. A. Talebi; A. Niknam-Kumle, in the IEEE Transactions on Industrial Electronics After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This authorship of this paper was revised without providing attribution to, or permission from, the coauthors of an earlier version of the manuscript. Alireza Niknam-Kumle was responsible for removing the original coauthor names and replacing them with new coauthors, and then resubmitting the content in a new manuscript to IEEE Transactions on Industrial Electronics Due to the nature of this violation, reasonable effort should be made to remove all past and future references to this paper. Control of electronically-coupled distributed energy resources and topology/switching strategy of power converters have the essential roles to provide high quality power for microgrids including unbalanced and nonlinear loads. Multi-level inverters (MLIs) are used to act as high quality converters with more conversion efficiency, and less switching-frequency/switching-losses. Nevertheless their nonlinearity, complexity, and solvability features of selective harmonic elimination strategy that has affected its industrial applications, it can provide desirable output waveform by keeping fundamental component in its desired value as well as eliminating/minimizing of low-order harmonics. This paper presents adaptive neuro-fuzzy inference system (ANFIS) as a switching angle estimator for harmonic optimization problem in cascade MLIs, which is trained based on the database including optimal switching angles obtained for inverter with non-equal DC sources by artificial bee colony (ABC) algorithm. The reliable search ability, accuracy and convergence time of ABC have been compared with particle swarm optimization algorithm. Also, performance of ANFIS is compared with artificial neural network based on correlation coefficient and root mean square error. Simulation results, experimental tests and real-time simulations reveals the effectiveness of proposed estimator for online elimination of low-order harmonics of MLIs in microgrids and its superiority over other methods.

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

Optimization of scheduling and control for a combined cooling, heating, and power microgrid system based on GDMOPSO

Optimization of scheduling and control for a combined cooling, heating, and power microgrid system based on GDMOPSO

An Improved Power Quality in a Renewable Energy-based Microgrid System Using Adaptive Hybrid UPQC Control Strategy

Due to its ability to integrate renewable energy, improve energy efficiency, and fortify the power system's resilience, microgrids are widely used as regional energy systems. But these advantages do have certain drawbacks in terms of control and Power Quality (PQ). In order to guarantee the proper operation of equipment that is connected and the system's general health, PQ is crucial in microgrids. For microgrids to operate successfully, governing stratagems in concurrence with front-line power electronics devices bid a firm context to knob PQ challenges like Voltage Sag and Swell, Source/Grid current harmonics, Voltage imbalances, Active and Reactive power compensation etc. Multifunctional systems that can integrate clean energy generation and as well as enhance PQ are necessary to meet the demands of complex loads that have the capability of operating in the situation of an unavailable network grid and power electronics devices, which necessitates the need for clean energy. Hence, this paper provides enactment of an adaptive hybrid control strategy based on an Adaptive Leaky Least Mean Square (AL_LMS) algorithm combined with Fuzzy Logic (FL) to the Unified Power Quality Conditioner (UPQC) in a Solar-PV energy based Microgrid system in improving microgrid power quality. The concert of UPQC is assessed by the conventional PI controller and Fuzzy Logic control with MATLAB/SIMULINK software platform, simulation results are conversed with proportionate studies in improving the PQ by minimizing Total Harmonic Distortion (THD), the Voltage Sag & Swell and the result comparison shows the effectiveness of the Fuzzy Logic in coordination with the AL_LMS algorithm resulting improved power quality within the IEEE Power Quality-519 Standards.

Integration of Solar Photovoltaic Plant in the Eastern Sumba Microgrid Using Unit Commitment Optimization

Integrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia’s Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia.

The Role of Artificial Intelligence in Enhancing Energy Management in Microgrid Systems

This look at investigates the effect of artificial intelligence (AI) on microgrid overall performance thru a quantitative evaluation of strength performance, reliability, and real-time optimization metrics. Two hypothetical microgrid systems, System A and System B, are examined, revealing that AI-pushed power management in System A outcomes in superior effects in comparison to System B. Descriptive facts exhibit that System A reveals higher strength efficiency (85.2%), increased reliability indices, and more advantageous actual-time adaptability, showcasing the capacity advantages of AI integration. These findings align with current literature, emphasizing the transformative position of AI in optimizing decentralized energy structures. The look at indicates that making an investment in AI technologies for microgrid power management holds promise for attaining sustainability and resilience. Future research need to consciousness on empirical research with actual-global facts to validate these findings.

Optimal design of multiuse hybrid microgrids power by green hydrogen–ammonia

The design and operation of hybrid microgrids (MGs) has attracted much interest. The creation of adaptable standalone hybrid systems that can satisfy interconnected clients’ energy needs using coupled green hydrogen–ammonia has been studied less. Additionally, the capability of scaling up hydrogen generation and fuel decarbonization is little studied. This study proposes an optimized design approach for multiuse hybrid MG supplying power gas, heat, and oxygen with green hydrogen and ammonia carriers. The suggested one-layer technique is implemented using PSO and performs both the optimal size and operation of the proposed system based on hourly data intervals to better track load variations. The proposed technique aims to minimize costs besides ensuring maximum reliability in light of load demand and RERs variations. CHMS is implemented to constantly fulfill heat loads by dispatching power and heat of ICE, FC and EBs. Decarbonization of natural gas is suggested by hybrid hydrogen–ammonia rather than hydrogen-based methanation only. The Proposed methodology is tested on a number of scenarios with various setups. The outcomes are evaluated against existing literature from both the same and different research regions. The best LCOE is 0.0519, 0.04122, and 0.05383 $/kWh, with nearly no energy supply loss and no emissions, according to the results of the vital scenarios SC6, SC8, and SC9. The obtained results reveal the following: (1) When CHMS is utilized, a significant reduction in investment cost results. (2) A single-layer optimization strategy can effectively locate the optimal solution with minimum ratios of curtailed energy. (3) The oxygen produced from hydrogen and ammonia-related conversions is essential in reducing total costs.

Optimization of DC Microgrid Power and Energy Management in the Presence of Small-Scale Wind Turbine Integration and Renewable Load Shedding

Improvement of Direct Current Microgrid (DCM) Power and Energy Management is the methodical process of increasing the DCM system’s performance, dependability, and effectiveness. In this study, the suggested a Stochastic Dove Swarm Optimized Deep Reinforcement Learning Algorithm approach to estimate the availability of wind energy, allowing for proactive management tactics. Initially, the data was collected and preprocessed by performing min–max normalization to clean the unwanted data. Recursive Feature Elimination is used to determine the greatest informative attributes. This study analyzes the use of significant performance metrics, including accuracy (91%), precision (92%), recall (93%), Root Mean Square Error (0.154), Normalized Root Mean Square Error (0.0526), and Mean Absolute Percentage Error (4.21). The use of optimization methodology focuses on the seamless integration of SWTs and the application of renewable LS methods to ensure the efficient management of power and energy in DC microgrids. The intention is to keep operational expenses to a minimum while providing a stable and robust energy supply. To ensure power balance, which serves as the primary objective of the monitoring, it is considered imperative for carrying out electricity limitations in this instance of the energy excess generated by solar power plants and the small-scale wind turbine.

An optimized frequency control of green energy integrated microgrid power system using modified SSO Algorithm

An optimized frequency control of green energy integrated microgrid power system using modified SSO Algorithm

Modelling the operation modes of local MicroGrid networks with mobile wind and solar power plants

The basis for the development of the global energy sector is the introduction of alternative energy and, accordingly, a complete change in approaches to the structural reorganisation of energy systems. An analysis of the state of electric power networks in Ukraine shows their almost complete depreciation, low reliability and structural obsolescence, which makes further development and expansion difficult to ensure the rapid growth of electricity consumption. Therefore, a trendy solution to the problem of modernising electric power networks while increasing their capacity and reliability is the use of MicroGrid systems, which are structural elements of the Smart Grid concept based on the intellectualisation of the management of the full cycle of electricity generation, use and storage. The miniaturisation and localisation of smart electric networks makes it possible to minimise power losses and ensure full control over their operating modes. A MicroGrid provides management of distributed energy resources and is essentially a hybrid, as it includes renewable energy sources alongside traditional ones. Regulation of MicroGrid operation modes and power flows in it is the key to increasing the sustainability of the operation processes. The article proposes the use of mobile wind-solar power plants as sources of distributed generation in the MicroGrid system, which is a new technical solution for improving the structural scheme of its implementation and optimising the management of operating modes. The structure of MicroGrid is substantiated and developed, and mathematical modelling of autonomous and hybrid modes of operation of the system with mobile renewable energy power plants is performed. The obtained results show the feasibility and energy efficiency of the developed MicroGrid system. Conclusions are drawn regarding the prospects for further development of MicroGrid with mobile power plants.

Optimal Scheduling of a Renewable Integrated Combined Heat Power Microgrid with Energy Storage and Load Uncertainties

Optimal Scheduling of a Renewable Integrated Combined Heat Power Microgrid with Energy Storage and Load Uncertainties

Research on Optimal Configuration of Integrated Energy Storage for Source Network, Load Storage Considering Carbon Emissions

This study endeavors to construct an optimized operation model for a Combined Heat and Power Microgrid that utilizes renewable distributed power generation. The model decouples the thermoelectric connection via energy storage equipment and takes into account carbon emissions based on multi-energy complementarity. This approach offers an optimization space for the thermal and electrical production of microgrids. Numerical simulation verification demonstrates that the optimization strategy proposed in this study enhances the flexibility of the system’s energy supply and improves the operational economy. Furthermore, the study explores the optimal storage battery capacity under the integration of a specific area’s source-grid-load-storage.

Techno-economic analysis of reactive power management in a solar PV microgrid: A case study of Sunyani to Becheam MV feeder, Ghana

Solar photovoltaic (PV) electricity offers a promising solution for the increasing demand for clean and sustainable energy, particularly in developing countries like Ghana. Power utility operators maintain power quality as their utmost priority per benchmarks established by regulators. Some researchers argue that increasing variable renewable sources in the grid compromises its stability, leading to poor power quality. This paper aims to evaluate the suitability of reactive power management (technical aspect) in a solar PV microgrid in meeting regulators' power quality benchmarks. It also presents the economics associated with reactive power management. The study uses Python optimisation algorithms to achieve the technical objective. The net present value (NPV), profitability index (PI) and discounted payback period (DPP) are the econometrics used to assess the economic viability of reactive power management in this study. The results show that optimising reactive power in microgrids reduces power losses, increasing the utility's energy sales. An annual energy savings of 1,985,600 MWh was made due to the reactive power optimisation, leading to significant economic returns for the microgrid. The NPV, PI and DPP calculated from the study were GHS 12,528,583,512, 10,404.3, and one year, respectively. Therefore, it suggests reactive power optimisation to microgrid and grid operators as a viable solution to meet regulators' benchmarks.

Harmony search meta-heuristic algorithm based on the optimal sizing of wind-battery hybrid micro-grid power system with different battery technologies

Wind power is a clean and renewable energy source that can contribute to sustainable development. To improve the utilization rate of wind power and reduce the cost of its use in grid-independent areas, this study performs an in-depth evaluation and analysis of the optimal design of wind-battery hybrid micro-grid power schemes with different battery technologies such as Nickel-Iron, Lithium-Ion, and Lead-Acid (LA). First, a mathematical model of a stand-alone wind-battery hybrid micro-grid power scheme is established, to evaluate the overall operation cost and reliability and estimate the optimal design of the micro-grid power scheme with different battery technologies. Then, an improved harmony search meta-heuristic algorithm is presented to optimize the system cost and reliability as the output, which was conceptualized using the musical process. The proposed meta-heuristic algorithm is compared using the original harmony search algorithm. Finally, the effects of wind speed, load demands, interest rate, battery lifetime, and the battery's depth of discharge are investigated for the overall operation cost and reliability. Experimental results show that the proposed method is suitable for solving optimal design problems and can reduce the total cost by 13.5 % and can reduce the average simulation time by 1.5 % compared to the original harmony search method. In different scenarios, when an LA battery is selected for the hybrid micro-grid power system; it is suitable for solving the power supply problem and can reduce the total cost and can increase the reliability of the scheme compared to the other case. Finally, system optimization can effectively contribute to the expansion of wind energy use and sustainable development.

A cooperative resilience-oriented planning framework for integrated distribution energy systems and multi-carrier energy microgrids considering energy trading

Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.