Microgrid Energy Research Articles

Design of a New Energy Microgrid Optimization Scheduling Algorithm Based on Improved Grey Relational Theory

Design of a New Energy Microgrid Optimization Scheduling Algorithm Based on Improved Grey Relational Theory

An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system.

The cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introduced in the proposed work to close this gap and promote load curtailment during peak hours. IBDR policy rewards participant customers with incentives for load curtailment which in turn lowers emissions and generation costs. Furthermore, a trade-off approach ensures both environmental and economic sustainability by striking a balance between cost reduction and emission reduction. Considering the fact in view that the 30-40% of the microgrid customers are willing to participate in the IBDR program, six different scenarios that have been analysed, each of which involves various levels of grid participation and different approaches to pricing in the electricity market. These scenarios also include the implementation of demand response programmes. Differential evolution algorithm was used as the optimization tool for the study. The results achieved for all the scenarios demonstrate the suitability and effectiveness of implementing the suggested IBDR strategy in terms of cost savings. According to numerical results reported, the generating cost decreased by 10-13% with the inclusion of IBDR. Additionally, a 6-8% reduction in peak and 4-5% improvement in load factor was also realised as a positive impact of the IBDR policy. The weighted economic emission dispatch algorithm offered a balanced solution that considered both the minimum generation cost and emissions for various load models in the microgrid system.

Artificial Intelligence Based Hybrid ASFO-ESVM for Load Demand Prediction in Micro Grid Energy Management

Predicting load demand is relevant when used in microgrid energy management systems to address issues such as nonlinear and dynamic consumption data. In this research, the author presents a fusion of Adaptive Sunflower Optimization (ASFO) and Enhanced Support Vector Machine (ESVM) methods to predict the load demand in micro grid environment. The ASFO algorithm enhances the efficiency of the ESVM through a fine-tuning meta-heuristic algorithm based on the sunflower natural organisms. This integration of ASFO and ESVM eliminates many of the drawbacks associated with the basic performance of the task, namely low speed of convergence, overtraining, and the presence of local minima in choosing the parameters. Some of the general parameters used in training and validating the model include load and meteorological data features involving, weather, temporal, load histories are the main contributors in the analysis. Comparisons with other ML algorithm ‘shave been made in respect of relative performance against established methods, such as Random Forest (RF) and Particle Swarm Optimization based with ESVM (PSO-ESVM). The findings infer that lower values of Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) and higher consistency index (d) are yielded by the proposed hybrid ASFO-ESVM model. For instance, even on working days of the week, the precision of the load forecasts was higher with the hybrid model than with the other options. The outcomes do prove that the proposed ASFO-ESVM model is very reliable and precise in its concerning aspect of load demand forecasting as it can be seen in the results obtained for different situations. Relatively, this work estimates a cost effective and feasible method for micro grid energy predictions which can enhance decisions in matters concerning power production, distribution, and control of energy. The study shows how these techniques are relevant towards the complexity and dynamism of the contemporary energy systems.

A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm

A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm

Research on centralized protection scheme of new energy microgrid based on graph theory matrix algorithm

Research on centralized protection scheme of new energy microgrid based on graph theory matrix algorithm

A MINI REVIEW OF RENEWABLE ENERGY STRATEGIES FOR PULAU PERHENTIAN NET ZERO JOURNEY

Sustainable energy solutions and carbon emissions reduction is imperative in the face of escalating environmental challenges. Pulau Perhentian, a pristine island paradise, serves as a compelling case study for exploring energy transition strategies towards achieving a net zero footprint. This paper review critically assesses existing literature on Pulau Perhentian energy transition initiatives, with a focus on renewable energy integration, microgrid systems, and energy efficiency measures. The review highlights the significance of energy consumption on the island's ecosystem and socio-economic development, emphasizing the urgency of reducing fossil fuel reliance. Microgrid systems emerge as pivotal for localized energy production, resilience, and community engagement. The paper explores energy storage, management strategies, and the challenges of implementing sustainable practices across sectors. By synthesizing existing knowledge, the review informs effective energy transition pathways for Pulau Perhentian and beyond. Insights extend to global policymakers, researchers, and practitioners engaged in similar efforts. Ultimately, this review underscores energy transition's critical role in preserving Pulau Perhentian's natural beauty while fostering sustainability and resilience.

Distributed Low-Carbon Energy Management of Urban Campus for Renewable Energy Consumption

In order to solve the mismatch between renewable energy and load in urban building microgrids, that is, the problem of renewable energy consumption in building microgrid clusters, while preserving the privacy of each user, this paper proposes a distributed low-carbon energy management method for urban building microgrid clusters. First, a low-carbon energy management method for the urban building microgrid is proposed in order to coordinate the power sharing of various subjects to minimize the total economic cost, unleash the consumption potential of low-carbon building clusters for renewable energy, and reduce carbon emissions on the spatial and time scale. Second, an ADMM-based distributed optimal energy management method is proposed to meet user energy needs while preserving local privacy; this includes energy storage systems, renewable energy generation, and the loads within each urban building microgrid. Finally, simulation experiments are conducted based on actual data from a certain area in Hangzhou, China, and the results verify the effectiveness of the model.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

The role of hybrid hydrogen-battery storage in a grid-connected renewable energy microgrid considering time-of-use electricity tariffs

Hybrid hydrogen (H2)-battery BT integrated microgrid has gained significant interest lately as a key element for achieving a zero-emission future, thanks to its wide range of applications. The energy management strategy (EMS) of the H2- BT storage-based microgrid is critical for ensuring efficient and cost-effective electricity generation by controlling the operating point of the storage systems to achieve economic and operational benefits. This paper presents an optimal energy management and sizing strategy for a hybrid H2-BT storage-based grid-connected microgrid, considering two scenarios of Time-of-Use (ToU) electricity tariffs. The uniqueness of this study lies in its improvement of microgrid effectiveness through the optimization of component size and EMS. Among the two scenarios, the “Time of Use-Flat” scenario is more cost-effective and the best option. This conclusion is based on the results which indicate that LSC-SSA method achieved the lowest Annual System Cost (ASC) of $544,818 and the lowest Levelized Cost of Energy (LCOE) of $0.273 in this scenario. These metrics are better compared to the “Flat-Time of Use” scenario, where the LSC-SSA method achieved an ASC of $552,773 and an LCOE of $0.277. Therefore, the “Time of Use-Flat” scenario provides greater cost-effectiveness for the hybrid H2-BT integrated microgrid.

Micro-grid source-load storage energy minimization method based on improved competitive depth Q - network algorithm and digital twinning

Aiming at the frequency instability caused by insufficient energy in microgrids and the low willingness of grid source and load storage to participate in optimization, a microgrid source and load storage energy minimization method based on an improved competitive deep Q network algorithm and digital twin is proposed. We have constructed a basic framework structure for the coordinated operation of source grid load and energy storage, and analyzed the modules on the power supply side, grid side, load side, and energy storage side. Under the improved competitive deep Q network algorithm, modifications were made to the energy storage of microgrid loads. Based on the processing results, the objective function for optimizing microgrid source load energy storage is constructed using digital twin technology, and the optimization of the objective function is achieved to solve the optimization objective function for microgrid source load energy storage and complete the optimization of microgrid source load energy storage. The experimental results show that this method can control the distortion rate within 5.12%, with frequency fluctuations around 50.0 Hz, and relatively good MSE, MAE, and R2 values. This method can effectively control frequency fluctuations and has a good effect on optimizing energy storage for microgrid power sources and loads.

Feasibility of renewable energy microgrids with vehicle-to-grid technology for smart villages: A case study from India

The socioeconomic development of a nation is inextricably linked to the availability of electricity. A significant portion of the worldwide inhabitants, specifically 840 million people, still lacks access to electricity, as per the World Sustainable Energy Goals Report. The Integrated Rural Energy Planning program aims to provide environmentally sound energy planning for domestic utilization. This paper adopts the 100 % household survey methodology for primary data collection for estimating energy potential and energy utilization patterns in the alamarathupatti village of Dindigul district, Tamil Nadu. The survey comprises the systematic questioner to obtain the accurate primary data of the hamlet. Based on the survey, hamlet-wise peak power demand was identified. Therefore, systematic energy planning is formulated for peak shaving by incorporating renewable energy generations with energy storage system. This paper elaborates on the proposed renewable integrated smart Micro (or) nano grid for peak shaving of the power grid. The primary source of the smart microgrid is solar photovoltaic-powered vehicle-to-grid (V2 G) energy storage technology and biomass energy conversion. Biogas generation through anaerobic digestion and producer gas generation through gasification meet the village's commercial electrical energy demand through a dual-fed generator set coupled with a smart microgrid to enhance the rural farmer's livelihood. Solar photovoltaic-powered V2 G shares 75 %, and biomass energy conversion shares 25 % of peak demand for effective peak shaving by the proposed smart microgrid. Therefore, proper rural energy planning with the renewable integrated microgrid system could enhance livelihood and leading to its economic development in an environmentally sustainable manner.

Virtual Generator to Replace Backup Diesel GenSets Using Backstepping Controlled NPC Multilevel Converter in Islanded Microgrids with Renewable Energy Sources

This work presents an islanded microgrid energy system that uses backstepping control applied to neutral point clamped (NPC) multilevel converters coupled with batteries to behave as virtual generators, able to absorb surplus renewable energy, therefore increasing the penetration of renewable energy sources. Additionally, on a charged battery the virtual generator allows turning-off the backup diesel generator set (GenSet). Aside from improving energy efficiency, the battery-connected multilevel converter aims to regulate frequency, improves power quality, and keeps the microgrid operational in the event of a GenSet failure. The backstepping controlled NPC multilevel converter emulates a virtual generator injecting power to perform as the primary and secondary microgrid frequency controller. Additionally, AC voltage control is implemented, which enables running the islanded microgrid only with multilevel converters, supplied by the battery while integrating solar and wind energy sources. Energy demand and renewable energy forecasts are used to manage the battery state-of-charge. Simulation results, obtained from switched and phasor models show that energy storage and the backstepping frequency control enables the compensation of power fluctuations from renewable energy sources. Furthermore, in the event of the main GenSet failure, the controlled virtual generator keeps the microgrid running for a few minutes, until another GenSet is ready to supply the microgrid. Therefore, the microgrid integration of the battery-connected multilevel converter results in a significant boost in energy efficiency by allowing the disconnection of the backup GenSet.

RETRACTED ARTICLE: Prioritizing customer and technical requirements for microgrid battery integration via a house of quality-driven decision-making approach

The purpose of this study is to make evaluation regarding significant issues about the customer expectations and technical competencies for successfully integration of batteries in microgrid systems. In this direction, six different customer expectations and technical requirements are identified by considering literature review results. The weights of the criteria are computed with sine trigonometric Pythagorean fuzzy (STPYF) decision making trial and evaluation laboratory (DEMATEL). Moreover, technical requirements are also ranked using a newly developed technique in this study called “ranking technique by geometric mean of similarity ratio to optimal solution” (RATGOS). This new methodology is also integrated with STPYF sets. The main contribution of this study is that it can be much easier to increase the efficiency of battery integration in microgrid systems by making the priority analysis. Moreover, proposing a new ranking-based decision-making technique (RATGOS) has an increasing impact on the methodological originality. On the other side, owing to considering DEMATEL approach in criteria weighting, the causal directions between these factors can be understood. This situation can be accepted as an important superiority of this model by comparing with the previously generated ones. It is determined that efficiency of storing energy is the most critical customer expectation to increase the effectiveness of this process. Furthermore, the ranking results also demonstrate that generating smart battery control systems is the most important technical requirements to have higher performance in microgrid energy systems. It is identified that the proposed model generates similar findings with the previous ones. Based on these results, some strategies should be implemented to increase the efficiency of energy storage processes in microgrid systems. Within this framework, choosing the right battery is of vital importance. Technological infrastructure is necessary to achieve this goal. Similarly, it is important to provide cyber security to increase the efficiency of energy storage processes. In this way, it is possible for the batteries to work more safely against external interventions.

Optimization Strategy for Integrated Energy Microgrids Based on Shared Energy Storage and Stackelberg Game Theory

The implementation of community power generation technology not only increases the flexibility of electricity use but also improves the power system’s load distribution, increases the overall system efficiency, and optimizes energy allocation. This article first outlines the operational context of the system and analyzes the roles and missions of the various participants. Subsequently, optimization models are developed for microgrid operators, community power storage facility service providers and load aggregators. Next, the paper explores the game relationship between microgrid operators and load aggregators, proposing a model based on the Stackelberg game theory and proving the presence and singularity of the Stackelberg equilibrium solution. Finally, simulations are conducted using Yalmip tools and the CPLEX solution on the MATLAB R2023a software platform. A combination of heuristic algorithms and solver methods is employed to optimize the strategies of microgrid operators and load aggregators. The research findings show that the proposed framework is not only able to achieve an effective balance of interests between microgrid operators and load aggregators but also creates a win–win situation between load aggregators and shared energy storage operators. Additionally, the solution algorithms used ensure the protection of data privacy.

Efficient voltage control strategy: observability design for multistage DC–DC buck converter

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential.

Microgrid energy optimization management considering consumer segmentation optimization and dynamic time intervals

ABSTRACT To prioritize critical loads and enhance microgrid energy management efficiency, this study introduces a method that combines consumer segmentation optimization and dynamic time intervals. A penalty-based approach is used to minimize unmet load demands, prioritizing energy for critical loads. A joint classification model for multi-type consumers is developed using a convolutional neural network autoencoder and hierarchical clustering. Dynamic time intervals are applied to reduce iterations and improve memory and processor efficiency in solving the energy management model. Simulation tests on a microgrid with distributed generation, battery storage, and consumption units confirm the method’s effectiveness. Results show that configuring dynamic time intervals and segmenting loads by priority yield solutions similar to fixed interval methods. Finally, the energy optimization management effects of the proposed method were compared with those of two latest methods. The comparison results demonstrate that if a microgrid underwent four different disconnection scenarios from the main distribution network, the proposed method saves 23.15%, 23.08%, 23.79%, and 34.61% time to achieve energy optimization management compared with that of the first latest method, and 24.20%, 23.87%, 25.11%, and 36.18% time than that of the second latest method.

Integrated energy microgrids participating in voltage regulation ancillary services: An improved ADMM based distributed optimization approach

AbstractThe integrated energy microgrid (IEM) has good potential for both active and reactive power regulation, which are gradually becoming critical resources for participating in power system ancillary services. This paper constructs a distributed optimization model for the IEMs participating in voltage regulation ancillary services by considering multiple physical network constraints. To reduce computational complexity, the DistFlow power flow model and the second‐order cone relaxation method are introduced to transform the optimization model into a second‐order cone programming problem. Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. Finally, the proposed optimization model and solution method are tested in the modified IEEE‐33 node test case to verify their effectiveness and feasibility.

Micro-Energy Grid Energy Utilization Optimization with Electricity and Heat Storage Devices Based on NSGA-III Algorithm

Micro-Energy Grid Energy Utilization Optimization with Electricity and Heat Storage Devices Based on NSGA-III Algorithm

Efficient framework for energy management of microgrid installed in Aljouf region considering renewable energy and electric vehicles

This paper proposes an efficient one-to-one-based optimizer as a new energy management method for a grid-connected microgrid in order to address both environmental and economic concerns. The suggested approach is distinguished by its robust exploration capabilities that allow the technique to reach the global solution and avoid local ones, along with its ease of deployment. The microgrid under consideration consists of conventional resources, microturbine, fuel cell, storage batteries, and electric vehicles, as well as renewable energy sources like photovoltaic and wind turbine. Real-time 24-hour solar irradiance, wind speed, and air temperature data of Sakaka, Aljouf region in Saudi Arabia located at 29° 58′ 15.13″N latitude and 40° 12′ 18.03″E longitude are utilized while the stochastic natures of renewable resources have been modeled using Beta and Weibull probability distribution functions. Various scenarios of renewable resources’ generations as well as electric vehicle’s charging states are analyzed. A thorough comparison is made with the published krill herd optimizer, in addition to other programmed algorithms such as grey wolf optimizer, Runge Kutta optimization, salp swarm algorithm, hippopotamus optimization algorithm, and Newton Raphson based optimizer. Also, the suggested approach is validated statistically through the use of Kruskal Wallis, Friedman, ANOVA, and Wilcoxon rank tests. With renewable resources working normally, the recommended strategy outperformed the published krill herd optimizer in terms of operating cost savings and emission reductions, which were 53.85 % and 46.62 %, respectively. While during the rated operation of renewable resources, the net savings and emission reductions were 10.14 % and 38.91 %, respectively. Additionally, the greatest cost savings during connecting electric vehicles at smart charging mode was 55.69 % as compared to the published approach. The suggested strategy can be recommended as an effective method for managing microgrid energy.

Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs.

Recent advancements in sensor technologies have significantly improved the monitoring and control of various energy parameters, enabling more precise and adaptive management strategies for smart microgrids. This work presents a novel model of an energy management system (EMS) for grid-connected polygeneration microgrids that allows optimizing the management of electrical storage systems, electric vehicles, and other deferrable loads such as heat pumps. The main novelty of this model is that it incorporates both climate comfort variables and the consideration of the degradation of the energy storage capacity in the control strategy, as well as a penalty for the dumping of surpluses. The model has been applied to a smart, sustainable building as a case study. The results show that the proposed model is highly adaptable to diverse weather conditions, minimizing renewable energy losses while satisfying the energy demand and providing comfort to the building's users. The study shows (i) that EVs' dynamic charging schedules play a crucial role, (ii) that it is possible to minimize a battery's degradation by optimizing its cycling, averaging one cycle per day, and (iii) the critical impact of seasonal weather patterns on microgrid energy management and the strategic role of EVs and storage systems in maintaining energy balance and efficiency.