Microgrid Energy Research Articles

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.

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

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.

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model.

Short‐term energy forecasting using deep neural networks: Prospects and challenges

AbstractThis study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

Capacity planning of storage batteries for remote island microgrids with physical energy storage with CO2 phase changes

Energy storage devices based on the CO2 thermal cycle (CTC) lose competitiveness in cold regions because of the decreased energy volume density and charge–discharge efficiency at low ambient temperatures. The CO2 hybrid thermal cycle (CHS) combines the CTC with the CO2 hydrate thermal cycle (CHT) to maintain a high energy volume density and charge–discharge efficiency regardless of the ambient temperature. The CHS-based energy storage system has been shown to have an energy volume density of 80–140 kWh/m3 and charge–discharge efficiency of 60 %–106 %. However, the economic feasibility of this system has not been considered. In this study, a numerical analysis was performed on the practical application and economic feasibility of CHS-based energy storage for the 100 % renewable energy microgrid of a pair of remote islands. In a case analysis of CHS installation in a microgrid for a remote island with 100 % renewable energy, the construction cost of CHS was 2/3 of that of pumped storage; the equipment cost reduction of CHS strongly depends on the type and amount of renewable energy to be interconnected, and therefore, the overall optimization of the power system is necessary.

ENHANCING EFFICIENCY AND SUSTAINABILITY: GREEN ENERGY SOLUTIONS FOR WATER SUPPLY COMPANIES

Water supply enterprises significantly impact local communities by providing essential services. These companies face high electricity costs for water extraction, purification, and transportation, affecting efficiency and reliability. Therefore, implementing innovative technologies to enhance sustainability in the water supply sector is crucial. This article explores the opportunities and strategies for renewable energy transition at water supply companies. Using investment, sensitivity, and strategic analyses, as well as a case study approach, the research examines technologies, conceptual frameworks, mechanisms, and approaches to integrate green power into water supply operations, addressing high energy costs and promoting sustainability. The article identifies solar, wind, hydroelectric, biomass, and geothermal energy technologies as the most prominent renewable power solutions for water supply enterprises. The developed conceptual framework for implementing these technologies includes needs assessment and goal setting; resource assessment and technology selection; system design and integration; financing and investment; regulatory compliance and permitting; stakeholder engagement and capacity building; and monitoring, evaluation, and continuous improvement. The mechanisms and approaches to integrate green energy solutions within the developed framework can involve on-site renewable energy generation, power purchase agreements, energy storage and microgrid systems, energy efficiency and demand management, and collaborative and community-based models. As a case study, the article examines a 120-kW solar power plant project for a water supply enterprise, demonstrating profitability with a net present value of 60,370 USD and an internal rate of return exceeding 21%. The project's payback period is estimated at 8.38 years, acceptable within industry standards. Sensitivity analysis indicates the project's financial resilience. Increasing electricity prices will boost profitability, justifying the solar power plant investment amid inflation and economic instability. Additionally, the project ensures reliable water transport and environmental benefits by reducing CO2 emissions through solar energy use. Thus, transitioning to renewable energy at water supply enterprises is feasible and essential for long-term sustainability, transforming operations to be more resilient, efficient, and environmentally friendly.

Energy Microgrids: Exploring Technology Trends and Prospects for Efficient Energy Management

Objective: The objective of this paper is to explore technology trends and prospects for efficient energy management in microgrids by identifying and analyzing distinct research lines in this field. Method: A Scopus search was conducted using keywords such as "microgrids" and "new technologies." The gathered information was processed using Bibliometrix software to cluster the data. This analysis identified five distinct research lines related to microgrids and energy management. Results and Discussion: The analysis revealed five distinct research lines: (1) microgrids related to energy generation and storage, (2) electric power system control, (3) electric power transmission networks, (4) optimization, and (5) power markets. The paper develops and explains these research lines comprehensively, detailing technology trends and prospects for efficient energy management. Additionally, a combined analysis of relevance and development classifies the research lines as either emerging or declining and determines whether they represent basic or motor themes. Research Implications: The findings provide insights into current and future trends in microgrid technology, offering valuable information for researchers, practitioners, and policymakers. Understanding these trends can help guide future research, development, and implementation strategies for efficient energy management in microgrids. Originality/Value: This paper offers a comprehensive analysis of technology trends and prospects in microgrids, utilizing advanced bibliometric tools to identify and classify research lines. The study provides a unique perspective on the development and relevance of different aspects of microgrid technology, contributing valuable insights to the field.

Enhanced Microgrid Energy Optimization: Integrating Load Prioritization and Dynamic Temporal Adaptation

Enhanced Microgrid Energy Optimization: Integrating Load Prioritization and Dynamic Temporal Adaptation

Practical solutions for microgrid energy management: Integrating solar forecasting and correction algorithms

Nowadays, the shift towards renewable energies is happening at an exponential pace. Microgrids are an integral part in integrating renewable energies into the global energy mix. Due to the volatility of renewables, such as solar or wind, proper energy management is needed to avoid generation or load curtailment. This issue is addressed throughout the literature using a wide variety of strategies, ranging from classical linear to nature inspired metaheuristic optimization algorithms. This paper goes beyond the simulation phase of different optimization algorithms and addresses the optimal energy management problem by proposing a novel framework to integrate optimization strategies into the daily operation of microgrids. The proposed framework showcases 3 practical methods for controlling the distributed generators, as well as a communication scheme which facilitates the data transfer between the machine running the optimization strategy and the energy management system of the microgrid. The framework is demonstrated on a small-scale islanded microgrid setup, located at the Technical University of Cluj-Napoca. The microgrid setup consists of a photovoltaic array, a battery energy storage system and two dispatchable generators. A two-stage optimization strategy is used, consisting of a day-ahead stage, which uses the solar forecast provided by the Global Forecast System, prior to each day, to issue a working plan for the dispatchable generators, and an intra-day stage, which uses hourly solar forecasts, issued during the day, to adjust the operation of the microgrid and minimize the error between the day-ahead solar forecast and the actual weather conditions. Mixed integer linear programming is used to solve the optimization problem and validate the correct operation of the proposed framework over two case studies. The results show that the microgrid works as intended, in both scenarios, as well as the benefits of using an intra-day correction stage.

Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

Energy management of hybrid AC/DC microgrid considering incentive‐based demand response program

AbstractIncreasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

DeepEMS: Multimodal optimal energy management of microgrid systems based on a hybrid multi‐stage machine learning model

AbstractThe effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Development of Smart Micro-grid Energy Efficiency Technologies on Workplace Level

Development of Smart Micro-grid Energy Efficiency Technologies on Workplace Level