ADVANCED MODEL FOR OPTIMAL MANAGEMENT OF ENERGY DEFICITS AND SURPLUSES IN STAND-ALONE DIRECT-CURRENT MICROGRIDS

The transformation of energy management systems (EMS) in microgrids has evolved from traditional optimization techniques to intelligent, decentralized architectures driven by artificial intelligence (AI). This review work highlights the limitations of conventional programming methods such as linear, nonlinear, and mixed-integer models in handling the variability of renewable sources. Modern AI techniques, including deep reinforcement learning, bidirectional long short- term memory networks, and fuzzy logic controllers, provide real-time adaptability and predictive accuracy. Additionally, blockchain-based peer-to-peer (P2P) energy trading introduces secure, transparent, and autonomous coordination among distributed agents. Through a comparative analysis, this work underscores the benefits, challenges, and integration potential of these approaches, advocating for hybrid AI and optimization frameworks to enable resilient, efficient, and user-centric microgrid operations.

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

The integration of renewable energy source (RES) and energy storage systems (ESS) in microgrids has provided potential benefit to end users and system operators. However, intermittent issues of RES and high cost of ESS need to be placed under scrutiny for economic operation of microgrids. This paper presents a two-layer predictive energy management system (EMS) for microgrids with hybrid ESS consisting of batteries and supercapacitors. Incorporating degradation costs of the hybrid ESS with respect to the depth of charge and lifetime, long-term costs of batteries and supercapacitors are modeled and transformed to short-term costs related to real-time operation. In order to maintain high system robustness at minimum operational cost, a hierarchical dispatch model is proposed to determine the scheduling of utilities in microgrids within a finite time horizon, in which the upper layer EMS minimizes the total operational cost and the lower layer EMS eliminates fluctuations induced by forecast errors. Simulation studies demonstrate that different types of energy storages can be utilized at two control layers for multiple decision-making objectives. Scenarios incorporating different pricing schemes, prediction horizon lengths, and forecast accuracies also verify the effectiveness of the proposed EMS structure.

Small productive processes (SPPs) have recently emerged as attractive alternatives to contribute to the socio-economic development of communities, primarily in rural contexts. However, SPPs have a complicated electrical behavior involving the interaction between various types of loads, such as, conventional, and complex ones. Further, the SPPs generally include voltage-dependent loads, which may increase/decrease the load consumption. Thus, these characteristics make the integration of the SPPs into energy management systems (EMSs) for microgrids (MGs) a challenging task. This work proposes a novel integration of the SPPs into an EMS considering the SPPs’ voltage sensitivity and complexity. For this purpose, we extended a previously proposed multi-zone ZIP load model (EMZ-ZIP) to be integrated into a convex AC multi-nodal EMS approach. The latter was formulated based on invertible nonlinear convex transformations and a binomial approximation method. The associated framework was assessed using a modified 9-bus test system with the characteristics of a low-voltage isolated MG with the integration of an SPP. The results demonstrate that the MG operation exhibits better technical and economic performance when the EMZ-ZIP model is an integral part of the EMS. In addition to an operating cost reduction of approximately 5% when considering voltage dependency, relevant practical advantage in scenarios of work shift and solar radiation variability are also presented. These results were compared with those obtained using other approaches like the time-variant ZIP and the constant power model.

This paper presents both an extensive literature review and a qualitative and quantitative study conducted on nearly 200 publications from the last six years (based on international experience and a top-down analysis framework with five classification levels) to establish the main trends in the field of centralized energy management systems (EMS) for microgrids. No systematic trend analyses have been observed in this field in previous literature reviews. EMS attributes for several features such as objective functions, resolution techniques, operating models, integration of uncertainties, optimization horizons, and modeling detail levels are considered for main trend identification. The main contribution of this study is the identification of four specific existing research trends: (i) dealing with uncertainties (comprises 33% of the references), (ii) multi-objective strategy (29%), (iii) traditional paradigm (21%), and (iv) P-Q challenge (17%). Each trend is described and analyzed based on the main drive of these separate research fields. The key challenges and the way to cope with them are described based on the rationality of each trend, the results of previous reviews, and the previous experience of the authors. Overall, finding these main trends, together with a complete paper database and their features, serve as a useful outcome for a better understanding of the current research-specific challenges, opportunities, potential barriers, and open questions regarding the creation of future centralized EMS developments. The traditional numerical analysis is insufficient to identify research trends. Therefore, the need of further analyses based on the clustering approach is emphasized.

Microgrids are becoming a viable solution for satisfying energy demand of rural and remote areas. Indeed, energy demand of islands can be met by renewable energy sources, energy storage systems, and micro-conventional generation sources-based microgrid systems. The optimal scheduling of these energy sources requires an energy management system for microgrids. Bretagne region in France has huge potential in marine renewable energy sources. Therefore, islands in this region can use tidal turbines with other energy sources to meet their local energy consumption. A case study of stand-alone marine microgrid system for Ouessant island is proposed in this paper. The considered microgrid includes PV system, tidal turbine, diesel generator, and Li-ion battery. The architecture and optimal scheduling of the developed microgrid system is presented to reduce operating and maintenance costs. The developed energy management architecture can help microgrid systems planning for islands in the near future.

Integration of renewable energy source (RES) and energy storage system (ESS) in microgrids has a potential benefit to users and system operators. However, new operating issues brought by RES and high cost of ESS need to be scrutinized for economic operation of microgrids. In order to evaluate the economic operation, this paper presents a predictive energy management system (EMS) for microgrids that manifests the process of battery degradation under the minimum system operating cost. The proposed EMS provides the power dispatch based on the hourly-ahead price and forecast data in a most cost-saving way, in which the battery degradation cost with respect to the depth of charge and lifetime is incorporated, transforming the long-term installation cost to the short-term operational cost that accounts for the real-time scheduling. The effectiveness of the proposed method is illustrated by case studies in the simulation.

This paper proposes a proficient hybrid approach based energy management system (EMS) of microgrid (MG) in grid-tied mode. The microgrid connected system like photovoltaic (PV), wind Turbine (WT), and microturbine (MT) battery. The major intention of the proposed approach is “to reduce the cost of electricity and enhancing the power flow (PF) among the source and load side.” The proposed method is the combination of Artificial Neural Network (ANN) and Hybrid Whale Optimization (HWO) approach, hence it is known as ANN-HWO. The necessary load requirement of grid connected microgrid scheme is constantly monitored by ANN. The HWOA develops the optimal combination of the microgrid based on the predicted load demand. Moreover, the two methods for microgrid energy management have been employed to minimize the impact of renewable energy predicting faults. The first method is focused on the various renewable energy sources (RESs) programming to reduction of electricity cost while the MG function. The second method is to balance the PF and minimize the forecasting errors impacts depending upon rule outlined from the scheduled power reference. The proposed technique is activated in MATLAB/Simulink site, and then the efficiency is classified with and without grid of MG structure. The effectiveness can be compared based on cost analysis and power generation of PV, MT, WT, and battery of existing methods. The power generation from the various sources based upon the ANN-HWO and existing methods is evaluated. Also, the total cost of the system also analyzed with different existing methods. The statistical evaluation, like mean, median, and standard deviation is also analyzed. The accuracy, specificity, recall and precision, RMSE, MAPE, MBE and consumption time of ANN-HWO, and existing techniques are also conducted. The mean, median, and standard deviation for 50 numbers of trails of the proposed technique is 1.3116, 1.2931, and 0.0354. The mean, median, and standard deviation for 100 numbers of trails of the proposed technique is 1.3232, 1.2786, and 0.0946. The RMSE, MAPE, MBE, and execution time for 50 numbers of trails of the proposed technique is 7.840, 0.748, 0.9971, and 1.011s. The RMSE, MAPE, MBE,and execution time for 100 numbers of trails of the proposed technique is 5.21, 0.93, 1.93, and 2.005 s. The proposed technique in 100, 200, 500, and 100 numbers of trails, the efficiency is 99.9673%, 99.7890%, 99.89402%, and 99.77879%.

An advanced real time energy management system for microgrids

An Energy Management System for microgrids including costs, exergy, and stress indexes

Integrated energy management system for Microgrids of building prosumers

In this paper, a multi-objective Energy Management System (EMS) for polygeneration microgrids is presented. The proposed tool has been developed within the LIVING GRID project, funded by the Italian Ministry of Research (actions related to the Italian Technology Cluster on Energy), and it is characterized by a detailed representation of generation units and flexible loads, as well as electric/thermal networks and storage systems that can be present in microgrids and sustainable districts. An optimization model has been developed in which the objective function is related to the minimization of costs and emissions, and the maximization of the overall exergy efficiency of the system. The decision model is applied to a real case study represented by the Savona Campus of the University of Genoa in Italy.

This research makes a strong focus on improving fluid dynamics inside the reservoir after stimulation for enhancing oil and gas well performance, particularly in terms of increasing the Gas–oil ratio (GOR) and injectivity leading to a better productivity index (PI). Advanced stimulation operation using new formulated emulsified acid treatment greatly improves the reservoir permeability, allowing for better fluid movement and less formation damage. This, in turn, results in injectivity increases of at least 2.5 times and, in some situations, up to five times the original rate, which is critical for sustaining reservoir pressure and ensuring effective hydrocarbon recovery. The emulsified acid outperforms typical 15% HCl treatments in terms of dissolving and corrosion rates, as it is tuned for the reservoir’s pressure, temperature, permeability, and porosity. This dual-phase technology increases injectivity by five times while limiting the environmental and material consequences associated with spent and waste acid quantities. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficient penetration within the rock pores to enhance oil flow and sweep. This increase in performance is linked to the creation of the wormholing impact of the emulsified acid, resulting in improved fluid dynamics and optimized reservoir efficiency, as shown by the enhanced gas–oil ratio (GOR) in the four mentioned cases. A critical component of attaining such improvements is the capacity to effectively analyze and forecast reservoir behavior prior to executing the stimulation in real life. Engineers can accurately forecast injectivity gains and improve fluid injection tactics by constructing an advanced predictive model with low error margins, decreasing the need for time-consuming and costly trial-and-error approaches. Importantly, the research utilizes sophisticated neural network modeling to forecast stimulation results with minimal inaccuracies. This predictive ability not only diminishes the dependence on expensive and prolonged trial-and-error methods but also enables the proactive enhancement of treatment designs, thereby increasing efficiency and cost-effectiveness. This modeling approach based on several operational and reservoir factors, combines real-time field data, historical well performance records, and fluid flow simulations to verify that the expected results closely match the actual field outcomes. A well-calibrated prediction model not only reduces uncertainty but also improves decision making, allowing operators to create stimulation treatments based on unique reservoir features while minimizing unnecessary costs. Furthermore, enhancing fluid dynamics through precise modeling helps to improve GOR management by keeping gas output within appropriate limits while optimizing liquid hydrocarbon recovery. Finally, by employing data-driven modeling tools, oil and gas operators can considerably improve reservoir performance, streamline operational efficiency, and achieve long-term production growth through optimal resource usage. This paper highlights a new approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. The use of real-time monitoring has boosted the study by enabling for exact measurement of post-injectivity performance and oil flow rates, hence proving the efficacy of these advanced stimulation approaches. The study offers unique insights into unconventional reservoir growth by combining numerical modeling, real-world data, and novel treatment methodologies. The aim is to investigate novel simulation methodology, advanced computational tools, and data-driven strategies for improving the predictability, reservoir performance, fluid behavior, and sustainability of heavy oil recovery operations.

This paper proposes an energy management system (EMS) of direct current (DC) microgrid. In order to implement the proposed EMS, the control and operation method of EMS is presented in this work. While most of the studies have individually examined the grid-connected mode used in building and the stand-alone operation mode applicable to the island, the proposed EMS allows it to be used in both grid-connected mode and stand-alone mode with 10 modes. In order to determine each mode in EMS, not only the amount of generated power, load power, and the state of charge (SOC) of the battery, but also the rated power of the energy storage system (ESS) converter that performs charging and discharging operations is additionally considered. Thus, various uncertainties that may occur in the actual DC microgrid environment can be improved. A laboratory-scale DC microgrid is fabricated to conduct experimental validation of proposed EMS. Experiments of DC microgrid with proposed EMS were performed for each mode, and the experiment waveforms of each power conversion device are included in detail.

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.