Energy Resources Research Articles

A real‐time decision support system to improve operations in electric bus networks

ABSTRACTElectrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Due to the limited driving range of electric buses, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, the impact on sustainability is most profound when the buses are powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning charging schedules amid these various sources of uncertainty. We develop a real‐time decision support system that uses real‐time data, predictions, and mathematical optimization to update the charging schedules and mitigate the impact of operational uncertainties. Our results show that the online strategy can maintain higher reliability and renewable energy utilization levels compared to other charging strategies. The study has been carried out in cooperation with the public transport operator in Rotterdam in the Netherlands to assist them in their TBN transition process.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

Effects of governmental supportive policies on waste management for two substitutable products made of virgin and waste materials: A game-theoretic approach.

In recent years, the concept of sustainability has attracted a great amount of attention, due to increasing energy resources scarceness. Waste recycling is known as an efficient approach to improve sustainability and save energy. In this view, a sustainable supply chain is established in the current study to investigate the effects of waste recycling on sustainable development. The considered supply chain consists of the government, two manufacturers, a supplier, a waste depot, and a recycler. Under this structure, two substitutable products are made of the virgin and recyclable waste materials. The supplier provides the virgin materials for the first product. The waste depot collects the non-recycled waste, whereas the recycler recycles it and supplies the recycled waste for the second product. Also, the government supports the second product to provide an incentive for its members to collect and recycle more waste. Then, the game theory is applied to make decisions under the considered structure. Finally, the results are revealed and some managerial insights are provided. It is derived that the governmental supportive policies play a significant role in resources conservation and energy storage. Moreover, increasing the quality of the product made of the recyclable waste improves the government's utility.

Enhancing Solar Power Forecasting using Grasshopper optimization and Whale Optimization Algorithm

Solar power forecasting is essential for effectively integrating solar energy into the power system. Accurate forecasting allows for more effective power system planning, operation, and management. In this research work, we employed the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) to choose features for solar power forecasting utilizing time series data from the OPSD dataset. The dataset contains measurements taken at 15-minute intervals, giving a wealth of data for training and verifying forecasting algorithms. The WOA is used to adjust the parameters of a forecasting model, hence increasing its accuracy and reliability. The suggested approach's performance is evaluated on a large-scale dataset, with training, validation, and test sets of 100,000, 50,000, and 51,347 data points, respectively. The results show that the WOA is effective at improving solar power forecasting accuracy, which contributes to the efficient use of renewable energy resources.

Enhancing grid resiliency in distributed energy systems through a comprehensive review and comparative analysis of islanding detection methods

Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning.

Agriculture 4.0: Polymer Hydrogels as Delivery Agents of Active Ingredients.

The evolution from conventional to modern agricultural practices, characterized by Agriculture 4.0 principles such as the application of innovative materials, smart water, and nutrition management, addresses the present-day challenges of food supply. In this context, polymer hydrogels have become a promising material for enhancing agricultural productivity due to their ability to retain and then release water, which can help alleviate the need for frequent irrigation in dryland environments. Furthermore, the controlled release of fertilizers by the hydrogels decreases chemical overdosing risks and the environmental impact associated with the use of agrochemicals. The potential of polymer hydrogels in sustainable agriculture and farming and their impact on soil quality is revealed by their ability to deliver nutritional and protective active ingredients. Thus, the impact of hydrogels on plant growth, development, and yield was discussed. The question of which hydrogels are more suitable for agriculture-natural or synthetic-is debatable, as both have their merits and drawbacks. An analysis of polymer hydrogel life cycles in terms of their initial material has shown the advantage of bio-based hydrogels, such as cellulose, lignin, starch, alginate, chitosan, and their derivatives and hybrids, aligning with sustainable practices and reducing dependence on non-renewable resources.

TURNING FOOD WASTE INTO GREEN ENERGY A BIO-GAS REVOLUTION

The Bio Gas Revolution stands as a beacon of sustainable innovation, revolutionizing waste management by converting discarded food into valuable green energy through biogas production. This abstract encapsulates the multifaceted approach of this revolution, encompassing waste food diversion strategies, cutting-edge anaerobic digestion technologies, renewable energy generation processes, circular economy principles, and the ensuing environmental-economic sustainability benefits. It underscores the transformative potential of harnessing waste food as a fundamental resource for renewable energy, heralding a new era of environmental preservation, resource optimization, and economic viability in the global energy land scape. It highlights the transformative potential of utilizing waste food as a critical resource for renewable energy, leading to significant environmental benefits, resource efficiency gains, and economic opportunities.. It emphasizes the transformative potential of harnessing waste food as a valuable resource for renewable energy production, contributing significantly to environmental preservation, resource efficiency, and economic viability” Key Words: Food Waste Management, Green Energy, Biogas Production, Sustainable Waste Disposal Submit Research Paper

Improvement the resiliency of distribution network with routing, scheduling, and prepositioning mobile energy resources with limited fuel

Mobile energy resources (MERs) are equipment which are used to improve the resiliency of distribution networks after natural disasters. An important point about using these resources is optimal routing and scheduling them to supply essential loads. In this paper, to make the problem more actual, a new formulation considering limited fuel for the trunks that move these energy resources is presented. Then, refueling vehicle is used to lessen the effect of limited fuel. Therefore, an optimization algorithm is presented for optimal allocating refueling vehicle, simultaneously optimal routing and scheduling of limited-fuel MERs. In the next stage, to achieve a better result and reducing the restoration time, a new formulation is presented for prepositioning MERs considering the stochastic occurrence of damages in various points of the network. Latin hypercube sampling method is used as scenario reduction method to implement the stochastic problem formulation. To illustrate the efficiency of the proposed method considering limited fuel for MERs, refueling vehicles, and MERs prepositioning, simulations are carried out on IEEE 33-bus test system and the results are compared with the previous studies’ results.

An integrated model with interdependent water storage for optimal resource management in Energy–Water–Food Nexus

With the rapid increase in population and the industrial revolution, the demand for clean energy and water has substantially increased, underscoring their importance for sustainable economic development. Although energy and water infrastructures are often viewed as separate and uncoupled due to distinct processes in power generation and water production, they are fundamentally interlinked within their respective domains. This necessitates a strong coupling to optimally manage power and water resources simultaneously. To address this, a joint optimization algorithm has been developed to manage the supply-side resources of the Energy–Water–Food Nexus (EWFN), including the power, water, food, cogeneration, and storage networks. A mathematical model is first developed to dispatch clear power, potable water, and storage resources, considering constraints related to supply, demand, production, flow, and ramping. Additionally, the integration of a water storage facility alleviates binding constraints, enabling flat production to reduce costs and CO2 emissions. The proposed methodology also allows for the real-time quantification of production costs, energy mix, reserve and curtailed capacities, and energy imbalances. This methodological extension to EWFN includes flexible resources within the grid’s portfolio to promote cleaner production, ensuring that the required amount of water is consumed across all sectors. Finally, the proposed algorithm is tested on freely available datasets, demonstrating that the co-dispatch of energy and water resources in the presence of constraints leads to optimal generation and distribution of power and water without heavily relying on a single-product plant.

Study on influence of turbulence intensity on blade airfoil icing mass & aerodynamic performance

Wind is an emerging renewable energy resource, but more useful in cold regions. With the increasing threat of climate change and global warming, the unpredictability of wind energy patterns has been affected. With continual threats from extremes and uncertainties, icing on wind turbines has been noted to grow affecting aerodynamic performance. The effect of turbulence intensity at its impact on aerodynamic performance was numerically done using ANSYS Fluent and FENSAP ICE software. Conditions considered for the study included turbulence intensities, median volume diameter (MVD), liquid water content (LWC), angle of attack, and ambient temperature for 180 min. The study's conditions aimed at providing a wide range of effects covering the in-cloud icing and freezing drizzle. The mass of ice increased with an increase in LWC when it increased from 0.05 g/m3 to 0.3 g/m3, and MVD with 1000 μm compared to 40 μm, but when temperature decreased led to an increase from −1 °C to −15 °C. Increasing the angle of attack led to reduced aerodynamic performance with stall angle occurring at α = 0–18°. An increase in the turbulence intensity from 0.01 % to 50 % resulted in decreased CL/CD.

Getting the Most Out of Every Training Day: The Influence of Instructors on Self-Regulated Learning During Firefighter Leadership Courses

Leadership courses in the fire services are highly challenging, and they can seriously exhaust trainees and hamper their self-regulated learning efforts (for example, setting goals, focusing attention, seeking feedback). We theorize that experiences of failure or overload can curtail trainees’ available energy resources on subsequent training days, which, in turn, should affect trainees’ learning efforts. Given instructors’ central role in leadership courses, we hypothesize that supportive and humble instructor behaviors decrease experiences of failure and overload and, thus, increase self-regulated learning. Moreover, we argue that supportive instructor behavior may amplify the positive effects of high energy resources, while humble behavior may alleviate the negative impact of low resource levels. We tested preregistered hypotheses with 118 firefighters participating in two-week leadership courses at a German fire academy. The participants completed short web-based questionnaires before and after classes each day. Multilevel analyses confirmed that perceived daily supportive and humble instructor behavior predicted trainees’ reports of daily self-regulated learning activity. Notably, this effect was independent from positive effects of trainees’ reported energy resources in the morning. Supportive and humble behavior did not moderate the effect of energy resources. Our findings suggest that instructors play a crucial role in facilitating effective learning under challenging training conditions. Furthermore, we offer implications for leaders in fire services, who often conduct trainings with their subordinates.

Optimization of Work Systems and Ergonomics to Improve Comfort and Efficiency Through The Implementation of Energy Management

This research aims to implement mall operations that are comfortable, efficient, and effective in energy use and determine the value of Energy Consumption Intensity (IKE). This research uses a qualitative method with a literature study approach. Research data was obtained from measurements carried out by researchers at mall buildings in South Jakarta. Instrument data is obtained from the installed Kwh meter. Data analysis technique by comparing measurement results with data results standardized by the Ministry of Energy and Mineral Resources for IKE and Indonesian National Standards for thermal comfort levels. The research results obtained show that the IKE value is classified as efficient. The IKE value during 2023 ranges from 8,79 – 11,37 kWh/m2/month with the total annual IKE value being 263,16 kWh/m2/year. In terms of air conditioning, the average comfort standard in 2023 will be 25℃ and RH (humidity) at 62.9%.

Late Carboniferous–Early Permian sedimentary–tectonic evolution in the eastern Qaidam Basin, Western China: Insight into the zircon U–Pb geochronology of ash-fall tuffs and sandstones

The Late Carboniferous–Early Permian period is a significant transitional phase within the late Paleozoic ice age and to investigate the sedimentary–tectonic evolution of the Qaidam Basin in this period is crucial for comprehensively understanding the tectonic history of the northeastern Paleo-Tethys Ocean and the related response to energy and mineral resources. This paper focuses on the Carboniferous–Permian strata of the Wanggaxiu-Dongdagou outcrop that was taken to represent the southern Oulongbruk terrane in the eastern Qaidam Basin. We determined the ages of tuff interbeds within the Zhabusagaxiu Formation originally identified as the upper Carboniferous and obtained the crystallization age spectra of detrital zircon grains from Carboniferous to lower Permian sandstones using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) isotope geochemical techniques. The origin of the volcanic ash and the sedimentary–tectonic evolution in this period were analyzed. The results indicate that the original Zhabusagaxiu Formation can be divided into two chronostratigraphic units: the upper Carboniferous Zhabusagaxiu Formation and the lower Permian. The earliest age obtained from tuffaceous layers within the dark shale interval is 297.8 ± 3.0 Ma (mean square weighted deviation (MSWD) = 0.87), which suggests a stratigraphic boundary between the Carboniferous and Permian periods. Detrital zircons from Carboniferous–Permian samples in the southern Oulongbruk terrane predominantly originated from the Qaidam block-North Qaidam ultrahigh-pressure belt continental island arc to the south. The ultrahigh-pressure belt had a significantly increased contribution to the detrital source supply system from the Late Carboniferous to Early Permian, while the Qaidam block experienced frequent volcanic eruptions from 298 to 293 Ma, dispersing volcanic ash only northward to the Oulongbruk back-arc basin and the Zongwulong Trough. The upper Carboniferous Zhabusagaxiu Formation was marked by siliceous clastic rock–carbonate rock interbedding in an epicontinental sea, while the lower Permian was characterized by shallow to bathyal marine shelf to subaqueous sedimentation. The Early Permian transgression potentially represents the largest transgression during the late Paleozoic ice age, indicating an enriched hydrocarbon-generating interval.

Global Conditions and Changes in the Level of Renewable Energy Sources

The progressing globalization of the contemporary economy impacts its volatility and unpredictability. The directions of changes in the socioeconomic development of the contemporary global economy are determined by a number of interrelated diverse factors. Factors clearly influencing the development of the modern international economy include innovation, digitization processes, instability of the economy caused by armed conflicts or pandemic outbreaks, the concept of sustainable development, climate policy, as well as issues related to the depletion of energy resources and the necessity of ensuring global energy security. The purpose of the article is to identify the factors of the development of the contemporary economy along with the analysis and evaluation of their impacts on changes in the level of renewable energy sources (RESs) in the EU countries. The time scope of the analysis covers the years 2013 and 2022 (a 10-year perspective). The study used the methods of literature study, literature criticism, statistical data analysis (statistical databases: EUROSTAT and IRENA), and linear ordering methods (TOPSIS and EDAS). The study results indicate that the levels of the RESs have changed in response to the factors diagnosed in the study. In the years studied, the leaders in terms of levels of RES development were France, Spain, and Denmark.

The role of load and distributed energy resources modeling in voltage recovery studies

Load modeling in power systems is crucial to stability studies. The analysis of certain dynamic phenomena requires proper load modeling. Fault Induced Delayed Voltage Recovery (FIDVR) relates directly to Residential Air Conditioner (RAC) stalling. Therefore, the modeling of induction motor (IM) loads is essential to this analysis. Delayed voltage recovery in the system can result in delayed power recovery in inverters connected to the transmission system, which connects photovoltaic and wind power plants to the Brazilian Interconnected Power System (BIPS). In the last few years, there has been a considerable growth of Distributed Energy Resources (DER) in Brazil. Modern and legacy ride-through settings coexist in power systems around the world. Inverters with legacy settings are more likely to disconnect in systemic events. This creates a context that has been assessed in this paper. The modeling of induction motors in the Acre-Rondonia system leads to a deterioration of this system’s dynamic performance, which can lead to collapse. DER’s voltage support can contribute to post-fault system voltage recovery, mitigating the delay caused by RAC stalling and avoiding collapse. Thus, more robust ride-through settings are required to avoid inverter disconnection. Moreover, dynamic voltage support and reactive current priority requirements are demands from inverters connected to the system. The results obtained show that this consideration enable more consistent results than the ones obtained by the classic ZIP load model.

Pulsed-field ablation of atrial fibrillation in elderly and frail patients: procedural efficacy and safety from a single-center experience

Abstract Introduction The demographic data shows a growing population, mostly elderly and with multiple comorbidities, therefore becoming increasingly frail. In this context Atrial fibrillation (AF) is the predominant sustained arrhythmia, and over the years trans-catheter isolation of pulmonary veins (PVI) has become a crucial approach in AF treatment. Among the technologies and energy resources employed through the years to perform atrial fibrillation transcatheter ablation, a novel technique has recently begun to gain ground, specifically pulsed-field ablation (PFA). PFA employs sequential brief and high amplitude electrical pulses to induce selective and irreversible electroporation of the myocardium, leading to cell death. The high selectivity of the lesion, ensured by the lower electroporation threshold of myocardial tissue compared to adjacent anatomic structures, allows for a decrease in potential complications associated with conventional thermal ablation. Purpose To assess the efficacy and safety in the use of pulsed field ablation in a selected population of elderly and frail patients undergoing atrial fibrillation ablation. Method We conducted a retrospective study focusing on elderly and frail patients admitted to our centre for paroxysmal or persistent AF ablation. The inclusion criteria were: age greater than 75 years and a Clinical Frailty Scale ≥ 5. The study population consisted of 26 patients [male gender 18, mean age 78 ± 3 years] including 8 who were affected by an oncological or haematological disease at the time of admission. The majority of patients were affected by paroxysmal AF while two of them presented persistent AF (9%). The primary ablation endpoint was the pulmonary vein isolation (PVI) that was achieved by delivering an energy of 2 kV over 8 applications, employing an alternating approach between the two configurations of the basket and flower catheter for each individual vein. Results The short-term success, represented by the complete and verified electrical isolation of the veins was achieved in all 26 patients without any periprocedural complications. First-pass pulmonary vein isolation (FPI) was universally achieved among the patient in study, with a mean skin-to-skin procedural time of 70 ± 30 minutes. From the procedural data perspective, ablation was achieved using an average fluoroscopy time of 22 ± 8.4 minutes and an average dose area product (DAP) of 1320 cGy×cm2. Conclusions In elderly and frail patients, we assessed the efficacy and safety of ablative procedures using the PFA technique. Successful electrical PVI was achieved without additional touch-ups, thanks to a high FPI rate. This results in lower procedural times and shorter intra-operative radiation exposure. These data confer an augmented value within the elderly-frail population, wherein shorter procedural times contribute to a better postoperative course, characterized by reduced bed rest and shorter intra-hospitalization periods.

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MkgCO2 annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets.

CUTTING-EDGE BIOFUEL TECHNOLOGIES FOR ENVIRONMENTAL MANAGEMENT AND SUSTAINABILITY

Researchers focus on finding Renewable Energy Sources (RES) and green energy for a sustainable future owing to ecological sustainability standards, growing power demands, depletion of traditional energy resources, and ecological damage caused by global warming and climate change. Bioenergy is a very viable option as it may be used for various energy needs via appropriate conversion methods. This analysis focuses on the promise and problems of producing biofuels using different feedstocks and advancements in process technology. The use of biofuels, such as biodiesel, ethanol, bio-oil, syngas, Fischer-Tropsch Hydrogen, and methane (CH4), generated from agricultural crop left-overs, micro- and macroalgae, and other biological waste via thermo-bio-chemical procedures, has been determined to be an environmentally beneficial option for energy production. Developing and implementing biofuels in industry and transportation significantly reduces reliance on petroleum and coal. The literature study indicated that biofuels derived from crops and microalgae have the potential to be the most effective and appealing method. The area of biofuels has made significant advancements via genetic engineering, which has opened up new possibilities for large-scale production for commercial use. However, the process of producing biofuels on a big scale remains difficult. Therefore, it is crucial to address this issue by converting biomass into biofuels using innovative technology to meet present and future energy demands.

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.