Battery Storage System Research Articles

A novel modified artificial rabbit optimization for stochastic energy management of a grid-connected microgrid: A case study in China

The energy management (EM) solution of the microgrids (MGs) is a crucial task to attain most economic, reliable and sustainable operation state of the MGs. This paper aims to solve the optimal scheduling and stochastic EM problem of a smart MG without and with demand side response (DSR) including MT, FC, PV, WT, and a battery storage system (BSS). A study case in Wenzhou city of China is conducted to reduce the operation cost and maximize the utilization of renewable energy. The system uncertainties like loading, temperature, solar irradiance and wind speed are considered which were obtained from real meteorological data. The normal, lognormal, Weibull PDFs as well as Monte-Carlo and RBS methods are used for uncertainty modelling. A modified artificial rabbit optimization (MARO) is proposed for EM solution based on three strategies including fitness-distance balance, exploitation mechanism of PDO and quasi-opposite-based learning (QOBL) to boost exploration and exploitation phases of traditional ARO. The statistical and non-parametric tests are applied via benchmark functions to validate the performance of MARO. As per the obtained results, the MARO is superior for EM solution compared to other techniques and the operation cost is reduced from 252.0721€ct/day to 184.8435€ct/day with saving of 26.86 % considerably with application of DSR.

Analysis of partial load loss of the PCS and internal storage battery loss in residential PV power generation and storage battery systems

AbstractThis study aims to quantify the amount of loss due to partial load of power conditioning system (PCS) and internal loss of storage battery in residential photovoltaic (PV) power generation and storage battery system by using home energy management system (HEMS) data. After identifying the configuration of the PV power generation and storage battery system, HEMS data on PV power generation, household demand, storage battery charging, storage battery discharging, purchased power and sold power were extracted and analyzed according to the research purpose. As a result, the loss due to partial load of the PCS and the average internal loss of the storage battery were quantified. In addition, it was confirmed that a more realistic system simulation could be performed by considering the impact of the partial load of the PCS and the average internal loss of the storage battery.

Negawatt-Driven Sustainable Energy Management Based-on Nanomaterial-Enhanced Batteries

With the dramatic increase of energy demand around the world, it has become a necessity to monitor and optimize energy consumption to conserve its use and decrease carbon emissions. This paper presents a comprehensive analysis of MicroGrids (MGs) that encompass a diverse array of energy sources, including renewable energy, Diesel Generators (DGs), and Battery Storage Systems (BSS). In this study, critical constraints are addressed such as distributed generator limits and grid power exchange. Moreover, the potential integration of nanomaterials within BSS to enhance energy efficiency and achieve Negawatt energy are explored. The main objectives are to evaluate the performance of various energy system configurations, with a specific focus on nanomaterial-enhanced batteries, and identify the optimal strategy to minimize operating costs while maximizing Negawatt energy production. By considering technical, economic, and environmental factors, this research aims to provide valuable insights into sustainable and efficient Microgrid solutions for future energy management.

Dynamic numerical modeling and performance optimization of solar and wind assisted combined heat and power system coupled with battery storage and sophisticated control framework

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.

Review on Economic Analysis of Grid-tied Battery Storage Systems and Optimization Strategies

This comprehensive review paper provides an in-depth exploration of the landscape of energy management strategies within the context of emerging energy markets. It underscores the significance of effective energy management and optimization, particularly in light of the surging integration of renewable energy sources, growing sustainability concerns, and the dynamic shifts within the energy sector. This review extensively analyses three fundamental domains: electricity demand estimation, battery storage systems, and energy arbitrage methodologies placing a significant emphasis on the body of the research related to electricity storage optimization.Furthermore, it delves into the widespread strategies that are adopted in battery storage systems, which have assumed a pivotal role in energy storage and the equilibrium of supply and demand in the modern energy landscape. Nevertheless, amidst this extensive examination, a considerable research gap arises, specifically highlighting the lack of dedicated studies addressing energy arbitrage methods tailored to the unique attributes of emerging energy markets. This gap is particularly prominent in the context of Sri Lanka's growing energy market, where the prominence of renewable energy sources is on the rise.The review underscores that in order to fully harness the vast potential of renewable resources and optimize energy supply within this distinctive setting, it is crucial to develop and implement customized energy arbitrage strategies. These strategies should be designed to accommodate the evolving energy dynamics and the integration of renewable energy, that would thus enable energy markets to function effectively while maximizing economic benefits. Therefore, as the global energy landscape continues to transform, with a growing focus on sustainability and renewable energy, this paper sheds light on the need for innovative and tailored energy solutions in emerging markets.

Unique solvation structure induced by anionic Cl in aqueous zinc ion batteries

Aqueous zinc ion batteries (AZIBs) have garnered significant attention in large-scale static energy storage battery systems due to their low cost, high safety and environmental friendliness. However, it has some inherent problems during operation, such as the occurrence of side reactions (hydrogen evolution reaction, HER) and anode corrosion, formation of by-products and growth of metal dendrites. To analyze the mechanism of generation from aspect of the electrolyte solvation structure and make cell efficiency further improvements based on it, so we use DFT calculations to find the most stable solvation structure in AZIBs with ZnCl2 as the electrolyte and analyze it. We define the relative concentration Cr, and calculate different groups metal cation cluster structures such as [Zn(H2O)n]2+, [ZnCl(H2O)n]+, [ZnCl2(H2O)n] and [ZnCl3(H2O)n]‐ that exist at different Cr. We discuss the effect of different clusters formed due to the Cr variations on the battery performance in terms of three aspects: the structural conformation, the cluster characteristics (including the hydrogen bonding network, bond lengths, bond angles, as well as the electrostatic potential ESP) and the cluster performance (including the adsorption energy Ea, binding energy Eb, and desolvation energy Edes). The results shows that the electrolyte metal cation Zn2+ can be coordinated with up to six H2O molecules in first shell, and this metal cation solvation structure contributes to the occurrence and formation of side reactions and by-products, which reduces the battery efficiency. Increasing the electrolyte anion Cl− concentration by appropriately increasing the Cr helps to desolvate the metal cation cluster structure, which greatly improves the battery efficiency and suppresses the side reactions and by-products. Yet the improvement effect was not obviously further improved by further increasing the Cl− concentration.

Design and Performance Evaluation of Fuzzy Controller based Energy Storage Enabled Statcom for Electrical Distribution Network

In order to fulfill the demand for power, the production of locally accessible resources, such as solar and wind energy, has increased dramatically during the past ten years. Their integration into the distribution of electrical network (EDN) has affected the operation of the network. Variations in the real power flow and an increase in reactive power demand have impacted the EDN voltage/frequency profiles and distribution transformer strain. Hence to improve the voltage regulation and reduce the active power fluctuations, there is a need for a system network of electrical distribution (EDN). It has the ability to support both reactive and active power. Thus, the energy storage device with STATCOM is introduced i.e. an energy storage device linked to Statcom (EsSt) has been proposed in this paper. The Common Coupling Point (CCP) voltage can be adjusted by energy storage connected to Statcom (EsSt) to satisfy the amount of power required actively at EDN. The EsSt has an integrated battery storage system and may be controlled using a voltage source converter by employing the proper control strategy to deliver the necessary energy support and VAR compensation. The voltage at the dc-link has been integrated using DC-DC converter based half bridge. The use of conventional PI controllers provides poor transient response. To improve the transient performance, It is suggested that a fuzzy logic controller be used in place of the PI controller. Analysis of comparison using MATLAB. The rule-based fuzzy logic controller performs better than the PI controller, according to the Simulink environment.

Risk Assessment Analysis of Hybrid Renewable Energy Systems in Remote Areas: Case Study of Domadgee, Australia

This study comprehensively analyses the energy requirements and options for Doomadgee, a remote indigenous community in Australia. Currently, the electricity provider relies on a 2.44 MW diesel power generator, resulting in high operational costs and carbon emissions. This study explores transition options to renewable energy sources to address these challenges while ensuring reliable and cost-effective power supply. This study suggests utilising a 2.4 MW wind energy source, a 1.2 MW solar energy source, and a 4 MW battery storage system that can last 8 hours. This configuration aims to reduce diesel consumption, optimise the internal rate of return (IRR), and establish a sustainable energy mix with low capital expenditure. The wind and solar capacities recommended for Doomadgee also consider seasonal fluctuations, such as wet and dry seasons in energy demand, ensuring efficient power production all year round. The study commences with the development of a risk matrix to determine the hurdle rate. The next step involves conducting ten distinct scenarios to evaluate the investment's financial feasibility, considering various variables such as wind, solar, equipment, and battery storage capacities. Sensitivity analysis also determines the most significant factors impacting financial valuation. This analysis provides valuable insights that can aid in the decision-making process.

Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development

The recent persistent power interruption in Nigeria has significantly disrupted commercial activities, resulting in a magnificent economic loss, supply chain ripples and revenue loss. As a result, harnessing renewable energy sources to generate electricity has become a popular choice for satisfying ever-increasing load demand and reducing apprehensions on global warming and reliance on depleted fossil fuels. The goal of this research is to determine whether powering a remote community with a hybrid energy system (HES) is technologically, financially and environmentally viable. The optimum design of a standalone HES with the diesel generator (DG), photovoltaic (PV) and battery storage system (BSS) is provided in this study to satisfy the electrical power needs of a farm settlement in Kura, Nigeria by considering generation constraints and load demand. This research work presents a genetic algorithm (GA) to minimize wearing cost of the system (WCS), minimize the land needed for the installation of the DG and PV system, minimize the total annual cost of the system (TAC) and maximize the benefit to cost ratio and revenue from electricity consumption. The findings of the research showed that PV/BSS/DG system is a prospective solution to satisfy the load requirements with least TAC of 67374 $/yr, annual maintenance cost (AMC) of 2808.2 $/yr, annual fuel cost (AFC) of 32300 $/yr and annual emission cost (AEC) of 774.2023 $/yr. The outcomes of the study show that a considerable TAC, AMC, AFC and AEC savings of 26766 $/yr (28.43 %), 2808.2 $/yr (53.09 %), 32300 $/yr (25.4 %) and 774.2023 $/yr (60.69 %) are recorded when compared with using DG alone. The control approach applied in this study has reduced the operational capacity of the DG and prevented about 41157 kg/yr, 419.19 kg/yr and 22.52 kg/yr of CO2, NOx and SO2 emissions from being injected into the atmosphere. The simulation outcomes of the research demonstrate that the developed model can significantly reduce cost of electricity in rural communities with the application of HES. Hence, a 45.36 % cost of energy saving has been accomplished through the energy management system introduced in the proposed HES. The study's outcomes can be used as benchmarks to help many countries to enhance access to electricity, raise their living standards and stimulate economic growth. The application of the proposed HES in remote communities can result in greater economic and environmental benefits to a general population of rural dwellers. The findings of the research work are beneficial to designers, independent power providers, investors, researchers and electricity consumers that are looking for a feasible power solution.

On the operation and implications of grid-interactive renewable energy communities

The increasing installations of photovoltaic systems, electric vehicle chargers, heat pumps, and battery storage systems challenge distribution system operators to prevent critical grid operations. Nonetheless, flexibility potential from customer-owned assets remains untapped due to lacking federated coordination and incentives. In Europe, emerging renewable energy communities can address this challenge by facilitating collective optimal operation of flexible assets and providing grid services. This paper presents an approach for renewable energy communities to offer participants’ flexibility through grid-interactive operations. Distribution system operators then based on optimal power flow calculations request flexibility use to prevent voltage violations, transformer overloads, and line overloads. A case study with simulations for a future rural low-voltage grid is conducted. The results show that, compared to business-as-usual operations or renewable energy communities with fixed import and export limits, the grid-interactive approach ensures non-critical grid operations cost-efficiently and reduces curtailment by 60% to 90%, down to 1.1% of total generation. By providing both upward and downward flexibility and aligning with distribution system operators’ requirements, grid-interactive renewable energy communities can be key in enhancing the efficient use and stability of distribution grids.

Study on the Economic and Technical Optimization of Hybrid Rural Microgrids Integrating Wind, Solar, Biogas, and Energy Storage with AC/DC Conversion

Under the guidance of the 'dual carbon' goals and 'rural revitalization' strategy, the development of microgrids primarily based on wind, solar, and biogas energy is rapidly advancing in rural areas. A critical and challenging area of current research is how to optimally configure the capacity of these microgrids of varying sizes, taking into account the availability of resources in the system's environment and specific climatic conditions, to maximize economic benefits. Based on this, the article constructs a model of a hybrid AC/DC microgrid system powered by wind, solar, and biogas energy. It undertakes multi-objective optimization to achieve the highest utilization of renewable energy, the most economical cost, and the minimum carbon emissions while ensuring the reliability of the system's power supply. The study explores the economically and technically optimal configuration of this microgrid energy system under certain climatic conditions. The results indicate that the optimal configuration for a rural microgrid powered by wind, solar, and biogas energy should include a 2.6 kW biogas generator, 30.00 kW solar panels, 5.24 kW wind turbines, a 2.6 kW battery storage system, and a 10.00 kW bidirectional inverter. This configuration results in the lowest total net cost of the system, achieving optimal outcomes in terms of total net cost, cost per kilowatt-hour, and supply reliability.

Distributed Optimization of Multi-Microgrid Integrated Energy System with Coordinated Control of Energy Storage and Carbon Emissions

The mutual optimization of a multi-microgrid integrated energy system (MMIES) can effectively improve the overall economic and environmental benefits, contributing to sustainability. Targeting a scenario in which an MMIES is connected to the same node, an energy storage coordination control strategy and carbon emissions management strategy are proposed, and an adaptive step-size method is applied to improve the distributed optimization of MMIESs based on the alternating direction multiplier method (ADMM). Firstly, the basic framework of MMIESs is established, and a coordinated control strategy limiting the time of charge and the discharge of the battery storage system (BSS) is proposed. Then a multi-objective optimization model based on operating and environmental cost is formulated. Considering that different microgrids may be managed by different operators and a different convergence speed of multi-objective optimization iteration, an adaptive step-size distributed iterative optimization method based on ADMM is used, which can effectively reduce the cost and protect the privacy of each microgrid. Finally, a system composed of three microgrids is taken as an example for simulation analysis. The results of distributed optimization are accurate, and the proposed coordinated control strategy can effectively enhance the revenue of ESS, which verifies the effectiveness of the proposed method.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Monolithic design of self-adaptive CMOS converter and robust event-triggered consensus control for integration of multi-renewable energy sources with battery storage system

Recently, integrating renewable energy sources (RESs) has been one of the most effective ways to overcome the power shortage problems due to the cumulative load demand, which cannot be covered using conventional power production. DC-DC converters with a wide voltage conversion range are highly important when integrating RESs to achieve voltage matching. In recent years, many large-scale improvements have been made to the DC-DC converters to improve reliability, effectiveness, flexibility, modularity, and cost-effectiveness. This study investigates the feasibility of integrating RESs and designing the most effective structure using complementary metal-oxide semiconductors (CMOS) for DC-DC converters. A novel monolithic CMOS buck converter with an adaptive sensing feedback system (ASFS) circuit is proposed for renewable integrated systems. The proposed converter based on ASFS provides a wide range of conversion voltages, low cost, and reliability. It depends on the pulse-width modulation (PWM) technique with a voltage-control duty-cycle (VCDC) circuit. The proposed multi-input DC-DC converter is tested to integrate three RESs to form an integrated hybrid system. The proposed converter is built to integrate the PV, wind turbine (WT), wave energy source, and battery energy storage system (BESS). The proposed DC-DC converter is tested to adjust the output voltage of the RESs at 12 V with a load current of 2.4 A to facilitate the integration process. The results show the success of the proposed converter in maintaining the output voltage at 12 V, with maximum efficiency of the CMOS buck DC-DC converter reaching 96.25 % over a wide input voltage range from 15 V to 35 V. Moreover, the maximum peak-peak ripple over the output voltage reaches 600 mV at the maximum input voltage of 35 V. A control method-based event-triggered consensus algorithm has been proposed to regulate the system voltage and ensure active power sharing in the microgrid system. The controller's settling time and rise time have been measured, and a robustness analysis has been performed based on the connections and disconnections of DGs and the impact of faults.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

Subsynchronous Damping by Battery Storage System in Grid-Forming Control

Subsynchronous Damping by Battery Storage System in Grid-Forming Control

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

Stock price forecast model for CATL based on BP neural network regression

With the introduction of the "Dual Carbon" policy and the increasing environmental awareness among residents, the new energy vehicle industry is experiencing positive growth momentum. New energy vehicles use non-traditional energy sources as their power supply, effectively reducing carbon emissions, enhancing energy efficiency, and contributing to the improvement of China's existing energy landscape, thus supporting environmental protection and the early realization of "carbon peak" and "carbon neutrality" goals. Contemporary Amperex Technology Co., Ltd. (CATL), a prominent and competitive player in China's emerging clean energy industry, focuses on researching, developing, manufacturing, and marketing power battery and energy storage systems specifically designed for new energy vehicles. Moreover, in recent years, machine learning and deep learning have gained wide application in various domains, including stock price prediction and financial investment. This paper constructs a stock price prediction model for CATL based on a BP neural network regression, considering factors related to traditional energy, carbon trading, environmental aspects, and industry-specific factors.

Optimal Economic Research of Microgrids Based on Multi-Strategy Integrated Sparrow Search Algorithm under Carbon Emission Constraints

In the global transition towards sustainable energy, microgrids are emerging as a core component of distributed energy systems and a pivotal technology driving this transformation. By integrating renewable energy sources such as solar and wind power, microgrids not only enhance energy efficiency and reduce reliance on traditional energy sources but also bolster grid stability and mitigate the risk of widespread power outages. Consequently, microgrids demonstrate significant potential in improving the reliability of power supply and facilitating flexibility in energy consumption. However, the operational planning and optimization of microgrids are faced with complex challenges characterized by multiple objectives and constraints, making the reduction in operational costs a focal point of research. This study fully considers an operational model for a microgrid that incorporates distributed energy resources and comprehensive costs, integrating a battery storage system to ensure three-phase balance. The microgrid model includes photovoltaic power generation, wind power generation, fuel cells, micro-gas turbines, energy storage systems, and loads. The objectives of operating and maintaining this microgrid primarily involve optimizing dispatch, energy consumption, and pollution emissions, aiming to reduce carbon emissions and minimize total costs. To achieve these goals, the study introduces a carbon emission constraint strategy and proposes an improved Multi-Strategy Integrated Sparrow Search Algorithm (MISSA). By applying the MISSA to solve the operational problems of the microgrid and comparing it with other algorithms, the results demonstrate the effectiveness of the carbon emission constraint strategy in the microgrid’s operation. Furthermore, the results prove that the MISSA can achieve the lowest comprehensive operational costs for the microgrid, confirming its effectiveness in addressing the operational challenges of the microgrid.