Operational Flexibility Research Articles

Single-Input Multiple-Output (SIMO) Cascode Low-Noise Amplifier with Switchable Degeneration Inductor for Carrier Aggregation.

This paper presents a single-input multiple-output (SIMO) cascode low-noise amplifier with inductive degeneration for inter- and intra-band carrier aggregation. The proposed low-noise amplifier has two output ports for flexible operation in carrier aggregation combinations for band 30 and band 7. However, during inter- and intra-band operation, gain variation occurs depending on the output mode. To compensate for this, a switching circuit is proposed to adjust the degeneration inductor, optimizing gain performance for both modes. The switching operation can minimize the control for the dynamic range in the receiver system to support carrier aggregation. The designed low-noise amplifier was fabricated using a 65 nm CMOS process, occupying an area of 2.1 mm2. In inter-band operation, the small-signal gain was measured by 18.9 dB for band 30 and 18.6 dB for band 7, with the noise figures of 1.03 dB and 1.07 dB, respectively. For intra-band operation, the small-signal gain was 17.3 dB and 17.2 dB, with the noise figures of 1.3 dB and 1.41 dB. The IIP3 values were measured by -7.6 dBm and -6.7 dBm for inter-band, and -6.3 dBm and -6.2 dBm for intra-band. Power consumption was 8.04 mW and 7.68 mW in inter-band, and 17.04 mW and 17.64 mW in intra-band depending on the output configuration.

Study on control strategies of the MW-scale supercritical CO2 recompression Brayton cycle for lead-cooled fast reactor

The supercritical CO2 (sCO2) Brayton cycle for the lead-cooled fast reactor (LFR) is an ideal power generation system for the distributed energy supply in remote islands. To figure out the optimal operating modes of the MW-scale sCO2 LFR unit in different load demands, this work performs a dynamic simulation based on the developed control modules. A parametric analysis is conducted about the effects of rotational speed, split ratio, and main gas temperature on the dynamic characteristics. Control strategies are further studied based on the thermodynamic performance and safety under four variable temperature load regulation modes and three fixed temperature rapid load regulation modes. Results show that the tracking αopt mode has the highest average net efficiency (ηnetav) of 19.13 %. The tracking αsafe mode allows compressors to operate in the safest range by sacrificing a little ηnetav. Excellent load change rates are revealed by using the main circuit regulating valve control mode and the turbine bypass control mode, with −12.48 %Pe/min & 11.92 %Pe/min, and −8.25 %Pe/min & 9.70 %Pe/min, respectively. Though the load change rate of the RS control mode is −3.96 %Pe/min & 3.47 %Pe/min, its ηnetav is higher. Our work guides the efficient, flexible, and safe operation of the sCO2 LFR unit.

An Optimal Road Network Extraction Methodology for an Autonomous Driving-Based Demand-Responsive Transit Service Considering Operational Design Domains

In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Control Voltage Effect on Operational Characteristics of Vehicle Magnetorheological Damper

Considering the increasingly large-scale application of magnetic fluids in various industries, we can confidently state that in the near future magnetorheological dampers will be widely used in adaptive automotive suspensions due to their operational flexibility and simplicity of controlling damping forces by changing the magnetic fluid properties according to parameters of surrounding electromagnetic field. The antivibration efficiency during operation is achieved by regulating the hydraulic resistance of the “magnetic” shock absorber by applying voltage to the windings of its coil. In addition to the physical properties of the oil used in the “magnetic” shock absorber, the viscosity of the working magnetorheological fluid is greatly influenced by the shape of the control signal. The paper focuses on the theoretical aspects of constructing a mathematical model of ac magnetorheological damper and presents the results of a computer experiment to assess effectiveness of its use as part of the adaptive suspension a passenger vehicle. In this case, the actual parameters of the “magnetic” shock absorber, used in modeling the dynamic process, were determined experimentally on a test bench, and the adequacy of the developed mathematical model was confirmed by the results of a semi-natural experiment. Using a verified model, the magnetorheological damper characteristics were obtained and compared for various forms of control signal, including rectangular voltage pulses of various frequencies and duty cycles, sinusoidal pulses and constant voltage signals. The analysis of the antivibration efficiency was carried out on the basis of the developed “quarter” model of a semi-active car suspension with a verified submodel of a magnetorheological damper integrated into its structure. Moreover, the simulation scenarios were based on the selected strategy for controlling the voltage supplied to the windings of the “magnetic” shock absorber. As the results of theoretical and experimental studies have shown in terms of energy consumption, expansion of the working area of the damping characteristic and achieving smooth control of the damping force, the most effective is the use of a sinusoidal pulse voltage signal in the control circuit, which ensures a reduction in both the amplitude and damping time of oscillations. However, when de-signing and manufacturing a controller, creating a pulse modulator for generating sinusoidal pulses coinciding in phase and frequency with the vibrations of the car body is very difficult due to the random nature of external disturbances from the road surface. When a constant voltage is applied to the magnetorheological damper winding, the damping properties of the suspension are also improved compared to the basic design based on a traditional hydraulic shock absorber. Moreover, there is a proportional relationship between the voltage supplying the damper, the amplitude and damping time of the vibrations of the car body is observed. An increase in the control signal voltage from 1 to 2 V leads, in comparison with passive control of a magnetic shock absorber, to a decrease in the maximum amplitude of vibrations of the car body by 6.25 and 11.25 %, respectively, and a decrease in the vibration damping time by 0.72 and 1.41 s.

Study on the Economic Operation of a 1000 MWe Coal-Fired Power Plant with CO2 Capture

The flexible operation of carbon capture units is crucial for the economic performance of coal-fired power plants equipped with CO2 capture systems. This paper aims to investigate the impact of electricity, CO2, and fuel prices on the economic operation of such plants. A novel economic optimization model is proposed, integrating a static model of the carbon capture system with a particle swarm optimization algorithm. A new concept, the CO2 boundary price, is introduced as a key metric for determining the operating conditions of CO2 capture units. The CO2 boundary price rises when the power load decreases due to the decline in power generation efficiency, and it also increases with rising fuel prices, as the cost of steam for CO2 capture increases. Additionally, when the objective is to meet power load demand, CO2 prices have a great influence on the operation of CO2 capture units, assuming fixed coal and electricity prices. However, when the primary goal is to maximize plant profitability, the system’s operational conditions are strongly influenced by the relative prices of electricity and CO2. The proposed optimization model and the uncovered price-effect mechanisms provide valuable insights into the economic operation of carbon capture power plants.

AES67 based microphone array for sound source localization

Within the research project EAV-Infra, a novel microphone array system was developed. The system stands out for its utilization of Ethernet-based signal, control data and power transmission, ensuring a high degree of flexibility in operation. The key feature is its ability to facilitate real-time multichannel audio streaming via AES67, allowing for scalable and distributed recording and computing. With a total of 64 digital miniaturized microphones integrated into its printed circuit board-based design, the system allows for analysis of audio data for beamforming and other sound localization mechanisms. Adherence to audio and networking standards ensures synchronized transmission of audio data over packet-based networks. The system's emphasis on scalability, flexibility, and affordability makes it well-suited for a wide range of applications in fields like acoustic metrology and the entertainment industry, providing a comprehensive solution for high-quality audio processing needs across diverse domains.

Hydrochar and Value-Added Chemical Production Through Hydrothermal Carbonisation of Woody Biomass

This study investigates the optimisation of hydrothermal carbonisation (HTC) parameters for transforming Whitewood biomass into hydrochar, focusing on bioenergy production and valuable chemical extraction as by-products. The optimal carbonisation was achieved at a process temperature of 240 -260 °C, which optimised the higher heating value of the hydrochar to 27-30 kJ/g and ensured a structural integrity similar to lignite coal. Increasing the temperature beyond 260 °C did not significantly enhance the energy content or quality of the hydrochar, establishing 260 °C as the practical upper limit for the HTC process. Residence times between 30 to 60 min were found to have minimal impact on the yield and quality of hydrochar, suggesting significant operational flexibility and the potential to double throughput without increasing energy consumption. The study also revealed that the process water by-product is rich in furan compounds, particularly furfural and hydroxymethyl furfural, with their highest concentration (125 mg/g of feedstock) occurring at 220 °C. The implementation of these findings could facilitate the development of a large-scale HTC facility, significantly reducing reliance on fossil fuels and enhancing economic viability by producing high-energy-density biofuels and high-value chemical by-products.

Addressing cybersecurity challenges in robotics: A comprehensive overview

As robotics technology becomes increasingly integrated into various sectors, ensuring the cybersecurity of robotic systems is paramount. This article provides an in-depth exploration of the cybersecurity challenges confronting robotics and offers strategies to address these concerns. With the growing connectivity and networking capabilities of robots, vulnerabilities such as unauthorized access, data breaches, and network attacks are significant threats [1]. Protecting sensitive data collected and processed by robots is crucial to preserving privacy and trust. Remote access features, while enhancing operational flexibility, also pose security risks if not adequately secured. Weak authentication mechanisms and insecure interfaces could allow malicious actors to compromise robot functionality. Furthermore, robots are susceptible to malware and cyber-attacks, including viruses, worms, and ransomware. To mitigate these risks, a comprehensive approach is necessary, incorporating secure design principles, robust authentication mechanisms, encryption techniques, and cybersecurity training. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is essential to develop resilient robotic systems capable of withstanding evolving cyber threats. This article underscores the importance of addressing cybersecurity challenges in robotics to ensure the safety and security of robotic deployments across diverse domains. As robotics technology evolves and becomes integral across various sectors, prioritizing cybersecurity [2] is crucial to protect these systems from unauthorized access, data breaches, and network attacks. The interconnected nature and remote access features of robots pose significant vulnerabilities. Comprehensive measures, including secure design, encryption, and cybersecurity training, are essential. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is vital for developing resilient robotic systems. This article highlights the urgent need to address cybersecurity challenges to ensure the safety and integrity of robotic deployments.

A real-time phase transition modeling of supercritical steam cycle and load variation rate enhancement of thermal power plants under deep peak shaving

The ambitious green revolution to renewable energy sources in global power grids necessitates massive integration of solar and wind energy, which involves intermittent and unpredictable challenges. Thermal power plants are crucial in stabilizing the grid and addressing these challenges through flexibility reformation including deep peak shaving and frequent load variations since the unsteady state energy transfer and thermal dynamics during combustion and heat transformation in thermodynamic processes vary significantly. These conditions lead to issues such as furnace instability and latent heat of phase transition. This study introduces a novel approach to modeling phase transitions of supercritical steam cycle, and investigatesthe length, position, temperature, and energy transfer of the working medium and components under normal and low operational states. Conducting and analyzing the thermal feasible region associating the security of components and working medium this study establishes a control strategy for dynamic heat transfer to reduce component degradation effects andenhance load variation rate under flexible operations. Simulation model of a supercritical power unit based on the proposed method demonstrates an accuracy of 97.94%. Results from the optimal approach in maximizing load variation rate show the effectiveness and achieve 1.2%p.e./min most under transition process.

Unlimited sampling beyond modulo

Analog-to-digital converters (ADCs) act as a bridge between the analog and digital domains. Two important attributes of any ADC are sampling rate and its dynamic range. For bandlimited signals, the sampling should be above the Nyquist rate. It is also desired that the signals' dynamic range should be within that of the ADC's; otherwise, the signal will be clipped. Nonlinear operators such as modulo or companding can be used prior to sampling to avoid clipping. To recover the true signal from the samples of the nonlinear operator, either high sampling rates are required, or strict constraints on the nonlinear operations are imposed, both of which are not desirable in practice. In this paper, we propose a generalized flexible nonlinear operator which is sampling efficient. Moreover, by carefully choosing its parameters, clipping, modulo, and companding can be seen as special cases of it. We show that bandlimited signals are uniquely identified from the nonlinear samples of the proposed operator when sampled above the Nyquist rate. Furthermore, we propose a robust algorithm to recover the true signal from the nonlinear samples. Compared to the existing methods, our approach has a lower mean-squared error for a given sampling rate, noise level, and dynamic range. Our results lead to less constrained hardware design to address the dynamic range issues while operating at the lowest rate possible.

Flexible, transparent, and low-temperature usable electromagnetic shielding film based on orthogonally arranged silver nanowire network/graphene oxide conductive network

With the increasing diversification of application environments of new electronic products, demands are being placed on flexibility, transparency, and stable operation capability even under harsh conditions of electromagnetic shielding films. Aiming at the issue of high contact resistance of simple silver nanowire (AgNWs) conducting network, this work utilizes orthogonally arranged AgNWs and graphene oxide (GO) to establish unique AgNWs/GO conductive network, achieving more conductive nodes and enhancing the stability of the conductive network. The resulting AgNWs/GO film exhibits a reduced resistance of 9.8 Ω/sq, an impressive light transmittance of 85.7 % (λ = 550 nm), and excellent electromagnetic interference shielding efficiency (EMI SE) reaching 30.3 dB in the X-band (8.2–12.4 GHz). Moreover, the AgNWs/GO film exhibits the potential for long-term operation in harsh environments. After undergoing numerous bending cycles (10,000 times), prolonged exposure (90 days), and repeated energization (800 times), the resistance of the AgNWs/GO film remains extremely stable. Furthermore, the film demonstrates remarkable electrothermal conversion capability in cold environments, rapidly heating to 73.3 ℃ under a 4 V voltage and maintaining stability thereafter. After 800 h of operation, there is negligible change in its electrothermal properties. The development of such a flexible electromagnetic shielding film holds significant implications for various applications.

Labor unionization and trade credit: A regression discontinuity design

This paper examines how labor unionization affects suppliers’ trade credit provisions. Using a regression discontinuity design based on close-call NLRB union elections, I find that firms narrowly passing union elections experience a significant decrease in trade credit from suppliers. This effect is attenuated in right-to-work states and for firms with better financial performance. These findings suggest that suppliers view unionization as a risk factor on labor costs, financial distress likelihood, and reduced operational flexibility. Overall, this paper adds to the literature on labor unions’ economic impacts, determinants of trade credit, and the interactions between different corporate claim holders.

Coordinated price-based control of modulating heat pumps for practical demand response and peak shaving in building clusters

With a high share of renewable energy in the power grid, it becomes increasingly difficult to ensure a continuous balance between power generation and consumption, thereby endangering grid stability. A substantial opportunity to address this challenge and align the heating demand with intermittent power production is offered by space heating and domestic hot water, which account for 80% of the energy consumption in buildings. Further research on the control of building clusters is required, where peak load management of multiple buildings can ensure grid stability during peak hours and contribute to avoiding power outages. In this paper, a rule-based controller is presented, called Extended Price Storage Control+ (EPSC+), for the practical and flexible operation of heating systems in a building cluster. Under dynamic pricing, the loads of electric heating devices for the provision of space heating and domestic hot water are shifted by EPSC+ while accounting for peak load constraints. The performance of EPSC+ is evaluated in a nine-week winter simulation study with a building cluster of ten buildings in Germany. For comparison, a hierarchical model predictive controller (MPC) and a hysteresis two-point controller are employed as benchmarks. Results close to those of MPC are achieved by EPSC+by reducing the median peak load by 38.8% and median electricity costs by 15% compared with the hysteresis controller. In contrast to MPC, EPSC+ does not require models, forecasts, or optimization and is computationally inexpensive, rendering it more attainable for real-world implementation.

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method

With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses the growing uncertainties. Notably, a comprehensive evaluation method for operational flexibility assessment is first established. Based on this, this paper further proposes a flexibility-oriented operation optimization model using the distributionally robust chance-constrained (DRCC) method. A customized solution method utilizing second-order cone relaxation and sample average approximation (SAA) is also introduced. The results of case studies indicate that the flexibility of AC/DC hybrid distribution networks is enhanced through sharing energy storage among multiple feeders, adaptive reactive power regulation using soft open points (SOPs) and static var compensators (SVCs), and power transfer between feeders via SOPs.

Electrical Substation Design for Safety, Reliability and Sustainability

Abstract: Electrical substations’ design is critical for safe, efficient and reliable electricity distribution. Safety and operational flexibility demand for optimized layout design. The fundamental ideas and elements of creating an efficient electrical substation structure that guarantees reliability and maximum performance. Selecting the optimum substation site in industrial operations necessitates a delicate balance of safety, efficiency, and environmental sensitivity. The main ideas and considerations that support the optimization of electrical substation design have been painstakingly addressed in this paper. Adequate space and smooth grid connectivity improve distribution efficiency and allow for future growth. Regulation adherence and effective security measures ensure ethical and secure siting. Adequate assessment of electromagnetic and radio interference is essential. Substation placement optimization connects industrial competence with environmental responsibility in this sophisticated decision-making process, establishing a healthy balance for reliability and sustainability. Beginning with the careful selection of a site that strikes a delicate balance between closeness to power sources and environmental sensitivity, each decision has far-reaching consequences for the grid's integrity. The thorough layout of equipment provides not only performance but also ease of maintenance, thus increasing the operational flexibility and reliability of the substation.

Test Stand for Microjet Engine Prototypes

To investigate the functionality and performance of a prototype microjet engine, we constructed a versatile test stand tailored to the specifications of a 400 N prototype. This test stand facilitated a comprehensive study by enabling real-time recording of 45 essential parameters for analysis, encompassing temperatures, pressures, speed, fuel flow, thrust, vibration, and various other monitored metrics. All parameters and control elements were seamlessly integrated via a data acquisition and control system, utilizing a compactDAQ (Data Acquisition) system from National Instruments and a custom Virtual Instrument programmed with graphical language. The test stand offers both manual and automated operation modes, with the flexibility for hybrid operation. For instance, following the idle regime, manual control using a potentiometer can seamlessly transition from automated control via a proportional control (P control) mechanism. Before the experimental campaign, rigorous verification and validation tests were conducted to ensure the reliability and accuracy of the setup. The experimental campaign comprised a series of manual tests focusing on the fuel system and automated tests covering starting, idle, working, and stopping regimes. This structured approach allowed for a comprehensive evaluation across different operational scenarios, providing insights into the engine’s behavior and performance under varying conditions.

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.