Integrated System Framework Research Articles

A resilience-oriented approach to integrated energy management systems: Addressing energy conversion unit unavailability and cost efficiency

As integrated energy systems become increasingly complex, their vulnerability to energy conversion units unavailability poses a considerable threat to system resilience and cost efficiency. This research is motivated by the need to address the growing threat of energy conversion unit unavailability and its significant impact on both system performance and operational costs. The development of a comprehensive vulnerability assessment framework, coupled with the integration of machine learning techniques, is hypothesized to significantly enhance the resilience and cost-efficiency of integrated energy management systems when faced with various unavailability scenarios. A novel methodology is introduced to enhance integrated energy management systems, focusing on the critical role of diverse energy conversion units. The methodology includes a new vulnerability index to quantify the impact of energy conversion unit unavailability on system performance, alongside an energy conversion unavailability attack model from the malicious actor perspective. Results indicate that under worst-case energy conversion unavailability attack scenarios, integrated energy systems operational costs can increase by up to 26.19%. The proposed solution, incorporating a Deep Q-Network into the integrated energy management system, achieves an 85.24% F1-score and a 96.68% reduction in additional costs associated with energy conversion unavailability attacks. This research demonstrates the varied impacts of different energy conversion units on integrated energy systems and proposes an enhanced integrated energy management system framework that improves system resilience and cost efficiency.

Multi-objective optimization and comprehensive Thermodynamic, economic, and environmental analysis of an innovative solar-geothermal integrated system for synergistic power generation and water desalination

ABSTRACT This paper presents a novel multi-objective optimization and sustainability analysis framework for an innovative solar-geothermal integrated system for power generation and water desalination. Harnessing renewable solar and geothermal energy, the system offers a sustainable solution for energy, water, and environmental challenges. Advanced thermodynamic modeling techniques, validated by industry simulations, accurately characterize system performance. Departing from conventional analyses, the research incorporates exergy-based economic and environmental assessments and the emergy concept to quantify the total available energy required, enabling a comprehensive sustainability evaluation. Through multi-objective optimization using genetic algorithms, the system’s design and operational parameters are optimized to enhance exergy efficiency, economic emergy performance, and environmental emergy performance simultaneously. The optimization framework considers turbine inlet conditions, pressures, and desalination configurations to identify optimal operating points balancing energy efficiency, economic feasibility, and environmental impact. Results demonstrate significant improvements of up to 23.49% in exergy efficiency, 25.97% in economic emergy performance, and 16.04% in environmental emergy performance compared to the baseline system. This study focuses on the multi-objective optimization of a novel solar-geothermal integrated system for synergistic power generation and water desalination. The methodology combines thermodynamic, economic, and environmental analyses to evaluate system performance. Key findings include a significant improvement in overall efficiency and sustainability compared to conventional systems, highlighting the potential for large-scale implementation.

SPATIAL PARAMETERS FOR MULTIMODAL INTEGRATION AT METRO STATIONS: A CONDUCIVE CASE STUDY OF METRO STATIONS IN DELHI

Urban public transit contributes to the multisector growth of a developing economy and is an essential component of sustainable urban development. Public transit has seen considerable upsurge in ridership over the past decade. In order to make public transportation more appealing to regular commuters than private automobiles, the current infrastructure in the majority of India's metropolitan cities must be modernized in a sustainable way. The Delhi Metro was designed with an identical objective in consideration, anticipating the increasing concern over traffic in and around the Delhi region, and after being implemented, it significantly reduced the number of individual vehicles on the road. Delhi Metro operates within the framework of the citywide multi-modal integrated transportation system, reinforced by other modes such as buses and IPT. It is the lifeline of Delhi's urban transit system. This research paper aims to study numerous aspects of multimodal integration at metro stations in order to increase the passengers' comfort and convenience. Convenient transitions in a multimodal transportation system are greatly aided by station amenities like the physical infrastructure, which includes the parking space, transfer area, metro ridership data, pedestrian network, cycling facilities, feeder shelter, taxi stands (IPT Stands), etc. Furthermore, it intends to promote walkability by establishing a high-quality public realm that is both welcoming and secure for anyone to use. This research is centered around Delhi Metro stations on the Yellow line. An extensive inventory of the station area and a commuter travel time questionnaire were used to evaluate the current state of affairs. Several standards of design were analyzed to optimize the output of research. The inferences yield specific recommendations and modifications for the present system, in addition to identifying several best practices that could be reiterated across all stations.

An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements

AbstractElectricity system decarbonisation poses several challenges to network stability and supply security, given renewables' intermittency and possible reduction of system inertia. This manuscript presents a novel integrated system framework to determine optimal generation investments for addressing decarbonisation challenges and achieving cost‐effective electricity systems while ensuring frequency stability and reserve requirements are met at the operational level in a net‐zero system. The novel planning framework is a mixed‐integer bilinear programming problem accurately modelling clustered variables for the on/off status of generation units and seconds‐timescale frequency requirements at an operational and planning level. The benefits of the decision framework and effects of dispatch decisions in a year are illustrated using the Great Britain case study. The results provide optimal trade‐offs and cost‐effective investment portfolios for including detailed modelling of unit‐commitment and frequency stability constraints versus not including them in the planning model. Making investment decisions for a net‐zero electricity system without these constraints can lead to very high system costs due to significant demand curtailment. Although the model's computation burden was increased by these constraints, complexity was managed by formulating them tightly and compactly. Non‐convex quadratic nadir constraints were efficiently solvable to global optimality by applying McCormick relaxations and branching techniques in an advanced solver.

A Health Systems Policy Framework on “How to” Build Cross-Sector Collaboration: Perspectives From Health Administrators and Leaders

There are many barriers/challenges bringing multiple stakeholders within health and non-health together to collaborate to address population health. This study aims to identify the key components to build successful cross-sector collaboration and develop a policy framework for health systems integration and transformation. We conducted quantitative surveys and qualitative interviews with health administrators and leaders who volunteered to participate on six newly established teams or “Tables” to improve population health locally in Ontario, Canada. Using thematic analysis and methodological triangulation, we identified emergent themes that were confirmed by member checking. The Relational Coordination survey response rate was 62% (n=45). The survey results were correlated with the twelve interviews and member checking. Drawing from the perspectives of the health administrators and leaders of the “Tables”, the emergent themes identified for successful cross-sector collaboration were: 1) systems change mindset, 2) inter-dependency, 3) inter-organizational relationships, and 4) self-organizing capacity. A health systems policy framework on “how to” build cross-sector collaboration was developed to support and achieve health systems integration.

A novel multi-objective optimization framework for optimal integrated energy system planning with demand response under multiple uncertainties

The planning of integrated energy systems (IES) faces significant challenges due to the presence of multiple uncertainties caused by stochastic demands and renewable energy generation. Particularly, how to balance different conflicted objectives in IES planning under multiple uncertainties is a major obstacle with few studies. Thus, this paper proposes a novel multi-objective optimization framework (MOOF) for uncertain IES planning with demand response to achieve the synergistic enhancement of multiple performance indicators of the system while ensuring its flexibility and safety. The proposed MOOF encompasses several key steps. Firstly, a multi-objective optimization model under various uncertainties is constructed, with the minimization of investment and operating costs, maximization of exergy efficiency, and minimization of carbon emissions as optimization objectives. Secondly, the chance constraint approach is used to convert the constraint conditions into deterministic ones. Subsequently, the Pareto dominance concept is incorporated into robust, interval, and opportunistic optimization techniques to obtain three deterministic transformation methods for multi-objective optimization with uncertainty. Further, a high-efficiency constrained multi-objective coevolutionary algorithm (CMCA) is developed to solve the proposed planning model, which has the characteristics of nonlinearity, and high-dimensional complexity. Finally, the effectiveness of the proposed MOOF and CMCA is verified through numerous case studies.

Mass-Balance-Consistent Geological Stock Accounting: A New Approach toward Sustainable Management of Mineral Resources.

Global resource extraction raises concerns about environmental pressures and the security of mineral supply. Strategies to address these concerns depend on robust information on natural resource endowments, and on suitable methods to monitor and model their changes over time. However, current mineral resources and reserves reporting and accounting workflows are poorly suited for addressing mineral depletion or answering questions about the long-term sustainable supply. Our integrative review finds that the lack of a robust theoretical concept and framework for mass-balance (MB)-consistent geological stock accounting hinders systematic industry-government data integration, resource governance, and strategy development. We evaluate the existing literature on geological stock accounting, identify shortcomings of current monitoring of mine production, and outline a conceptual framework for MB-consistent system integration based on material flow analysis (MFA). Our synthesis shows that recent developments in Earth observation, geoinformation management, and sustainability reporting act as catalysts that make MB-consistent geological stock accounting increasingly feasible. We propose first steps for its implementation and anticipate that our perspective as "resource realists" will facilitate the integration of geological and anthropogenic material systems, help secure future mineral supply, and support the global sustainability transition.

Setting the Stage for Agents’ Cooperation: Digital Twin Family as Framework for Systems Integration

Given present conditions of VUCA and BANI, production systems face frequent shifts in requirements, demanding efficient processing to maintain flexible. Production control, pivotal in such systems, navigates diverse target conflicts, requiring mutual representation of agent properties, targets, and conditions for joint optimization and self-reconfiguration. Dynamic representation of current information is essential, with Digital Twin technology showing promise. However, practical implementation often falters due to differing understandings and capabilities. Our work aims to propose a framework for designing and implementing specific, deliberate sets of networked Digital Twins, systematically analyzing existing concepts for integration, primarily through the Digital Twin Family approach.

New digital low-level RF controls based on the Red Pitaya STEMlab for the TLS Linac system

The Linac system at Taiwan Light Source (TLS) has been in operation for almost a quarter of a century and requires upgrades to improve its reliability. To achieve this, some components of the control system have been replaced with new digital low-level RF control units that use emerging technologies. A new unit is based on the open-source hardware platform which is named “Red Pitaya STEMlab” and offers a compact size and low power consumption. The unit features DAC (Digital-to-Analog Converter) blocks for downloading arbitrary waveforms with external trigger play and ADC (Analog-to-Digital Converter) blocks for waveform acquisition, enabling the development of real-time diagnostic toolkits. The new low-level RF control interface has been fully integrated into the existing EPICS software framework for system integration. The new digital low-level RF control system supports I/Q (in-phase/quadra-ture-phase) data with online amplitude and phase set-tings, and a waveform digitizer for inspecting low-level RF signals from the klystron modulator. Specific graphical applications have been designed and integrated into the existing operation interfaces. The system has been successfully achieved during routine operations. This paper describes the details of these efforts.

Electronic Health System Integration Framework for Secure M-Health Services: A Case of University of Nairobi Hospital

The purpose of this article sought to design a secure framework that can be used in M-Health systems development. The researcher used the integrated information theory as a framework for enforcing system security as a holistic approach. To actualize this study, objectives that were meant to guide in carrying out the research were: To evaluate the significance of Confidentiality, Integrity and availability on the security of M-health systems and to develop a framework for secure integration of M-Health systems. The researcher used University of Nairobi Hospital because of ease of accessibility and financial resources available to conduct the research. The study adopted a cross section survey design methodology that included a sample size of 44 ICT personnel and users of University Health System at the University of Nairobi Hospital. Data collection methods were observation, conducting interviews and filling questionnaires that were administered to the target population in the University Hospital. The target population were handed the questionnaires and had them filled. The filled in questionnaires were then picked later from the respondents. SPSS version 23 was used for data analysis, then presented in frequency tables, bar charts, pie charts and standard deviation.

A cooperative resilience-oriented planning framework for integrated distribution energy systems and multi-carrier energy microgrids considering energy trading

Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.

Sky-view images aided NLOS detection and suppression for tightly coupled GNSS/INS system in urban canyon areas

Although the global navigation satellite system (GNSS) and inertial navigation system (INS) integration framework has many advantages compared to the standalone GNSS and INS systems, its accuracy can be severely degraded in urban canyon areas, due to non-line-of-sight (NLOS) signals. Detection and suppression of NLOS signals are the key to improving the location accuracy of urban areas. Therefore, this paper proposes a method for NLOS detection using the improved region growing algorithm on sky-view images captured by a fish-eye camera. After obtaining NLOS detection results, this paper constructs a weighted model with an adaptive scale factor to suppress the influence of NLOS signals. Experimental results show that the proposed method can effectively improve the performance of NLOS detection and suppression. On this basis, a tightly coupled GNSS/INS integration system based on an extended Kalman filter is developed and tested on the vehicle equipment. Results show that the proposed method outperforms the traditional GNSS single point positioning (SPP) and tightly coupled GNSS SPP/INS integration on positioning accuracy. The root mean square error can be reduced by 33.7% and 24.6% in the north and east directions respectively. It indicates that the tightly coupled GNSS SPP/INS integration aided by the fish-eye camera has certain research value and potential on positioning in urban canyon areas.

A three-layer planning framework for regional integrated energy systems based on the quasi-quantum theory

With the rapid development in social informatization, more and more factors, such as regional economy, technological development, and people’s living needs, will affect the supply–demand relationship of regional integrated energy systems (RIES), which involve multiple energy forms. These factors turn the supply–demand relationship of an energy system into a nonlinear and time-varying chaotic system. This makes it difficult to predict and balance these relationships, which poses a huge challenge to the prediction, planning, operation, etc. Existing conventional methods attempt to quantify the prediction bias caused by various external environmental factors of energy systems and to weaken the uncertainty caused by multiple energy loads through random programming. However, uncertainty factors increase with the development of an energy system, thereby inreasing the requirements of such uncertainty quantification methods. Furthermore, in conventional methods, the prediction or planning decisions are often made by an observer while neglecting the impact of the observer’s decision-making behavior on prediction and planning. Therefore, this paper proposes a three-layer planning framework for to solve the above problems. This architecture includes quasi-quantum uncertainty periodic evaluation, stochastic planning based on information gap decision theory, and rolling planning based on model predictive control. First, we establish a quasi-quantum model of multi-energy system prediction error based on the quasi-quantum wave function model to qualitatively analyze prediction errors affected by uncertainty before and after planning. Simultaneously, combined with the evaluation model of the quasi-quantum potential energy function, a quasi-quantum uncertainty period evaluation model is proposed. Based on the minimum equivalent planning entropy, the planning cycle of multistage rolling planning is divided to minimize uncertainty. Second, the information gap decision theory is used to quantify the influence range of source and load uncertainty in the planning process and the multistage planning cycle is randomly planned. Then, the model predictive control method is used to control the actual error, and the planning strategy is changed in time to reduce the planning deviation caused by the prediction error. Finally, adopting a Beichen demonstration area in Tianjin, China, as a case study, the effectiveness of the proposed method in uncertainty analysis and long-term planning improvement is verified. The three-layer planning framework can improve the adaptability of the regional integrated energy system in the long-term planning process, and can timely adjust the planning scheme to cope with the impact of unpredictable uncertainties in the planning process.© 2017 Elsevier Inc. All rights reserved.

An optimal step-size simulation framework for large-scale heat-electric integrated energy system considering fault states

Dynamic simulation plays a crucial role in the development of integrated energy systems, which are typically governed by a set of partial differential equations. The selection of an appropriate simulation step-size is essential for balancing the accuracy and speed of the system simulation. However, determining the optimal simulation step-size is challenging, particularly for large-scale and fault-state systems. To address this issue, this paper proposes a novel approach for determining the optimal step-size in simulations of large-scale systems and fault-state systems. Firstly, an iteration simulation approach and a multi-step-size simulation framework are introduced to accelerate the simulation of large-scale systems without compromising accuracy. Additionally, a fault hazard evaluation model and a fault-state system variable-step-size simulation framework are developed to ensure accurate simulation results for fault-state systems. To validate the proposed methodology, comprehensive analyses are conducted on different scenarios using a 4536-node and a 38-node testbed. The results demonstrate that the proposed large-scale system simulation strategy reduces simulation time by 69.1%, while the fault-state simulation approach improves simulation accuracy by 61.99%.

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.

Modeling and optimization of collaborative computing in regional multi-energy systems for energy Internet

Multi-energy systems (MES) exploit advanced physical information technology and innovative management pattern to achieve collaborative control of multiple heterogeneous energy. The refined control of MES bursts massive delay-sensitive computing tasks, requiring precise system modeling and collaborative computing strategies. However, the unbalanced spatial and temporal distribution of resources and real-time requirements of tasks further increase the difficulty of computing. To address these challenges, a precise modeling and optimization method of collaborative computing in MES is proposed. First, an integrated system framework for regional multi-energy systems (RMES) in northeastern China is designed. Then, the collaborative computing problem with energy and delay constraints in RMES is modeled. It is proved that the problem can be transformed into a joint convex and nonconvex optimization problem. Moreover, an improved simulated annealing-based joint resources optimization (ISA-JRO) scheme is proposed. Finally, extensive simulation results show that ISA-JRO significantly improves the computational resource utilization ratio and reduces the total cost in MES. ISA-JRO reduces at least 25 % of the total cost compared with the traditional methods.

Low carbon optimal operation of integrated energy system based on concentrating solar power plant and power to hydrogen

A new integrated energy system (IES) framework is created in order to encourage the consumption of renewable energy, which is represented by wind and solar energy, and lower carbon emissions. The connection between the units in the composite system is examined in this research. In-depth analysis is done on how energy is transferred between electricity, heat, gas, and hydrogen. The system model and constraints are used to build an objective function with the lowest total operating cost. The calculation of carbon trading includes the ladder carbon trading method. And set up 6 cases for analysis, which verifies the effectiveness of the participation of the concentrated solar power plant (CSPP) in the heat supply and power to hydrogen system (P2HS) in reducing the total operating cost of the system, reducing wind curtailment and light curtailment, and reducing carbon emissions. Under the model considered in this paper, reduces the total operating cost reduces by 27.04% when the concentrated solar power plant is involved in the supply of thermal load. And the carbon emission is reduced by 14.529%. Compared with the traditional power to gas, considers the power to hydrogen system in this paper, which reduces the total operating cost by 4.79%.

A two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints

After proposing the carbon peaking and carbon neutrality target, China further proposed a series of specific carbon emission growth limit sub-targets. How to decarbonize the energy system to ensure the realization of the carbon growth limit sub-targets is a meaningful topic. At present, generation expansion planning of renewable energy in integrated energy systems has been well studied. However, few of the existing studies consider specific carbon emission growth targets. To address this research gap, a two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints is proposed in this paper, which takes into account multiple uncertainties. In this framework, the objective function is to minimize the total operation cost and wind turbine investment cost. The first stage is the decision-making level of the wind turbine capacity configuration scheme. The second stage is the optimal economic dispatching in the worst-case scenario, which is a bi-level problem of max-min form. Thus, the two-stage robust optimization framework constitutes a problem of min-max-min form, which is pretty hard to solve directly with a commercial solver. Therefore, a nested column-and-constraint generation algorithm is adopted and nested iterations are performed to solve the complex problem. Finally, case studies are carried out on a regional electric-gas integrated energy system. The MATLAB/YALMIP simulation platform with the Gurobi solver is used to verify the effectiveness and superiority of the proposed framework. Compared with other four cases, 5,000 Monte Carlo scheduling tests demonstrate that the proposed framework can ensure the system carbon emission to be controlled within a certain limit even in the worst scenario. Due to the consideration of multiple uncertainties, the proposed framework planning results are both robust and economical for investment. This study can provide theoretical support for the actual regional integrated energy system to achieve a certain carbon growth target.

Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired "neuromorphic" processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital-computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Stunting Convergence Management Framework through System Integration Based on Regional Service Governance

The acceleration of stunting reduction in Indonesia is one of the priority agendas in the health sector, its implementation being through various regional and tiered approaches. This paper aims to manage management using an integrated system framework approach at the regional level and to support the acceleration of stunting reduction nationally. It takes a quantitative description approach that uses secondary data sourced from the Directorate General of Regional Development, Ministry of Home Affairs, the Republic of Indonesia in 2019–2021. The locus of papers is in five provinces, North Kalimantan, South Kalimantan, Central Kalimantan, West Kalimantan, and East Kalimantan, Indonesia. The data collection and processing consisted of twenty stunting convergence coverage referring to regulations in Indonesia. The analysis used is an integrated framework based on five dimensions. Management based on an integrated framework in a regional-based system for stunting convergence can be a solution to accelerating stunting reduction. This paper provides an option to accelerate the handling of stunting through the Integration of Service Governance-Based Systems in Districts/Cities, considering the achievements in the last three years that have not been maximally carried out in every district/city in five provinces in Kalimantan, Indonesia. This study explains that the local government needs to socialize and disseminate the commitment to stunting reduction results to reaffirm commitment and encourage all parties to actively contribute to integrated stunting reduction efforts. This paper has limitations in the implementation of dimensions that can develop in a context that is correlated with several perspectives, such as regional planning, budgetary capacity, and regional capacity.