Operational Planning Research Articles

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

Adversarial attacks on reinforcement learning agents for command and control

Given the recent impact of deep reinforcement learning in training agents to win complex games such as StarCraft and DoTA (Defense Of The Ancients)—there has been a surge in research for exploiting learning-based techniques for professional wargaming, battlefield simulation, and modeling. Real-time strategy games and simulators have become a valuable resource for operational planning and military research. However, recent work has shown that such learning-based approaches are highly susceptible to adversarial perturbations. In this paper, we investigate the robustness of an agent trained for a command and control task in an environment that is controlled by an active adversary. The C2 agent is trained on custom StarCraft II maps using the state-of-the-art RL algorithms—Asynchronous Advantage Actor Critic (A3C) and proximal policy optimization (PPO). We empirically show that an agent trained using these algorithms is highly susceptible to noise injected by the adversary and investigate the effects these perturbations have on the performance of the trained agent. Our work highlights the urgent need to develop more robust training algorithms especially for critical arenas like the battlefield.

УПРАВЛЕНИЕ КОМАНДОЙ ВНУТРИКОРПОРАТИВНОГО СТАРТАПА: ПРОЦЕССНОЕ ИССЛЕДОВАНИЕ ДИНАМИКИ РАЗВИТИЯ

Innovative development of the Russian economy is impossible without increasing the efficiency of small innovative enterprises (startups), including those created within companies. The problems of managing internal startups and especially startup teams have been relatively poorly studied by modern science. The high dynamics of startups causes high variability and intensive transformations in team management practices. To create a highper formance startup team, it is necessary to understand the internal logic of change in the parameters of teams and team management. The article studies the dynamics of changes in team characteristics and team management within the startup development process. The research methodology is based on process research and case studies. The work investigates the dynamics of the startup team a cloud gaming service. The startup was organized within one company but was then sold to a large IT corporation. The article examines certain characteristics of the team in their development including management methodologies, team composition, communications, culture, motivation, operational and strategic planning pro cedures.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

Enhancing Emergency Department Management: A Data-Driven Approach to Detect and Predict Surge Persistence.

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77-8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises.

Space Environments Applied to the Gateway Program

NASA’s Gateway is a crewed space station with a planned mission duration of 15 years in lunar orbit. It is an international collaboration, including partnerships with the European Space Agency, Canadian Space Agency, and Japan Aerospace Explorations Agency. Additionally, the program relies on important partnerships with industry for designing and building hardware. This complex mission poses significant challenges related to space environments. Gateway’s hardware will be directly exposed to the extremes of space weather posing challenges to avionics and materials as well as operations planning. The space environments are defined in the “Design Specification for Natural Environments” and applied through program requirements to all design partners. This paper describes the space environments applicable to the Gateway program, the flow of the environments to system requirements, and the challenges that the program is currently facing with regard to the space environments.

METHODOLOGICAL APPROACH TO THE ASSESSMENT OF INTERNATIONAL LOGISTICS MANAGEMENT IN RECREATIONAL ENTERPRISES: THE IMPACT OF RISKS ON FINANCIAL SECURITY

In the article, we present a methodological approach to evaluating international logistics management in the context of ensuring financial security through the development of an appropriate model for assessing indicators and risks that influence it. The research focuses on enterprises in the recreational sector. Structural-logical analysis methods were applied for modelling and graphical methods for their visualization. The fundamental method was the technique of evaluation using fuzzy sets. In this context, the MATLAB system served as the basis for more effective modelling. The article demonstrates that the evaluation of international management begins with the analysis of key performance indicators and risks. As a result of the research, a methodological approach to evaluating the effectiveness of international logistics management in the recreational sphere was formed. The main goal of such management is efficient planning and execution of logistics operations, which ensure continuity and high quality of recreational services at an international level, cost optimization, and increased customer satisfaction. However, to achieve this, it is necessary to properly assess risks and key indicators in the context of ensuring an adequate level of financial security. The proposed approach to evaluation focuses on identifying logistics risks, providing a comprehensive understanding of the problems and opportunities in the context of enhancing financial security. Looking ahead, it is necessary to more closely consider the possibility of practical application for specifically selected enterprises in the recreational sphere and to evaluate not only risks but also threats to financial security.

Operational planning of the production and the processing of chickens in multi-products slaughter unit

Operational planning of the production and the processing of chickens in multi-products slaughter unit

Charting the Course: A Policy Perspective on the Evolution of Kebbi State's Health Sector Annual Operational Planning

<i>Aim</i>: This study aims to provide a comprehensive overview of the Annual Operational Planning (AOP) process, a key health sector planning and harmonization approach, in Kebbi State, Nigeria, from 2020 to 2024, focusing on nine optimal planning parameters: state ownership, stakeholder participation, sector harmonization, evidence-based, timeliness, executive sign-off, quarterly tracking, dissemination, and sustainability. <i>Methods</i>: Review of AOP program reports, state-relevant reports, and stakeholders’ interviews. Employing a mixed-methods research design, the study integrates quantitative data derived from secondary sources like annual reports and policy documents with qualitative data from semi-structured interviews with key stakeholders. Descriptive statistics and time-series analysis are used for quantitative data to study progress and trends in each milestone, while thematic analysis of interview transcripts helps to elucidate factors influencing implementation and outcomes. <i>Results:</i> Findings highlight a progressive improvement in the annual operational planning across several of the planning parameters over the years from the baseline in 2020, including in participatory engagement of the lower levels of the health sector (bottom-up approach), using evidence to inform planning; timely development of the AOP to inform health budget; and regular tracking of the plan. However, challenges persist in the government funding of stakeholders’ engagement, particularly the bottom-up approach at the Local Government Health Authorities (LGAs) level, which may affect the inclusiveness of the process in the future. <i>Conclusion</i>: The study concludes that the annual operational planning in Kebbi State has seen notable improvement across multiple planning parameters, assuring the effectiveness and sustainability of the process as a key planning tool. However, allocating adequate resources, including budget and human resources, to facilitate a participatory approach in the AOP development process is critical for continuous success. This study contributes to the broader discourse on health sector planning in developing contexts, offering valuable insights for policymakers, health managers, and stakeholders in designing and implementing effective and harmonized health plans.

The impact of budget allocation by research service and platform providers on R&D outcomes: Reassessing the value of investment in research infrastructure in a knowledge-based society

This study aims to empirically analyze the impact of budget allocation by the Korea Institute of Science and Technology Information (KISTI) on national research competitiveness, thereby reassessing the value of investing in research infrastructure within a knowledge-based society. In the 21st century, research and development (R&D) have emerged as a pivotal element of national competitiveness, underlining the increasing importance of investments aimed at constructing and enhancing research infrastructure. However, empirical studies examining the causal relationship between research infrastructure investment and national research competitiveness are still notably scarce. Accordingly, this research endeavors to systematically delineate the effect of research infrastructure investment, with a focus on KISTI’s budget allocation, on enhancing national R&D outcomes. To achieve this, the structural relationship between KISTI’s budget, national R&D budget, and various academic and industrial performance indicators was analyzed using multiple regression and simple regression analysis. In particular, by demonstrating the mechanism through which the budget management of research support organizations like KISTI contributes to strengthening national research competitiveness, this study aims to shed new light on the strategic value of research infrastructure investment in a knowledge-based society. Furthermore, these findings are expected to provide valuable evidence for the formulation of national R&D policies in Korea and the strategic planning of budget operations for research support organizations. Through strategic investment of limited budgets, this could enhance the efficiency of national R&D investments and contribute to strengthening the capacity for scientific and technological innovation required in a knowledge-based society.

Novel coupled hydromechanical model considering multiple flow mechanisms for simulating underground hydrogen storage in depleted low-permeability gas reservoir

Depleted low-permeability gas reservoirs are the primary storage media for underground hydrogen storage (UHS). An accurate assessment of the geomechanics and complex flow mechanisms in a depleted low-permeability gas reservoir is crucial for predicting the injection and production capabilities of UHS facilities. This paper proposes a novel hydromechanical multicomponent model that couples non-Darcy flow, relative permeability hysteresis (RPH), and geomechanics to evaluate the hydrogen storage capacity of UHS facilities in a depleted low-permeability gas reservoir. The coupled model was solved using a fully coupled strategy, employing the finite volume method to solve non-Darcy flow equations and the virtual element method to solve geomechanical problems. The results indicate that the high-velocity non-Darcy (hvnD) and geomechanical effects reduce the working gas volume by 12.82% and 10.56%, respectively. Additionally, the low-velocity non-Darcy (lvnD) effect causes the third stress/strain distribution to exhibit a “focusing” phenomenon, resulting in a 13.19% reduction in the working gas volume. This paper also describes a case where residual gas is captured by mechanisms such as “water lock” and “capillary capture” through the RPH effect. Considering the RPH effect, the gas-water transition zone expanded by 30%, and the peak volume of gas-containing pores in the water zone increased by 1.48 times. Ignoring the RPH effect led to a 19.73% overestimation of the utilization rate of the gas-containing pore space. Hence, this study achieves safe and successful seasonal hydrogen storage and provides theoretical support for designing multicycle operational plans.

Optimization methods in water system operation

AbstractOperational water management is a critical global challenge, and decision making can be improved by using mathematical optimization. This paper provides an overview of optimization techniques, both exact and heuristic, used in water management. It focuses on the use of optimization techniques in the short term: operational planning in reservoir management, control of open channels, hydropower scheduling, and operation of polder drainage pumps. Principles of model predictive control, methods for optimization under forecast uncertainty, and approaches for conflict resolution are explained with the help of educational examples and practical cases. Challenges and research questions to be addressed in the future are presented as an outlook.This article is categorized under: Engineering Water > Methods Science of Water > Water and Environmental Change Water and Life > Conservation, Management, and Awareness

Evaluation of Public Transportation System through Social Network Analysis Approach

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

Local community's engagement and enactment of social value from hydropower infrastructure

PurposeSocial needs of local community are highly essential in the context of public infrastructure and have an impact on their performance. This paper explores the local community subjectivity in interaction with primary stakeholders to deepen our understanding of social value and this category of misunderstood stakeholders.Design/methodology/approachThe paper presents a partnership framework that aims to help stakeholders be reflexive and construct knowledge about social value of the infrastructure. The empirical material includes an extensive review of the public infrastructure documents published between 1981 and 2021 and 13 interviews with key members of local community.FindingsThe main contribution of this study is an integrated model to study the social value of an infrastructure and a dynamic approach to study how a local community engages and enacts social value. The dynamic approach highlights three plans of stakeholder’s subjectivity, which are relational, representational and operational plans to promote inclusive stakeholder’s management (“of” and “for”).Originality/valueThe study combines an analytical and a theoretical framework to investigate the enactment of social value.

Optimal operational planning of a bio-fuelled cogeneration plant: Integration of sparse nonlinear dynamics identification and deep reinforcement learning

This paper presents a novel data-driven approach for short-term operational planning of a cogeneration plant. The proposed methodology utilizes sparse identification of nonlinear dynamics (SINDy) to extract a dynamic model of heat generation from operational data. This model is then employed to simulate the plant dynamics during the training of a reinforcement learning (RL) agent, enabling online stochastic optimization of the production plan in real-time. The incorporation of SINDy enhances the accuracy of capturing the plant's nonlinear dynamics and significantly improves the computational speed of plant simulations, enabling efficient RL agent training within a reasonable timeframe. The performance of operational planning with the RL agent is compared to that of dynamic programming, a widely used method in the literature. The evaluation metric encompasses energy efficiency, unmet demands, and wasted heat. The comparison investigates the effectiveness of RL and dynamic programming under various scenarios with different qualities of energy demand forecasts. The RL agent exhibits robustness and notably improves the operational planning performance, particularly when faced with uncertain energy demands in the environment. Furthermore, the findings show that the RL agent, trained on a school building data, could successfully perform planning tasks for a hotel building, indicating the transferability of learned planning knowledge across different cogeneration use cases.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

The effect of stope size and cut-off grade on the design and scheduling of an underground mining operation in the presence of a pre-concentration system

ABSTRACT Stope layout optimisation is considered one of the main steps in the design and strategic planning of underground mining operations. This paper investigates the influence of stope size and cut-off grade in an underground sublevel stoping operation in the presence of a preconcentration system using a newly developed mathematical optimisation model. The utilisation of a bulk ore sorting system, its effects on the resource utilisation, and the final discounted cash flow of a case study are presented. Results show that incorporating a bulk sorting system to a sublevel stoping operation can significantly increase the viability of a deposit.

Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry’s output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy.

Metaheuristic Optimization of the Agricultural Biomass Supply Chain: Integrating Strategic, Tactical, and Operational Planning

Metaheuristic Optimization of the Agricultural Biomass Supply Chain: Integrating Strategic, Tactical, and Operational Planning