Planning Of Systems Research Articles

Joint Planning and Operation Optimization of Renewable Energy Systems Considering Bi-Directional Electric-Thermal Storage and Conversion

Renewable energy systems have emerged as a crucial research area due to the escalating demand for sustainable energy solutions. With the advancement of renewable energy, the electric-thermal coupling within multi-energy systems has become more intricate. Bi-directional electric-thermal storage and conversion technologies have emerged as a potential solution to address the challenges associated with efficient energy utilization. This paper focuses on the joint planning and operation optimization of renewable energy systems considering bi-directional electric-thermal storage and conversion. The integrated framework for renewable energy systems incorporating a bi-directional electric-thermal storage and conversion unit is designed, and the joint planning and operation optimization method is proposed. Case studies are conducted based on typical annual energy demand and solar radiation characteristics in Beijing, China. Numerical results show that the proposed method can effectively handle the coupling and bi-directional conversion characteristics of electrical and thermal energy, achieving energy cost savings while fulfilling the energy demands of the system. The proposed system has a capital expenditure of USD 261,251.4 and an operating expenditure of USD 177,007.1, which shows a total cost reduction of 12.28% compared to the lithium-ion battery system, providing better economic performance while further enhancing the flexibility of energy utilization. These research findings contribute to the development of more efficient and sustainable renewable energy systems, providing a valuable reference for future research and practical applications within the energy field.

A two-stage optimization approach for big-scale problems in long-term planning of municipal solid waste management systems

A two-stage optimization approach for big-scale problems in long-term planning of municipal solid waste management systems

Forecasting Public Bicycle Utilization in New York City Utilizing the CitiBike Dataset

The integration of public bicycles into urban transportation systems has gained popularity due to their potential to reduce traffic congestion and air pollution, while also addressing the “last kilometer” problem. This study examines the usage patterns and predictive analysis of public bicycles in New York City using the CitiBike dataset from 2013 to 2015. We employ the Spring algorithm for networkx visualization and analyze the loan and return data to understand spatial-temporal usage dynamics. Additionally, clustering techniques, including K-means and DBSCAN, are applied to the dataset to uncover usage trends and patterns. The findings provide valuable insights for the planning and optimization of public bicycle systems, such as CitiBike, and contribute to the promotion of sustainable and environmentally friendly urban transportation.

Forecasting Public Bicycle Utilization in New York City Utilizing the CitiBike Dataset

The integration of public bicycles into urban transportation systems has gained popularity due to their potential to reduce traffic congestion and air pollution, while also addressing the “last kilometer” problem. This study examines the usage patterns and predictive analysis of public bicycles in New York City using the CitiBike dataset from 2013 to 2015. We employ the Spring algorithm for networkx visualization and analyze the loan and return data to understand spatial-temporal usage dynamics. Additionally, clustering techniques, including K-means and DBSCAN, are applied to the dataset to uncover usage trends and patterns. The findings provide valuable insights for the planning and optimization of public bicycle systems, such as CitiBike, and contribute to the promotion of sustainable and environmentally friendly urban transportation.

The First Fifth-Generation District Heating and Cooling System in Kazakhstan: Planning and Design

This paper focuses on the potential for integrating Geographic Information System (GIS) software into Fifth-Generation District Heating and Cooling (5GDHC) systems to promote efficient and sustainable energy management, particularly in Kazakhstan. By reviewing the key literature, we identify three main areas where GIS software enhances the planning of 5GDHC systems: decision-making in the context of energy market regulations, operational data utilization, and modeling/simulations for technical design. The technical design emphasizes the role of heat pumps, ultra-low temperature district heating (ULTDH) networks, and end-user buildings. Previous research has explored various methodologies for integrating network and demand-side strategies, developing sustainable district heating and cooling (DHC) systems, and mapping urban areas suitable for DHC deployment. However, none has presented an open-source model incorporating GIS-based decision-making in designing 5GDHC systems. This study, for the first time, addresses this gap through a case study conducted in the Northern Industrial Zone of Karaganda, Kazakhstan, demonstrating how GIS-enhanced modeling can be effectively applied in a developing, industry-driven economy. We outline the mathematical framework for comparing existing simulation tools and developing a custom model suited to the region’s needs. Additionally, we discuss validation and calibration methods, which remain underexplored in the current literature. The proposed model incorporates waste heat recovery from local sources, including nearby wastewater treatment plants, showcasing a sustainable energy solution for the industrial park. The results indicate that a well-structured 5GDHC system, supported by GIS tools, can markedly enhance energy efficiency and sustainability, presenting a scalable and adaptable approach for other regions in Kazakhstan and beyond.

Safe motion planning and formation control of quadruped robots

This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems. First, in order to improve the efficiency and safety of quadruped robots navigating in complex environments, this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field. The framework is suitable for both static and slow dynamic obstacle environments, aiming to achieve multiple goals of obstacle avoidance, minimizing energy consumption, reducing impact, satisfying dynamic constraints, and ensuring trajectory smoothness. This approach differs in that it reduces energy consumption throughout the movement from a new perspective. Meanwhile, this method effectively reduces the impact of the ground on the robot, thus mitigating the damage to its structure. Second, we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control. Finally, the proposed algorithm is validated through both simulations and real-world scenarios testing.

Optimizing operations of flexible assembly systems: demonstration of a digital twin concept with optimized planning and control, sensors and visualization

AbstractThis paper presents the development of an optimized planning and control method for flexible manufacturing and assembly systems. While the significant potential of flexible manufacturing concepts to help producers adapt to market developments is recognized, the complexity of the flexible systems and the need to optimally plan and control them is a major obstacle in their practical implementation. Thus, this paper aims to develop a comprehensive digital planning method, based on a digital twin and to demonstrate the feasibility of the approach for practical application scenarios. The approach consists of four modules: (1) a simulation-based optimization module that applies reinforcement learning and genetic algorithms to optimize the module configuration and job routing in cellular reconfigurable manufacturing systems; (2) a synchronization module that links the physical and virtual systems via sensors and event handling; (3) a sensor module that enables a continuous status update for the digital twin; and (4) a visualization module that communicates the optimized plans and control measures to the shop floor staff. The demonstrator implementation and evaluation are implemented in a learning factory. The results include solutions for the method components and demonstrate their successful interaction in a digital twin, while also pointing towards the current technology readiness and future work required to transfer this demonstrator implementation to a full-scale industrial implementation.

A New Methodology for Estimating the Potential for Photovoltaic Electricity Generation on Urban Building Rooftops for Self-Consumption Applications

As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments.

Fine-grained simulation model for PV power output interval based on two-stage scenario clustering and dual-ensemble compatible learning

Photovoltaic (PV) power simulation is indispensable for the planning and operation of renewable-rich power systems. Among various simulation methods, data-driven indirect simulation methods are the most efficient, primarily involving scenario extraction and power output generation. Typical scenario extraction methods often feature coarse characterizations, which also cannot be expressed by traditional power output generation models lacking the ability of quantifying the uncertainty of the output results. This paper proposes a fine-grained simulation model for PV power output interval, i.e., upper bound and lower bound, based on two-stage clustering of multiple typical scenarios and dual-ensemble compatible learning. The first stage of scenario extraction, considering the impact of total daily irradiance on PV output, employs a line distance index for preliminary scenario division of daily PV output. These scenarios are further divided into five characteristic scenarios with an angular distance index considering the impact of intra-day irradiance variations on power output. PV power output interval is generated from dual-ensemble compatible learning including Stacking and Bagging, which are adaptively selected with a Bias-Variance Selection Index (BS) calculated using the value of scenario characteristics. Stacking and Bagging are driven by three neural network base learners with Bayesian layers, which can output the lower bound and upper bound with confidence at a certain level, thus quantifying the uncertainty of the result accurately. The actual data of Daxing and Gangjialing power stations are used in case study. Results form case study show that compared with the existing methods, the proposed method can improve the Power Coverage Rate (PCR) and Average Power Simulation Accuracy (APA) of uncertainty simulation results by 27.44% and 5.39%, respectively. Meanwhile, the Average Daily Maximum Power Simulation Accuracy (AMPA) is similar to that of existing methods.

Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification

Developing hybrid renewable energy systems (HRES) is crucial for ensuring affordable, sustainable, and reliable energy access in remote rural and island regions. Rich and detailed data play a pivotal role in the optimal design of HRES. However, remote areas often face data limitations due to inadequate metering infrastructure, which further increases uncertainties in HRES design. This paper proposes a planning optimization method that quantifies multiple uncertainties, requiring only limited information for the optimal HRES design. First, uncertainties in HRES planning are classified into different types based on intrinsic randomness, data availability, and impact level. Then, by integrating uncertainty quantification with credibility theory, knowledge-based scenario generation models are developed to leverage limited available information to generate extreme and typical operating conditions for a tailored HRES design. Finally, a fuzzy credibility-constrained stochastic-robust optimization model is proposed to optimize the design of HRES. A practical case facing significant data scarcity and gaps is used to validate the effectiveness of the proposed method. The proposed approach reduces the loss of load probability for HRES to 0.02% and lowers the levelized cost of energy to $0.16/kWh—33% lower than that of the deterministic method and 79% lower than that of the traditional stochastic method.

Convex geometric motion planning of multi-body systems on lie groups via variational integrators and sparse moment relaxation

This paper reports a novel result: with proper robot models based on geometric mechanics, one can formulate the kinodynamic motion planning problems for rigid body systems as exact polynomial optimization problems. Due to the nonlinear rigid body dynamics, the motion planning problem for rigid body systems is nonconvex. Existing global optimization-based methods do not parameterize 3D rigid body motion efficiently; thus, they do not scale well to long-horizon planning problems. We use Lie groups as the configuration space and apply the variational integrator to formulate the forced rigid body dynamics as quadratic polynomials. Then, we leverage Lasserre’s hierarchy of moment relaxation to obtain the globally optimal solution via semidefinite programming. By leveraging the sparsity of the motion planning problem, the proposed algorithm has linear complexity with respect to the planning horizon. This paper demonstrates that the proposed method can provide globally optimal solutions or certificates of infeasibility at the second-order relaxation for 3D drone landing using full dynamics and inverse kinematics for serial manipulators. Moreover, we extend the algorithms to multi-body systems via the constrained variational integrators. The testing cases on cart-pole and drone with cable-suspended load suggest that the proposed algorithms can provide rank-one optimal solutions or nontrivial initial guesses. Finally, we propose strategies to speed up the computation, including an alternative formulation using quaternion, which provides empirically tight relaxations for the drone landing problem at the first-order relaxation.

The impact of hydrogen on decarbonisation and resilience in integrated energy systems

The lack of clarity and uncertainty about hydrogen's role, demand, applications, and economics has been a barrier to the development of the hydrogen economy. In this paper, an optimisation model for the integrated planning and operation of hydrogen and electricity systems is presented to identify the role of hydrogen technologies and linepack in decarbonising energy systems, improving system flexibility, and enhancing energy system security and resilience against extreme weather events. The studies are conducted on Great Britain's (GB) 2050 net-zero electricity and gas transmission systems to analyse the hydrogen transport and capacity requirements within the existing infrastructure under different scenarios. This includes sensitivities on the level of flexibility, high gas prices, hydrogen production mixes, enabled reversibility of electrolysers, electricity generation cost, and hydrogen storage facilities. In all sensitivity scenarios, efficient hydrogen transport within the existing infrastructure is enabled by the optimal allocation of green and blue hydrogen sources, distributed storage facilities, and the intra-day flexibility provided by linepack. The findings highlight that increased renewable deployment transfers intermittency to the hydrogen network, requiring greater linepack flexibility compared to the current paradigm (up to 83%). Furthermore, the necessity of synergy between different gas and electricity systems components in providing flexibility, security, and resilience is quantified.

Framework for evaluating the successful implementation of solar home systems in public housing projects in the United Arab Emirates

Successful planning and implementation of solar home systems (SHS) remains a significant challenge for government agencies, particularly from economic and sustainability perspectives. This study aims to develop a stakeholder-based framework for evaluating SHS programs in the public housing sector of the United Arab Emirates (UAE). A mixed-method approach was employed, incorporating both qualitative and quantitative data collection. A comprehensive review of SHS was conducted to identify the main global success factors for implementing the system in public housing projects. Subsequently, a structured questionnaire was distributed to 56 specialized participants comprising institutional stakeholders involved in SHS implementation in the UAE, leading to the identification of the critical success factors (CSF). Furthermore, 24 domain experts validated the data through focused group interviews. The findings indicate that the success of SHS programs is significantly influenced by regional factors, with stakeholders playing a pivotal role in defining the success factors. The research identified 44 potential CSFs, categorized into six fundamental groups: political, economic, technical, social, environmental, and marketing. Among these, economic factors were the most significant in the UAE, followed by technical, political, environmental, marketing, and social aspects. Government support was identified as the most CSF, followed by annual savings, maintenance and monitoring, installation quality, and payback period. The proposed framework emphasizes the government's role as the primary stakeholder, guiding the evaluation of success based on specific goals and performance metrics. Each stakeholder's unique success factors are weighted to reflect their importance, and the analytic hierarchy process (AHP) method is employed to derive key performance indicators (KPI) for consistent success measurement. This study provides a robust tool for stakeholders to assess and compare the success of different SHS programs by considering region-specific factors. It is intended to help UAE policymakers and government authorities make decisions, ensuring more effective SHS implementation, with the potential to be applied in the other Gulf Cooperation Council Countries that share similar environmental, economic, and political conditions.

A Combined Capacity Planning and Simulation Approach for the Optimization of AGV Systems in Complex Production Logistics Environments

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

The Action Plan of the Ministry of Energy as a tool to conduct structural modernization and ensure Russia's technological sovereignty. Part 2

Subject. The article addresses problems of official data systematization in the energy sector, rule-making activities of ministries and departments of the Russian Federation. Objectives. The purpose of the study is to assess the situation in the energy sector, determine the relevance of the current Action Plan of the Ministry of Energy of the Russian Federation for 2019–2024 in view of the new economic sanctions. Methods. The study employs methods of comparative analysis. Results. The paper established that the Action Plan of the Ministry of Energy of the Russian Federation for 2019–2024 cannot be considered as an effective tool to carry out structural modernization of the fuel and energy complex and secure the technological sovereignty of the country. Conclusions. To change the current situation, it is necessary to rethink the principles of building the entire system of strategic planning.

Modelling of wind and solar power output uncertainty in power systems based on historical data: A characterisation through deterministic parameters

The inherent uncertainty associated with wind and solar energy poses challenges in ensuring the reliability of the power system with a high penetration of renewable energy. Therefore, incorporating uncertainty is vital during the planning of power systems. Traditional analytical methods often necessitate transforming the uncertainty of large-scale power systems into deterministic optimisation planning, while those approaches tend to be overly conservative, yielding unsolvable solutions, or rely on assumptions and data fitting, creating a significant gap with real-world scenarios. To address this, this study employs upper α-quantiles originating from abundant historical data to replace power output expectations to consider uncertainty during the planning. Additionally, random sampling based on historical data generates potential wind and solar output scenarios, and Monte Carlo simulations are used to validate the proposed method's reliability and assess system investment distribution. Using China's power system as a case study, 64 quantile choices are evaluated. Compared to planning through output expectations, using quantiles can reduce the load loss rate from 10−1 to 10−5, and both the cost expectations and the extreme cost risk decrease. The use of fossil fuels has also decreased by 10%. Results underscore the necessity of choosing upper α-quantiles with higher α, such as 85, to account for the long-tailed wind power distribution. However, even overly conservative wind power output α is still challenging to mitigate residual load loss. The combination of photovoltaics and intraday energy storage ensures that the α selection of photovoltaics should not be too low, due to the reason that there is a need to balance the gradual decrease of load loss rates and the steep ascent of expected investment costs in the power system.

Historical synthesis of the International Commission on Water Resources Systems

ABSTRACT The planning, designing, management, and operation of water systems has been one of the most important activities throughout the history of humankind. However, as a scientific and applied discipline, it evolved about 70 years ago. The International Commission on Water Resources Systems (ICWRS) has primarily focused on developing and implementing improved methods for water resources management for the benefit of society since the 1970s. This paper reviews the history of the water resources systems (WRS) discipline over these six decades after the 100th anniversary of the International Association of Hydrological Sciences (IAHS) and aims to make a synthesis of the history of ICWRS. In the concluding section, an outlook for researchers and decision makers on the future development of WRS research is also provided.

Optimizing location planning of tourism signage systems based on space syntax theory- a case of Lukang Old Street in Taiwan

ABSTRACT This study explores the optimization of tourism signage systems location planning in Lukang Old Street, Taiwan, using space syntax theory. The tourism signage systems plays a crucial role in providing movement and tourism route information in developing old street areas. While Lukang Old Street attracts many visitors due to its unique local culture, architecture, and landscape, the current lack of an effective signage system often leads to disappointment among tourists who struggle to navigate to famous attractions. This research aims to configure and plan the location of a signage system for Lukang Old Street tourism based on space syntax theory. The study focuses on wayfinding behavior and indicator information, discussing the relationships between wayfinding and signage systems. By analyzing the spatial configuration and connectivity of movement patterns in Lukang Old Street using Depthmap software, the research identifies the most convenient routes for tourism and 10 important intersections where people gather. The findings provide valuable insights for optimizing the configuration and planning of the tourism signage system, with the ultimate goal of enhancing visitors’ travel satisfaction and promoting tourism development in Lukang Old Street.

Use Of The Ward And Peppard Method In Planning Strategic System Information: Systematic Literature Review

Strategic planning of information systems needs to be used, because it can support business strategies in organizations or companies in order to achieve business goals more quickly. One method that can be used in strategic planning of information systems is the Ward and Peppard method. This method has a clear framework and also has analysis techniques that focus on business needs & information technology for the future. The purpose of this study is to see the use of the Ward and Peppard method in strategic planning of information systems. The method used in this study is a literature study. The results of this study are that the Ward and Peppard method in strategic planning of information systems can be used as one of the preferred methods that can be used in carrying out strategic planning of information systems in various sectors of life.

Grand challenges of wind energy science – meeting the needs and services of the power system

Abstract. The share of wind power in power systems is increasing dramatically, and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Recognising the fundamental objective of power systems, maintaining supply–demand balance reliably at the lowest cost, and integrating all these technologies are significant research challenges that are driving radical changes to planning and operations of power systems globally. In this changing environment, wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. A needs and services paradigm is adopted here to highlight these research challenges, which should also be guided by a balanced approach, concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied, with control and coordination internally being a focal point in parallel with a strong recommendation for a holistic approach targeted at where wind has an advantage over its competitors and in coordination with research into other technologies such as storage, power electronics, and power systems.