Planning Of Systems Research Articles

Optimum ground-mounted on-grid connected photovoltaic system

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively.The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis.The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km2) compared to the traditional criterion (76.28 km2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden.The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

Charging facility planning and scheduling problems for battery electric bus systems: A comprehensive review

The adoption of battery electric buses (BEBs) has gained significant momentum in the public transportation sector due to their environmental and energy-saving merits. Nonetheless, challenges such as limited driving range and battery degradation, to some extent, hinder the extensive deployment of BEBs. This paper provides a comprehensive review of BEB adoption and utilization over the past decade, categorizing the state-of-the-art development and our discussion on BEB charging facility planning (BEB-CFP) and BEB charging scheduling (BEB-CS). In this review paper, we first survey the prevailing charging technologies in the BEB market and evaluate their applicability and limitations. Subsequently, the paper synthesizes the development and application of these technologies in terms of related infrastructure planning and charging scheduling operation, focusing on their unique considerations, modeling approaches, solution algorithms, and practical applications. Last, the paper concludes by identifying avenues for future research. Our findings contribute to the literature by providing a holistic revisit of BEB-CFP and BEB-CS with different types of charging technologies, offering selection recommendations and insights for both practitioners and policymakers aiming to optimize the utilization and sustainability of BEB fleets.

Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System

Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations.

Assimilate Remobilization in Five Spring Grain Legumes Under Mediterranean Conditions

A comparative evaluation of grain legumes is essential for the effective planning of legume-based agricultural systems in a given environment. The goal of this work was to contrast the growth, translocation of assimilates, and grain yield of spring-planted common vetch (Vicia sativa L.), red pea (Lathyrus cicera L.), lentil (Lens culinaris Medik.), chickpea (Cicer arietinum L.), and field pea (Pisum sativa L.) under rainfed Mediterranean conditions. Two cultivars of each species were cultivated on a silty clay soil in northeastern Greece for 2 years (2014 and 2015) with contrasting rainfall patterns. Chickpea and field pea exhibited better early crop growth rate than any other legume. Species differences in assimilates availability prior to grain filling affected the remobilization of assimilates to seed, which increased by 45% for every kg ha−1 rise in early dry matter accumulation. Dry matter translocation efficiency varied from 9 to 51% depending on species and year. Red pea was the best option in terms of seed yield, regardless of the seasonal rainfall. Chickpea in the drier year (2015) and field pea in the wetter year (2014) produced seed yields that were comparable to that of red pea. Lentil and common vetch were generally less productive species in terms of seed yield. Species seed yield was associated with their ability to accumulate biomass either before podding (r = 0.52, P < 0.05) or at maturity (r = 0.51, P < 0.05), but not with harvest index or translocation of dry matter. Findings provide new knowledge regarding growth attributes and reallocation of assimilate in five legume species grown simultaneously in the same environment, which has never been studied before. In addition, results highlight that selecting species with enhanced early or final biomass potential as well as adopting cultural practices that promote biomass accumulation in the growing season appear to be effective management strategies for improving seed yield of the tested grain legumes under Mediterranean conditions.

Optimal voltage set-point of automatic tap changer transformers and generators and reactive power compensation to increase power system predictability

In recent years, renewable energy sources (RESs) such as wind turbines (WTs) have received much attention in both the operating and planning of power systems. These sources have caused many uncertainties in the operation of power systems due to their unpredictable nature. Mentioned uncertainties besides load demands uncertainty, and possible failures in power system components, make the system state unpredictable. The operational decisions are completely challenging in these situations. Any efforts to increase the network operator awareness from the system state will be completely valuable for making accurate decisions.This paper tries to increase the predictability of the power system state through optimal regulating of the voltage setpoint of automatic tap changer transformers and generators besides the reactive power of shunt capacitors. However, conventional objectives such as decreasing losses are considered, simultaneously. This paper uses the K-medoids data clustering method to evaluate the effect of uncertainties. Also, the correlation between input uncertain variables is considered and handled by the Cholesky decomposition technique combined with the Nataf transformation technique. The multi-objective optimization problem is tackled using an improved version of the non-dominated sorting genetic algorithm (NSGA-II). To ensure for validity of the results, studies are repeated using the multi-objective particle swarm optimization (MOPSO) algorithm as another evolutionary-based method.The proposed approach is applied to the IEEE 14 and 30 bus standard system. The results showed that the predictability of the power system can be considerably increased while keeping the losses approximately at its minimum amount.

Cooperative operational planning of multi-microgrid distribution systems with a case study

Clustering historical electricity consumption data is very important for creating representative demand profiles for the planning and operation of the power grids. This paper investigates a multi-dimensional framework for data clustering, which takes scattering and separation metrics, as well as the number of clusters into account. A combination of wavelet mutation with the Invasive Weed Optimization (IWO) method for clustering features is proposed. One notable advantage of the IWO method over other metaheuristic optimization algorithms is its ability to dynamically adapt the number of weed colonies during the search process, resulting in improved exploration and exploitation of the search space. The proposed strategy is applied to cluster the electricity consumption data from a large municipal government center in Perth, Western Australia. The suggested method is then evaluated by comparing it with the well-known method in the literature, namely, the k-means technique. After the data clustering, the obtained results are implemented in the design of a multi-microgrid system under two different scenarios of cooperative and noncooperative modes. To evaluate the performance of the proposed method, the proposed method is implemented on the operational planning of a real multi-microgrid distribution system in Western Australia using linear programming to take the advantage of the mathematical-based solvers. After performing some investigations, the cooperative mechanism, where the microgrids have participated in supplying the demand of microgrids was found to yield to greater operational and investment cost minimimzation. In terms of numerical comparison, the total cost in the cooperative model is 6.5% lower than that in a non-cooperative situation.

Strategic Identification and Selection of Information Systems Projects

Strategic Information Systems Planning (SISP) stands at the intersection of technology and organizational strategy, serving as a guiding compass for businesses navigating the increasingly complex digital landscape. This research report explores the critical aspects of Strategic Information Systems Planning (SISP), focusing on various approaches to SISP and best practices to avoid common mistakes during the process. Systems analysts play a pivotal role in this process, ensuring the alignment of IS projects with organizational goals and objectives. By examining different SISP approaches and highlighting best practices, this report aims to provide valuable insights into effective project identification and selection for achieving organizational success.

Path planning method for unmanned aerial vehicles

Unmanned aerial vehicles, commonly known as drones, have seen tremendous growth and wide-spread use in the last ten years, largely because of unheard-of technological improvements. It is currently one of the most significant study subjects since it involves many different areas of robotics and control systems. This study aims to thoroughly assess the most popular approaches for unmanned aerial vehicle path planning, while also outlining their benefits, drawbacks, potential uses, and overall effectiveness. The objective is to present the academic community with a summary of the existing environment and the development direction of future unmanned aerial vehicle path planning algorithms to encourage more research for quickly evolving planning of intelligent systems.

The interface language of the RAGICAL automated system of planning and management of a technical system with elements of artificial intelligence

The interface language of the RAGICAL automated system of planning and management of a technical system with elements of artificial intelligence

Federal Development Institutions as a tool for ensuring economic security in the system of strategic planning of the region's economy: problems and prospects of development (on the example of the Far Eastern Federal District)

The article examines the role of development institutions in ensuring the economic security of the state. Within the framework of this study, an analysis of the impact of the results of the activities of the Far East Development Corporation on the socio-economic indicators of the Far Eastern Federal District was carried out, based on the results of which the need to improve the existing model of strategic economic planning in order to improve the efficiency of regional economic management is considered. The object of the study is the socio-economic development of the Russian Far Eastern Federal District, the subject of the study is the role of federal development institutions in ensuring the socio-economic development of the Far Eastern Federal District. The purpose of the study is to substantiate the need to form a strategic planning system and a management system for development institutions that ensure the implementation of economic security priorities. In order to perform research tasks, the article uses both general scientific methods such as analysis, comparison, generalization, and specific ones, including methods of econometric modeling such as the construction of a multifactorial linear regression model. The results of the study demonstrate the relevance of the introduction of economic cybernetics into the system of strategic planning of the economy. The analysis of the results of the activities of the joint-stock company "Corporation for the Development of the Far East", which ensures the attraction of investments to the points of advanced development of the Far East, through the construction of a multifactorial linear regression model demonstrates the absence of influence of this development institution on the dynamics of the gross regional product of the regions of the Far Eastern Federal District. The problems of the system of strategic planning of the economy and the functioning of development institutions noted in the article actualize the need to improve the existing model of strategic planning of the economy. The existing model of planning for the development of socio-economic development and ensuring economic security is formed on the basis of economic policy instruments applied within the framework of a market economy, while in order to solve the existing problem of disproportionate socio-economic development in the country, which poses a threat to national security, it is necessary to introduce centralized planning mechanisms that ensure the achievement of the set results the account of the specified resources, the formation of which is possible due to the current level of development of digital technologies and artificial intelligence and the available theoretical and methodological base in the face of economic cybernetics.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Long‐term scenario generation of renewable energy generation using attention‐based conditional generative adversarial networks

AbstractLong‐term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long‐term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long‐term yearly sequence generation and intraday scenario generation of wind–solar energy. In the long‐term yearly sequence generation process, the k‐means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long‐term features of wind and photovoltaic energies. Furthermore, an attention‐based conditional generative adversarial network (ACGAN) was proposed to capture short‐term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real‐world dataset.

Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.

Low diversity and abundance of predatory fishes in a peripheral coral reef ecosystem.

Semi-enclosed seas are often associated with elevated local threats and distinct biogeographic patterns among marine fishes, but our understanding of how fish assemblage dynamics vary in relation to relatively small semi-enclosed seas (e.g., the Gulf of Aqaba) remains limited. Baited remote underwater video surveys (n = 111) were conducted across ~300 km of coral reef habitats in the Gulf of Aqaba and the northern Red Sea. A total of 55 predatory fish species were detected, with less than half of all species (n = 23) observed in both basins. Relative abundance patterns between the Gulf of Aqaba and the northern Red Sea were variable among taxa, but nearly twice as many predatory fish were observed per unit of effort in the northern Red Sea. In general, assemblages in both basins were dominated by three taxa (Epinephelinae, Carangidae, and Lethrinidae). Large-bodied and threatened species were recorded at very low abundances. Multivariate analysis revealed distinct assemblage structuring of coral reef predators between the Gulf of Aqaba and the northern Red Sea. Most of the species driving these differences were recorded in both basins, but occurred at varying levels of abundance. Environmental factors were largely unsuccessful in explaining variation in assemblage structuring. These findings indicate that biological assemblages in the Gulf of Aqaba are more distinct than previously reported and that reef fish assemblage structuring can occur even within a relatively small semi-enclosed sea. Despite inter-basin assemblage structuring, the overall low abundance of vulnerable fish species is suggestive of overexploitation in both the Gulf of Aqaba and the northern Red Sea of Saudi Arabia. As the region surveyed is currently undergoing large-scale coastal development, the results presented herein aim to guide spatial management and recovery plans for these coral reef systems in relation to this development.

Quantifying the overall spatial distribution characteristics of urban heavy truck trips: The case of China

Urban freight systems serve as an essential part of city livability. Heavy trucks are critical for urban freight system operation. Although understanding the overall spatial distribution characteristics of heavy truck trips is important for urban freight system planning and management, quantifying the spatial distribution characteristics of heavy truck trips remains scarce. In this study, we employ two indices, the flow hierarchy index and the urban centrality index, to quantify the overall spatial distribution characteristics of heavy truck trips from two perspectives: the heavy truck flow hierarchical organization and outflow spatial distribution centrality. We use these approaches to quantify the overall spatial distribution characteristics of heavy truck trips for typical Chinese cities and classify them into four patterns. We further analyze the characteristics and underlying mechanism of each pattern and then calculate the overall heavy truck trip efficiencies for cities in different patterns. Our research offers a framework for quantifying the overall spatial distribution characteristics of urban heavy truck trips and provides policymaking references for the planning and management of urban freight systems.

Estimating Riparian Water Movement in an Incipient Karst Landscape—Minnesota, USA

Understanding rain, ground, and surface water interactions in riparian zones is essential for hydrologic and environmental management. The novelty of this study lies in its revelation of isotopic shifts and consequent evaluation of hydrologic pathways and processes within the forested riparian zones of three distinct streams, valleys, and riparian systems in a carbonate-sandstone incipient karst landscape. We collected water samples from three southeastern Minnesota catchments: Trout Brook, Crystal Creek, and Bridge Creek. A Picarro L2130i was used to measure δ18O for oxygen, and δD for deuterium in units of ‰ (per mil). We estimated the global meteoric line for the study sites, built upon aquifer age dating, and explored aquifer transit time for the study sites using a simple seasonal amplitude model. The results showed small amplitudes for 2020 and 2021, suggesting that bedrock aquifer water was the primary source water with a mean core transit time greater than 10 years. All three catchments were different but had similar bedrock valley types and riparian sediment. The primary driving factor was the seasonal precipitation input mixing with existing water. In a normal to wetter year, the isotopic data showed larger amplitude shifts between seasons with trendlines that adjusted depending on the temperature of the new water additions. The proposed approach is valuable in revealing complex hydrologic processes and pathways and can contribute extensively to the planning and management of karst riparian systems.

FORMATION OF THE COST SYSTEM BASED ON THEIR OPTIMAL STRUCTURE

The functioning of business entities of the machine-building industry is closely related to the consumption in the process of manufacturing and selling products (goods, services) of a wide range of material, energy, labor, financial, and intellectual resources. Accordingly, the amounts incurred by the enterprise constitute a generalized monetary estimate of the cost of consumed production resources, and also reflect the optimal use of resources, the consumption of which is important for providing all the needs of the enterprise at the stage of production processes. Effective management influence at the stage of formation and implementation of expenses requires ensuring an equally significant degree of differentiation of the defined management system, as well as the justified application of norms and methods of solving management problems related to the optimal regulation of consumption norms. The basis of the integrated cost management system is a hierarchical system of performance indicators of the enterprise, which provides an opportunity to implement the principle of decentralization in the implementation of management decisions. The budgeting system creates prerequisites for attracting additional financial resources, finding channels for their mobilization, adequate centralization of cash flows, and regulates their rational use. The proposed algorithm for implementing an integrated cost management system is shown in Fig. 3.6. determines the procedure for implementing a set of systematized measures regarding the implementation of management technology, the system of intra-firm cost planning, which prescribes key regulations and management procedures.

Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning

Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission–fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission–Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission–fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission–fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission–fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm.

Long-term cost planning of data-driven wind-storage hybrid systems

—To address the long-term cost planning problem accurately, typically a significant number of formulas are required. However, this increases computational complexity and limits the generalizability of the approach to practical problems. To overcome these challenges, this study adopts a data-driven approach that considers uncertainties to evaluate the long-term cost planning problem accurately for wind power generation with hybrid energy storage. A method for predicting wind power output is proposed using temporal convolutional networks to handle long-term uncertainties. Additionally, an instantaneous phase-synchronous wind power output feature extraction method based on Hilbert transform theory is introduced to capture time-series fluctuation characteristics and address short-term uncertainties. Furthermore, the long-term operational costs and planning of high-percentage new energy wind farms with hybrid energy storage are discussed by combining the wind power output characteristics with the operating characteristics and lifespan of hybrid energy storage devices. The experimental results demonstrate the significant advantages of the deep learning probabilistic prediction model combined with wind power feature extraction. The algorithm validates the effectiveness of considering multiple uncertainties in the planning of microgrids with hybrid energy storage, particularly in considering the uncertainty of the decreasing trend in the investment cost of battery energy storage.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.