Bus System Research Articles

Planning renewables in distribution system with electric vehicles to improve hosting capacity and energy losses: Two-stage stochastic optimization framework

Excessive penetration of renewables in distribution system can create several operational issues. One of the technologies that can maximize accommodation of renewables is electric vehicles (EVs). However, along with EVs additional measures are required while maximizing hosting capacity (HC) to avoid negative impact on system and account for uncertain parameters. In this regard, this paper proposes a two-stage stochastic optimization approach for planning of renewables in distribution system integrated with EVs. In the first stage, capacity of renewables is determined while maximizing HC whereas in the second stage operating strategy of EVs, renewables and grid power is evaluated to minimize expected energy losses. The proposed framework includes uncertainties in load demand, renewables and EVs characteristics through stochastic programming approach. The optimization problem is formulated as second order cone programming problem. The proposed model is tested on modified IEEE-33 bus and real-life 108-bus Indian distribution systems with different charging/discharging patterns and penetration rates of EVs. Moreover, an economic based cost analysis to compare planning cost and EV charging cost is also provided. Simulation results reveal that in 33-bus system with 500 EVs, the HC with vehicle-to-grid capability (V2G) is higher as compared to uncoordinated and coordinated charging by 29.86% and 6.36% respectively, while in 108-bus system with 1000 EVs, it is higher by 18.51% and 5.69% respectively. Further, the energy losses with V2G are 27.54% and 1.36% less in 33-bus system and 7.31% and 3.22% less in 108-bus system as compared to uncoordinated and coordinated charging respectively.

Conflict management at urban bus stops through cooperative trajectory planning

Conflicts due to frequent bus dwelling and entering/exiting maneuvers at bus stops have significantly undermined the efficiency, safety, and sustainability of the urban bus system. This paper proposes a cooperative trajectory planning strategy in a Vehicle-to-Infrastructure (V2I) environment for conflict management between buses and cars at bus stops. A dynamic multi-stage-based model along with solution algorithm is developed to search trajectories that eliminate the conflicts between cars and buses entering and leaving the bus stop in real time.The assessment of the proposed strategy is carried out at both the bus stop and corridor levels within a simulated environment. In-depth trajectory analyses during a typical weaving process at a bus stop reveal that the proposed strategy effectively eliminates bottlenecks and enhances operational performance across multiple approach lanes affected by the bus stop. A thorough evaluation involving multiple cycles of weaving among buses and vehicles at the bus stop reaffirms the strategy’s effectiveness under varying levels of bus occupancies, traffic demand, and bus stop locations. The case study at the corridor level illustrates that the proposed strategy successfully improves operational performance and bus schedule adherence across diverse scenarios, encompassing different roadway link lengths, traffic demands, bus headways and dwelling times, and bus stop locations. It notably excels in enhancing the performance of an urban corridor with longer link distances and near-side located bus stops, particularly under high traffic demand levels and shorter bus dwelling times.

Optimal Artificial Intelligence Technique for LVRT Capability Improvement of a Grid-tied Wind Energy Conversion System: A MGOANFIS-PI Methodology

This paper proposes an innovative algorithm-based control model for improving the doubly fed induction generator (DFIG) system’s low-voltage ride-through (LVRT) capabilities. The Mountain Gazelle optimization (MGO) algorithm is combined with an adaptive neuro-fuzzy inference system (ANFIS)-based proportional-integral (PI) regulator, called the MGOANFIS-PI approach. In the proposed method, the data set of an MGO-based PI regulator is used after rectification to train the MGOANFIS controller, so updating the ANFIS technique improves MGO’s searching behavior. The implemented control method ensures LVRT capability on grid-tied wind conversion plants based on DFIG in case of a malfunction or a voltage drop. Multiple metrics relating to LVRT are monitored in the proposed system, comprising current, active, and reactive power and voltages. The objective to be achieved, which is resolved through the cascaded MGOANFIS-based PI scheme, is defined. Based on the collected data, ANFIS performs and anticipates the optimal control signal from converters on the rotor and network sides. The MGOANFIS-PI methodology effectively handles system performance and voltage instability during four fault circumstances. The LVRT functionality of the DFIG system and the power quality are enhanced with the assistance of the proposed methodology. Simulations are carried out using MATLAB/Simulink framework. Two test systems are examined: the IEEE 39 bus and the 9 MW wind power plant (WPP) test systems. The WPP’s ability to withstand grid voltage sags is analyzed as part of an effectiveness assessment. The relevance and efficacy of the proposed MGOANFIS-PI method are evaluated, and proved the better performance of the cascaded MGO-based ANFIS-PI controller when compared to PI controllers optimized by comparing it with other well-known optimization techniques, i.e., Gorilla troop optimizer, particle swarm optimization, and genetic algorithm. In the three-phase IEEE 39 bus system and the 9 MW test scheme, detailed wind power plants could persevere in the face of every fault condition.

A new framework for hierarchical multi-objective energy hub planning considering reliability

The concept of an Energy Hub (EH) is a promising approach for constructing a Multi-Energy System (MES). The configuration of equipment within the EH is crucial for ensuring the efficiency, cost-effectiveness, and reliability of MES. This paper introduces a novel hierarchical energy hub planning (H-EHP) approach for EH configuration, employing a multi-objective optimization algorithm to plan optimal EH configurations. Utilizing an epsilon constraint-based Pareto front technique, H-EHP optimizes EH capacity, location, and number of installations at each hierarchical level, and economic and reliability feasibility of the system is hierarchically evaluated. The H-EHP is modeled in three stages for result analysis: 1) Power-to-Power (P2P) equipment-based modeling, 2) Power-to-Gas (P2G) equipment-based modeling, and 3) Sector Coupling (SC) equipment-based modeling. The distribution network and various operating characteristics, along with constraints of EH equipment, are modeled. A novel analysis method based on Branch Rolling is introduced for the reliability assessment of EH-linked distribution network. Mixed-integer linear programming (MILP) is employed to identify the optimal H-EHP that minimizes both economic and reliability aspects through an epsilon-constrained Multi-Objective Problem (MOP). Testing on the IEEE-33 bus system and a 7-node gas system confirms the superiority of the proposed theory through three casestudies. The results show that cost-effectiveness and system reliability are analyzed for each EH stage. Notably, HL1, relying solely on electrical equipment, exhibits poor cost-effectiveness. HL2, incorporating P2G-based equipment, demonstrates significant improvements with F1 enhanced by 29.64 % and F2 by 19.07 %. The HL3 outcome highlights the Sector-Coupling (SC) effect, with F1 and F2 showing improvements of 34.85 % and 45.58 %, respectively, compared to HL1. The analysis of hierarchical EH configuration and results underscore the efficacy of the H-EHP method for optimal EH planning.

Optimal economic environmental power dispatch by using artificial bee colony algorithm

<span>Today, most power plants worldwide use fossil fuels such as natural gas, coal, and oil as the primary resource for energy reproduction primarily. The new term for economic environmental power dispatch (EEPD) problems is on the minimum total cost of the generator and fossil fuel emissions to address atmosphere pollution. Thus, the significant objective functions are identified to minimize the cost of generation, most minor emission pollutants, and lowest system losses individually. As an alternative, an Artificial Bee Colony (ABC) swarming algorithm is applied to solve the EEPD problem separately in the power systems on both standard IEEE 26 bus system and IEEE 57 bus system using a MATLAB programming environment. The performance of the introduced algorithm is measured based on simple mathematical analysis such as a simple deviation and its percentage from the obtained results. From the mathematical measurement, the ABC algorithm showed an improvement on each identified single objective function as compared with the gradient approach of using the Newton Raphson method in a short computational time.</span>

A hybrid optimization approach for strategically placing electric vehicle charging stations in a radial distribution IEEE-33 bus system

The main objective of this study is to demonstrate the effectiveness of using Hybrid Particle Swarm Optimization (HPSO), a unique methodology, to optimize an IEEE-33 bus system for optimal placement of electric vehicles. This methodology has been specifically designed to effectively regulate voltage fluctuations while reducing power dissipation. The exploration capabilities of PSO and the exploitation capabilities of SA make HPSO a robust algorithm for a specific objective function. HPSO has shown superior performance compared to current traditional methods after careful study. Simulation results illustrate power loss reductions ranging from 1.01% to 8.21% compared to various metaheuristic methodologies. The inclusion of this approach demonstrates that combining the capabilities of PSO and SA facilitates the development of a highly efficient optimization framework by combining their stochastic search and fast convergence capabilities. This methodology not only sets innovative benchmarks for power system development, but also represents a significant paradigm shift in managing and improving durable and efficient electrical networks.

Retaining bus riders: A lifecycle longitudinal analysis of behavioral status transitions from entry to exit

Amidst a global decline in bus ridership, this study pioneers a longitudinal approach to understanding individual-level transitions and churning in urban bus systems. Utilizing a novel framework that leverages smart card data, we construct and analyze user behavior transition matrices over time, employing Markov processes and the Chapman-Kolmogorov Equation. Our analysis, derived from a 22-month dataset from Shenzhen, reveals a two-stage churning process: users first decrease travel frequency before transitioning to irregular travel patterns. Crucially, this study introduces targeted retention policies, including tiered usage incentives and personalized communication strategies, aimed at different stages of the user lifecycle. By offering free subsequent trips to irregular travelers and combining policy approaches for users at high risk of churning, we provide actionable insights for transit operators to counter the trend of declining ridership.

Research on Simulation and Power Regulation Control Strategy of Fengning Variable-Speed Pumped Storage Unit

Abstract Variable-speed pumped storage unit demonstrates rapid power response and flexible adjustment of unit speed, significantly enhancing unit operational efficiency and the power system’s stability. This paper focuses on simulating the control strategy of the Fengning variable-speed pumped storage unit. First, the water pump/turbine and motor models are established based on the relevant parameters of the Fengning variable-speed pumped storage unit. Second, according to the distinct responsibilities of the governor and the excitation system, the control strategies of the unit in the power priority mode and speed priority mode are designed. Finally, we compare and analyze the power response characteristics of a single-machine infinite bus system under various operating conditions and control strategies. We also summarize the applicable scope and existing problems under different operating conditions.

Assessing the Environmental, Economic, and Route Optimization Impacts of Implementing a Green Bus System in Urban Areas

The proliferation of e-bus has swept the globe. Many countries have begun the transformation of electric buses. Electric buses are embraced worldwide for their environmental and low-cost advantages. This work will help local governments establish a complete e-bus system.The first question mainly addresses the ecological impact that would be brought by the impact. In this work, the Ecological Impact Quantification Model (EIQM) is developed to address gaseous pollution including Greenhouse gas carbon dioxide, pollutants nitric oxide and PM2.5. The ecological effect enhancement with e-bus system is presented, and especially, the gaseous pollution caused by electricity production is approximated by the Monte Carlo simulation method. As a result, this team figures out that with the development of e-bus system, the average CO2 emission per person decreases, having a reduction of 11391g at first, and 14039g after five years.The second question mainly addresses the financial problem that the government needs to face. In this work, the Quantification of Financial Impact Model (QFIM) is developed. This team utilizes the differential equation to obtain the exact varying rate of diesel fuel and electricity fee, and finally determined the possible range of the government deficit. By synthesizing these data, this team discovers that the government could start reducing its deficit after 8 years of the transformation from diesel bus to e-bus, having deficit from $-88,412,180 to $-64,182,722.The third question addresses the 10-year plan for the selected city and 2 additional cities. This team develops Optimized Deployment Model based on Greedy Algorithm (ODGA) to optimize the transformation plan over 10 years of existing bus routes and the number of e-bus to be constructed per year. Monetary and time utility is considered and Greedy Algorithm is introduced to find out the best travelling route for e-buses. This team successfully figures out the best transformation plan of travelling routes and number of e-bus to be constructed per year to maximize the efficiency, including 5-min interval departure time and 867 buses are required to run the proposed route in Chicago. Finally, the team writes a piece of letter to Chicago Transit Authority and proposes the 10-year plan of e-bus system construction to guarantee the steady implementation of e-bus.

A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

Small‐signal rotor angle stability of multi‐virtual synchronous machine (n‐VISMA) microgrid

AbstractAutonomous microgrid is known to lack appropriate inertia and damping for grid stabilization. Due to this, virtual synchronous machine (VISMA) has been introduced to provide necessary ancillary services through control of power converters. In a multi‐VISMA (n‐VISMA) microgrid, relative rotor angle stability of the power system is dependent on the active power balance after small perturbation. Thus, the use of relevant analytical models are essential issues for microgrid stability analysis. This paper presents a comprehensive small‐signal stability analysis to study inherent electromechanical oscillations in the virtual rotors. The subsystems of the microgrid consisting of VISMA, network, load and the outer power control were all modelled in Synchronous Reference Frame. The small‐signal model (SSM) was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines on the network. To validate the developed numerical analytics, dynamic responses of the SSM are compared with those of the non‐linear (NL) system dynamics and the results reveal that the developed linearized SSM is sufficient to accurately characterize behaviour of the VISMA microgrid when operated in autonomous mode. Eigenvalues analysis and parameter sensitivities of the critical modes were investigated. Oscillatory participations of the VISMAs and steady state stability limit of the microgrid have also been investigated.

Stability analysis of single-machine infinite bus system with renewable energy sources using sine cosine chimp optimization

Stability analysis of single-machine infinite bus system with renewable energy sources using sine cosine chimp optimization

Integrating life cycle sustainability assessment in power flow optimization

The electric power network management is an important field of improving to achieve sustainability. Optimal Power Flows (OPFs) allow the efficient operation of the system. However, OPF algorithms only consider the operation stage of the power plants involved in the power system, neglecting the rest of stages of its life cycle. To evaluate the total effects of the power generation mix, this work develops a methodology to perform a Multi-Objective OPF (MOOPF) with impact variables in electrical systems that integrate different generation technologies. These variables result from the application of a Life Cycle Sustainability Assessment (LCSA), consisting in environmental, economic, and social indexes with “cradle-to-grave” dimensions. To achieve this objective, the MOOPF is carried out operating a MATLAB algorithm over the IEEE57 bus system. It uses the ɛ-constraint method subject to the imposition of two or three impact variables. To examine the applications of this work, several simulations are performed optimizing different impact variables. Two different Spanish electricity scenarios are considered: the 2019 generation, and the 2030 objective generation proposed by the Spanish National Energy and Climate Plan. This tool broadens the scope of MOOPF assessment that can be used in a decision-making process. It allows to identify important environmental, economic, and social affections derived from system designs and limits. The study proves that the application of LCSA with MOOPF obtains realistic results making sustainability solutions technically viable. Additionally, different benefits and drawbacks, linked to the studied scenarios and grid disposition, are compared when conducting the proposed methodology for hourly generation management.

An adaptive heuristic approach for Volt-VAr optimization in distributed generation integrated system

Volt-VAr optimization (VVO) is an important tool in improving the efficiency of distribution system by simultaneously coordinating voltage and reactive power both. Today, loads are largely dependent on voltage and thus, this voltage-dependence affects both real and reactive power. So, minimizing apparent power is more important than minimizing the energy. VVO enables distribution system utilities to run their network on comparatively lower voltage which ultimately increases energy savings. Devices such as, on load tap changers (OLTC), and voltage regulators (VRs) are commonly adopted techniques to regulate/boost the voltage. Capacitive banks (CBs) are also used to inject reactive power (VAr) into the system keeping the operational constraints and system constraints specified and satisfied. This paper presents a new methodology of Volt-VAr optimization using Genetic algorithm considering a variety of constraints. The test is performed on IEEE-69 bus system and results validate the efficacy of the proposed algorithm.

Active Planning for Virtual Microgrids with Demand-Side and Distributed Energy Resources

In this paper, the notion of a cohesive and self-sufficient grid is proposed. Based on a cohesive and self-sufficient virtual microgrid, an active distribution network is optimally planned, and an optimal configuration of demand-side resources, distributed generations, and energy storage systems are generated. To cope with stochastic uncertainty from forecast error in wind speed and load, flexibility reserves are needed. In this paper, the supply relation between flexibility and uncertainty is quantified and integrated in an innovative index which is defined as cohesion. The optimization objectives are a minimized operational cost and system net-ability cohesion as well as self-sufficiency, which is defined as the abilities both to supply local load and to deal with potential uncertainty. After testing the optimal configuration in the PG&E 69 bus system, it is found that with a more cohesive VM partition, the self-sufficiency of VMs is also increased. Also, a case study on uncertainty-caused system imbalance is carried out to show how flexibility resources are utilized in real-time operational balance.

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Investigations on application of curve fitting in power system inertia estimation

ABSTRACT As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation.

Design of OFDM‐based two‐wire bus communication system for fire safety

SummaryIntelligent fire safety systems place higher demands on the speed and reliability of data communication between sensors, controllers, and other devices. The two‐wire bus has become a significant trend in developing fire safety communication systems because of its low cost and simple maintenance. This paper proposes a two‐wire bus communication system for fire safety based on orthogonal frequency division multiplexing (OFDM) technology. First, we introduce the low‐voltage power line channel model and conduct relevant simulations, providing the conditions for subsequent simulation work. Then, we design the system's transmission and reception schemes, provide the parameters, and introduce each technology used. Finally, we conducted system simulations based on three different channel models, and the system performance is compared among different encoding, modulation, and channel estimation methods. The simulation results show that the system designed in this paper has superior performance compared with other systems and can meet the requirements of intelligent fire safety systems for communication speed and reliability.

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.