System Constraints Research Articles

Safe reinforcement learning based optimal low-carbon scheduling strategy for multi-energy system

Multi-energy system with distributed energy resources has become the inevitable trend in recent years due to their potential for creating the efficient and sustainable energy infrastructure, with a strong ability on carbon emission reduction. To accommodate the uncertainties of renewable energy generation and energy demand, model-free deep reinforcement learning methods are emerging for energy management in multi-energy system. However, traditional reinforcement learning methods still have operation safety issue of violating the physical constraints of multi-energy system. To address the challenges, a low-carbon scheduling strategy based on safe soft actor-critic algorithm is proposed in this paper. Firstly, an electricity-thermal-carbon joint scheduling framework is constructed, where carbon trading mechanism is incorporated to further motivate carbon emission reductions. Secondly, the energy cost and carbon trading cost are simultaneously integrated in the objective function, and the dynamic optimization problem of multi-energy system is modeled as a constrained Markov decision process by taking into account the diverse uncertainties. Then, a novel safe soft actor-critic method is proposed to achieve the benefits of economic and carbon emissions, where the security networks and Lagrangian relaxation are introduced to deal with operation constraints. The case study validates that the proposed scheduling strategy can reduce the energy cost and carbon trading cost by up to 26.24% and 33.73% within constraints, compared with existing methods.

An online energy-saving offloading algorithm in mobile edge computing with Lyapunov optimization

Online computing offloading is an effective method to enhance the performance of mobile edge computing (MEC). However, existing research ignores the impact of system stability and device priority on system performance during task processing.To address the problem of computing offloading for computing-intensive tasks, an online partial offloading algorithm combining task queue length and energy consumption is proposed without any prior information. Firstly, a queue model of IoT devices is created to describe their workload backlogs and reflect the system stability. Then, using Lyapunov optimization, computing offloading problem is decoupled into two sub-problems by calculating the optimal CPU computing rate and device priority, which can determine the task offloading amount and offloading location to complete resource allocation. Finally, the online partial offloading algorithm based on devices priority is solved by minimizing the value of the drift-plus-penalty function’s upper bound to ensure system stability and reduce energy consumption. Theoretical analysis and the outcomes of numerous experiments demonstrate the effectiveness of the proposed algorithm in minimizing system energy consumption while adhering to system constraints, even in dealing with dynamically varying task arrival rates.

Research on power allocation strategy and capacity configuration of hybrid energy storage system based on double-layer variational modal decomposition and energy entropy

This paper deals with the study of the power allocation and capacity configuration problems of Hybrid Energy Storage Systems (HESS) and their potential use to handle wind and solar power fluctuation. A double-layer Variable Modal Decomposition (VMD) strategy is proposed. Firstly, using the Sparrow Search Algorithm with Sine-cosine and Cauchy mutation (SCSSA) to optimize the parameters in VMD. Considering the different characteristics of batteries and supercapacitors and the richness of information that may be contained in the high frequency residuals obtained from the primary VMD, the secondary VMD is carried out to decomposition. In response to the modal aliasing phenomenon of the secondary VMD, the high and low frequency demarcation points of the secondary VMD are discriminated by combining the energy entropy (eN) to enable the more accurate power allocation strategy. Secondly, a mathematical model for cost analysis of the HESS is established by considering the daily integrated operating cost of the HESS and the relevant constraints of the renewable generation system. Finally, HESS capacity configuration and validation are based on the power allocation strategy proposed in this paper. The simulation results show that the power allocation strategy proposed in this paper is more accurate and has a better economy for the capacity configuration of HESS.

Uncovering multi-level mental healthcare barriers for migrants: a qualitative analysis across China, Germany, Netherlands, Romania, and South Africa

BackgroundForced displacement is a significant issue globally, and it affected 112 million people in 2022. Many of these people have found refuge in low- and middle-income countries. Migrants and refugees face complex and specialized health challenges, particularly in the area of mental health. This study aims to provide an in-depth qualitative assessment of the multi-level barriers that migrants face in accessing mental health services in Germany, Macao (Special Administrative Region of China), the Netherlands, Romania, and South Africa. The ultimate objective is to inform tailored health policy and management practices for this vulnerable population.MethodsAdhering to a qualitative research paradigm, the study centers on stakeholders’ perspectives spanning microsystems, mesosystems, and macrosystems of healthcare. Utilizing a purposive sampling methodology, key informants from the aforementioned geographical locations were engaged in semi-structured interviews. Data underwent thematic content analysis guided by a deductive-inductive approach.ResultsThe study unveiled three pivotal thematic barriers: language and communication obstacles, cultural impediments, and systemic constraints. The unavailability of professional interpreters universally exacerbated language barriers across all countries. Cultural barriers, stigmatization, and discrimination, specifically within the mental health sector, were found to limit access to healthcare further. Systemic barriers encompassed bureaucratic intricacies and a conspicuous lack of resources, including a failure to recognize the urgency of mental healthcare needs for migrants.ConclusionsThis research elucidates the multifaceted, systemic challenges hindering equitable mental healthcare provision for migrants. It posits that sweeping policy reforms are imperative, advocating for the implementation of strategies, such as increasing the availability of language services, enhancing healthcare providers’ capacity, and legal framework and policy change to be more inclusive. The findings substantially contribute to scholarly discourse by providing an interdisciplinary and international lens on the barriers to mental healthcare access for displaced populations.

The Political Psychology of Cancel Culture: Value Framing or Group Identity?

“Cancel culture” has become a prominent phrase in US political commentary, with supporters and opponents relying on different value-based arguments to express their views. At the same time, these camps often fall along predictable partisan political lines. What, then, are the real motivations for promoting and opposing behaviors sometimes labeled as “canceling” in contemporary American politics? We explore this question through survey data from Pew in 2020 and two original survey experiments, conducted on the 2021 CES and a 2023 online sample. We examine how Americans define the term “cancel culture,” to what extent cancel culture is linked to both a range of core democratic values, and the role of partisanship in shaping support or opposition to specific behaviors. We observe a significant range of views about cancel culture and document connections to various political values. From our experiments, we find that partisanship is a potent driver of support for and understanding of cancel culture and that value-based framing has a weaker impact as compared to partisanship. Cancel culture provides an important case study of how Americans process conflicting norms and values, including free speech and political accountability, beyond the typical constraints of the formal political system.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

Diagnostic and prognostic for prescriptive maintenance and control of PEMFC systems in an industrial framework

This paper designed and defined the framework of prescriptive maintenance and its four axes, for hydrogen-energy systems, considering the constraints of actual systems, to improve durability, availability, reliability, performance, safety and operating costs of these systems. Different data-driven approaches for diagnostic and different approaches for prognostic of Remaining Useful Life (RUL) for Proton Exchange Membrane Fuel Cell (PEMFC) systems are discussed. The use of measures always available on actual systems allows to validate the approach in an industrial framework. Several diagnostic algorithms (classification and clustering) are compared to find the best compromise between computation time and accuracy in an online diagnostic framework. For the prognostic part, the data set comes from experimental tests performed under steady load for a PEMFC stack. A degradation trend of PEMFC stack voltage is extracted from these data and two well-known prediction algorithms namely Bidirectional Echo State Network (BiESN) and Bidirectional Long-Short Term Memory network (BiLSTM) are tested and compared to evaluate the robustness of the PEMFC voltage prediction for RUL estimation. The diagnostic and prognostic approaches proposed in this paper are first steps towards future work related to prescriptive maintenance for hydrogen-energy systems (PEMFC, Proton Exchange Membrane Water Electrolyzer and hybridization of the two).

Red line the red line: Optimizing emergency medicine physicians and surgeons collaborative roles on trauma teams.

It has long been the standard for surgical and EM teams to both be present upon patient arrival and work together for the sickest trauma patients, yielding improved outcomes. It is important to dismantle divisive perceptions, confront system constraints, and promote new strategies that optimize the engagement of trauma team members. The focus should be on the patient, whose injury care starts with prevention and extends seamlessly through prehospital, hospital and rehabilitation.The authors address several myths that impact collaborative teamwork among emergency medicine physician and surgeons.Leaders, especially at GME sites, need to foster collaborative relationships, rather than adversarial. The red line mentality is a divisive construct that should be dismantled.

Optimizing Economic Dispatch with Renewable Energy and Natural Gas Using Fractional-Order Fish Migration Algorithm

This work presents a model for solving the Economic-Environmental Dispatch (EED) challenge, which addresses the integration of thermal, renewable energy schemes, and natural gas (NG) units, that consider both toxin emission and fuel costs as its primary objectives. Three cases are examined using the IEEE 30-bus system, where thermal units (TUs) are replaced with NGs to minimize toxin emissions and fuel costs. The system constraints include equality and inequality conditions. A detailed modeling of NGs is performed, which also incorporates the pressure pipelines and the flow velocity of gas as procedure limitations. To obtain Pareto optimal solutions for fuel costs and emissions, three optimization algorithms, namely Fractional-Order Fish Migration Optimization (FOFMO), Coati Optimization Algorithm (COA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II) are employed. Three cases are investigated to validate the effectiveness of the proposed model when applied to the IEEE 30-bus system with the integration of renewable energy sources (RESs) and natural gas units. The results from Case III, where NGs are installed in place of two thermal units (TUs), demonstrate that the economic dispatching approach presented in this study significantly reduces emission levels to 0.4232 t/h and achieves a lower fuel cost of 796.478 USD/MWh. Furthermore, the findings indicate that FOFMO outperforms COA and NSGA-II in effectively addressing the EED problem.

Principal Landau determinants

We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Our algorithms build on the Julia package Landau.jl and are implemented in the new open-source package PLD.jl available at https://mathrepo.mis.mpg.de/PLD/. Program summaryProgram title:PLD.jlCPC Library link to program files:https://doi.org/10.17632/7h5644mm4n.1Developer's repository link:https://mathrepo.mis.mpg.de/PLD/Licensing provisions: Creative Commons by 4.0Programming language:JuliaSupplementary material: The repository includes the source code with documentation (PLD_code.zip), a jupyter notebook tutorial providing installation and usage instructions (PLD_notebook.zip), a database containing the output of our algorithm on 114 examples of Feynman integrals (PLD_database.zip).Nature of problem: A fundamental challenge in scattering amplitude is to determine the values of complexified kinematic invariants for which an amplitude can develop singularities. Bjorken, Landau, and Nakanishi wrote a system of polynomial constraints, nowadays known as the Landau equations. This project aims to rigorously revisit the Landau analysis of the singularity locus of Feynman integrals with a practical view towards explicit computations.Solution method: We define the principal Landau determinant (PLD), which is a variety inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ). We conjecture that it provides a subset of the singularity locus, and we implement effective algorithms to compute its defining equation explicitly.

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

Energy Cost Optimization for Incorporating Energy Hubs into a Smart Microgrid with RESs, CHP, and EVs

The energy carrier infrastructure, including both electricity and natural gas sources, has evolved and begun functioning independently over recent years. Nevertheless, recent studies are pivoting toward the exploration of a unified architecture for energy systems that combines Multiple-Energy Carriers into a single network, hence moving away from treating these carriers separately. As an outcome, a new methodology has emerged, integrating electrical, chemical, and heating carriers and centered around the concept of Energy Hubs (EHs). EHs are complex systems that handle the input and output of different energy types, including their conversion and storage. Furthermore, EHs include Combined Heat and Power (CHP) units, which offer greater efficiency and are more environmentally benign than traditional thermal units. Additionally, CHP units provide greater flexibility in the use of natural gas and electricity, thereby offering significant advantages over traditional methods of energy supply. This article introduces a new approach for exploring the steady-state model of EHs and addresses all related optimization issues. These issues encompass the optimal dispatch across multiple carriers, the optimal hub interconnection, and the ideal hub configuration within an energy system. Consequently, this article targets the reduction in the overall system energy costs, while maintaining compliance with all the necessary system constraints. The method is applied in an existing Smart Microgrid (SM) of a typical Greek 17-bus low-voltage distribution network into which EHs are introduced along with Renewable Energy Sources (RESs) and Electric Vehicles (EVs). The SM experiments focus on the optimization of the operational cost using different operational scenarios with distributed generation (DG) and CHP units as well as EVs. A sensitivity analysis is also performed under variations in electricity costs to identify the optimal scenario for handling increased demand.

Responsible migrants: rights-claiming, risks and costs at the shelter

ABSTRACT Spaces of migration comprise multiple processes of knowledge-production operating at the nexus of state, non-state actors and individuals. Looking at a domestic migrant shelter in Singapore reveals how it acts as an alternative site of knowledge-production. Interviews with migrant workers at the shelter describe how they learn to ‘stand up’ for themselves by learning about laws, rights and job alternatives. At the same time, they reveal how risks and costs lie on the other side of rights-claiming; similarly, while jobseeking is an empowering act, opportunities for entrepreneurship fuel the neoliberal subjectification of migrant workers and distances the state from its duty of care. Using ‘responsibilisation’ as a lens helps make sense of the overlaps between systemic constraints and forms of agency that co-exist within the migration regime. Where the shelter offers opportunities for change, it also problematically reproduces a status quo where rights-claiming, burden of proof and jobseeking are constructed as the responsibility of migrants and NGOs, and beyond the remit of the state.

“We Have to Charge Levies, Else We Will Close Down the Schools”. Delivering Ghana’s education capitation grant programme within chronic resource constraints: impacts and implications

ABSTRACT Financial barriers to education such as the payment of school fees have long been identified as a key driver of the perennially high out-of-school rates in developing countries. Accordingly, Ghana implemented the education capitation grant (CG) policy in 2005 as part of efforts to universalise access to primary education. At its core, the policy abolishes the payment of school fees and levies and replaces these with a government-funded per capita allocation to every basic school. Despite its critical role in widening access to education, the CG programme has received very little scholarly curiosity. The extant studies have largely focused on the CG programme's impacts on enrolment and retention. The funding component of the programme has generally been under-researched. This qualitative study, therefore, examines the implication of funding challenges for the delivery of the CG programme. Relying on one-on-one semi-structured interviews with 16 head teachers and 16 chairpersons of School Management Committees (SMCs), the article shows how a system of long-lasting funding constraints has compelled school administrators to adopt unsanctioned coping strategies such as charging fees and padding enrolment figures. Ultimately, these coping strategies undercut the CG’s objectives of removing financial barriers to basic education in Ghana and also raise critical questions about the viability of the CG’s goal of addressing the wealth bias that has characterised accessing education.

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

A wind farm planning method considering frequency security constraint based on wind farm participation in primary frequency regulation

Abstract Renewable energy sources such as wind power, connected to the grid on a large scale, reduce the rotational inertia and frequency regulation of the power system. Many studies have included frequency security constraints (FSC) in power system scheduling to ensure system frequency stability, but few studies have included FSC in grid planning. Therefore, this paper establishes a planning model for wind farms that considers the FSC based on the wind turbines’ participation in the primary frequency regulation (PFR) by obtaining the maximum deviation value constraint of the system frequency through the system frequency response (SFR) model. Tests were conducted on the IEE30-node system, and the results show that the proposed model enhances the system frequency response capability and ensures system security.

Bidirectional DC-DC Converter and Improved Electrical Vehicle Dynamic Response Control

Background: In automotive applications where bidirectional power flow is necessary to lighten the power system, dual active bridge (DAB) converters are frequently employed. Variations in the required output voltage, erratic input voltage, and shifting loads all have an impact on this converter. As a result, converter performance has to be improved. To increase efficiency, the current stress of the DC-DC converter must be optimised. This paper proposes a control scheme for the coupled inductor bidirectional DC-DC (CIB DC-DC) converter utilising both model predictive control (MPC) and a proportional-integral (PI) controller. The integration of these control techniques aims to enhance the performance and efficiency of the converter. Methods: The MPC algorithm is employed to predict the converter's future behaviour based on a dynamic model, taking into account system constraints and performance criteria. By optimising the control action over a finite time horizon, the MPC algorithm ensures an optimal response, considering the current state and anticipated changes. Additionally, a PI controller is incorporated to augment the control strategy. The proportional component of the PI controller enables a fast initial response to the error between the desired and actual converter outputs. The integral component eliminates steady-state errors and provides robustness against disturbances, resulting in improved overall system performance. Results: The proposed control scheme is implemented and evaluated through simulations and experimental tests on a prototype converter. The results demonstrate the effectiveness of the combined MPC and PI controller approach. Conclusion: The coupled inductor bidirectional DC-DC (CIB DC-DC) converter using MPC can provide precise control of power flow between two voltage domains, enabling efficient bidirectional power transfer. The predictive capabilities of MPC allow it to adapt to varying load conditions and respond quickly to changes, ensuring stable operation and accurate regulation of voltage and current. Overall, the coupled inductor bidirectional DC-DC converter controlled using MPC over PI offers improved performance, efficiency, and flexibility compared to traditional control methods. MPC can handle the complex dynamics response and non-linear characteristics of the converter, making it suitable for bi-directional vehicle charging applications, where precise control and high efficiency can be achieved.

Reconstruction of Cenozoic δ11Bsw Using a Gaussian Process

AbstractThe boron isotope ratio of seawater (δ11Bsw) is a parameter which must be known to reconstruct palaeo pH and CO2 from boron isotope measurements of marine carbonates. Beyond a few million years ago, δ11Bsw is likely to have been different to modern. Palaeo δ11Bsw can be estimated by simultaneously constraining the vertical gradients in foraminiferal δ11B (Δδ11B) and pH (ΔpH). A number of subtly different techniques have been used to estimate ΔpH in the past, all broadly based on assumptions about vertical gradients in oxygen, and/or carbon, or other carbonate system constraints. In this work we pull together existing data from previous studies, alongside a constraint on the rate of change of δ11Bsw from modeling. We combine this information in an overarching statistical framework called a Gaussian Process. The Gaussian Process technique allows us to bring together data and constraints on the rate of change in δ11Bsw to generate random plausible evolutions of δ11Bsw. We reconstruct δ11Bsw, and by extension palaeo pH, across the last 65Myr using this novel methodology. Reconstructed δ11Bsw is compared to other seawater isotope ratios, namely , , and δ7Li, which we also reconstruct with Gaussian Processes. Our method provides a template for incorporation of future δ11Bsw constraints, and a mechanism for propagation of uncertainty in δ11Bsw into future studies.

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

ℓ -Proca stars

Initially applied to the scalar case, we extend the applicability of the multi-field generalization with angular momentum of bosonic stars to the vector case, in order to obtain new configurations that generalize the one-field spherical Proca stars. These new objects, which we call ℓ -Proca stars, arise as stationary and spherically symmetric bosonic stars solutions of the Einstein-(multi)Proca system, whose matter content is formed by an arbitrary odd number of 2ℓ+1 of complex Proca fields with the same mass, time-frequency, radial profile and angular momentum number ℓ . We analyze the system of constraint and evolution radial equations for the matter content to show the consistency of our proposal, and obtain numerically the ground states of these new solutions for the first few values of ℓ using spectral methods.