Charge Constraints Research Articles

Supersymmetric charge constraints on AdS black holes from free fields

Supersymmetric AdS black hole solutions exist only when their angular momenta and charges satisfy a certain constraint that depends on the dimension. We show that these nonlinear relations on the conserved charges agree with a computation in the dual supersymmetric CFT in its free limit, with interactions entering only through a uniform rescaling of all charges. Our computations apply to the highly non-trivial charge constraints for AdS4, AdS5 and AdS7 black holes, and generalize an earlier one for the analogous constraint in AdS3. Our results suggest a microscopic understanding of AdS black holes beyond the scope of supersymmetric indices.

Optimizing energy consumption in smart buildings: A model for efficient energy management and renewable integration

In the current landscape of sustainable energy, integrating photovoltaic (PV) generation and electric vehicles (EVs) into household energy management systems (EMSs) has become a pivotal strategy to enhance grid sustainability in residential areas and provide economic benefits to homeowners. This article introduces an innovative approach to an energy administration framework designed for intelligent households, to seamlessly integrate EVs, both with and without PV generation. The proposed plan meticulously optimizes residential electricity expenses. It standardizes the load gradient using Time-of-Use (TOU) cost, residential electricity use fluctuations, PV generation patterns, and specific variables of EVs, including entry and exit times and charging statuses. The management method is divided into two distinct phases based on charging constraints and initial charge status. Stage A comprises three operational states contingent upon the availability of PV generation, while Stage B involves five operational states predicted based on anticipated PV generation. Through this research, the advanced control strategy facilitates precise adjustment of energy absorption by EVs from the grid and/or PV generation, along with injected energy derived from EVs to the grid. It ensures minimal residential electricity expenses and synchronizes the electrical load profiles. The findings indicate a significant reduction in residential electricity expenses and a substantial normalization of power load characteristics. Furthermore, simulation results emphasize the superior performance of the proposed plan for intelligent households fitted with both EVs and PV generation, compared to households with EVs alone, in terms of lowering expenses and normalizing load scenarios.

Optimal electric bus scheduling considering battery degradation effect and charging facility capacity

ABSTRACT Electric buses are environmentally friendly with the features of low noise levels and zero-emissions. However, higher upfront costs due to the battery degradation effect, charging facility capacity and operational issues are the main obstacles to large-scale application of electric buses. This study proposes a mixed-integer nonlinear model for optimizing electric bus scheduling with a partial charging strategy, battery degradation and constraints of charging facility capacity. A genetic algorithm is then proposed to solve the model. Last, a case study based on a real transit network in Nanjing, China is conducted. The experimental results show that compared with the existing scheduling scheme, the optimal scheduling model can reduce the total system costs by 8.20%, which validates the effectiveness of the proposed model. The findings in this paper can provide a reference for operators to formulate electric bus scheduling schemes and promote the sustainable development of public transport.

Optimal Power Model Predictive Control for Electrochemical Energy Storage Power Station

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge (SOC) constraint of the battery, partition the SOC into different states. Using SOC as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery SOC to prevent SOC from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station.

Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Energy management strategies based on soft actor critic reinforcement learning with a proper reward function design based on battery state of charge constraints

The environmental concerns have pushed our society to find solutions to reduce the emission of transportation systems. One promising solution is the fuel cell hybrid electric vehicle (FCHEV), with a proton exchange membrane fuel cell (PEMFC) and a lithium-ion battery. The presence of two different energy sources requires a smart energy management strategy (EMS) which must not only optimize energy consumption (conventional approaches), but also mitigate component’s degradation (New approaches : Health-conscious strategies). For that, a wide range of techniques has been proposed in the literature, and the most recent techniques are based on machine learning. Reinforcement learning (RL) is usually the most chosen strategy, but as in the optimization techniques, a strong design of the objective function (called reward function in RL) is required. The main challenge in the reward function is to encourage a proper battery state of charge (SOC) management, while reducing the energy consumption and avoiding the factors that accelerate the FC aging. The current advancement in the literature shows satisfactory results, but the reward function design have great improvement potential in the chosen approach. Indeed, most approaches in RL for SOC management with continuous actions are based on the reference SOC principle, which reduces the EMS ability to optimize the other part of the reward function. This paper reveals the challenges associated with the introduction of SOC limits in the reward function and proposes an approach, based on SOC boundaries in the reward function. The contribution allows to better consider the parts to optimize, as it gives more freedom than previous reference SOC techniques all reducing the consumption by 12.9%.

Controllable Meshing of Distribution Grids through a Multi-Leg Smart Charging Infrastructure (MLSCI)

The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being provided, which means actively controlling power exchanges between radial distribution feeders can significantly increase the hosting capacity of the power system. Remarkable benefits are expected when the distribution networks to which the charging infrastructure is connected differ in terms of main characteristics, e.g., rated voltage level, end-user type and operating profiles, and the number and type of renewable plants. The paper focuses on technical targets, such as loss reduction and power quality in terms of admitted voltage deviation from the rated value. The power exchanges between distribution feeders are made possible by a controlled DC link, where bi-directional DC/DC converters are connected so as to charge or discharge vehicles according to the Vehicle-To-Grid approach. A multiplexer topology in which several vehicles can be alternatively connected to the same DC/DC converter is modeled. The proposed concept can contribute to network flexibility by controllably meshing distribution feeders and, jointly, by modulating charging processes according to assigned charging constraints.

Electronic metal-support interaction-induced space charge polarization for boosting photoelectrochemical water splitting

Overcoming the inherent transportation of charge constraint of the photoanode has been critical for seeking feasible photoelectrochemical (PEC) water splitting. Here, we propose to utilize electron metal-support interactions (EMSI) between Cu nanoparticles (NPs) and N-C support for modifying the electronic structure of CuNPs-N-C/SnS2 photoanode and analyze the impact of EMSI on the process of PEC water splitting. The combination of detailed theoretical simulation calculations and comprehensive characterizations indicates that the charge imbalance induced by EMSI within the CuNPs-N-C and SnS2 interfaces results in an enhanced interfacial polarized electric field and boosts the separation of photo-induced carriers at the CuNPs-N-C and SnS2 interfaces. The optimal CuNPs-N-C/SnS2 photoanode displays remarkable properties with a considerably upgraded photocurrent of 2.33 mA cm−2 at 1.23 VRHE, which is 6.30 beyond the value of SnS2 (0.37 mA cm−2). This work paves a way to developing high-performance photoanodes for efficient PEC water splitting.

Hierarchical control combined with higher order sliding mode control for integrating wind/tidal/battery/hydrogen powered DC offshore microgrid

Due to the intermittent nature of renewable energy sources, energy storage devices play a key role in achieving power balance for microgrid. To reduce the overall carbon footprint, this paper considers fuel cells and batteries as auxiliary sources with wind and tidal energy as primary sources. A two-stage control system has been designed, which has been subdivided into system-level and local-level control systems. The system-level control includes an energy management system that optimizes load distribution, minimizes system cost, and meets battery state of charge and hydrogen level constraints. To make the microgrid independent and minimize transportation costs, on-site hydrogen production has been deployed using an electrolyzer system. At the local level, a fast reaching law-based terminal sliding mode controller has been implemented for accurate and robust tracking of the references provided by the system-level control. The stability of the proposed framework has been validated using the Lyapunov stability criteria. The proposed 600 V and 400 kW microgrid structure has been realized using MATLAB/Simulink simulations. Furthermore, the effectiveness of the proposed control scheme has been compared with PID and ITSMC in terms of accuracy and robustness under both power deficit and surplus modes. Finally, the real-life efficacy has been validated by utilizing controller hardware-in-loop experiments.

Integrated optimization of automated guided vehicles and yard cranes considering charging constraints

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed.

Current Loss Mechanism of Magnetic Insulation Transmission Line With Helical Inductance Support

Accurate adjustment of long magnetic insulation transmission lines (MITLs) is crucial in high-power pulsed systems, which must guarantee insulation and high transmission efficiency of current under complicated electromagnetic environment. In this study, a spiral inductance-supported insulation structure in a coaxial MITL was designed, which achieved high efficiency of current transmission up to 90%. Moreover, the current transmission efficiency of the MITL under various working states and specific mechanism of the current loss was investigated by combining circuit with particle-in-cell (PIC) simulations. Results show that the current loss of the inductance-supported MITL in the under-matched state is caused by the inductance current on the support structure, as the electron charge layer is strongly constrained by magnetic field. However, the charge constraint among MITL electrodes is relatively weak in self-limited flow. The electrons bombard the supporting rod, which results in charge accumulation and unneglectable current loss in self-limited flow. This work uncovers the mechanism of current loss in the spiral inductance-supported MITL, which provides guidance for engineering design.

Integrated optimization of electric bus scheduling and charging planning incorporating flexible charging and timetable shifting strategies

In a battery electric bus (BEB) network, buses are scheduled to perform timetabled trips while satisfying time, energy consumption, charging, and operational constraints. Increasing research efforts have been dedicated to the integrated optimization of multiple planning tasks to reduce system costs. At a high integration level, this study determines the BEB scheduling and charging planning with flexible charging and timetable shifting strategies. We first formulate an integrated arc-based model to minimize the total costs considering the power grid pressure cost and subsequently reformulate it into a two-stage model, for which we develop an effective solution method. The first stage minimizes the total operational costs including the fleet, charging, and battery degradation costs based on the column generation technique, and the second stage minimizes the peak power demand through two timetable shifting strategies. It is found through numerical experiments that the proposed integrated optimization model and solution method can achieve significant improvement in the utilization rate and reductions in the fleet size, operational costs, and peak power demand compared to the two baseline models.

On the electromagnetic symmetry producing fields charges at four bosons association

Physics would like to know what electric charge is. As a matter property it generates EM fields, coupling constant,conserved current. Nevertheless Maxwell is uncomplete. It requires to be extended. An approach is supported byelectric charge transfer phenomenology. Consider on three flavours charges {+, 0, −} transmission.A generic electric charge is defined by the triad {+, 0, −}. It provides an exchange charge physics through thequadruplet {Aμ, Uμ, V ± μ }. An electromagnetic symmetry is constituted. It associates the four vectorial bosons. TheEM completeness of particles carrying three electric charges is found. A four photons EM is derived. It includes, Aμ asthe usual photon, Uμ massive photon, V±μ massive charged photons. A new electromagnetic physics is expressed through an enlarged abelian symmetry, Uq(1). Maxwell relationshipsbetween charge and fields are extended. Fields charges are more primitive than electric charge. Noether theoremimproves the attributes on electric charge conservation, symmetry equation and constraint. The symmetry equationwhich govern the electric charge dynamics shows a charge behaviour beyond matter, as fields flux. EM interaction isextended from fine structure constant to modulated and neutral charges.The four bosons electromagnetism introduces a non-univoque electromagnetic symmetry. A pluriformity of EM modelsis performed under similar abelian group, Uq(1). Opportunities for different EM models are constituted preservingcharge conservation law and sharing a common Lagrangian. Physical varieties on Noether theorem, fields strengths,Lagrangian coefficients, equations of motion, fields charges are expressed. Electric charge is englobed by fields charges.The simplest four bosons model is selected. That one which fields strengths are gauge invariants. Propitiatingmeasurable granular and collective fields strenghts. Four kinds of charges are expressed through equations of motion.Electric, modulate, neutral and Bianchi. Allowing to include new EM sectors. Extend Maxwell for nonintegers charges,nonlinearity, neutral EM, spintronics, weak interactions, photonics. A new EM energy emerges.

Evaluation of Distinct EV Scheduling at Residential Charging Points in an Unbalanced Power Distribution System

Electric vehicles (EVs) have significant potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system’s average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach. HIGHLIGHTS Framework for home charging schedule of distinct EVs Compliance with multi-objective power supply efficiency Probabilistic modeling of load demand from real collected data Optimization-driven solution for a test case in Mysore city in India

Comparative Analysis of Lithium-Ion and Lead–Acid as Electrical Energy Storage Systems in a Grid-Tied Microgrid Application

Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable attribute. It cannot be effectively deployed due to its application’s power quality and stability issues. Therefore, an energy storage system is employed to alleviate the variability of RERs by stabilizing the power demand against irregular generation. Electrical energy storage systems (EESSs) are regarded as one of the most beneficial methods for storing dependable energy supply while integrating RERs into the utility grid. Conventionally, lead–acid (LA) batteries are the most frequently utilized electrochemical storage system for grid-stationed implementations thus far. However, due to their low life cycle and low efficiency, another contending technology known as lithium-ion (Li-ion) is utilized. This research presents a feasibility study approach using ETAP software 20.6 to analyze the performance of LA and Li-ion batteries under permissible charging constraints. The design of an optimal model is a grid-connected microgrid system consisting of a PV energy source and dynamic load encompassed by Li-ion and LA batteries. Finally, the comparative study led to significant conclusions regarding the specific attributes of both battery technologies analyzed through the operation, revealing that Li-ion is a more conducive energy storage system than LA.

Hybrid distributed finite-time neurodynamic optimization of electric vehicle charging schemes management in microgrid considering time-varying factors

In this paper, two distributed finite-time neurodynamic algorithms are proposed to collaboratively manage the charging scheme of electric vehicles (EVs) in the microgrid scenario. First, the upper level model is constructed to optimize the disorderly charging problem of EV users under private charging posts , and explore the optimal charging scheme under charging constraints and time-varying conditions to ensure the benefits of users. The lower layer model explores the optimal public charging scheme under the system operation constraint and the supply–demand balance constraint with the objective of minimizing the overall microgrid operation cost. The optimal solution of the upper model, i.e., the load of EV users under the private charging post, is considered as a parameter of the lower model. In this context, two finite-time neurodynamics with fast convergence rate, executed in a distributed manner, are proposed to track the optimal solution of the problem in real time. Furthermore, the stability and convergence in finite time of the two proposed algorithms are proved using Lyapunov theorem and finite time theorem. Numerical case studies of small-scale and large-scale power systems demonstrate the effectiveness, robustness, and real-time performance of the two proposed algorithms.

Optimal scheduling of ancillary services provided by an electric vehicle aggregator

Massification of Electric vehicles (EVs) is becoming a worldwide reality as a means to combat climate change and local pollution. Considering that most of the time vehicles are in parking places, there is an opportunity for using EVs to provide some valuable services to the power network. In particular, EVs can provide ancillary services in electricity markets through an aggregating agent. To this end, EVs aggregators need to develop decision support tools to optimally allocate energy and regulation resources considering power network constraints. Unlike optimization models for EVs aggregators currently available in the literature, in this paper we propose an optimization approach for EVs aggregators that jointly considers the most important aspects influencing EVs profitability, such as uncertainty, drivers’ patterns, capacity constraints, state of charge constraints, regulation demand constraints, regulation offer constraints, regulation bounds constraints, and power-system security constraints. The optimization problem is formulated as a mixed-integer linear programming problem, thus ensuring global optimality. Results are presented in the form of the hourly allocation for charging/discharging power profiles, distinguishing between day-ahead energy and capacity/energy for regulation, and the profit that can be reached, while accounting for network constraints.The proposed model is illustrated through a case study, which allows us to show that EVs aggregators allow for leading to a more reliable power system operation, avoiding transmission lines congestion, while providing important profits for EV owners who are able to provide regulation services.

Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems

In this paper, distribution systems are optimized to accommodate different renewable energy sources, including PhotoVoltaic (PV) and Wind Turbine (WT) units with existing Electric Vehicles Charging stations (EVCS) connected to specific locations of distribution systems. Battery Energy Storage systems (BES) are provided at the exact locations of the PV and WT units. The BES units are optimized to control the power flow inside the distribution systems and enhance their performance by minimizing the proposed fitness functions. Each BES's generated or consumed power depends mainly on its State of Charge (SoC) and system constraints. The Gorilla Troop Optimizer (GTO) algorithm is adopted to solve the optimization problem either as a single-objective or multi-objective optimization. The adopted fitness functions minimize power loss and total voltage deviation (TVD) of the 108-bus distribution system. Two stages are involved in the GTO solution of the optimization problem. The GTO algorithm finds the ideal positions and sizes of the PV and WT units in the first stage. Then the GTO optimizes the BES operation in the second stage after integrating the EVCS into the system, using distinct profiles with two charging levels on weekdays and weekends. The obtained results show that the percentage reductions in the active energy loss with optimal allocations of the PV/WT are 45.29 % and 45.56 % for Level 1 and Level 2 of EVCS charging. The counterpart reductions increase to 48.22 % and 48.33 % when integrating the BES units. Furthermore, with PV/WT integration, utility power consumptions are reduced by 65.74 % and 65.65 % for the two charging levels, while the reductions increase to 70.63 % and 70.54 % when including BES units. These findings emphasize the importance of optimal BES operation in distribution networks equipped with renewable energy sources. The findings demonstrate the superior capabilities of the GTO algorithm during the optimization phase on an equal footing.

Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system.