Power Loss Research Articles

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

Implementation of fuzzy logic controller for positive output LUO converter

This paper describes the fuzzy logic controller (FLC) for a positive output LUO converter (POLC), which uses a voltage controller to achieve the output voltage regulation. The fourth-order POLC DC–DC converter produces positive output voltage utilizing the voltage lift technique. Due to the converter’s switching functions and its inherently time-varying nature, it exhibits a highly nonlinear dynamic response. To enhance the dynamic performance of the converter, controllers are designed using small signal model and frequency response analysis. The closed loop is used to evaluate converter performance in the presence of variations in reference voltage, source voltage and load disturbances. The power loss analysis of the converter is performed. The comparative results show that FLC gives good dynamic response for wide range of variations than the proportional–integral (PI) controller. The prototype model is built using dsPIC (30F4011) microcontroller, and the experimental results are presented to validate the simulation results.

Simulation and Application of a New Type of Energy-Saving Steel Claw for Aluminum Electrolysis Cells

Aluminum electrolysis is a typical industry with high energy consumption, and the energy saving of aluminum electrolysis cells is conducive to the sustainable development of the ecological environment. The current density distribution on the steel claws of conventional aluminum electrolysis cells is uneven, resulting in a large amount of power loss. Therefore, a new type of current-equalized steel claw (CESC) is designed in this paper. The ANSYS simulation study shows that the CESC can achieve a more uniform current density distribution and reduce the voltage drop by about 36 mV compared with the traditional steel claw (TSC). In addition, the use of CESC optimizes the temperature distribution of the steel claws and reduces the risk of cracking and deformation. The results of the industrial application tests are highly consistent with the simulation results, confirming the accuracy of the simulation results. The economic benefit analysis shows that using CESC saves 114.1 kWh of electricity per ton of aluminum produced. If this technology can be promoted throughout China, it is expected to save up to 4.75 billion kWh of electricity annually. The development of CESC is promising and of great significance for improving the overall technical level of the aluminum electrolysis industry.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

A Facile Low Prevacuum Treatment to Enhance the Durability of Nonfullerene Organic Solar Cells

Herein, a straightforward vacuum‐assisted method is introduced to enhance the stability of nonfullerene organic solar cells (OSCs). The method, termed “prevacuum” involves subjecting the active layer (D18:Y6) to a low‐pressure vacuum (−1 bar) before thermal annealing at 100 °C. Compared to untreated devices, prevacuum‐treated OSCs exhibit a notable increase in power conversion efficiency from 13.71% to 14.90%. This enhancement is attributed to improved light absorption and charge extraction, as evidenced by external quantum efficiency measurements. Moreover, prevacuum treatment significantly improves device stability under operational conditions, with a 30% power loss occurring after 8.25 h compared to 4.5 h for untreated devices. This improvement is attributed to the removal of volatile components and impurities during the vacuum process, leading to a more hydrophobic and stable active layer. The study demonstrates the efficacy of prevacuum treatment as a simple and accessible method for enhancing the performance and longevity of OSCs, paving the way for their broader application in sustainable energy technologies.

Optimization of high-speed reducer in electric vehicle based on analysis of lubrication

Purpose The purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results. Design/methodology/approach The traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects. Findings The flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient. Originality/value According to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Power Semiconductor Junction Temperature and Lifetime Estimations: A Review

The lifetime of power electronic systems is the focus of both the academic and industrial worlds. Today, compact systems present high switching frequency and power dissipation density, causing high junction temperatures and strong thermal fluctuations that affect their performance and lifetime. This paper is a review of the existing techniques for the electro-thermal modelling of Mosfet and IGBT devices regarding lifetime estimation. The advantages and disadvantages of the methodologies used to achieve lifetime prediction are discussed, and their benefits are highlighted. All the factors required to predict power electronic device lifetime, including Mosfet and IGBT electrical models, the computation of power losses, thermal models, temperature measurement and management, lifetime models, mission profiles, cycle counting, and damage accumulation, are described and compared.

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

Multi-heterogeneous interfaces boost dielectric polarization in PBA-derived Co/MnO@NC ternary composites for electromagnetic wave absorption

The elaborate construction of multi-component composites has been deemed as a promising strategy to enhance dielectric polarization response capability in the preparation of highly efficient microwave absorbers. However, the rational design and integration of homogeneous composites with diverse components continues to pose a great challenge. Herein, we propose a straightforward self-assembly-carbonization strategy for fabricating Co/MnO@NC ternary composites derived from CoMn-Prussian Blue Analogous precursors, featuring enriched heterogeneous interfaces and balanced magneto-electric coupling synergy. The ternary composites effectively introduce diverse dissipation mechanisms, including interfacial polarization behavior originated from the constructed heterogeneous interfaces, dipole polarization relaxation triggered by atomic defects, and optimized magnetic loss ability from well-dispersed metallic Co species. Benefiting from these advantages, the Co/MnO@NC composites demonstrate superior electromagnetic wave absorption performances, with a minimum reflection loss value of −61.37 dB at the thickness of 3.5 mm and effective frequency absorption bandwidth of 6.32 GHz, covering the full Ku-band. Furthermore, power loss density and radar cross-section simulations validate that the Co/MnO@NC ternary composites possess exceptional microwave signal attenuation abilities, with a maximum radar cross-section reduction value of up to 27.98 dBm2 at 0°. Such outstanding absorption properties exceed those of most currently reported ternary composites and exhibit significant potential to replace conventional ferromagnetic-based absorbers. This work offers a straightforward strategy to fabricate ternary composites with excellent electromagnetic wave attenuation properties and sheds novel insights into the dissipation mechanisms.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Hercules: Enabling Atomic Durability for Persistent Memory with Transient Persistence Domain

Persistent memory (pmem) products bring the persistence domain up to the memory level. Intel recently introduced the eADR feature that guarantees to flush data buffered in CPU cache to pmem on a power outage, thereby making the CPU cache a transient persistence domain . Researchers have explored how to enable the atomic durability for applications’ in-pmem data. In this article, we exploit the eADR-supported CPU cache to do so. A modified cache line, until written back to pmem, is a natural redo log copy of the in-pmem data. However, a write-back due to cache replacement or eADR on a crash overwrites the original copy. We accordingly developed Hercules, a hardware logging design for the transaction-level atomic durability, with supportive components installed in CPU cache, memory controller (MC), and pmem. When a transaction commits, Hercules commits on-chip its data staying in cache lines. For cache lines evicted before the commit, Hercules asks the MC to redirect and persist them into in-pmem log entries and commits them off-chip upon committing the transaction. Hercules lazily conducts pmem writes only for cache replacements at runtime. On a crash, Hercules saves metadata and data for active transactions into pmem for recovery. Experiments show that, by using CPU cache for both buffering and logging, Hercules yields much higher throughput and incurs significantly fewer pmem writes than state-of-the-art designs.

Hurricanes make headlines, but chronic utility failure drives energy (in)security in the U.S. Virgin Islands

Natural disasters and extreme events expose significant challenges to electricity infrastructure. We explore concepts of energy security and energy resilience in the aftermath of Hurricanes Maria & Irma in the U.S. Caribbean in 2017. We look at household consequences of regular and storm related power outages for communities, and investigate the motivations for household adoption of solar energy in the wake of a natural disaster, using surveys and interviews in the U.S. Virgin Islands. Results indicate that the storm-related utility failure was widely acknowledged as catastrophic by Islanders, severely limiting the ability for people to work, purchase and store food, and live healthy and productive lives – with lasting consequences. We also find that residents are just as impacted by chronic utility failure as they were disaster-related outages. In the wake of perceived failure from the local utility to provide energy security or energy resilience, households are making decision to invest in self-provided systems to meet these needs by purchasing generators and solar PV. Yet, provision of solar PV follows systemic and racialized patterns of access. We suggest that the practical ways in which utilities make decisions about resilience are outdated, and call for a new paradigm of energy security.

Integrating hydrogen into a hybrid system to meet a laboratory's electricity demand

Providing quality electricity to improve the living conditions of rural and urban populations in Africa, particularly in Cameroon, represents a challenging but achievable task. Indeed, Cameroon has the second largest hydrographic network in Central Africa, and thanks to this network, over 90% of electricity comes from aging hydroelectric dams that cause permanent power outages. The desire to take advantage of other benefits that nature has given this country has directed this work towards renewable energies. Thanks to the HOMER Pro software, a renewable energy hybrid system with hydrogen gas serving as storage and energy vector was adopted. Photovoltaic panels and wind turbines are proposed as primary energy sources for the permanent, low-cost supply of the IRAD laboratory at the University of Maroua. The software proposes three systems, one with the wind turbine as the only primary source, and considered as the most optimal. It has an energy cost equal to $0.0633, an NPC of $20330, and an initial capital of $8500. The other two systems are hybrids with a renewable fraction equal to 100%, and a load distribution strategy that is the charge cycle (CC) like the first system. The first less expensive hybrid system has an energy cost and an NPC respectively of $0.0778 and $24986. As for the second hybrid system, it has an NPC and an energy cost respectively of $26195 and $0.0778. Hydrogen fully plays its role as an energy vector and storage in all three systems. A sensitivity analysis was conducted in this work to ensure that the system proposed for the laboratory can also be adapted in the 9 other regions of the country. It appears that the values of wind resources, solar radiation, and temperature have a significant impact on the energy cost. The cities of Yaoundé and Ebolowa, displaying respective wind speeds of 2.07 m/s and 1.76 m/s, are recognized for having the lowest wind speeds in the country, which hinders the overall energy production and profitability of the system. The hybrid system suggested for these cities proves to be expensive, with energy costs exceeding the standard electricity price set by the energy distribution and marketing organization in the country.

A 13.56-MHz 93.5%-Efficiency Optimal On/Off Timing Tracking Active Rectifier with Digital Feedback-Based Adaptive Delay Control.

This paper presents an adaptive active rectifier with digital feedback delay controllers (DFDC) which quickly tracks optimal on/off timing against input voltage and load variations. To efficiently generate the on/off transition, the proposed active rectifier adopts dynamically controlled coarse/fine delay lines rather than using conventional power-hungry static comparators, while removing the risk of unwanted multiple driving pulses to pass transistors. DFDC conducts the dual-loop digital feedback to independently adjust on/off timing with high-speed 13.56-MHz loop bandwidth, improving the voltage conversion ratio (VCR) and power conversion efficiency (PCE). DFDC can enable real-time power-saving mode control that automatically masks clock-toggling to non-essential blocks to minimize dynamic power loss while driving power transistors. To validate the efficacy of the proposed adaptive rectifier during digital feedback and settling procedures, experiments were carried out with 0.25 μm CMOS prototype at the carrier frequency of 13.56-MHz, input voltages between 1.7 and 2.6 V, and load ranges from 0.33 to 2.2 kΩ. The proposed active rectifier employing DFDC achieves a peak PCE of 93.5% and the peak VCR of 96.3% at the output power of 12.52 mW and 2.02 mW, respectively.

The effect of smart transformers on the optimal management of a microgrid

This study examines the impact of smart transformers on the optimal management of microgrids within a combined heat and power framework. Utilizing a Genetic Algorithm for optimization, the research identifies optimal settings for control variables and resource capacities. The integration of smart transformers significantly enhances performance, improving voltage profiles and reducing electrical losses, while minimizing costs and pollution levels.Key gaps in existing literature include insufficient exploration of smart transformers' advantages and a lack of holistic approaches that integrate technical and economic objectives. This study proposes a comprehensive optimization framework that simultaneously addresses multiple goals, such as reducing power losses and environmental impacts.The contributions include the innovative application of smart transformers for accurate control of active power flow and the development of a robust optimization strategy. Comparative analyses with other techniques, such as Particle Swarm Optimization and Interior Point Method, demonstrate the superior performance of the Genetic Algorithm approach in achieving optimal microgrid management.

Optimised rule-based peak shaving and its impact on annual energy cost reduction

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important energy management method which helps consumers and grid operators. Peak shaving reduces voltage drop and voltage-rise issues, ratings of the different components and energy consumption costs and power losses. In this work, some of the most effective peak-shaving techniques to handle the problem of peak shaving and a few real-world implementations are discussed. Furthermore, an optimised rule-based peak-shaving (RBPS) method, which considers grid import and export power limits, is presented. The impact of this peak-shaving method, on a case study of an educational institute with a photovoltaic (PV)-battery energy storage system (BESS) based on its net load data over an annum for annual operating energy cost reduction, is presented. The results indicate that the total cost reduction for a year is 68.48% using PV and BESS with the peak-shaving application.

Pre-disaster allocation and post-disaster dispatch strategies of power emergency resources for resilience enhancement of distribution networks

With the frequent occurrence of large-scale power outages caused by extreme disasters, coordinated dispatch of power emergency resources (PERs) and repair crews (RCs) has gradually become an essential method to enhance the resilience of distribution networks (DNs). Previous works solve this coordinated dispatch problem in a risk-neutral approach with the assumption that the supply of power repair materials (PRMs) is still fully available after the disaster. However, growing evidence indicates that certain extreme disasters may lead to limited availability of PERs in the DN, rendering risk-neutral decision making impractical. To fill this gap, this paper proposes a resilience-oriented PERs pre-disaster allocation and post-disaster dispatch strategy in a risk-averse manner. In the pre-disaster prevention process, a two-stage distributionally robust optimization (DRO) model based on mean-CVaR is developed, considering the uncertainties of both system damage and PERs demands, along with the decision maker's preference for risk. In the post-disaster restoration process, a collaborative spatio-temporal dispatching model for PERs and RCs with the goal of minimizing outage losses is established. To tackle the computational challenges associated with PRMs allocation based on DRO, robust counterpart transformation and dual theory are utilized. Case studies are conducted using a real world modified 118-bus distribution network. In comparative analysis, five control groups are established to validate the necessity to consider the limited supply of PRMs and the risk-averse attitude of decision makers in enhancing DNs resilience.

Universal strategy study of applying passive metasurfaces to an intra-DC wireless-optical interconnection

Free-space optical (FSO) communication can reduce the routing complexity of data center networks (DCNs), not only ensuring high-capacity optical switching, but also owning similar flexibility to the wireless connectivity. Presently, mainstream wireless-optical switching units (WOSUs) have adopted serial beam control for unicasting. As a two-dimensional planar structure composed of meta-atoms with special electromagnetic properties arranged in a certain way, a passive metasurface (PMF) has strong parallel beam regulating capability. In this paper, we propose a wireless-optical intra-DC interconnection scheme based on PMFs. Through a real PMF chip, by adjusting the polarization of an incident beam, the power distribution between normal and abnormal reflected beams can be controlled. When both reflected beams are assigned with power, we obtain a pair of beams reflected in parallel, thus simultaneously communicating with two racks. In fact, we can perform 1-to-N (N≥2) multicast by using cascaded PMFs, and 1-to-N means that each source rack can communicate with N destination racks, i.e., a wide communication coverage range. However, the cascaded PMFs may result in huge chip costs and high accumulated power loss. In this paper, we only demonstrate the 1-to-2 communication ability of our PMFs, so the communication coverage may be still limited. Hence, by introducing electrostatic drive to control the PMF rotation, each source rack can achieve a wider communication coverage range than before. As a result, all racks within the coverage range have the ability to establish communication links with the source rack. To this end, we also design a neural network to mimic the PMF rotation, further improving the communication capability between racks. We then propose a DCN-topology reconfiguration algorithm supporting the coexistence of unicasting and multicasting, in order to make real-time decisions on the matching between FSO ports with minimized latency. Finally, we established a proof-of-concept prototype system to demonstrate the parallel beam control of our PMF. The test results show that the angle error is less than 0.1°, satisfying the accuracy requirements of WOSUs, and our PMF always has a good polarization response with the insertion loss of less than 2 dB under various azimuth angles or rotation planes.

Optimal‐droop voltage‐restorer for zonal dc distribution system with simultaneous consideration of dynamic current balance and power loss reduction

AbstractA dc voltage restorer (dc‐VR) with optimal droop control is developed to reduce the power loss and solve the dynamic current imbalance simultaneously of the zonal dc distribution system integrating multi‐parallel energy storage. Firstly, the power loss model composed of line loss and converter loss is built and it is proved to be a strictly convex function with respect to droop coefficients, which indicates the power loss can be mitigated by optimizing the current distribution. Thus, an image‐based droop optimization method is proposed to search for the optimal droop coefficients. Then, the economy of voltage compensation on power loss reduction is investigated and an ESS‐combined dc‐VR is designed, including an interleaved parallel architecture and a capacitor‐integrated electric spring (C‐ES). Unified virtual inertia is proposed for interleaved parallel bridge arms to make their dynamic performance consistent. And the newly introduced C‐ES can keep the bus voltage at the rated level to reduce the system cost. Therefore, the problem of dynamic currents imbalance is addressed from the points of converter structure (dc‐VR) and control system (unified inertia), and the power loss can be reduced by voltage recovery (C‐ES) and optimizing the current reallocation which is achieved by updating sharing coefficients from the image‐based droop optimization method. Finally, the simulation cases validate the effectiveness of the proposed optimal‐droop dc‐VR on dynamic current balance and power loss reduction, and hardware in the loop experiment results prove the consistent dynamic characteristics of the dc‐VR's arms.