Optimal Power Supply Research Articles

Analysis of the critical current and electromagnetic forces of HTS segments in multiple pairs of current leads used in the current lead box of a fusion reactor

The high-temperature superconducting current leads (HTS CLs) used in fusion reactors require multiple HTS CLs to be placed inside the current lead box (CLB) of specified dimensions. Each HTS CL not only carries a large current but also has very high safety requirements. This study, based on the first generation HTS tapes (Bi-2223/Ag-Au), analyzed the critical current distribution of a 60 kA HTS CL and evaluated optimal power supply configurations for eight HTS CLs within the CLB. It also determined the optimal distance of each lead from the center of the CLB. Through ANSYS APDL calculations, we first obtained the critical current distribution of each panel of a single CL. The analysis results indicated that the HTS segment has a current carrying capacity of 78 kA at 65 K. Secondly, the optimal power supply option adopted a positive–negative alternating approach. When the distance is 1200 mm, the total current carrying capacity of the eight CLs is 605 kA, with relatively minor differences among the minimum panels of each lead, and the electromagnetic forces for each current lead were all below 1000 N. In addition, the relationships of the critical currents, surface areas of the CLB and electromagnetic forces of eight CLs with distance were obtained. Based on the above analysis, the distance of each lead from the center of the CLB was determined to be at least 800 mm.

Exploration of Energy-Saving Technologies in Building Electrical System Design

Green energy conservation is the mainstream trend in the current development of the construction industry. The application of energy-saving technology in building electrical system design can effectively reduce energy consumption, avoid unnecessary energy consumption, and truly achieve energy conservation and environmental protection. Based on this, the article elaborates on the principles of energy-saving design in building electrical systems, and actively explores the application of energy-saving technologies from different perspectives such as optimizing power supply and distribution system design, adopting high-efficiency energy-saving lighting equipment, applying renewable energy, promoting smart home technology, and improving the efficiency of building electrical equipment.

A study on power supply restoration strategy considering flexible resource regulation ability

AbstractThe rapid development of new energy technology can make this type of energy the primary power source in the future. However, new energy power generation is considerably affected by the environment, resulting in intermittent and fluctuating characteristics. Given the shortage of power generation in urban distribution networks during extreme weather, a study on power supply restoration strategy considering flexible resource regulation ability is proposed in this study. First, the typical adjustable flexible resources, air conditioners, and electric vehicles in urban distribution networks are modeled, and their adjustment ability is evaluated. Second, on the basis of the evaluation results, the optimal power supply recovery path is solved to ensure the power supply of essential loads in the distribution network. Last, the IEEE 33‐bus system proves that this method can quickly find the optimal path of power supply recovery and effectively reduce the power loss rate of essential loads, thus improving the toughness of urban distribution networks.

Temperature variation and urban electricity consumption in China: Implications for demand management and planning

With the pressing challenges of climate change and rapid urbanization, understanding the impact of temperature variation on electricity consumption in urban areas is crucial for effective demand management and planning. This study investigates the relationship between temperature and electricity usage in Chinese urban centers, shedding light on critical patterns that can inform sustainable energy policies. Using panel data analysis reveals a significant impact of temperature change on household electricity consumption. The findings indicate an optimal temperature range of 18–24 °C and a non-linear association between temperature and power usage. Notably, the rise in summer temperatures has a greater impact on electricity consumption than the decrease in colder winters. The study also highlights variations in the influence of temperature on electricity consumption based on regional economic development and north-south disparities. The study suggests implications for optimizing power supply planning.

Choosing Optimal Supply Radius of Transformer Substations (TSs) in Iraq’s Cities Using Geometric Programming with Neutrosophic Coefficients

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, then y is very likely to occur," "x should not be less than a," etc. To design an optimal radius of power supply in the electrical transformer substation, this article presents a new method for creating primal and dual neutrosophic geometric programming problems. It also provides a numerical example to evaluate the approximate optimal economic power supply radius.

Supply energy management methods for a direct current microgrid: A comprehensive review

AbstractThe revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction.

Multi-scenario research on the low-carbon transition in energy-deficient cities-view from the power supply side

Cities are the places where power supply demand is highly concentrated. How to ensure power supply security while realizing low-carbon transition is the key challenge faced by the construction of new power systems. In large cities with a shortage of local power generation resources, this problem is particularly prominent. The study takes Guangzhou city as a case study area, which has the typical characteristics of “serious energy shortage and extremely high-power load density” in China. This study explores the differences in power supply capacity, structure, carbon emissions, and power supply costs under the six different scenarios set up from the power supply side for future power supply structures. The optimal power supply modes have been matched for cities with different development characteristics and needs. The change in power supply structure and the realization path under the optimal power supply modes are discussed. The results show that the power supply capacity of Guangzhou is constantly improving under different scenarios, with the highest self-sufficiency rates of power production capacity and power production quantity reaching 80% and 50%, respectively, by 2030. Despite this, Guangzhou still does not have the basic conditions for building a new power supply system through the development of local high-proportion renewable energy only due to the limitation of resource endowment. The combination with increasing the ratio of renewable energy in outsourcing power is an inevitable choice for low-carbon transition for the city. This study provides a new idea on how to build a new power supply system for an energy shortage city. It can be used as a reference for safe power supply and low-carbon transition for the power supply sectors of other cities.

Optimizing Power Supply Scheduling for Offshore Stand‐Alone Microgrids: A Novel Framework considering Load and Fuel Procurement Uncertainties

Optimizing Power Supply Scheduling for Offshore Stand‐Alone Microgrids: A Novel Framework considering Load and Fuel Procurement Uncertainties

A Voltage-Level Optimization Method for DC Remote Power Supply of 5G Base Station Based on Converter Behavior

Unlike the concentrated load in urban area base stations, the strong dispersion of loads in suburban or highway base stations poses significant challenges to traditional power supply methods in terms of efficiency and cost. High-voltage direct current (HVDC) remote supply have better application potential in this scenario due to their low transmission losses, attracting much attention. However, existing research has problems such as ambiguous optimal power supply distance under different voltage levels and a lack of behavioral models for converters. Therefore, this paper starts from the behavior of underlying converters, analyzes the loss composition of different converters in HVDC long-distance supply, and establishes a refined model for converters by determining the mathematical relationship between converter losses and operating power. Considering the economic feasibility of power supply solutions throughout the lifecycle, a modeling method is proposed that optimizes the voltage level of converters considering the behavior of converters for different supply distances. The optimal voltage level for different supply distances is discussed, and the effectiveness of the model is verified through examples, providing valuable guidance for optimizing the voltage level in HVDC long-distance supply for 5G base stations.

Optimal Electric Vehicle Parking Lot Energy Supply Based on Mixed-Integer Linear Programming

E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.

Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming

The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

Real-time demand response strategy base on price and incentive considering multi-energy in smart grid: A bi-level optimization method

Real-time pricing (RTP) is an efficient approach for demand side management, which mainly aims at reducing peak-to-average ratio (PAR) and balancing power supply and demand. However, in real life, users’ real power consumption sometimes deviates significantly from the ordering power, which results in waste of power resources. Only the price-based demand response (FBDR) is difficult to tackle the deviation problem, especially in the multi-energy generation system. To address this issue, a two-stage hybrid demand response (DR) strategy, which combines RTP and real-time incentive, has been proposed in this paper to minimize the difference between the real power consumption and the ordering power. The first stage is to determine the optimal ordering power via RTP strategy, and then the power supplier and users reach an agreement of power ordering (APO). The second stage is to minimize the difference between the real power consumption and the ordering power via real-time incentive and APO. According to the random dynamic interaction between users and the power supplier, a bi-level model is constructed. Multi-energy generation and energy storage system (ESS) on the supply side, and photovoltaic (PV) generation on the demand side are also considered in this model. Considering the characteristics of the upper and lower objectives and the benefits of both supply and demand sides, the corresponding distributed optimization algorithm is designed to solve the model. Finally, the real-time price, optimal power consumption and optimal power supply are obtained. The simulation results show that the proposed DR strategy and algorithm not only fully guarantee the benefits of users and the power supplier, but also have a good performance in shaving peak and filling valley.

Electric Heating System of Residences without Central Heating: Power Supply Optimization

For conservation and environmental purposes, three electricity consumption models, power grid, power grid + PV and power grid + PV + battery, are proposed in this paper, the optimization of the electric heating energy consumption model is carried out with the objective of the highest annual net return of the system. The results showed that for the single-layer residence with a housing area of 100m2, the use of the power grid + photovoltaic system has the highest annual net income, as well as good economic and environmental benefits.

On the design and optimization of distributed energy resources for sustainable grid-integrated microgrid in Ethiopia

This paper presents a study that focuses on alleviating the impacts of grid outages in Ethiopia. To deal with grid outages, most industrial customers utilize backup diesel generators (DG) which are environmentally unfriendly and economically not viable. Grid-integration of hybrid renewable energy systems (HRES) might be a possible solution to enhance grid reliability and reduce environmental and economic impacts of utilizing DG. In this study, an optimization of grid integrated HRES is carried out for different dispatch and control strategies. The optimal power supply option is determined by performing comparative analysis of the different configurations of grid integrated HRES. The result of the study shows that grid integrated HRES consisting of photovoltaic and wind turbine as renewable energy sources, and battery and hydrogen as hybrid energy storage systems is found to be the optimal system to supply the load demand. From the hydrogen produced on-site, the FC generator and FCEVs consume 143 620 kg/yr of hydrogen which is equivalent to 394 955 kg/yr gasoline fuel consumption. This corresponds to saving 1 184 865 kg/yr of CO2 emissions and 605 703 $/yr revenue. Besides, this system yields 547 035.4 $/yr revenue by injecting excess electricity to the grid. The study clearly shows the economic and environmental viability of this new technology for implementation.

Design of optimal power supply parameters for industrial expansion

The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value.

Power supply optimization method based on multivariate load peak-valley complementary characteristics

The power grid load shows a trend of diversified development. With the continuous increase of the power grid construction scale, it is very important to optimize the power supply of the distribution network and improve the utilization of assets and equipment by using the peak valley complementary characteristics of multiple loads. In this paper, the peak-valley fluctuation characteristics of load are classified and analyzed. Cluster analysis is used to study the load classification with stable peak-valley characteristics in the complex load composition. Based on the peak-peak superposition and peak-valley complementary characteristics of the classified load, and considering the load space dimension, the multi-load optimization combination method for the whole period and time segment is proposed, and verified by simulation.

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are noexception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise andvibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technologyis proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHDthrusters and power supply for ion propulsion drones along with performance optimization of high-voltage powersupply for endurance in earth’s atmosphere.

Development of magneto elastic transducers of mechanical quantities invariant to destabilizing factors

This article analyzes the transducers of mechanical quantities developed by various authors, invariant to destabilizing factors and ways to reduce the sources of errors affecting the main characteristics. At the same time, special attention is paid to the causes of various errors associated with the properties of the magnetic material, such as magneto elastic hysteresis and the nonlinearity of the transformation function. Additional errors caused by temperature changes and their studies have shown the possibility of reducing the temperature error by selecting the material of the magnetic circuit and choosing the optimal power supply modes. Fluctuations in the frequency and amplitude of the supply voltage are factors that significantly affect the sensitivity, while causing additional error, and the use of the classic Royer multivibrator scheme increases sensitivity.