A wireless transcutaneous energy transfer (TET) system is researched. The characteristic functions of system voltage gain and transmission efficiency are obtained by circuit analysis. Meanwhile, according to the typical technical parameters of the TET system, a characteristic analysis is conducted. Therefore, based on the energy injection technique, a variable frequency constant voltage control method is proposed which enables the TET system to operate at a high efficiency all time when both the load and the transfer distance change. The experimental set of the TET system is arranged. The experimental results have verified the correctness of the theoretical analysis and design scheme. When the transfer distance is fixed, the overall efficiency remains constant in the whole load variation range. The overall efficiency of the TET system is above 83% within typical transfer distances. The multi-physics simulation software is used to simulate the human tissue safety. The simulation results show that the maximum electric field strength, the specific absorption rate, and the maximum temperature are lower than their corresponding limitations.
In order to avoid the disadvantages of the traditional slip ring, such as easy wear and easy accumulation of static electricity, a novel slip ring design method based on series series parallel (SSP) compensation and variable frequency control is proposed. First, the magnetic circuit of the non-contact slip ring is modeled. The leakage inductance and the magnetizing inductance of the loosely coupled transformer, namely the primary side series compensation and secondary side series parallel compensation, are compensated respectively. Next, due to the influence of temperature and other factors, the sensitivity of some components is analyzed, and the relationship between component parameter variation and resonance frequency variation is obtained. After that, a phase difference detection method based on Hanning window fast Fourier transform is proposed, which can avoid the restriction of hardware performance and improve the anti-interference ability. Finally, according to the calculated phase difference and critical quality factor, a variable frequency control method based on zero phase angle is proposed to help the system work at zero phase angle, thereby reducing the loss of reactive power. The simulation results show that when the component parameters change, the proposed method based on SSP compensation and variable frequency control can quickly help the system work in zero phase angle state, and then improve the efficiency of the system. A prototype is designed to verify the proposed method, and the experimental results are basically consistent with the simulation results. Compared with no compensation method, the efficiency of the proposed method is increased by 5%. The research results show that the method proposed has a high robustness.
The performance of lithium-ion batteries is greatly affected by the temperature change. Therefore, it is of great significance to get the temperature information to develop the thermal management system. In order to solve the problem that the local temperature caused by the traditional temperature sensor arrangement cannot replace the overall average temperature, it is necessary to conduct studies of average temperature estimation for 10 A·h ternary nicke clobalt manganese lithium-ion batteries at variable environment temperature. First, a lithium-ion battery is modeled and the experimental platform is built to obtain the parameters which are necessary for modeling. Then, an average temperature estimation method for lithium-ion batteries based on recursive least squares and the extended Kalman filter algorithm is proposed. Finally, to verify the effectiveness of the proposed method, the experimental data of the battery at variable environment temperature is obtained under improved hybrid pulse power characteristic (IHPPC). The results show that the proposed method can estimate the average temperature of lithium-ion batteries in real time. The maximum error of the temperature estimation model after the algorithm convergence is 1.8 ℃, and the average error is only 1 ℃.
The large-scale access to renewable energy such as wind power and photovoltaics brings great uncertainty in power system planning and operation. In order to enhance the ability of high-proportion renewable energy grid to respond to uncertain events and ensure the safe and economic operation, it is necessary to improve the flexibility of the power system. First, based on the perspective of line transmission capacity and safe operation, the flexibility index of the transmission line was defined. Next, considering the economic operation strategy of the system, a multi-objective transmission line planning model based on flexibility and economy was proposed to optimize the flexibility index, investment cost, operating cost, and renewable energy consumption. After that, the NSGAII optimization algorithm was used to solve the model. Finally, the improved Garver-6 and IEEE RTS-24 node systems were taken as examples to analyze the effectiveness of the proposed model. The results show that the planning scheme can improve the transmission capacity of power grids, reduce the probability of renewable energy abandonment, and improve the flexibility and economy of power grid operation.
The development of renewable energy represented by wind, photovoltaic, and hydropower can increase the uncertainty of power systems. In order to ensure the flexible operation of power systems with a high proportion of renewable energy, a power system flexibility evaluation method based on typical operating scenarios was proposed. Through a modified K-means algorithm, the operating scenarios of renewable energy and load were clustered to obtain typical scenarios. The flexibility evaluation indexes were proposed from three perspectives including regional supply and demand balance, regional power flow distribution, and inter-regional transmission capacity. The flexibility evaluation index of each scenario, and the comprehensive evaluation index based on the appearance probability of each scenario were calculated to evaluate the flexibility of the system. Based on the actual historical output data of new energy and the load of a certain region in the south, the flexibility evaluation was performed on a modified IEEE 39 system. The results show that the proposed clustering method and flexibility index can effectively reflect the flexibility of the system.
Nowadays, the third energy revolution has taken place. Many developed countries have formulated clean energy development strategies and announced the time for phasing out thermal and nuclear power to reduce carbon emissions. Meanwhile, China has made a commitment to the world that the carbon emissions of China will peak before 2030, and the carbon neutrality will be achieved before 2060. Therefore, it is of great significance to study the development pathway of clean electricity of China. The reserves and characteristics of clean energy such as hydro, wind, and solar in China are analyzed. The medium and long-term power demand of China is projected, and the power system structure in 2030 and 2050 are respectively estimated based on the electric power and energy balance equations. In addition, the trend of carbon emissions is also analyzed. Some suggestions are proposed to guide the development of China’s clean electricity. The results indicate that the “carbon peaking” of China’s power system would arrive in 2027, and the clean electricity of China is projected to exceed 50% of the total energy production in 2030. Thermal and nuclear power can be replaced by clean electricity such as hydro, wind, and solar energy in 2050, the power industry will achieve “zero CO2 emission”, and the transformation of the green power system will be achieved in response to carbon peaking and carbon neutrality goals.
To improve the accuracy of photovoltaic (PV) power prediction, this paper proposes a novel weather classification method. First, it distinguishs the clear days and cloudy days according to the total cloud cover. Then, it further classifies the cloudy days into three subtypes to investigate whether the sun is obscured by clouds. This method can effectively identify the characteristics of key meteorological environmental factors that affect PV output and form a new classification index sky condition factor (SCF) by weighted summation. This method has clear physical meanings, good discrimination, and easy quantification. The reasonable classification of weather types can eliminate the coupling relationship between many meteorological environmental factors and reduce the dimension of input variables, which makes it easy for statistical modeling. Based on the theoretical and the statistical approachs respectively, the modeling and verification are conducted and the results show that the method can effectively improve the accuracy of PV power prediction.
To respond to the demand of achieving carbon peaking and carbon neutrality goals, and to construct a complete “source-grid-load-storage” new energy power system, a distributed photovoltaic net load forecasting model based on Hamiltonian Monte Carlo inference for deep Gaussian processes (HMCDGP) is proposed. First, direct and indirect forecasting methods are used to examine the accuracy of the proposed model and to obtain spot forecasting results. Then, the proposed model is used to perform probability forecasting experiments and produce interval prediction results. Finally, the superiority of the proposed model is verified through the comparative experiments based on the net load data of 300 households recorded by Australia Grid. After obtaining the exact net load probabilistic forecasting results, the photovoltaic production can be fully utilized via power dispatch, which can reduce the use of fossil energy and further reduce the carbon emission.
Due to the difference in load characteristics and influencing factors in large-scale distribution transformer load forecasting, if all the distribution transformers share a unified model, the prediction accuracy is low, and if the model is built for each distribution transformer, the computational resources will be excessively consumed. An Attention-LSTM short-term forecasting method of distribution load based on multi-dimensional clustering is proposed. The non-parametric kernel method is used to perform probability fitting on the daily load characteristics to form a typical daily load sequence. Improved two-level clustering is applied for load clustering, taking the Euclidean warping distance and influence factors as the similarity evaluation criteria. AP clustering is utilized for obtaining similar time-series, and training sets are formed to train the Attention-LSTM model. Different Attention-LSTM models are obtained by training for different distribution load types and time-series. The effectiveness and practicability of the method proposed are verified by the load data and meteorological data of a municipal distribution network. The accuracy rate is increased by 2.75% and the efficiency is increased by 616.8%.
At present, new elements such as distributed new energy and electric vehicles have emerged in the distribution network, which changes the composition of loads, enriches the connotation of loads, and poses severe challenges to load forecasting. In fact, loads are aggregated in a bottom-up manner in multiple voltage levels of the distribution network, but such hierarchical characteristics are rarely considered in current load forecasting researches. Therefore, a multi-level load collaborative forecasting method based on the distributed optimization algorithm is proposed aimed at ensuring the bottom-up aggregation consistency of loads and jointly improving the performance of load forecasting at all levels. First, the distributed optimization concept based on the alternating direction method of multipliers is adopted to construct a multi-level load collaborative forecasting framework which adapts to the hierarchical characteristics of distribution network and has less data interaction. Then, a specific forecasting method based on the long short term-memory neural network and federated learning is proposed. By aggregating the bottom load forecasting results step by step, the bottom-up integrated load forecasting of distribution network can be realized. The results of calculation examples show that the proposed method has a high accuracy and a great application prospect.
With the objective of carbon neutrality, renewable energy resources gradually become the main power supply, whose variability poses great challenges to the operation and optimization of the system, especially to the power distribution network. In order to solve the problem of reactive power caused by high penetration of renewable energy sources, a centralized optimization model is proposed, which takes reactive power optimization and “generation-network-load-storage” multi-energy integration into account. The model aims at optimizing the operating cost and minimizing network losses and carbon emissions of the system. Reactive power compensation, regulation of energy storage, and energy conversion are considered to achieve safe and low-carbon economic dispatch of the power and gas systems. An improved second-order cone relaxation method is used for the convex relaxation of non-linear equality constraints concerned with the distribution network. The switching capacity produced by discrete reactive power compensators can be exactly linearized by the application of the big M approach. The simulation results demonstrate that the proposed method could effectively compensate the reactive power required by the grid-connection point of wind turbine, coordinate the energy interaction between the power and gas, thus improving the stability and elasticity of the distribution network after integration of large-scale renewable energy sources, which helps promote the consumption of renewable energy.
In order to simulate the impacts of carbon peaking and carbon neutrality goals on power system supply side transformation from, the system dynamics method is used to analyze the main influencing factors for carbon emissions in the process of power structure transformation and their correlations under four different development scenarios. The evolution of power generation structure and power carbon emission in four development paths are studied. The results show that the power system supply side transformation would be affected by many factors. Under the premise of policy support, the development of market absorption mechanism and absorption technology would contribute to the transformation of the power generation structure, which is of great significance to the realization of the dual carbon targets.
An integrated port energy system planning model is established considering the flexibility of shore power load to finely model the shore power load. Next, the proposed model is decoupled into shore power load elasticity and integrated energy system planning. Then, the shore power load curve of the port-ship master-slave game model is calculated. Finally, the optimal response method is used to iteratively solve the optimal integrated energy system planning method considering the shore power load elasticity. The simulation results show that the model can help rationally allocate resources in the port area, effectively improve the energy efficiency of the port area, increase the revenue of port area, and help the port area to save energy and reduce emissions.
To improve the utilization rate of clean energy, reduce carbon emissions, and alleviate the global energy crisis and greenhouse effect, a low-carbon economic dispatch model of multi-energy park considering high proportion of new energy consumption is proposed. First, after introducing the gas storage and heat storage equipment to the park, the potential of energy coupled devices is further tapped, and the impact of electric vehicle charging mode is explored. Then, based on the stepwise price curve, a price-based integrated thermo-electric demand response model is established. Moreover, considering the low-carbon operation of the integrated energy system, a carbon capture and storage equipment model is built. Furthermore, a mixed integer linear programming model for low-carbon economic dispatch before the day of the multi-energy park is proposed. The example analysis shows that the proposed model can improve the energy utilization rate and the scheduling flexibility of the park, effectively reduce the carbon emissions of the park, increase the income of the park, and promote the consumption of high proportion of new energy.
In order to improve the competitiveness of wind power in participating in the power market, promote low-carbon operation of the power system, and meet the new requirements for the completeness and flexibility of the production simulation model due to the uncertainty of wind power output,this paper analyzes the electricity cost composition from the perspective of low-carbon economy, and applies the stochastic programming theory to propose a short-term production simulation model of power system containing wind power. Considering the participation of the carbon trading market, this model aims to minimize the expected cost of electricity production in a short-term time scale, and coordinately optimize the day-ahead power output, real-time power regulation, power reserve capacity, wind curtailment, and load shedding. Taking the modified IEEE 39-bus system as an example, this paper quantitatively evaluates the impact of carbon trading mechanism, carbon trading price, and wind power installed capacity on electricity costs and their contributions to carbon emission reduction. The simulation results show that the proposed model can effectively analyze the short-term electricity cost, carbon emissions, and operational risks of the power system containing wind power under the carbon trading environment, thus has a promise application prospect.
The goal of “carbon peaking and carbon neutrality” puts forward higher requirements for low-carbon operation of power system considering security and stability. The large-scale access of new energy easily leads to problems such as uneven distribution of power flow and electromechanical oscillation. As the representative device of the third-generation flexible AC transmission system (FACTS), interline power flow controller (IPFC) is greatly capable of power flow control, damping control and transient stability control, but the main objectives of IPFC vary considerably under different working conditions, and there is contradiction between the goals. First, based on the improved relative gain matrix (MRGA) theory, the system state equation with IPFC was linearized, the interaction between targets was quantitatively analyzed, the superposition position of the additional controller was selected, and the interaction between steady-state control and dynamic control was weakened. Then, for the transient process, combined with fuzzy logic theory, the IPFC multi-objective coordinated controller was designed. Finally, the controller parameters were optimized using the particle swarm algorithm. While improving the transient stability and small disturbance stability, the controller reduced the power flow overshoot during the transient process and enhanced the coordinated control ability of IPFC under different system operating conditions. It was helpful to solve the problems of energy transmission and consumption, safety and stability control caused by the large load, low inertia, and random fluctuations of the power system under the “dual carbon” background.
Line hardening and energy storage configuration are important parts of the pre-disaster planning defense strategy, which can effectively improve the disaster prevention and emergency response capabilities of the hybrid AC-DC distribution system (HDS). Under the background of frequent extreme events, a method to improve the resilience of hybrid AC-DC distribution system considering line hardening and energy storage resource allocation is proposed, and a two-stage robust optimization model is constructed. Essentially, the model is a tri-level mixed integer nonlinear programming problem. The outer level evaluates the active behavior of HDS to determine the line hardening and energy storage system configuration strategies, the middle level determines the worst line failure set after the extreme event occurs, which is the passive behavior of HDS, and the inner level evaluates the active behavior of HDS to determine the emergency response and the operation strategies. Based on the nested column and constraint generation algorithm (nested column and constraint generation, NC&CG), the 3-level mixed integer linear programming model is solved. Finally, a simulation analysis is conducted with a 9-node DC distribution network and an improved IEEE-33 node hybrid AC-DC distribution system coupled with a ring AC distribution network as an example. The results show that the proposed method can effectively improve the resilience of the distribution network and ensure its safe and reliable operation in extreme events.
It is urgent to vigorously develop renewable energy to achieve the goal of “carbon peaking and carbon neutrality”. Unlike traditional thermal units, the marginal cost of renewable energy units is zero. With the reform of the electricity spot market, renewable energy is bound to have a huge impact on the market-clearing results and the operation of the power system. Considering the actual spot market operating rules, an electricity spot market simulation framework is established based on the security-constrained unit commitment and economic dispatch models for the operation simulation of the electricity spot market. Taking the actual data of a provincial power grid as an example, a quantitative analysis of the impact of renewable energy on the market-clearing results in the spot market environment is conducted. The simulation results show that, in the spot market environment, the participation of renewable energy will reduce the average electricity price, the system operating costs, and the renewable energy accommodation. At the same time, the profit space of the market units will be compressed.
In order to further enhance the active power regulation capacity of East China power grid, this paper analyzes the necessity of constructing an operation reserve system of East China power grid under the new power systems construction from the aspects of receiving end characteristics, new energy development, net load fluctuation, and the demand of power market reform. Furthermore, it proposes an operating reserve proposal system of East China power grid under the new situation based on the status of typical power systems at home and abroad. The suggested operating reserve system reorganizes and revises the principles of reserve classification, response time, and minimum reserve configuration. The results verify the effectiveness of the proposed operating reserve system through the measurement and analysis of the actual operation data of the East China power grid.
A day-ahead optimal decision-making model is established for an integrated electricity-heat energy system to participate in both the electric energy market and the spinning reserve market, and the step-by-step carbon trading is introduced into the proposed model. The conditional value at risk method is used to manage the uncertainty risk of renewable energy and electrical load. With the objective to minimize the operation scheme cost and carbon emission cost, an operation plan is developed and the reserve resources are arranged for the integrated electricity-heat energy system. The results of a case study show that the proposed model improves the reliability, economy, and low-carbon level by taking the complementary advantages of the integrated energy system and reasonably arranging reserve resources to deal with the risks caused by uncertain factors.
In order to accurately quantify the carbon emissions of coal-fired units with different capacities and serve the goal of “carbon peaking and carbon neutrality” in China better, a novel CO2 emission characteristic model for environment-economic dispatch of power systems is established. First, the changes in the capacity and coal consumption of coal-fired units in China in recent years are summarized and analyzed. Then, the relationship between the load rate and CO2 emission intensity is analyzed using the K-Medoide cluster method, and the carbon emission characteristic model of new coal-fired units restricted to basic equations is established. Finally, combined with theoretical analysis and actual data, a simulation is conducted to verify the validity of the model.
In this paper, an optimal sizing and placement model for distributed generation (DG) is established, which includes active power losses, voltage profile, pollution emission, DG costs, and meteorological conditions. Since optimizing placement and sizing are discrete and continuous variables respectively, the model established is a highly nonlinear complex one with discrete optimization variables. Therefore, the adaptive manta ray foraging optimization (AMRFO) algorithm is applied to obtain the optimal Pareto front, which has a rich and diverse search mechanism, individual updating mechanism, and advanced Pareto solution selection mechanism. For this model, a better solution of high quality can be obtained. In order to avoid the influence of subjective setting of weight coefficient, the ideal point method based on Mahalanobis distance is used to make Pareto front decision. Finally, the simulation based on the IEEE 33, 69-bus distribution network and the IEEE 33, 69-bus distribution network in isolated network operation are implemented. The results show that compared with the traditional multi-objective intelligent optimization algorithm, AMRFO algorithm can obtain a more widely distributed and uniform Pareto front. While considering the economy, the optimized distribution network voltage profile and active power losses can be significantly improved.
Aimed at the problems of wide area distribution, resource dispersion, and inefficient aggregation of distributed energy storage, this paper proposes an aggregation model and evaluation method of distributed energy storage based on the adaptive equalization technology. First, this paper establishes an adaptive equalization function model based on dynamic characteristic parameters such as energy storage capacity, power, and state of charge. Then, based on the adaptive equalization function model, it establishes the aggregation model and evaluation method of distributed energy storage, which takes the power regulation rate, adaptive equalization rate, and capacity contribution rate as the dynamic parameters of aggregation degree. The example simulation verifies that the model can realize the fact that each energy storage unit can complete the aggregation from energy storage unit to energy storage aggregate with a smaller internal difference and a higher external aggregation rate. It can be applied to a large number of distributed energy storage aggregation participating in grid auxiliary services, and realize the efficient utilization of energy storage resources.
