Traditional model predictive control (MPC) is highly reliant on the system's mathematical model. When there are uncertainties in the model or parameter variations, the control performance is vulnerable to being affected. In view of this, model-free predictive control (MFPC) has gradually become the research focus in this field. Against this backdrop, this paper conducts in-depth research. The system's mathematical model is replaced with the auto regressive with extra inputs (ARX) model. This study deeply analyzes the internal reasons for the high computational complexity of the traditional recursive least square (RLS) algorithm during the parameter identification of the ARX model. Furthermore, it innovatively proposes the recursive least square-dichotomous coordinate descent (RLSDCD) identification algorithm as an advanced solution. The study combines rigorous theoretical analysis with extensive simulations and experimental validation. Results demonstrate that the proposed algorithm reduces computational complexity significantly while maintaining control robustness. It has been successfully applied to the control of GFI, providing a more efficient and reliable solution for the optimization of GFI control strategies.

This paper proposes an improved robust predictive current control strategy, combining an improved ultralocal model with a hybrid space vector modulation (HSVM) scheme. Compared to conventional ultralocal model, the proposed improved ultralocal incorporates grid voltage dynamics into the model architecture and replaces online estimations with direct sampled-value. The proposed control strategy enhances tracking accuracy during grid voltage fluctuations, especially under unbalanced and harmonically distorted conditions. Additionally, the HSVM strategy dynamically switches between three voltage vector sequences according to modulation index thresholds, which improves steady-state performance in non-ideal grid conditions. To address the prevalent issues of voltage fluctuations and harmonic distortion in practical power grids, existing solutions inject compensation into the complex power reference. However, existing methods overlook the negative-sequence current effects, which may lead to inaccurate results under unbalanced conditions. This paper incorporates the effects of negative-sequence currents and derives the power compensation term under non-ideal grid conditions, yielding an accurate analytical expression. Experimental results demonstrate that the proposed method exhibits strong robustness against inductance parameter variations and reduces current THD by 26% (ideal grid) and 11% (non-ideal grid) compared to conventional methods.

Model-based predictive current control based on space vector modulation (SVM-MBPCC) is widely used in three-phase pulse-width-modulated (PWM) AC/DC converters because of its good steady-state performance. However, the conventional SVM-MBPCC method depends on the accuracy of system model and parameters. Recently, model-free predictive current control based on SVM (SVM-MFPCC) has been proposed to solve the problem of parameter robustness. However, its steady-state performance is inferior to that of SVM-MBPCC with accurate parameters. This paper combines the conventional SVM-MFPCC with a hybrid SVM method (HSVM-MFPCC), which inherits the strong parameter robustness and reduces current harmonics significantly by selecting the optimal vector sequence of SVM online. The simulated results indicate that the proposed HSVMMFPCC can reduce the current total harmonic distortion (THD) by 27.4% than conventional SVM-MFPCC in high range of modulation indexes.

Traditional deadbeat predictive current control (DBPCC) based on space vector modulation (SVM) is widely used in the field of high-performance control of permanent magnet synchronous motor (PMSM) drives due to its simple concept, fast dynamic response, and fixed switching frequency. However, the performance of DBPCC depends heavily on the motor parameter accuracy, which severely deteriorates when the machine parameter mismatches happen in practice owing to temperature, saturation, and so on. Furthermore, the traditional DBPCC uses only one vector sequence in the full speed range, leading to a relatively high proportion of current harmonics at high modulation indexes. To solve the problems above, this article proposes a model-free predictive current control (MFPCC) method for PMSM drives. Different from the prior ultra-local model, both the nonphysical parameter and the unknown dynamic part of the system are updated online by using the voltage and current in the last two control periods, which makes the proposed method universal and adaptive while achieving strong robustness. Next, the reference voltage vector is calculated by the adaptive ultra-local model and synthesized by variable-sequence SVM (VS-SVM) to obtain minimal current harmonics. The proposed method is compared to the traditional DBPCC. The experimental findings support the idea that the proposed method offers better steady-state performance and stronger robustness and that it can lower the total harmonic distortion (THD) by more than 30% at high modulation indexes.

The technology of space vector modulation has been commonly adopted in model-free method for AC/DC inverters based on LCL-type filter, which can achieve strong robustness against parameters disturbance and good steady-state performance. However, the grid-side current harmonics of model-free method with SVM is commonly higher than that of model-based method with SVM when accurate system parameters are known. To reduce the current THD while maintaining the strong robustness, an improved model-free method has been proposed in this paper, which adopts modified ultra-local model and hybrid SVM to reduce the steady-state harmonics of grid-side currents. The robustness and current harmonics reduction of the proposed method have been verified by the simulated results.

Deadbeat predictive current control (DPCC) has been widely studied in induction motor drives due to its simple concept, quick response and excellent steady-state performance. However, conventional DPCC uses a lot of motor parameters to predict current and voltage. Therefore, the performance of DPCC is affected by the accuracy of motor parameters in the controller. The performance of DPCC will severely deteriorate when the motor parameters used in the controller deviate greatly from their actual values. To improve the parameter robustness of DPCC, this paper proposes a robust DPCC (RDPCC), which utilizes the error between the measured currents and predicted currents to compensate for the influence of machine parameter mismatches. The proposed method is compared with the conventional DPCC, and the experimental results confirm its strong robustness against motor parameter mismatch.

In order to ensure the operational safety of the battery energy storage power station (BESPS), a power allocation strategy based on fast equalization of state of charge (SOC) is proposed. Firstly, BESPS is divided into charging group and discharging groups, which can reduce the response number of battery energy storage system (BESS). Then, the charging and discharging power of the BESS is formulated as a Sigmoid function in relation to the SOC. Furthermore, a differentiated mapping of SOC is proposed to augment the distinctions in power allocation weights among BESS with varying SOC levels. This approach can enhance the rate of SOC convergence in BESS, thereby preventing their withdrawal from operation due to excessive SOC. Finally, the effectiveness of the proposed power allocation strategy is verified by simulation.

Model based predictive power control based on space vector modulation (SVM-MBPPC) is a popular control method for three-phase grid-connected inverter with the advantage of quick response and excellent performance. However, the excellent control performance of the conventional SVMMBPPC mainly depends on the accuracy of system parameters. To overcome the poor robustness of SVM-MBPPC, some scholars propose model-free predictive power control based on SVM (SVM-MFPPC). In spite of the strong robustness of the SVMMFPPC, the steady-state performance of conventional SVMMBPPC with accurate parameters is greater than the SVMMFPPC. This paper proposes an improved MFPPC based on hybrid SVM (HSVM-MFPPC), which has equal robustness to conventional SVM-MFPPC and achieves better steady-state performance. The proposed HSVM-MFPPC is also extended to unbalanced grid condition by adding an compensation power value. The simulated results confirm that the proposed HSVMMFPPC inherits strong robustness and can reduce the current total harmonic distortion (THD) by 30.6% than conventional SVM-MFPPC when the average modulation index is high enough.

Conventional deadbeat predictive current control (DBPCC) based on space vector modulation (SVM) shows quick dynamic responses and a good steady-state performance in induction motor (IM) drives. However, the motor parameters change during operation due to temperature and saturation changes, leading to inaccurate reference voltage vectors and a degraded performance. Furthermore, conventional DBPCC uses a fixed vector sequence of 0127 over the entire speed range, which results in large current harmonics at high modulation indices. To address the above issues, this paper proposes a robust deadbeat predictive current control (RDBPCC) for IM drives. Based on an ultra-local model, the proposed method updates the input voltage gain and unknown system components online according to the voltage and current of the previous two control cycles. Because the final control expression contains only the measured stator current and voltage values, the model shows a strong robustness. The steady-state performance is significantly improved by selecting the optimal vector sequence according to the modulation index based on the principle of current harmonic minimization. The experimental results confirm that, compared with conventional vector control and conventional DBPCC, the proposed method achieves a strong parameter robustness and reduces the current total harmonic distortion (THD) by more than 10% and 20% at high-modulation indices.

Model-based predictive current control based on space vector modulation (SVM-MBPCC) is widely used in three-phase pulse-width-modulated (PWM) AC/DC converters because of its good steady-state performance. However, the conventional SVM-MBPCC method depends on the accuracy of system model and parameters. Recently, model-free predictive current control based on SVM (SVM-MFPCC) has been proposed to solve the problem of parameter robustness. However, its steady-state performance is inferior to that of SVM-MBPCC with accurate parameters. This paper combines the conventional SVM-MFPCC with a hybrid SVM method (HSVM-MFPCC), which inherits the strong parameter robustness and reduces current harmonics significantly by selecting the optimal vector sequence of SVM online. The simulated results indicate that the proposed HSVMMFPCC can reduce the current total harmonic distortion (THD) by 27.4% than conventional SVM-MFPCC in high range of modulation indexes.

The concept of rapid control prototype provides great convenience for the research in power electronics and electric drives. In recent years, lots of rapid control prototype platforms have been commercialized. Although these commercial platforms boast powerful features and capabilities, the expensive price and the closed software environment also limit their implementation in some scenarios. This paper presents a low-cost rapid control prototype platform called MotorAST developed based on Texas Instruments F28388D controlCARD. The hardware and software architecture of the platform will be describe in detail in the article and the performance of the platform will be demonstrated through the experiment using model predictive control on a permanent magnet synchronous motor. Benefit from the robust real-time processing ability of F28388D and the flexible software configuration for each part of the platform, MotorAST can deliver excellent performance with low material costs.

Abstract In practical application, the motor speed may need to be greater than the rated speed, so field‐weakening control is required. However, the conventional flux‐weakening control requires much tuning work, because multiple PI controllers are used. In order to make the tuning work easier, this paper combines flux‐weakening control with deadbeat predictive current control (DPCC) to eliminate two PI controllers in current loop. In addition, this paper also proposes a speed adaptive flux‐weakening controller, which can further simplify the tuning work. The effectiveness of this method has been confirmed on a 2.2 kW surface permanent magnet synchronous motor (SPMSM) experimental platform.

This paper mainly studies the comprehensive evaluation of multi station integration project. Firstly, the multi station fusion evaluation index system including four first level indexes and ten second level indexes is constructed. Secondly, the evaluation model of multi station fusion comprehensive benefit based on AHP entropy fuzzy comprehensive evaluation method is constructed. Finally, three multi station fusion project cases are selected for empirical analysis. The comprehensive score of case 1 is the highest, which is 88.6402, and that of case 2 is the lowest, which is 86.6486, Case 3 is between the two.

Model predictive current control can achieve fast dynamic response and satisfactory steady-state performance for induction motor (IM) drives. However, many motor parameters are required to implement the control algorithm. Consequently, if the motor parameters used in the controller are not accurate, the performance may deteriorate. In this paper, a new robust predictive current control is proposed to improve robustness against parameter mismatches. The proposed method employs an ultra-local model to replace the mathematical model of the IM. Additionally, to improve the control performance, a linear extended state observer is developed for disturbance estimation. Experimental tests confirm that satisfactory tracking performance can still be obtained although the motor parameters may not be accurately set in the controller.

Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply- and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users' load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system's economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system's economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.

Energy is an important material basis for economic and social development, and energy security is an important part of national security. China strives to reach the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. Therefore, in the next development, China will build a new power system with new energy as the main body. The realization of multi-energy complementarity and the promotion of the integration of source, grid, load, and storage are conducive to the construction of a clean, low-carbon, safe and efficient energy system. This paper selects the actual source network load storage integration project as a case to study how to use the regional resource endowment to select the optimal source network load storage project power ratio scheme. First of all, this paper studies the multi-source complementary characteristics of the power side of the integration of source, network, load, and storage with new energy as the main source under the dual carbon target, that is, stationarity, probability distribution characteristics, complementarity, reliability and load matching degree, and studies the on-grid power of the power side. Secondly, according to the actual source network charging and storage project, five power supply schemes are put forward, and the abandonment rate and the proportion of new energy power of each scheme are defined. The power system operation simulation software SPER_ProS2013 to carry out the simulation analysis of different schemes, and calculate the main calculation indexes of each scheme; Finally, through the simulation calculation of production operation, from the economic point of view, the power ratio scheme 1 is selected. The scheme can make full use of the new energy resources in the agglomeration area and its surrounding areas, effectively increase the proportion of clean energy consumption in the agglomeration area, and help the region to achieve the "30.60 targets" as soon as possible.

The investment in power grid equipment maintenance is not only an important guarantee for the safe operation of power grid, but also an important part of the management and control of power grid enterprises. The restriction of transmission and distribution price reform on the operation and maintenance cost of power grid enterprises has led to the increasing pressure on the capital guarantee and cost control of maintenance cost. Based on the research on the differentiated input of operation and maintenance costs for different equipment states, this paper establishes equipment risk assessment models, and uses state evaluation and failure probability statistics methods to obtain quantitative risk indicators, and combines equipment maintenance cost models and failure risks. Cost model, with the goal of minimizing the overall cost, to optimize the maintenance plan, can effectively reduce the maintenance cost of the equipment and the failure risk caused by unreasonable maintenance, so as to scientifically arrange the maintenance time and projects to obtain the optimal maintenance plan.

Deadbeat predictive current control (DPCC) uses many machine parameters when calculating the reference stator voltage vector. The performance of DPCC may severely deteriorate if the motor parameters used in the controller are not accurate. To improve the parameter robustness of DPCC, this paper proposes a robust DPCC (RDPCC), which utilizes the error between the measured currents and predicted currents to compensate for the influence of machine parameter mismatches. The proposed method is compared with the DPCC, and the experimental results confirm its strong robustness against motor parameter variations.

Deepening the electric power system reform and building a new power system with new energy as the main body are the top priorities in the development of the energy and power field. The participation of new energy in electricity market transactions is in the early stage of development, and there are many problems in the mechanism design. Aiming at the problem of new energy participating in electricity market transactions, this paper summarizes and analyzes the current situation of new energy participating in the electricity markets at home and abroad, then puts forward the incentive mechanism and price mechanism of new energy participating in electricity market transactions, and finally makes some suggestions about this problem. The research objective is to better promote the participation of new energy in the electricity market, realize the efficient use of new energy, and help to achieve carbon peak and neutrality goals.

Abstract Based on the Cournot dual-oligonucleotide head model, this paper studies the Impact of carbon emission rights on Enterprises Based on Nash Equilibrium. The following finds analytical expressions of production enterprises’ output changes to analyze the impact of enterprises. As a result, it was found that the distribution of carbon emission rights would damage consumers’ rights and interests. In addition, some sufficient conditions for the historical emission law to affect producers’ residual changes in the carbon emissions trading system are also discussed.