An improved model-free predictive current control scheme based on double voltage vector for two-level voltage source inverters (2L-VSIs) is proposed in this article. First, two basic voltage vectors (VVs) are chosen in each control period, whose durations are calculated based on the cost function. Then, a novel current gradient updating method is developed by using the applied two VVs to calculate the current gradients of the remaining six VVs. As a result, the stagnation effect can be eliminated and current ripples and total harmonic distortion can be reduced effectively. In addition, due to the nature of vector diagram symmetry, the processing of current gradient updating is simplified and the calculation speed is increased. The effectiveness of the proposed scheme is verified by simulation and experimental results from the 2L-VSI system.

Model-free predictive current control (MFPCC) has been widely used in voltage source inverters (VSIs). The output current performance of look-up-table-based MFPCC is affected by the number of the applied voltage vectors (VVs), the current gradient updating and the sampling disturbance. To improve the current performance, a novel modulated MFPCC is proposed for VSI, while real-time current gradient updating and sampling disturbance suppression can be achieved. First, three basic VVs are picked, whose duration time are computed according to the current gradients. Second, an advanced current gradient updating method is presented by establishing the equations of different current gradients under different VVs. Furthermore, the sampling disturbance effect on MFPCC is analyzed and an extended state observer is designed to suppress such effect. Simulation model and experimental platform of the VSI system are structured to verify the preponderance of the proposed MFPCC in reducing the total harmonic distortion of output current.

Model-free predictive current control (MFPCC) methods based on look-up tables (LUTs) have been widely applied in voltage source inverters (VSIs) due to their simple implementation and excellent robustness. However, the performance of the VSIs is affected by the voltage vector used, accuracy of current gradients, and sampling noise. In order to improve these, a new double-vector MFPCC method is proposed in this article. First, a synthetic voltage vector composed of double vectors is applied in each control period to reduce current ripples, where the durations of the double vectors are calculated using predicted current gradients. Next, an extended LUT is designed by using the current gradients of the applied synthetic voltage vectors to update all current gradients of eight basic voltage vectors, so as to increase the accuracy of current gradients. Then, the effect of the sampling noise on MFPCC is analyzed in detail and suppressed by a sliding mode observer for the VSIs. Finally, simulation and experimental results have verified the effectiveness of the proposed method.

The parametric uncertainties have a great effect on the output voltage of grid-forming inverters when they are operated under conventional modulated model predictive control (MPC). To address this known issue, a novel modulated model-free predictive control (MFPC) is proposed in this article. First, a new look-up table (LUT) formed by capacitor voltage differences and inductor current differences of LC -filter is designed for voltage-current predictions. Then, the state-space equations of different voltage vectors are established and reconstructed to update each voltage-current difference in the LUT per control period. Next, the duration of each voltage vector and the voltage prediction are calculated based on the updated voltage differences, so as to remove the impact of parameter variation on duration calculation and prediction calculation, enabling their robustness. Finally, the practicality of the proposed approach is demonstrated experimentally under both ideal and mismatched parameters. In comparison with conventional modulated MPC, the proposed modulated MFPC not only achieves comparable voltage performance, but also exhibits greater robustness.

In this paper, a distributed peer-to-peer (P2P) electricity trading model was proposed to study economic interactions between load aggregators (LAs) and microgrid operators (MGOs) considering network losses in a distribution system. In this model, the economic interactions among market participants were formulated as a Nash bargaining game, where LAs and MGOs can bargain with each other on the trading volume of electricity and payment. To achieve the Nash bargaining solution, the game was divided into two sub-problems: social welfare maximization and payment bargaining. Then, the alternating direction method of multipliers was used to solve the two sub-problems with limited information exchange. Finally, we tested the proposed model on a 12 × 12 km² distribution system, and the results verify its effectiveness.