This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.

Due to the variability and stochastic nature of wind power, accurate wind power forecasting plays an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. The convergence of the forecasting results is also proved. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation in Ireland and that from a single wind farm to demonstrate the effectiveness of the proposed method.

Probabilistic wind power forecasting has become an important tool for optimal economic dispatch and unit commitment of modern power systems with significant renewable energy penetrations. Ensemble forecasting based on Monte Carlo simulation is commonly used by many grid operators, but other probabilistic approaches, such as multi-step iterative wind power forecasting have not yet been fully explored. The associated uncertainty analysis is an important yet challenging issue in this area. This paper proposes to use an analytic interval forecasting framework to estimate the forecasting uncertainty of a wind farm in Ireland based on the Temporally Local Gaussian Process (TLGP) model and evaluates the probabilistic forecasting metrics of reliability and sharpness. The key findings confirm that TLGP not only has better forecasting accuracy but is also less sensitive to uncertainty propagation than Gaussian Process (GP). This work provides an effective analytical framework for iterative multi-step interval forecasting.

With gallium nitride (GaN) switching speed of 3.7 ns, high dv / dt of 105 kV/μs for 390 V poses serious challenges for SR using extra circuit sensing high frequency signal of drain-to-source voltage method. A digital SR algorithm based on charge conservation is proposed, which deduces the SR on-time by calculating the time of current flowing the SR MOSFET junction capacitor. The proposed SR requires no additional detection circuit, which effectively avoids the effect of high dv / dt and minimizes the SR conduction loss to improve the efficiency. Furthermore, in high-output current applications, printed circuit board (PCB) planar transformers face problems of huge winding loss and limited winding current-carrying capacity. A design is proposed for planar transformer using secondary-parallel multiple interleaved copper foil winding. To diminish DC resistance, the secondary side of the transformer is connected in parallel with three copper foil layers. Additionally, the primary and secondary windings are interleaved to achieve lower AC resistance. A 1.5-kW GaN LLC prototype is built with 650 kHz resonant frequency. The LLC power density is 524 W/in ³ and the efficiency is up to 98% with 28 V/53.5 A output at full load.

Conventional LLC synchronous rectifier (SR) schemes use detection circuits to sense the signals of voltage or current. But it typically operates below 500 kHz, which is unsuitable for high-frequency applications as it may lose large SR duty cycle caused by high dv/dt and GaN devices usage. To address the serious challenge, a digital SR control using extended harmonics impedance model is proposed for GaN LLC converters. By building the impedance model, the SR on-time is calculated accurately in the microcontroller. It not only minimizes the on-time of SR body diode with high precision, but also owns high immunity. A 650-kHz 1.5-kW GaN LLC converter was built. With the output of 28 V/53.5 A, the efficiency is as high as 98.1% at 1.5 kW by using the proposed SR. The power density is also up to 524 W/in ³ . Compared to the conventional SR, the efficiency improves 1.6% at full load.