Aiming at the defects of gap light leakage and surface heat transfer in the use of a glass vacuum tube, in this paper, the glass tube embedded star multifins receiver is used as the concentrating photothermal conversion component of compound parabolic concentrating solar air device, and the air is selected as the heat transfer medium to alleviate the above defects and reduce the sealing requirements of the device and the low temperature burst tube´rate. The influence of the fin shape of the starshaped multifins receiver on the performance of the compound parabolic concentrating solar air device was comparative studied. The influence of different starshaped multifins receiver on the optical performance of the device was simulated and calculated. Based on this, the thermal properties of the device with embedded star sixfins receiver and the star fivefins receiver were tested and analyzed under actual weather conditions. The results show that when a<7.4°, the optical performance of the solar air collector device with star sixfins receiver is better than that of the star fivefins receiver. The average light receiving rate and concentrating efficiency of the former are 98.2% and 76.4% respectively, which are 1.2% and 1.1% higher than those of the latter. In the sunny day, when the air flow rate is 4.0 m/s, the average outlet temperature, maximum instantaneous heat collection and photothermal conversion efficiency of the solar air collector embedded with the star sixfins receiver are 37.0 °C, 896.45 W and 75.31%, respectively, which are 1.37%, 2.80% and 1.93% higher than that of the embedded star fivefins receiver. The testing results can provide a reference for the structural optimization design of the receiver of the compound parabolic concentrating solar air device.

Bifacial photovoltaic modules are designed to generate electricity by absorbing both front and rear irradiance at the same time, which enhances the power generation compared to monofacial photovoltaic modules. In this paper, a twomode power control strategy is proposed based on bifacial photovoltaic modules. Firstly, the modified equivalent irradiation model is used to construct a parametric model of the electrical characteristics of bifacial photovoltaic modules; according to the power output characteristics of bifacial photovoltaic modules and then based on the judgement of irradiance, a twomode control strategy with the maximum power output and the power reserve control is proposed; finally, the model is used to experiment and simulate the 365 W crystalline silicon bifacial photovoltaic module. The experimental results show that the relative error between the predicted power and the measured power is less than 3.5%, and the relative error of the voltage at the maximum power point under the measured condition is 0.3%. The twomode power control strategy can achieve the realtime active power reserve without increasing the energy storage, in which the maximum active standby capacity at the transient equivalent irradiance of 1006 W/m² is 69 W, and at the transient equivalent irradiance of 1031 W/m² is 78 W.

In order to solve the micrositing problem of wind farms under complex terrain, an optimization strategy based on improved discrete state transfer algorithm (RCDSTA) is proposed. Firstly, in order to solve the influence of ground flatness on fan placement, the terrain ruggedness index (TRI) is introduced to quantify the ground flatness numerically, and the points with excessive ruggedness are constrained. Secondly, a wind turbine layout optimization method based on discrete state transition algorithm (DSTA) is proposed to calculate power generation by multiwind downward threedimensional wake superposition, and the DSTA algorithm is improved to reduce the calculation time of fitness value, so as to shorten the calculation time of complex calculation optimization problems and improve the calculation efficiency. Finally, taking a wind farm with complex terrain in Xinjiang, China as an example, the algorithm is compared with the microscopic site selection results of genetic algorithm (GA) and engineering design under the background of the same terrain conditions and objective function. The simulation results show that the discrete state transition algorithm is more effective than the above two methods in providing a reasonable fan layout scheme considering the characteristics of terrain factors.

In order to solve the problem of unified setting of yaw control parameters of wind turbines and the delay of yaw startup of wind operation, a yaw control parameter optimization method based on wind direction fluctuation characteristic evaluation and multiobjective particle swarm optimization algorithm was proposed. The yaw control parameters under different wind speed ranges were optimized by taking the power generation of the unit and the rotation Angle of the engine room under yaw control as optimization objectives. A yaw control strategy optimization method based on VMDEEMD –LSTM –LSSVM wind condition prediction model is proposed. Through predicting the average wind speed in a period of time, the optimized yaw control parameters are set in advance, and through predicting the wind direction, whether yaw starts in advance to the wind action is judged and controlled. The results of example analysis show that this strategy can effectively improve the power generation of wind turbines and reduce the cabin rotation Angle under yaw control, which is beneficial to the economic benefit of wind farms.

In order to further reduce the cost of wind power, employing 15 MW or even higher capacity wind turbine has been the major development trend in the future wind energy market. This paper has developed the soil structure interaction model of the IEA 15 MW monopile wind turbine using a set of linear springs by improving the capability of FAST due to the demand of investigating the SSI effect on the dynamic responses of nextgeneration large offshore wind turbines. The nacelle vibration and bending moment of the support structure under normal power production and extreme conditions are obtained. The results indicate that the ignorance of SSI effect significantly underestimates the towertop and mudline bending moments under power production load cases. The SSI effect has an insignificant influence on the dynamic responses of the wind turbine under the extreme conditions. Specifically, the fatigue damage of the support structure at the mudline is only predicted with an error lower than 3% under the 50year return period extreme condition. Nonetheless, the SSI effect has a dominant influence for the low wind speed conditions. The tower top and mudline bending moments are underestimated by 37.6% and 20.1%, respectively, if the SSI effect is ignored. The axial mode of the foundation is activated, resulting in an intense nacelle vibration and a huge increase to the fatigue damage of the support structure. The study has verified that the SSI effect is mandatorily considered in the design of large offshore wind turbine structures.

Reasonably determining the renewable energy quota ratio to maximize the consumption of renewable energy while minimizing the costs for quotaobligated entities is a critical challenge that numerous studies urgently need to address. In this article, the renewable energy quota target suitable for China is analyzed, with the goal of maximizing the interests of market participants, while considering factors such as power source characteristics, market features, and resource attributes. A noncooperative game bilevel optimization model for renewable energy quota ratios is proposed. In this article, the renewable energy quota target suitable for China is analyzed, with the goal of maximizing the interests of market participants, while considering factors such as power source characteristics, market features, and resource attributes. A a noncooperative game bilevel optimization model for renewable energy quota ratios is proposed. Power generators are positioned at the upper level of the model, engaging in noncooperative games with other power generators to determine optimal pricing based on marginal costs for profit maximization. Quotaobligated entities are located at the lower level, adjusting their electricity purchase plans to optimize the reported electricity quantities and prices from power generators, thereby minimizing total electricity procurement costs. The case study analysis demonstrates that the constructed model not only enhances the benefits of market participants but also promotes the consumption of renewable energy.

In order to guarantee capacity adequacy for the power system with a high penetration of renewable energy resources, it is necessary to establish a capacity market mechanism that is compatible with the energy market. From the perspective of investment decisionmaking, this paper analyzes the efficiency of capacity market mechanism in the power system with high proportion of renewable energy by studying supplier's investment behavior in capacity energy market with high integration of renewable energy. Firstly, a threelayer optimization model is established for the investment decision problem, including unit decisionmaking, capacity market and energy market model. Then since the model is difficult to solve directly, a phased loop iterative solution algorithm is proposed, being able to solve the problem with the capacity and energy market decoupled. Finally, based on the actual data of a certain provincial network, this paper uses the proposed method to compare the effects of energyonly market and capacityenergy market on capacity adequacy for the power system with a high proportion of renewable energy.

Under the context of "dual carbon," an optimization scheduling model considering carbon emission flow and staged carbon trading mechanism is proposed in this paper to promote the lowcarbon economic operation of distribution networks. Firstly, the participation of distribution networks in the carbon trading market is taken into account, and the theory of carbon emission flow is introduced to determine the carbon emission status of each node within the distribution network. Subsequently, the stochastic states of electric vehicles are determined using the Monte Carlo algorithm, and the carbon quota of power generation equipment is obtained based on the entropy weight method. Simultaneously, a carbon quota model for electric vehicles is constructed, and a staged carbon trading mechanism is applied to model electric vehicles, photovoltaic units, wind power generation, and thermal power units. Finally, the system is optimized using an improved particle swarm optimization algorithm, with the objectives of minimizing the system operating cost and maximizing the system carbon income. The proposed model is verified through case studies conducted on an improved IEEE33 node distribution network system, where four operating scenarios are set. The research results demonstrate that the proposed model reduces carbon emissions by 539.43 tons, and the amount of wind and light discarded is reduced by 555.27 kW·h, which also makes the system's carbon revenue increase by 79.627 9 yuan.

The fourwire voltage source inverters have become an advisable interfaced converter between the source and loads. However, the switching state of the Finite Control Set Model Predictive control does not change regularly in each cycle, which results in variable switching frequency of the inverter switch and large ripple of the output power. In addition, the direction and size of the inverter output voltage are fixed under the traditional FCSMPC control, which may not reach the boundary within the whole control track. Therefore, this paper proposes an online duty cycle modulation predictive control based on capacitor split four wire inverter. This strategy takes into account the advantages of fast dynamic response of FCSMPC and fixed switching frequency of modulation strategy, so the controller can achieve fixed switching frequency and zero error tracking at low sampling rates.

To promote the realization of the "dual carbon" goal, the distributed new energy in different regions of the virtual power plant is coordinated and optimized through lowcarbon power generation. A multi region virtual power plant coordinated and optimized scheduling technology based on the Grey Wolf Optimization Algorithm is proposed. Firstly, construct an operational optimization model with the best economic benefits, connecting virtual power plants in different regions with distributed new energy, and jointly scheduling wind and solar power generation units and carbon capture units; Secondly, due to the difficulty in solving, strong nonlinearity, and high dimensionality of the constructed model, the advantages of the Grey Wolf Optimization Algorithm such as high search efficiency, fast convergence speed, and few optimization parameters are utilized to optimize the model. At the same time, an improved Grey Wolf Optimization Algorithm is proposed to improve the algorithm's global optimization ability and solve the problem of premature and local optima in the later stage of the algorithm; Finally, through simulation verification, the proposed method can achieve optimal scheduling of virtual power plants in different regions, reducing carbon emissions and net costs.