Search

A droop-free distributed energy storage control strategy based on the diffusion algorithm is proposed to address the inability of droop control to simultaneously achieve bus voltage stability and accurate power distribution among energy storage systems when the line impedance is mismatched and bus voltage is inconsistent. First, the diffusion algorithm is applied to the distributed estimation of direct current (DC) microgrid to obtain the global average, and the difference between the voltage rating and the average voltage is used as a compensation term to restore the bus voltage deviation. Then, in order to achieve the accurate distribution of power between energy storage systems with different rated capacities and different states of charge (SOC), a standard power of energy storage is designed and the difference between the standard power and the average standard power is used as a compensation term for the equilibrium of SOC between energy storage systems. Finally, a model based on RT-LAB is built to verify the effectiveness of the designed control strategy in four different operating modes. The experimental results show that the proposed control strategy can achieve the bus voltage recovery and the accurate distribution of energy storage power in the isolated DC microgrid.

Virtual power plant (VPP) with a carbon capture system provides a new path to improve energy efficiency and achieve the target of carbon peaking and carbon neutrality. At the same time, flexible coordination of multiple uncertainties in the VPP system is a key premise to realize low-carbon operation of the system. A low-carbon economic dispatch model of VPP considering ladder-type carbon trading in multiple uncertainties is proposed. The carbon capture system and demand response are modeled, and a carbon trading mechanism is introduced into the optimal dispatch model to build a ladder-type carbon trading cost model to restrict system carbon emissions. A variety of uncertain factors in VPP is modeled, including wind power generation, photovoltaic, load and electric vehicle, and a low carbon economic dispatch model of VPP is established considering opportunity constraints. The uncertainty of electric vehicles is dealt with by using adjustable robust optimization. Based on the sequence operation theory, the uncertain model with opportunity constraints is transformed into a mixed integer linear programming model. The decision optimization technology CPLEX solution is used to verify the effectiveness of the proposed model in an actual VPP example.

New energy power plants generate vast amounts of operational data and experience highly variable operating conditions， posing a significant challenge when it comes to diagnosing faults in generator sets. For this reason， a fault diagnosis model based on an improved SVM （support vector machine） algorithm is introduced. Firstly， the concept and principle of SVM for new energy power plants are analyzed. A multivariate SVM classifier is used to optimize the SVM. Secondly， the fault signal extraction method and fault characterization method of photovoltaic power stations and wind farms are studied. Moreover， a fault diagnosis model is proposed. Finally， sample data are obtained from new energy power plants， and an improved SVM fault diagnosis model based on decision level fusion is built. Additionally， the model is trained using fault feature vectors. The results show that the fault diagnosis accuracy for photovoltaic power plants reaches 97.5%， while the fault diagnosis accuracy for wind farms reaches 98.09%， which verify the accuracy of the method.

A carbon emission monitoring method based on carbon emission flow is proposed for the integrated electricity and gas systems （IEGS）. This method distributes carbon emissions on the generation side to various node loads and branch power， thereby achieving accurate tracking and traceability of carbon emissions. Firstly， the principle of carbon emission flow is introduced. Subsequently， the application of carbon emission flow in the power grid and gas network is investigated. A carbon emission monitoring model for the power grid and gas network is established， and a carbon emission monitoring method is proposed based on the theory of carbon emission flow. Finally， simulation results demonstrate that this carbon emission monitoring method for the integrated energy system can simultaneously elucidate the temporal and spatial carbon footprint of the system， overcoming the limitations of conventional carbon measurement methods that tend to be overly macroscopic.

Fully tapping into the role of user side regulation helps reduce the energy cost of integrated energy system (IES). Demand response (DR) and electric vehicle (EV) as schedulable resources on the user side are important regulation means for optimal scheduling of IES. However, in the actual operation process, due to the influence of load aggregator (LA) economic incentives and EV travel, the economic impact of the uncertainty of user side DR on IES cannot be ignored. Based on this, this paper proposes an IES optimal operation model considering the robust stochastic optimization of EV and the participation of LA which considers the energy purchase cost of IES from the superior network and the economic loss cost of LA. First, the response rate model and EV uncertainty model based on economic incentive are constructed. Then, the robust optimization model of EV is built, and the load demand of EV travel uncertainty is analyzed. Finally, a simulation example is given to analyze the impact of user DR uncertainty and EV uncertainty on IES operation economy and power balance. The simulation results show that considering the uncertainty of DR and EV can optimize the economic operation of IES and reduce the economic loss of LA and the total cost, which verifies the effectiveness and economy of the proposed models.