Hanlin Dong

Multi-port converter (MPC) gets growing applications due to its advantages of flexible control and high power density. But the fluctuation low frequency DC bus current ripples could degrade the operational stability of the system. In this paper, a frequency-adaptive repetitive control method is proposed to eliminate the effect of variable low frequency ripples. The virtual variable sampling (VVS) instead of traditional fixed sampling is used to achieve integral sampling number during the variable ripple period. Then a frequency-adaptive repetitive control strategy is adopted to suppress the ripples. Compared to the traditional methods designed for reducing fixed frequency ripples, the proposed one can eliminate the fluctuation low frequency ripples, showing better suppression ability and dynamic response speed. Moreover, the mechanism of DC chain current ripple is also analyzed, and the current ripple frequency identification method is improved. Finally, simulations and experiments are performed to verify the proposed strategy and analysis.

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Large electrolytic capacitors used in grid-connected and stand-alone photovoltaic (PV) applications for power decoupling purposes are unreliable because of their short lifetime. Film capacitors can be used instead of electrolytic capacitors if the energy storage requirement of the power conditioning units (PCUs) is reduced, since they offer better reliability and have a longer lifetime. Film capacitors have a lower capacitance than electrolytic capacitors, causing enormous frequency ripples on the DC-link voltage and affecting the standalone photovoltaic system’s dynamic performance. This research provided novel direct sliding mode controllers (DSMCs) for minimizing DC-link capacitor, regulating various components of the PV/BES system that assists to manage the DC-link voltage with a small capacitor. DSMCs were combined with the perturb and observe (P&O) method for DC boost converters to increase the photovoltaic system’s dynamic performance, and regulate the battery’s bidirectional converter (BDC) to overcome the DC-link voltage instabilities caused via a lower DC-link capacitor. The system is intended to power both AC and DC loads in places without grid connection. The system’s functions are divided into four modes, dependent on energy supply and demand, and the battery’s state of charge. The findings illustrate the controllers’ durability and the system’s outstanding performance. The testing was carried out on the MT real-time control platform NI PXIE-1071 utilizing Hardware-In-The-Loop experiments and MATLAB/Simulink.

This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.