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Abstract Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

Abstract Hydrogen fuel cell vehicles (HFCVs) are promising environmentally friendly technologies that are increasingly supported by governments around the world. In the integrated energy system (IES) of this paper, by adding the method of electrolysis to hydrogen production, the electrical energy is converted into chemical energy, which cannot only absorb the new energy with fluctuating output but also meet the use of more and more HFCVs. Aiming at the newly introduced hydrogen energy conversion and consumption pathways, this paper uses a statistical analogy to model HFVCs, taking into account the energy conversion efficiency and the needs of vehicle owners. Using the GUROBI solver in the YALMIP toolbox, the IES optimization problem is formed into a MILP problem and solved, and the corresponding co‐optimization scheduling method is given. Based on meeting the needs of HFCVs and electric vehicle (EV) owners, the system can schedule the number and timing of charging and discharging of EVs and the power of hydrogen production from electrolyzers on the time scale, and finally smooth the total power curve. Simulation results show that the proposed collaborative optimal energy scheduling method can meet the current demand for new energy in the system and improve the economy of the IES.

Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior works, the forecasting problem was formulated as a regression problem, however, which most cannot guarantee that the forecasted outputs is nonnegative. To solve this problem, we proposed a novel probabilistic model by using nonlinear regression and Bayesian learning method. In the paper, we present the detailed theoretical derivations and interpretations. The simulation results show the validity and feasibility of the proposed algorithm by comparing with the traditional SVM algorithm.