At present, research on the combination of quantum computing and artificial intelligence algorithms applied to the fault location of distribution networks is very sufficient. Because this type of algorithm usually uses the switching function in logical form, it leads to numerical instability and is difficult to expand to the problem of a larger scale distribution network. Therefore, in this study, we propose a hybrid classical quantum computing architecture model for the fusion of quantum annealing and fault location in distribution networks. Firstly, an algebraic objective function suitable for D-wave quantum computer was constructed according to the distributed idea, and the feasibility of the model was verified by using quantum platform. Then, a decoupling optimization model was proposed to improve the poor accuracy of the model in the case of multiple faults, and the decoupling optimization operation was completed by classical calculation and verified experimentally on the quantum platform. Then the original model and decoupled optimization model were applied to fault scenarios of 20-node single power distribution network, 33-node single power distribution network, 9-node distribution network with distributed generation source(DG), 16-node distribution network with DGs, and 33-node distribution network with DGs. The performance of the two models was compared in five aspects: the accuracy of fault location, quantum bit resource consumption, model parameter range, running time and fault tolerance performance. Finally, the quantum classical hybrid decoupling optimization model was compared with the simulated annealing algorithm, the quantum annealing algorithm and the improved quantum annealing algorithm in three aspects of accuracy, running time and quantum bit consumption resources. Both the comparison between the two models constructed in this paper and the comparison between the hybrid model and the classical model can reflect that the decoupled optimization model is committed to realizing the fault location of a larger scale distribution network with fewer quantum bits, and can play the advantages of the classical quantum hybrid architecture model.

As the last link of the power system, the distribution network is responsible for ensuring stable power consumption and improving power quality. Therefore, a more reliable and fast fault section location(FSL) method is essential for the stable operation and optimization of distribution networks. In this context, this paper adopts an effective method to apply the quantum annealing algorithm(QA) based on the quantum tunneling mechanism to the distribution network fault section location problem. A quantum Hamiltonian function consisting of potential and kinetic energy terms is constructed based on the theoretical knowledge of QA. Among them, FSL objective function is mapped to the potential energy term, and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect and approximate or even reach the global optimal solution. Based on the quantum Hamiltonian function construction, this paper modifies some parameters in the QA framework to propose an improved quantum annealing algorithm(IQA) to improve the accuracy. In the two test systems of IEEE 33-node distribution network and IEEE 33-node distribution network with distributed generation sources(DGs), QA and IQA are compared and analyzed with other intelligent algorithms using the average number of iterations and localization accuracy as indicators. We find that QA is more likely to obtain the global optimal solution compared with the simulated annealing algorithm(SA). IQA can search for faulty sections with 100% accuracy and the least number of average iterations in both single power distribution networks and distribution networks containing DGs. Under the scenarios of fault signal distortion and increasing fault sections, IQA shows superb competitive advantages by exhibiting good fault tolerance performance, global optimal search capability and stability.

Energy storage equipment can play a unique advantage to recycle the regenerative braking energy of metro, of which flywheel energy storage system (FESS) has a good application prospect. At present, the control topology of FESS is two-level converter, and the DC voltage of FESS is mostly DC 750 V. High speed maglev-flywheel energy storage system (HSM-FESS) is used to recycle the braking energy in transit transportation. In order to achieve stable operation of the HSM-FESS, the control strategy based on the voltage threshold of the DC1500 V traction grid is adopted. At the same time, the Neutral Point Clamped (NPC) three-level control method of high speed maglev-permanent magnet motor (HSM-PMM) based on square wave modulation-two phase conduction (SWM-TPC) is proposed. A starting and braking simulation model of metro with HSM-FESS is built in MATLAB/Simulink, and the relevant simulation verification is completed.

The majority of scholars believe that Shor’s algorithm is a unique and powerful quantum algorithm for RSA cryptanalysis, so current postquantum cryptography research has largely considered only the potential threats of Shor’s algorithm. This paper verifies the feasibility of deciphering RSA public key cryptography based on D-Wave, which is the second most effective RSA attack method after Shor’s algorithm. This paper proposes the influence of different column methods on the final integer factorization, puts forward a new dimension reduction formula, simplifies the integer factorization model based on quantum annealing, simulates it with the qbsolv quantum computing software environment provided by D-Wave, and factors the integer 1630729 (an 11-bit prime factor multiplied by an 11-bit prime factor). The research results show that choosing an appropriate number of columns and column width in the binary integer factorization multiplication table is very important for studying the optimization ability of the quantum annealing algorithm. In fact, Science, Nature, IEEE Spectrum, and the National Academies of Sciences (NAS) are consistent in asserting that the practical application of general-purpose quantum computers is far in the future. Therefore, although D-Wave computers were initially mainly purchased by Lockheed Martin, Google, etc., for purposes such as image processing, machine learning, combinatorial optimization, and software verification, post quantum cryptography research should further consider the potential of the D-Wave quantum computer in deciphering RSA cryptosystems in the future, and a discussion of this potential is one of the contributions of this paper.

The optimal planning in integrated electricity and natural gas systems (IEGS) generally considers future load growth, but at present most planning is only a single stage. It is a one-time allocation and investment of all equipment at the beginning of the planning period, which is easy to cause problems such as redundant allocation in the early planning period, insufficient capacity and aging of equipment in the later planning period. In addition, most natural gas systems (NGS) adopt the steady-state model, ignoring the dynamic characteristics. Therefore, this paper proposes the multistage optimal location and capacity allocation of gas turbines (GT) in IEGS and considers the dynamic characteristics of NGS, to meet the future load growth demand and improve the economic efficiency of IEGS. The planning model established with the life cycle cost as the objective function is a mixed integer linear programming (MILP) model. By solving the MILP model, the most optimal location and capacity of gas turbines in each planning stage are obtained.