Flexible operation of shared energy storage at households to facilitate PV penetration

This paper proposes a new methodology to enable high penetration of photovoltaic (PV) generation in low voltage (LV) distribution networks by using shared battery storage and variable tariffs. The battery installed at customer premises is shared between customers and local distribution network operators (DNOs) to achieve two goals-minimizing energy costs for customers and releasing distribution network constraints for DNOs. The two objectives are realised through a new concept - “charging envelope”, which dynamically allocates storage capacity between customers and the DNO. Charging envelope first reserves a portion of storage capacity for network operator's priority to mitigate network problems caused by either thermal or voltage limit violation in order to defer or even reduce network investment. Then, the remaining capacity is used by customers to respond to energy price variations to facilitate in-home PV penetration. Case study results show that the concept can provide an attractive solution to realise the dual conflicting objectives for network operators and customers. The proposed concept has been adopted by the Western Power Distribution (UK) in a smart grid demonstration project SoLa Bristol.</p

Flexible operation of shared energy storage at households to facilitate PV penetration

This paper proposes a new methodology to enable high penetration of photovoltaic (PV) generation in low voltage (LV) distribution networks by using shared battery storage and variable tariffs. The battery installed at customer premises is shared between customers and local distribution network operators (DNOs) to achieve two goals-minimizing energy costs for customers and releasing distribution network constraints for DNOs. The two objectives are realised through a new concept - “charging envelope”, which dynamically allocates storage capacity between customers and the DNO. Charging envelope first reserves a portion of storage capacity for network operator's priority to mitigate network problems caused by either thermal or voltage limit violation in order to defer or even reduce network investment. Then, the remaining capacity is used by customers to respond to energy price variations to facilitate in-home PV penetration. Case study results show that the concept can provide an attractive solution to realise the dual conflicting objectives for network operators and customers. The proposed concept has been adopted by the Western Power Distribution (UK) in a smart grid demonstration project SoLa Bristol.</p

A quantum circuit simulator and its applications on Sunway TaihuLight supercomputer

Classical simulation of quantum computation is vital for verifying quantum devices and assessing quantum algorithms. We present a new quantum circuit simulator developed on the Sunway TaihuLight supercomputer. Compared with other simulators, the present one is distinguished in two aspects. First, our simulator is more versatile. The simulator consists of three mutually independent parts to compute the full, partial and single amplitudes of a quantum state with different methods. It has the function of emulating the effect of noise and support more kinds of quantum operations. Second, our simulator is of high efficiency. The simulator is designed in a two-level parallel structure to be implemented efficiently on the distributed many-core Sunway TaihuLight supercomputer. Random quantum circuits can be simulated with 40, 75 and 200 qubits on the full, partial and single amplitude, respectively. As illustrative applications of the simulator, we present a quantum fast Poisson solver and an algorithm for quantum arithmetic of evaluating transcendental functions. Our simulator is expected to have broader applications in developing quantum algorithms in various fields.Comment: 21 pages, 9 figure

Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.Comment: 12pages, 6 figure

Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this letter, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first develop a new way of performing the sine transformation, and based on it the algorithm is optimized by reducing the depth of the circuit from n2 to n. Then, we analyze the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error mitigation and error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.Comment: 20 pages, 9 figure