Data-driven short-term load forecast method and demand-side management for distribution network

With the development of the power grid, the smart grid makes the system more intelligent, efficient, sustainable and reliable with integrated Information and Communication Systems. Moreover, the data from the advanced system provides chances to utilise machine learning algorithms to improve the system operation further. In addition, the load profile is undergoing altering and becoming more unpredictable because of the increase in smart home appliances, EVs, e-heating systems, energy storage devices, etc. These factors bring more challenges and opportunities for the future power system to improve operational efficiency and demand response quality. In this regard, considering load forecasting is crucial in smart distribution networks for utility companies, especially those employing the demand-side management alternatives, the short-term load forecast could be more accurate and robust, evolve for future load forecasting purposes, and reveal its value in improving demand-side management qualities. This research proposes a novel Dynamic Adaptive Compensation-Long Short-Term Memory (DAC-LSTM) forecast method. This method uses high time-resolution datasets and LSTM networks as fundamental to give short-term time-series load forecast results. The proposed method dynamically distinguishes the peak and off-peak hours and improves the forecast accuracy separately. For DNOs, the forecast errors, especially during peak hours, lead to penalties to start/stop backup generations or adjust the distribution schedules, and this will result in more operational costs. Further, the proposed method introduces a novel DAC block to compensate for forecast errors according to the error trend calculated by historical forecast and actual load, then applying dynamic adaptive parameters. The greater the current-to-average forecast error ratio or the closer the forecast step to the present time stamp, the larger the compensation factors. Besides, the factor caps are set to prevent the model from over-compensation conditions. The sensitivity of introduced parameters is analysed, providing the performance of the developed method under different parameter values. Afterwards, the proposed method is evaluated with six case studies, including varying the forecast steps (compared with LSTM and ARIMA), limiting the size and length of the training datasets (compared with ARIMA and Persistence), comparing with other state-of-art methods qualitatively, and comparing with ELEXON UK domestic load forecast results. Finally, the advantages of the DAC-LSTM method are validated, including providing accurate short-term load forecast results during peak and off-peak simultaneously, with a shorter length of or fewer households’ historical datasets, and compared with existing transmission network forecast methods. The system operators, like DNOs, can reduce the operational cost with more accurate forecasts during peak hours as well as own more load curtailment potentials during the off-peak hours. Additionally, more contributions, including the future bottom-up load scenarios establishment and the improved Stackelberg Game demand response for end-user utility bill reductions, will help system operators develop suitable DSM alternatives and tariffs based on more realistic and accurate analysis. To be more specific, first, based on the Ten Year Network Development Plan (TYNDP) 2018 and the UK government reports, bottom-up load profiles are designed and generated for the UK distribution network for the scenario years 2020, 2030 and 2040. The DAC-LSTM method is evaluated with these scenario profiles, yielding up to 0.989 and 3.79% (measured in R2 and MAPE) forecast accuracy, for various levels of electric vehicle and e-heating penetration when compared with the ARIMA and Persistence methods. Second, a DSM alternative is built based on a Stackelberg Game to reduce the consumers’ utility bill, which considered the forecast error as a constraint. In this game, given that the consumer offers maximum controllable power to the operator, the game achieves the Stackelberg Equilibrium while maximising the operator’s revenue and supplying the necessary power to consumers. The case study demonstrates that when considering forecast errors in demand response strategies, higher forecast accuracies reduce the electricity bill up to 10.4% in an ideal circumstance. The improved Stackelberg Game makes the forecast error one primary constraint that most existing DSM alternatives lack. This proves the value of utilising state-of-art forecast methods in the deployment of DSM alternatives

Graphene-Boron Nitride Heterostructure Based Optoelectronic Devices for On-Chip Optical Interconnects

Graphene has emerged as an appealing material for a variety of optoelectronic applications due to its unique electrical and optical characteristics. In this thesis, I will present recent advances in integrating graphene and graphene-boron nitride (BN) heterostructures with confined optical architectures, e.g. planar photonic crystal (PPC) nanocavities and silicon channel waveguides, to make this otherwise weakly absorbing material optically opaque. Based on these integrations, I will further demonstrate the resulting chip-integrated optoelectronic devices for optical interconnects. After transferring a layer of graphene onto PPC nanocavities, spectral selectivity at the resonance frequency and orders-of-magnitude enhancement of optical coupling with graphene have been observed in infrared spectrum. By applying electrostatic potential to graphene, electro-optic modulation of the cavity reflection is possible with contrast in excess of 10 dB. And furthermore, a novel and complex modulator device structure based on the cavity-coupled and BN-encapsulated dual-layer graphene capacitor is demonstrated to operate at a speed of 1.2 GHz. On the other hand, an enhanced broad-spectrum light-graphene interaction coupled with silicon channel waveguides is also demonstrated with ∼0.1 dB/μm transmission attenuation due to graphene absorption. A waveguide-integrated graphene photodetector is fabricated and shown 0.1 A/W photoresponsivity and 20 GHz operation speed. An improved version of a similar photodetector using graphene-BN heterostructure exhibits 0.36 A/W photoresponsivity and 42 GHz response speed. The integration of graphene and graphene-BN heterostructures with nanophotonic architectures promises a new generation of compact, energy-efficient, high-speed optoelectronic device concepts for on-chip optical communications that are not yet feasible or very difficult to realize using traditional bulk semiconductors

Enhanced Photodetection in Graphene-Integrated Photonic Crystal Cavity

We demonstrate the controlled enhancement of photoresponsivity in a graphene photodetector by coupling to slow light modes in a long photonic crystal linear defect cavity. Near the Brillouin zone (BZ) boundary, spectral coupling of multiple cavity modes results in broad-band photocurrent enhancement from 1530 nm to 1540 nm. Away from the BZ boundary, individual cavity resonances enhance the photocurrent eight-fold in narrow resonant peaks. Optimization of the photocurrent via critical coupling of the incident field with the graphene-cavity system is discussed. The enhanced photocurrent demonstrates the feasibility of a wavelength-scale graphene photodetector for efficient photodetection with high spectral selectivity and broadband response

High-Responsivity Graphene-Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal-oxide-semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cut-off at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we conclude that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron-phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers