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.