The following are the outputs of the research conducted to solve engineering problems using AI and optimization-based approaches.
Authors:Leloko J. Lepolesa, Kayode E. Adetunji, Khmaies Ouahada, Zhenqing Liu, Ling Cheng
Abstract
The transition from the Internal Combustion Engine Vehicles (ICEVs) to the Electric Vehicles (EVs) is globally recommended to combat the unfavourable environmental conditions caused by reliance on fossil fuels. However, it has been established that the charging of EVs can destabilize the grid when they penetrate the market in large numbers, especially in grids that were not initially built to handle the load from the charging of EVs. In this work, we present a dynamic EV charging pricing strategy that fulfils the following three objectives: distribution network-level load peak-shaving, valley-filling, and load balancing across distribution networks. Based on historical environmental variables such as temperature, humidity, wind speed, EV charging prices and distribution of vehicles in different areas in different times of the day, we first forecast the distribution network load demand, and then use deep reinforcement learning approach to set the optimal dynamic EV charging price. While most research seeks to achieve load peak-shaving and valley-filling to stabilize the grid, our work goes further into exploring the load-balancing between the distribution networks in the close vicinity to each other. We compare the performance of Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms for this purpose. The best algorithm is used for dymamic EV pricing. Simulation results show an improved utilization of the grid at the distribution network level, leading to the optimal usage of the grid on a larger scale.
Authors:Leloko J. Lepolesa, Kayode E. Adetunji, Khmaies Ouahada, Zhenqing Liu, Ling Cheng
Abstract
The adoption of electric vehicles (EVs) promises a reduction of carbon emissions and a crucial step towards a cleaner environment. While more EVs are expected to replace internal combustion engine vehicles to operate on the road worldwide, their adoption is inhibited by factors such as high power demand. Unregulated or poorly regulated charging of EVs can cause grid instability, especially in grids that were not initially designed to handle the charging of EVs. This calls for leveraging the available grid resources to control the charging of EVs in a manner that ensures optimal grid operation. This work proposes a distribution network-level dynamic pricing strategy for charging EVs to optimally utilize the distribution network and balance the load between residential and commercial/industrial distribution networks. Different EV charging probabilities that cause the EV load to differ from the optimal state with a mean average percentage error (MAPE) as high as 30% are explored. Simulation results show that with the dynamic pricing strategy as an incentive to the EVs users, EVs charging load will contribute to the optimal grid resources utilization.
Authors:Leloko J. Lepolesa, Shamin Achari, Ling Cheng
Key components of the project:
Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyperparameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.