Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

A robust optimization approach for optimal load dispatch of community energy hub

The vehicle-to-grid (V2G) technology is an effective and economic solution to enable the integration of electric vehicles (EVs) into power grids. As an effort to present the state of the art in relevant fields, the power interaction mode between EVs and power grids, and the scheduling methodology for the V2G implementation are overviewed comprehensively in this paper. To be specific, technical requirements, grid impacts and battery degradations regarding each power interaction mode are discussed; the operating processes of centralized and decentralized scheduling approaches are introduced and compared. More importantly, it is noted that few attentions are given to the solving algorithms of programming models used for optimizing the scheduling strategy of EVs and few works examine their feasibilities to acquire the optimal charging strategies especially for a large number of EVs. In this context, this paper also illuminates the mathematical foundation on optimization techniques for the optimal V2G strategy, and provides new insights for evaluating these models.

Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation

Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization

Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.

Xinhui Lu

Papers

A hybrid deep learning model for short-term PV power forecasting

A robust optimization approach for optimal load dispatch of community energy hub

Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles

Multi-objective optimal dispatch of microgrid containing electric vehicles

A robust optimization approach for coordinated operation of multiple energy hubs