Cost benefit analysis and data analytics for renewable energy and electrical energy storage

To accommodate with the global increase in the deployment of solar photovoltaic (PV) and energy storage system (ESS), a deterministic approach for sizing PV and ESS with anaerobic digestion biogas power plant; to meet a load demand will be presented in this plenary session. This aim is to maximize the sizing of PV to increase the security of energy supply. Energy economics for ESS will be a focus. Case study based on real-life data will be used to demonstrate the validity of the new approach

Application of Big Data in Smart Grid

In this paper, the state-of-The-Art of big data is reviewed. Challenges, opportunities and tools will be discussed. Some emerging technologies will be looked to promote big data applications. The applications of big data in smart grid in some countries will be summarized too.State Grid Corporation of Chin

IEEE

To achieve net-zero emissions economy, the transition to online entertainment and retail, aging populations, urban population growth, and pressures on public finance have created huge interests for human to run cities differently and smartly. A term titled smart city is created which is considered as an idealistic city, where the quality of life for citizens is greatly improved by utilizing information and communication technology (ICT), new services, and new city infrastructures to efficiently achieve the value, such as sustainable and resilient development. The eco-sustainable method has to be used in several aspects, such as energy, mobility, environment, and social services. Research and development in smart cities is expanding exponentially. SMC is one of the core sponsors of the IEEE Smart Cities

Interactive energy management for networked microgrids with risk aversion

Department of Finance and Education of Guangdong Province 2016[202]: Key Discipline Construction Programme, China; Guangdong Foshan Power Construction Corporation Group Co. Ltd., Foshan, China

Multi-View Collaborative Representation Classification

Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]

Agent-based modeling and neural network for residential customer demand response

In this paper, both bottom-up and top-down models for demand response with agent-base approach and neural networks have been investigated. Simulations have been carried out with practical load data from the UK and Canada. Results show that each approach has its advantages and disadvantages depending on difference application scenarios. © 2013 IEEE

Reinforcement learning-based profit maximization for battery energy storage systems with electric vehicles and photovoltaic systems

With the growing penetration of renewable energy and the increasing adoption of electric vehicles, the reliable and secure operation of the power grid is facing significant challenges. The inherent randomness and uncertainty associated with renewable energy generation and electric vehicle charging are major factors contributing to grid instability. To address this issue, this paper proposes the utilization of energy storage systems for actively regulating active and reactive power to mitigate grid supplydemand imbalances. Reinforcement learning algorithms are employed to schedule the active and reactive power of the energy storage system, and sensitivity and economic analyses are conducted. The results demonstrate that the integration of energy storage systems into the grid can effectively mitigate the uncertainties and randomness associated with electric vehicle charging and renewable energy generation. The real-time scheduling strategy outputted by the reinforcement learning algorithm reduces computation time, while the economic and sensitivity analyses confirm the profitability and robustness of the energy storage system.EPSRC Supergen Energy Storage Network + Early Career Researcher Committee Fund

Fusing Local and Global Information for One-Step Multi-View Subspace Clustering

Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.This research was funded by National Natural Science Foundation of China under Grant 61903091; Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010801)