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

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

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]

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

A Novel Load Shedding Strategy Combining Undervoltage and Underfrequency with Considering of High Penetration of Wind Energy

Low carbon emission is one of the main targets for smart grid planning. To achieve this goal, intermittent energies such as wind and solar are integrated to the power systems increasingly. However, this may create huge challenges to the power system operators for balancing the generation and demand at all times and guaranteeing the system reliability at the same time. With high penetration of renewable energies, power system operators are compelled to curtail the loads when the power system cannot rely on power from renewable energies continuously due to strong dependence on the environment. As an important defense to protect the power network from collapsing and to keep the system integrating, load shedding has been designed and proposed for decades. However, most of the shedding schemes consider the load increasing instead of lack of generation. This paper applies a load shedding scheme with considering both voltage and frequency changes when the generation is inadequate since the power system cannot obtain the expected renewable generation and renewable energies are highly penetrated into the grid.State Grid Corporation of Chin