Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Multi-stage Robust Scheduling for Community Microgrid with Energy Storage

Energy storage devices can effectively balance the uncertain load and significantly reduce electricity costs in the community microgrids(C-MGs) integrated with renewable energy sources. Scheduling of energy storage is a multi-stage decision problem in which the decisions must be guaranteed to be nonanticipative and multi-stage robust(all-scenario-feasible). To satisfy these two requirements, this paper proposes a method based on a necessary and sufficient feasibility condition of scheduling decisions under the polyhedral uncertainty set. Unlike the most popular affine decision rule(ADR) based multistage robust optimization(MSRO) method, the method proposed in this paper does not require the affine decision assumption, and the feasible regions(the set of all feasible solutions) are not reduced, nor is the solution quality affected. A simple illustrative example and real-scale scheduling cases demonstrate that the proposed method can find feasible solutions when the ADR-based MSRO fails, and that it finds better solutions when both methods succeed. Comprehensive case studies for a real system are performed and the results validate the effectiveness and efficiency of the proposed method.

Model Predictive Control Based Real-time Energy Management for Hybrid Energy Storage System

An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.