Model Predictive Optimization for Energy Storage based Smart Grids

• Main Authors: YANG,TSAI-CHEN, 楊采真
• Other Authors: HSIUNG,PAO-ANN
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/8a93p8

碩士 === 國立中正大學 === 資訊工程研究所 === 104 === A smart grid is usually composed of multiple micro-grids, each of which includes renewable power generators, energy storage systems (ESS) and power consumers. In most smart grids, ESS is used as an uninterruptible power supply (UPS), for power backup purposes. In recent years, ESS has also started to act as an active electricity supplier so as to minimize overall electricity costs in a smart grid. However, the ESS lifetime decrease with each charging/discharging. For a fair evaluation of the overall cost of electricity in smart grids, besides electricity trading costs, the cost incurred due to Loss of Life (LoL) via ESS usage should also be considered. The target problem solved in this Thesis is to find a near-optimal schedule which includes the electricity usage of the smart grid such as electricity trading and ESS usage. As a solution to the target problem, the Thesis proposes a Model Predictive Optimization (MPO) method for distribution management in smart grids. Future energy states are predicted using an Autoregressive Integrated Moving Average (ARIMA) model. According to the prediction results, a near-optimal ESS usage is found through the Genetic Algorithm (GA). A distribution management system architecture is also designed to support multiple micro-grids for a global scheduling with tradeoff between electricity trading and ESS LoL within a smart grid. The experimental results demonstrate two benefits of our proposed methods, namely reducing overall cost and ESS LoL. For predicting demand loads and renewable energy generation, the error rate of prediction model is less than 10%, and the average execution time of prediction method is 3.13 seconds. By applying the proposed MPO method, the overall cost in a smart grid is reduced by reducing both electricity cost and the ESS LoL cost. Compared to the MPC look-ahead dispatch method, MPO achieve an overall cost saving and ESS LoL reduction by 0.85% and 12.18%, respectively.