Mode‐based energy storage control approach for residential photovoltaic systems. Issue 1 (23rd January 2019)

• Record Type: Journal Article
• Authors: Henri, Gonzague; Lu, Ning; Carrejo, Carlos
• Is Part Of: IET smart grid. Volume 2:Issue 1(2019)
• Page Start: 69
• Page End: 76
• Publication Date: 2019-01-23
• Subjects: photovoltaic power systems -- energy storage -- power consumption -- predictive control -- load dispatching; continuous charging -- two‐stage algorithm -- mode selection -- economic model predictive control algorithm -- mode‐based energy storage control approach -- energy storage device -- real‐time control modes -- optimal mode -- residential electricity consumption data -- PECAN Street Project -- forecasting errors -- solar variability -- load patterns -- time 24.0 hour; B8110B Power system management, operation and economics -- B8250 Solar power stations and photovoltaic power systems -- C1330 Optimal control -- C3340H Control of electric power systems; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0159
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : This study presents a novel mode‐based energy storage control approach. Assuming that an energy storage device (ESD) is equipped with a set of predetermined real‐time control modes, the dispatch objective is to select an optimal mode instead of a continuous charging or discharging power value. A two‐stage algorithm is developed for mode selection. In the first stage, a sliding 24 h economic model predictive control algorithm is used to determine the power outputs of the ESD for the next 24 h. In the second stage, the output sign for the next time step determines the class of modes to be elected (charging or discharging). The information from the first stage is used to compute the total cost for each selected mode. The mode with the lowest day‐ahead cost is chosen. The residential electricity consumption data collected in the PECAN Street Project is used in the simulation to validate the performance of the proposed algorithm. Simulation results show that using a mode‐based approach reduces the sensitivity to forecasting errors along with load and solar variability. The algorithm performance is consistent across different load patterns.