A model predictive control based peak shaving application of battery for a household with photovoltaic system in a rural distribution grid. (December 2018)

• Record Type: Journal Article
• Authors: Dongol, Deepranjan; Feldmann, Thomas; Schmidt, Michael; Bollin, Elmar
• Is Part Of: Sustainable energy, grids and networks. Volume 16(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-12
• Subjects: Model predictive control -- Peak shaving -- Battery storage; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2018.05.001
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation. Highlights: Use of MPC for a single electricity price scenario. Peak shaving in the PV feed-in and the load demand. Maximum utilization of the self-produced electricity. Mixed Integer Quadratic Programming formulation of the optimal control problem. MPC implementation scheme in the laboratory. Preliminary experiments using the MPC.