PV-OPTIM: A Software Architecture and Functionalities for Prosumers

Abstract

The future development of the energy sector is influenced by Renewable Energy Sources (RES) and their integration. The main hindrance with RES is that their output is highly volatile and less predictable. However, the utility of the RES can be further enhanced by prediction, optimization, and control algorithms. The scope of this paper is to disseminate a smart Adaptive Optimization and Control (AOC) software for prosumers, namely PV-OPTIM, that is developed to maximize the consumption from local Photovoltaic (PV) systems and, if the solar energy is not available, to minimize the cost by finding the best operational time slots. Furthermore, PV-OPTIM aims to increase the Self-Sustainable Ratio (SSR). If storage is available, PV-OPTIM is designed to protect the battery lifetime. AOC software consists of three algorithms: (i) PV Forecast algorithm (PVFA), (ii) Day Ahead Optimization Algorithm (DAOA), and (iii) Real Time Control Algorithm (RTCA). Both software architecture and functionalities, including interactions, are depicted to promote and replicate its usage. The economic impact is related to cost reduction and energy independence reflected by the SSR. The electricity costs are reduced after optimization and further significantly decrease in case of real-time control, the percentage depending on the flexibility of the appliances and the configuration parameters of the RTCA. By optimizing and controlling the load, prosumers increase their SSR to at least 70% in the case of small PV systems with less than 4 kW and to more than 85% in the case of PV systems over 5 kW. By promoting free software applications to enhance RES integration, we estimate that pro-environmental attitude will increase. Moreover, the PV-OPTIM provides support for trading activities on the Local Electricity Markets (LEM) by providing the deficit and surplus quantities for the next day, allowing prosumers to set-up their bids.