Incorporating energy storage system into grid connected photovoltaic system with the application of wireless sensor network

Abstract

The growing use of residential photovoltaics (PV) poses several challenges for distribution system operators. Technical challenges arise when excess PV energy is integrated into the low-voltage utility grid. This leads to current and voltage increase. On the other hand, economic challenges show up when the energy storage system (ESS) is fully charged without a mechanism in place to store excess energy. Residential ESS not only mitigates these challenges but also assists in mitigating irregular PV power issues. Different methodologies have been developed to construct models assessing the techno-economic benefits of integrating ESS with PV systems. Nevertheless, these approaches do not account for PV-ESS operation with feed-in-tariff (FiT) incentives, different electricity rate structures, and the lifetime model of ESS. This paper proposes an energy management strategy (EMS) suitable for rooftop PV installations. The EMS incorporates a temperature model and optimizes scheduling to regulate the power output of the PV/ESS in response to changes/needs in electricity demand. In addition, tactical information exchange between the ESS and the utility grid is facilitated by Link 16 wireless sensor networks. Furthermore, hybrid gated recurrent unit (GRU) and graph convolutional network (GCN) model is introduced for predicting PV power and electricity load. Consumer electricity data, PV data, and tariffs are utilized in the proposed study to examine the impact of MESS on electricity charges. The impact of multi-ESS (MESS) on the objective function is evaluated using the optimization model. Simulation results on a typical system show that the proposed approach significantly enhances battery performance. By the end of the year, the SOC reaches 84.78 % for the proposed MESS and 84.64 % for the DESS. The results reveal that the proposed approach yields 22 % cost savings compared to a dual ESS system and 24 % cost reduction compared to a single ESS. Moreover, the elevated packet delivery ratio values corresponding to higher signal-to-noise ratio levels signify enhanced reliability and superior communication performance. The impact of jamming attacks on battery consumption employing additive white Gaussian noise (AWGN) is compared with AWGN and single-tone, and AWGN and multi-tone. The results show that when dealing solely with AWGN, less power is needed to filter out noise.