Distribution-Driven Optimization for Heat Pump and EV Hosting Capacity in LV Networks

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5 pound in BSSA, 4.5 pound in MRFO, and 5.5 pound in CGO, the proposed DDAO method achieves a lower total price of 3.2 pound. This suggests that the DDAO method is more cost-effective than the existing techniques.