Deterministic Robust Planning and Probabilistic Techno-Economic Assessment of a Sector Coupled Community Energy System

This paper proposes a systematic approach to examine the techno-economical impact of a renewable based prosumer centric hybrid energy system (HES) connected to the local distribution grid. Systematic approach includes deterministic and probabilistic framework for optimal planning, scheduling, management, and assessment of HES. During the initiation stage, major components of the HES are identified as solar, Li-ion batteries and biomass to enable cross-sectoral participation whereas electric vehicle (EV) charging to promote smart electric-mobility. Through stage-wise data collection, processing, and training, hourly solar-radiation historical data is classified into three seasons through kernelized space-vector approach. Similarly, probabilistic assessment of EV arrival rate and charging requests are estimated through historical charging data. Based on initial system architecture, deterministic approach includes objective function formulation and its optimal solution through aggrandized class topper optimization (ACTO), particle swarm optimization (PSO), and JAYA algorithm. The objective function is realized through upper-level economic problem and lower-level technical sub-problems. Analyzing the convergence characteristics, the optimal system dynamics and capacity estimated using ACTO is considered as the best with a minimum cost of energy of Rs. 4.235 per kWh. Finally, the probabilistic assessment is carried out through three levels of uncertainty to examine the system cost variations in each scenario.

Automated summary

This paper proposes a systematic approach to examine the techno-economical impact of a renewable based prosumer centric hybrid energy system (HES) connected to the local distribution grid. The approach includes deterministic and probabilistic framework for optimal planning, scheduling, management, and assessment of HES. The research uses solar, Li-ion batteries, biomass, and EV charging as major components and conducts probabilistic assessment and optimization using historical data. The results show that the optimization solution using ACTO algorithm is the best, with a minimum energy cost of 4.235 Rs.