# Capacity optimization configuration and multi-dimensional value evaluation of integrated energy system with power-to-hydrogen

## TL;DR

This paper introduces a new system to evaluate and optimize hydrogen production equipment in energy systems for better efficiency and sustainability.

## Contribution

A novel multi-dimensional evaluation system and improved optimization algorithm for power-to-hydrogen equipment configuration in integrated energy systems.

## Key findings

- The IBBPSO algorithm outperforms PSO, GWO, and WOA by 9.8%, 33.57%, and 17.7% respectively in solving the optimization model.
- The optimal P2H equipment configuration is achieved at 50 MW capacity.
- A comprehensive evaluation system using AHP-entropy weight method is developed for P2H value analysis.

## Abstract

The research on the value evaluation system of power-to-hydrogen (P2H) equipment configuration in integrated energy systems is of great value for optimizing resource allocation, improving energy utilization efficiency, and promoting clean energy technology development. However, there is no comprehensive evaluation system for evaluating P2H equipment configuration in integrated energy systems. Therefore, a multi-dimensional value evaluation system is proposed to realize the thorough evaluation of P2H equipment with different capacity configurations in the integrated energy system. Initially, a mathematical model considering flexibility benefit, new energy consumption benefit, economic benefit, and environmental benefit is established to maximize the comprehensive benefits brought by P2H equipment to the integrated energy system, and the model is solved using an improved backbone particle swarm optimization (IBBPSO) algorithm; subsequently, a multi-dimensional value evaluation system based on the analytic hierarchy process (AHP) -entropy weight method is constructed, and the value of P2H equipment with different capacity configurations in the integrated energy system is compared and analyzed when the comprehensive benefit is optimal. The experimental results show that the IBBPSO algorithm exhibits better performance in solving the optimization model. Compared to PSO, IBBPSO, GWO, and WOA algorithms, it improves by 9.8%, 11.09%, 33.57%, and 17.7%, respectively. The optimal solution is achieved when the P2H equipment is configured to 50 MW.