Optimizing CHP-based multi-carrier energy networks with advanced energy storage solutions.

Area of Science:

• Energy Systems Engineering
• Optimization Algorithms
• Network Control

Background:

• Large-scale combined heat and power (CHP) networks are crucial for energy efficiency.
• Integrating multi-carrier energy (MCE) systems with diverse storage technologies presents operational challenges.
• Stabilizing energy supply and managing energy flows in interconnected electrical and gas networks requires advanced control strategies.

Purpose of the Study:

• To develop an advanced operational framework for CHP-based MCE networks with integrated electrical and gas energy storage systems (EESS and GESS).
• To design a novel coordinated controller for managing energy flows and stabilizing supply to CHP units.
• To optimize the operational performance of these networks, focusing on cost reduction and enhanced system flexibility.

Main Methods:

• A novel coordinated controller was developed to manage charging/discharging cycles of EESS and GESS.
• The operational optimization problem was solved using a parameter-free Teaching-Learning-Based Optimization (TLBO) algorithm.
• The framework was validated on a testbed integrating the IEEE 14-bus power system, Belgian natural gas network, and district heating subsystems.

Main Results:

• The integration of EESS reduced total operation costs by approximately 0.075%.
• The integration of GESS led to a slight increase in operation costs by approximately 0.024%.
• The proposed framework demonstrated significant improvements in operational cost efficiency, energy flow stability, and network resilience.

Conclusions:

• The developed framework effectively integrates and coordinates multi-energy storage in CHP-based MCE networks.
• The findings contribute to the development of more sustainable, flexible, and resilient energy systems.
• The TLBO algorithm provides an efficient method for solving complex operational optimization problems in MCE networks.