Session: 09-06-01: Decarbonization with Hybrid Energy Systems

Paper Number: 167056

Optimal Planning of Flexible Hybrid Renewable Energy Microgrids for Decarbonized Multi-Vector Energy Systems 

This paper addresses the challenge of integrating renewable energy sources (RES) into energy systems by proposing a flexible-by-design strategy for the optimal planning of a hybrid renewable energy system (HRES). The most important challenge in increasing the penetration rate of RES is the fluctuating nature of their products, necessitating the development of HRES with energy storage integration. This paper presents a comprehensive analysis on the optimal planning and design of a decarbonized multi-vector energy system. This study proposes an HRES consisting of wind, photovoltaic (PV) and battery, thermal (heat and cold), and hydrogen energy storage units within a standalone micro-grid framework.

The primary objective is to minimize the total cost of investment and operation while considering technical limitations and ensuring reliable energy supply to meet electrical, heating, and cooling demand over a 25-year planning horizon. The optimization model incorporates constraints related to storage capacity limits, charge/discharge rates, and grid independence, and is solved using the General Algebraic Modeling System (GAMS) with the CPLEX solver. To enhance accuracy, the study employes detailed temporal modeling with 12 sample days per year to capture seasonal and daily variations in energy production and demand.

The research utilizes real-world data recorded for a location in Iran, including hourly measured values of solar power generation and wind speed. Load data is derived from comprehensive regional energy consumption profiles, with electrical load patterns reflecting typical residential and commercial usage in the studied area. The model accounts for projected growth with annual rates of 3% for electrical and cooling loads and 2% for heating loads. Component specifications and economic parameters for renewable generators, energy storage technologies, and conversion systems were sourced from technical literature, peer-reviewed publications, and industry reports from organizations such as the International Renewable Energy Agency (IRENA).

  

Three key scenarios are analyzed: (1) Base case with full demand coverage which is optimal capacity planning without load shedding, (2) Load shedding scenario optimization to assess its impact on system costs, and (3) Worst-case scenario analysis, ensuring system resilience under extreme conditions, where low renewable energy production coincides with peak demand, introducing an additional Loss of Power Supply Probability (LPSP) constraint. The model considers technical constraints such as power balance, state of charge limits for storage systems, and the maximum charge/discharge rates. The objective function minimizes the levelized cost of energy (LCOE) while considering economic constraints such as investment costs, operation & maintenance costs and load shedding penalties, as well as technical constraints such as storage degradation over time, power balance, charge/discharge limits of storage, and grid independence.

 

The Base Case demonstrates that a well-sized combination of wind, PV, battery, and thermal storage can fully meet demand, achieving an LCOE of $0.02614/kWh and a total system cost of $7.87 million over 25 years. The Load Shedding Scenario reduces system costs by 7.48%, highlighting that allowing a small percentage of unmet load (LPSP = 1%) can significantly reduce capital investments in renewable generation and storage capacity. Under the Worst-Case Scenario, findings show that additional battery storage is essential, increasing overall costs by only 0.1% but significantly enhancing system reliability by reducing LPSP by 66%. Additionally, the study examines excess energy production, revealing that 30–35% of potential renewable energy is curtailed. The Load Shedding Scenario reduces curtailment by 23.5%, further improving economic efficiency. Moreover, the study evaluates the role of hydrogen energy storage, considering future cost reductions and technology advancements. Finally, while hydrogen storage was initially excluded due to its high costs, simulations indicate its potential economic feasibility as prices decline. The findings advocate for multi-vector energy storage solutions, combined with renewable energy sources, can significantly enhance system reliability and reduce dependency on fossil fuels, paving the way for sustainable energy systems in micro-grid applications.

Presenting Author: Alireza Asadbeygi University of Pittsburgh

Presenting Author Biography: PhD candidate in Mechanical Engineering, with years of experience in biomedical engineering research.

Authors:

Maryam Hamidi Carnegie Mellon University
Alireza Asadbeygi University of Pittsburgh