[Paper Review] Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system

Sleiman Farah, Gorm Bruun Andresen|arXiv (Cornell University)|Sep 5, 2023

Hybrid Renewable Energy SystemsEnergy16 references3 citations

TL;DR

This paper proposes an investment-based optimization method for sizing and improving emerging grid-connected energy storage technologies in hybrid renewable systems. By decoupling linear system optimization from investment-driven parameter improvement, it identifies discharge efficiency as the most impactful parameter for thermal, pumped thermal, molten salt, and adiabatic compressed air energy storage—followed by discharge and storage capacity costs, with charge capacity and efficiency being least critical.

ABSTRACT

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimizing the the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this, a novel investment-based optimization method is proposed. The method involves linear optimization of the hybrid renewable energy system and subsequent investment optimization, accounting for diminishing improvements per investment. Applied to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, the results show that enhancing discharge efficiency is most valuable for all technologies. Reducing discharge capacity costs and energy storage capacity cost can also become important. Charge capacity cost and charge efficiency are found to be of lesser significance. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimization method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

Motivation & Objective

• Address the challenge of non-linear system modeling when optimizing energy storage design parameters for emerging technologies.
• Identify the most impactful technical and cost parameters to prioritize for R&D investment in grid-connected hybrid renewable systems.
• Provide a strategic roadmap for developers to allocate limited resources effectively across emerging storage technologies.
• Overcome limitations of traditional optimization by incorporating diminishing returns on investment in parameter improvements.
• Assess the sensitivity of optimal improvement pathways to varying operational conditions, including solar/wind capacity factors and grid connection requirements.

Proposed method

• Decompose the optimization into two stages: first, linear optimization of the hybrid renewable energy system to determine optimal system configuration.
• Second, apply investment-based optimization that models improvement of storage parameters with diminishing returns using a concave function.
• Use a parameterized investment function where the rate of improvement per unit investment is governed by slope (α) and maturity (β) parameters.
• Integrate the investment model with system-level optimization to rank the value of improving each storage parameter (e.g., efficiency, cost).
• Apply the method across four emerging storage technologies: thermal energy storage (TES), pumped thermal energy storage (PTES), molten salt energy storage (MSES), and adiabatic compressed air energy storage (aCAES).
• Evaluate results under diverse scenarios, including varying solar and wind capacity factors, grid capacity factors, and investment sensitivity parameters.

Experimental results

Research questions

• RQ1 Which energy storage design parameters yield the highest system-level value when improved through investment?
• RQ2 How does the order of importance of storage parameters vary across different emerging technologies under identical system conditions?
• RQ3 How do varying grid connection capacity factors and renewable resource availability affect the optimal improvement pathway?
• RQ4 How do investment sensitivity parameters (α and β) influence the prioritization of parameter improvements?
• RQ5 What is the relative impact of improving discharge efficiency versus capacity costs in reducing system-level costs and enhancing grid integration?

Key findings

• Discharge efficiency is the most valuable parameter to improve across all four energy storage technologies, delivering the highest system-level value per unit of investment.
• The second most impactful parameters are the costs of discharge capacity and energy storage capacity, which significantly influence system economics and scalability.
• Charge capacity cost and charge efficiency are the least impactful parameters, with minimal effect on system performance or cost reduction.
• The optimal improvement pathway is sensitive to grid connection capacity factor: higher requirements amplify the value of improving discharge efficiency.
• Sensitivity analysis shows that investment parameters α and β significantly affect the prioritization order, with higher α favoring faster improvements and higher β increasing potential for future gains.
• The method successfully identifies technology-specific R&D roadmaps, enabling developers to focus on high-impact improvements and reduce reliance on lithium-ion batteries.