Energy System Model Research Articles

NFDI4Energy Case-Study: Comparative Analysis and Visualisation of Long-Term Energy System Scenarios

Analysis and comparison of energy system scenarios provide valuable insights into potential transformation pathways. These studies on long-term developments can serve as new inputs for scientific research and decision-making processes, providing policymakers and other stakeholders with the necessary guidance to achieve sustainable energy systems. Generally, such scenarios are derived from energy system models which often seek a cost-optimal system design under a variety of boundary conditions, ranging from technical constraints to limits of land availability or a cap on overall greenhouse gas emissions [1]. For Germany, several larger energy system scenario studies have been published, addressing the goal of carbon neutrality in 2045 as prescribed in the German climate protection act [2]. These studies show differences in their specific methodology, sector representation, parameter settings or, more generally, overall scenario narratives. This diversity represents a challenge regarding the comparability of these studies, and consequently the ability to identify consensus and controversies in their findings. Often only limited access to data for parameter settings and scenario results is provided. Almost always the data is presented in different detail and formats, thus imposing further barriers for comparison and usability for the scientific community [3]. As one of the three use cases applied in Task Area 6 of the NFDI4Energy research project, we aim to address this challenge by providing transparent and open comparative information and data on long-term energy system scenarios. Selected scenarios for the transition towards a climate-neutral Germany will be annotated with terms form the Open Energy Ontology (OEO) [4]. The comparison is building on an already existing database infrastructure from the Open Energy Platform (OEP) [5]. Existing concepts for qualitative and quantitative comparisons will be used and improved to cover a wide range of existing energy system studies.

Robust inter-reliant resilience of cyber-physical smart grids

Nowadays, information and cyber technologies have been integrated into traditional energy systems to enhance their energy performance. While this integration can improve the cost-benefit of smart grids, it may not fully capture the benefits in extreme conditions or during cascading outages.Electrical and cyber-physical energy systems have become more interconnected due to integration, which has also resulted in electrical energy systems (EESs) being more vulnerable to extreme natural disasters. The other system can be infiltrated by an outage in one system, leading to a cascading outage process. To tackle these challenges, a robust model of cyber-physical energy systems constrained by resilience considerations has been proposed to model the interdependences of EESs and cyber-physical systems (CPS) (i.e., EES & CPSs) under extreme natural disasters. The objective function of the proposed model is to improve cost-benefit and energy performance under extreme natural disasters. In this paper, it is explained how the energy efficiency for electrical energy systems can be improved under extreme natural disasters by the proposed operation processes for bulk integrated EESs & CPS. However, the highly nonlinear integrated EESs & CPS model is linearized to moderate the model complexity. Finally, the effectiveness of the proposed integrated EESs & CPS model is evaluated by investigating the modified IEEE-30 bus energy system.

A flexible approach to GIS based modelling of a global hydrogen transport system

Beside the production of hydrogen, the transport is a key component to a successful development of a decarbonized global hydrogen economy. To answer the question, where the required amounts of hydrogen will be produced and along which routes and in which form they will be transported, a sector integrated global hydrogen transport system model needs to be established. This paper provides a flexible method and tool for geographical structuring of sector integrated energy transport systems. It enables the adaptability to existing energy system models and allow for flexible adjustment of the geographical scope. Combined with the integration of pipeline and ship transport it is a novelty, that enables the setup of energy transport systems to perform structured investigation of origin and transport in a future hydrogen economy. The model is validated by comparison with similar studies regarding the directions of hydrogen transport flows.

Hamiltonian Neural Network 6-DoF Rigid-Body Dynamic Modeling Based on Energy Variation Estimation

This study introduces a novel deep modeling approach that utilizes Hamiltonian neural networks to address the challenges of modeling the six degrees of freedom rigid-body dynamics induced by control inputs in various domains such as aerospace, robotics, and automotive engineering. The proposed method is based on the principles of Hamiltonian dynamics and employs an inductive bias in the form of a constructed bias for both conservative and varying energies, effectively tackling the modeling issues arising from time-varying energy in controlled rigid-body dynamics. This constructed bias captures the information regarding the changes in the rigid body’s energy. The presented method not only achieves highly accurate modeling but also preserves the inherent bidirectional time-sliding inference in Hamiltonian-based modeling approaches. Experimental results demonstrate that our method outperforms existing techniques in the time-varying six degrees of freedom dynamic modeling of aircraft and missile guidance, enabling high-precision modeling and feedback correction. The findings of our research hold significant potential for the kinematic modeling of time-varying energy systems, parallel system state prediction and control, inverse motion inference, and autonomous decision-making in military applications.

Overcoming challenges in life cycle assessment of smart energy systems – A map of solution approaches

Current research underlines the benefits of digitalization to integrate renewable energy sources (RES) efficiently and to coordinate generation and supply. This development towards smart energy systems (SES) includes emerging use cases at the distribution grid level, e.g., smart charging strategies for electric vehicles or energy management systems in buildings. An environmental assessment of such, however, is challenging. The standardized Life Cycle Assessment (LCA) approaches seem insufficient since SES are emerging systems at the interface of digitalization and energy with multiple intended and unintended environmental effects. This article aims to derive solution approaches for LCA professionals that investigate use cases enabled by information and communication technology (ICT), specifically in SES. To identify challenges during the first two phases of an LCA and solution approaches, we examine seven recent research projects and conduct a real-time Delphi workshop with LCA experts in the field of SES. As a result, the article provides approaches on setting up an LCA, filling data gaps, and methods to draw upon the long-term impacts of use cases in SES. Key findings include a modified taxonomy of potential environmental effects. Results show that systemic effects in SES have potentially the highest impact on the LCA, followed by effects caused by user behavior. Assessing these effects of ‘higher-order’ poses an increased complexity. Determined solution approaches include combining the standardized LCA with energy system modeling or surveys on user behavior. Overall, this article reveals orientation strategies and recommendations to guide LCA professionals toward a holistic environmental assessment of use cases in SES.

Enhancing energy system resilience using tidal stream energy

During autumn 2021, high global gas demand led to a 400% increase in imported wholesale gas prices in the UK. This coincided with (i) an extended period of low wind resource, where wind turbines provided 60% less energy than typical levels for the time of year, (ii) low nuclear power availability and (iii) depleting domestic natural gas reserves. These simultaneous events led to UK wholesale electricity prices more than doubling. The 2022 military and political impact of Russia’s invasion of Ukraine has led to additional increases in imported fuel prices. These types of disruption have the potential to cause damage worth years of sector revenues [1]. In the future, energy resilience challenges must be overcome whilst also achieving net-zero to limit global warming to within 1.5 degrees Celsius above pre-industrial levels, whilst electricity demand at least doubles.
 This research investigates the role of tidal stream energy in enhancing energy system resilience during periods when the energy system is highly stressed, such as during autumn 2021 in the UK. The concept of resilience refers to the ability of the system to survive strong and unexpected disruptions and to recover quickly afterward [1]. This research is motivated by the acknowledgement that the commercial viability of marine power relies on the sector being able to explain how it can provide contributions beyond decarbonisation [2].
 We build on an energy system modelling case study of the Isle of Wight [3], using the EnerSyM-RC energy system model. Wind data between 2012 – 2020 is analysed to identify and characterise periods of low wind resource. These data form the inputs to the EnerySyM-RC modelling. The model then applies a simple brute force optimisation method to investigate the most suitable mix of solar PV, offshore wind, tidal stream and energy storage that enhances system resilience on the Isle of Wight.
 Initial results show that the adoption of tidal stream energy compliments solar and wind generation by reducing reliance on reserve energy (e.g. imported gas) during high-stress periods. Reduced reliance on imported energy is also achieved because of the distinct generation pattern of tidal power (i.e. 4 periods of power generation, each separated by slack tide, every day), which enhances the utilisation of local energy storage.
 References
 [1] Jasiunas J, et al., 2021, Energy system resilience – A review, Renewable and Sustainable Energy Reviews, 150:111476
 [2] Pacific Northwest National Laboratory, 2021, Grid Value Proposition of Marine Energy: A Preliminary Analysis, PNNL -31123, Technical report
 [3] Coles DS et al., 2023, Impacts of tidal stream power on energy system security: An Isle of Wight case study, Applied Energy, 334:120686

Cooperative-game-based joint planning and cost allocation for multiple park-level integrated energy systems with shared energy storage

Under the carbon-neutrality goal, joint planning along with a fair cost allocation of shared energy storage becomes a promising solution to boosting the economic benefits and energy utilization efficiency of multiple park-level integrated energy systems. Hence, a joint planning and cost allocation method for multiple park-level integrated energy systems with shared energy storage is proposed in this paper to obtain optimal joint planning and cost allocation strategies of park-level integrated energy systems with shared energy storage. First, a joint planning model for park-level integrated energy systems with shared energy storage is established to minimize the total investment and operation costs of the grand cooperative coalition by considering the optimal construction time sequence of devices. Then, an ex-post cost allocation algorithm based on an improved Nucleolus method is proposed to realize a fair cost allocation for the joint planning of park-level integrated energy systems with shared energy storage. Finally, case studies on a grand cooperative coalition with six park-level integrated energy systems are performed for verifying the effectiveness of the proposed method. Simulation results demonstrate that the total investment and operation costs of park-level integrated energy systems are reduced, and the renewable energy consumption levels of park-level integrated energy systems are improved. Besides, the proposed cost allocation algorithm has less computing time, and better coalition rationality and stability compared to other similar algorithms.

Integration of wave energy into Energy Systems: an insight to the system dynamics and ways forward

Wave energy is a rich and highly accessible renewable energy resource, that has largely been under-developed. Studies from the sector have tried to show the potential of benefits wave energy in “simple cases” or via small hybrid systems, the large scale incorporation of wave energy has not yet been fully investigated. Our approach uses a fully dynamic climate driven energy system model, which has undergone modifications to include wave energy converters and their associated dependencies.
 This study explores the system dynamics and important elements that will be used for large scale wave energy integration; in a fully coupled European Energy System. We explore the cost pathways of different wave energy converters, the impact of climate data, and the impact of transmission capacity expansion under cost-optimal configurations of a multi-renewable European power system. From this preliminary approach we aim to provide the boundary conditions, and assumptions that will govern the integration of wave energy into the European Energy System up to 2050.

Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs' system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs' system-level impact and that a naïve aggregation of individual profiles leads to an overestimation of the fleet's flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2kWh/EV, only 10% of the result of a naïve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.

Design, modeling and simulation of nuclear-powered integrated energy systems with cascaded heating applications

Nuclear-renewable integrated energy systems (IES) consist of a variety of energy generation and conversion technologies and can be used to meet heterogeneous end uses (e.g., electricity, heat, and cooling demands). In addition to supply-demand balance, end-use heat demands usually require heat supply of certain temperature ranges. The effective and efficient utilization of heat produced within an IES is, therefore, a critical challenge. This paper examines design options of an IES that includes heating processes of multiple temperature grades. We investigate a cascaded design configuration, where the remaining residual heat after high-grade heating processes [e.g., hydrogen production through high-temperature steam electrolysis (HTSE)] is recovered to meet the low-grade heating needs [e.g., district heating (DH)]. Additionally, a thermal energy storage system is integrated into the DH system to address the imbalance between heat supply and demand. This paper primarily focuses on the design and modeling of the proposed system and evaluates its operation with a 24-h transient process simulation using a DH demand profile with hourly resolution. The results indicate that the residual heat from the HTSE exhaust is insufficient for the DH demand, and additional topping heat directly from the reactor process steam is needed. Furthermore, the inclusion of thermal energy storage within the DH system provides the necessary balance between thermal generation and demand, thereby ensuring a consistent rated temperature of the DH supply water. This approach helps minimize the control actions needed on the reactor side.

How, where, and when to charge electric vehicles – net-zero energy system implications and policy recommendations

A coordinated Charging Infrastructure (CI) strategy could accelerate the adoption of Battery Electric Vehicles (BEVs). Policymakers need to understand the tradeoffs between several types of CI developments. To support decision-makers, we apply the Swiss TIMES Energy system Model, which we extended with heterogeneous consumer segments with four trip types and several CI options. The novelty of this work lies in the interplay of such method advancements, representing BEV charging options with various CI types that can be accessed based on their location type at an hourly intraday temporal resolution. In explorative scenario analyses, we evaluate the effects of CI on car fleet deployment and their energy system implications in achieving net-zero CO2 emissions in Switzerland by 2050. Our analysis shows that the BEV share makes up 39%–77% of the fleet by 2050, and each BEV needs about 5 kW total charging capacity, split into 1.6–2.6 BEVs per private charger and 18–25 BEVs per public charger. Providing overnight charging access through private home chargers or public chargers in residential areas facilitates a 12%–20% increased BEV penetration compared to the reference scenario. For consumers without private home charging, improved public CI in non-residential areas increases BEV uptake by 24%. While low-power slow CI is cost-effective at home, high-power fast CI in commercial areas supports integration of solar PV. We highlight the need for coordinated CI policies and provide a variety of policy options based on our analysis and international insights.

Thermal design and heat transfer optimisation of a liquid organic hydrogen carrier batch reactor for hydrogen storage

Liquid organic hydrogen carriers (LOHCs) are considered a promising hydrogen storage technology. Heat must be exchanged with an external medium, such as a heat transfer fluid, for the required chemical reactions to occur. Batch reactors are simple but useful solutions for small-scale storage applications, which can be modelled with a lumped-parameter approach, adequately reproducing their dynamic performance. For such reactors, power is consumed to circulate the external heat transfer fluid and stir the organic liquid inside the reactor, and heat transfer performance and power consumption are two key parameters in reactor optimisation. Therefore, with reference to the hydrogen release phase, this paper describes a procedure to optimise the reactor thermal design, based on a lumped-parameter model, in terms of heat transfer performance and minimum power consumption. Two batch reactors are analysed: a conventional jacketed reactor with agitation nozzles and a half-pipe coil reactor. Heat transfer performance is evaluated by introducing a newly defined dimensionless parameter, the Heat Transfer Ratio (HTR), whose value directly correlates to the heat rate required by the carrier's dehydrogenation reaction. The resulting model is a valid tool for adequately reproducing the hydrogen storage behaviour within dynamic models of complex and detailed energy systems.

Modeling and simulation of a small-scale hybrid energy system using genetic algorithm for Yolan-Bayara Village, Bauchi State

To mitigate the downsides of conventional energy generation systems, such as contaminants in the air and the release of greenhouse gases that contribute to environmental degradation, hybrid energy systems (HES) are absolutely essential for revitalizing areas that are rural or remote to obtain favorable technical and economic benefits. It is very difficult to establish a hybrid energy system's ideal size while still trying to meet the reliability requirements and consumer demands for electricity for powering their homes, schools, hospital, and businesses. Using a Genetic Algorithm (GA) optimization approach, this abstract describes a study on the modeling and simulation of a small-scale hybrid system that integrates renewable energy sources with energy storage for Yolan-Bayara village Bauchi state. Photovoltaic (PV) panels, wind turbines, and a battery storage system make up the system. The battery storage system enables energy storage and retrieval when necessary, while the PV panels and wind turbines generate sporadic electricity. A mathematical model is created that includes several system parameters, such as environmental factors, and energy demand profiles in order to arrive at the best sizing approach. However, conventional optimization approaches may have trouble locating the overall optimal solution due to the complexity of the system and the uncertainty surrounding renewable energy sources. A Genetic Algorithm is used to solve this problem. To ensure effective energy use and low operating costs, the GA successfully adjusts the control settings to changing environmental circumstances and energy demand profiles. The outcomes show how GA-based optimization methods can improve the functionality and financial viability of small-scale hybrid systems. Such technologies could be extremely important for promoting green energy options and reducing climate change.

(Invited) An Industrial Perspective on Electrochemistry and the Energy Transition

As wind and solar become an increasingly larger share of primary energy, the use of electrochemical storage and conversions is growing. Traditionally, carbon-based fuels such as oil, coal and natural gas have been globally traded and served as energy storage media for the power and transportation sectors. With the growth of lower-carbon energy technologies, particularly wind and solar power, effective use of renewable electrons requires high-performing, cost-effective solutions for storage and distribution services that are analogous to traditional fuels. Electrochemical energy storage technologies are increasingly used for a range of applications from kW to GW scales, with this deployment momentum built from improved performance and declining costs.This talk will address where multiple types of storage exist in the historical & current energy system, their scales, and the roles of storage in the emerging energy transition from the perspective of an international energy company. Our energy storage and conversion research & development over the past decade will also be discussed, with a focus on current and emerging electrochemical systems designed for use in mobility, the power sector, and industry. Systems of active research include metal anodes used with high voltage cathodes and high energy density stationary batteries. This work has been driven by business challenges, with a range of modeling approaches used to generate insights that complement theory & experiment. Techniques include large-scale energy system modeling, modeling of power market operations, technoeconomic analyses to guide cost & performance targets of new technologies, and new ways to capture and predict physical phenomena that underpin device life and performance. Finally, commercial operation examples of energy storage operation will be provided, along with challenges still to be resolved through improved system understanding across scales.

Impact of Automation on Enhancing Energy Quality in Grid-Connected Photovoltaic Systems

Rapid growth in the integration of new consumers into the electricity sector, particularly in the industrial sector, has necessitated better control of the electricity supply and of the users’ op-erating conditions to guarantee an adequate quality of service as well as the unregulated dis-turbances that have been generated in the electrical network that can cause significant failures, breakdowns and interruptions, causing considerable expenses and economic losses. This research examines the characteristics of electrical variations in equipment within a company in the industrial sector, analyzes the impact generated within the electrical system according to the need for operation in manufacturing systems, and proposes a new solution through automation of the regulation elements to maintain an optimal system quality and prevent damage and equipment failures while offering a cost-effective model. The proposed solution is evaluated through a reliable simulation in ETAP (Energy Systems Modeling, Analysis and Optimization) software, which emulates the interaction of control elements and simulates the design of electric flow equipment operation. The results demonstrate an improvement in system performance in the presence of disturbances when two automation schemes are applied as well as the exclusive operation of the capacitor bank, which improves the total system current fluctuations and improves the power factor from 85.83% to 93.42%. Such a scheme also improves the waveform in the main power system; another improvement result is when simultaneously operating the voltage and current filter together with the PV system, further improving the current fluctuations, improving the power factor from 85.83% to 94.81%, achieving better stability and improving the quality of the waveform in the main power grid.

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

The impact of large-scale thermal energy storage in the energy system

In the last decade, pit thermal energy storage (PTES) systems have been used as a large-scale heat storage solution in district heating systems due to their low specific investment cost and high storage efficiency. Despite the existing knowledge on thermal energy storage (TES) technologies, their economic and environmental impacts have not been quantified in the literature, and very few studies have studied PTES as part of the energy system. For this reason, the energy system model Balmorel was used to quantify the impact of TES on the energy system, particularly PTES, and compare it to the tank thermal energy storage (TTES) alternative. The investigation was focused on Denmark and its neighboring countries. It was found that it was only the energy systems using TES that could achieve carbon neutrality by 2050. The main reason was the added flexibility due to the energy storage that allowed the system to have a 35% higher PV capacity, 10% higher wind capacity, and lower levels of curtailment. Additionally, systems with TES had 2.4 €/MWh lower average heat price (with 24% lower peak price). When comparing PTES with TTES, it was found that PTES systems were more advantageous, achieving a 1.5 €/MWh lower average price of heat.

Investigating biodiversity trends in different mitigation scenarios with a national integrated assessment model

ABSTRACT The inclusion of Sustainable Development Goals (SDGs) in climate mitigation pathways is critical and can be reached by assessing their consequences through the deployment of appropriate indicators to that end. Integrated Assessment Models (IAMs) are important tools for understanding possible impacts caused by adopting new policies. We investigate terrestrial biodiversity trends (life on land: SDG 15) for three different climate mitigation scenarios for Brazil: (1) A scenario compatible with a world that maintains its current policies with current deforestation rates in the Amazon and the Cerrado biomes; (2) A scenario in which Brazil fulfils its Nationally Determined Contribution (NDC); and (3) A scenario compatible with a world that limits warming to 1.5°C. We use the Brazilian Land-Use and Energy System model (BLUES), a national IAM, to show the implications of the transitions involved in the above-mentioned scenarios for the country up to 2050. We conduct a post-processing analysis using consolidated biodiversity indicators to emphasize how different IAM greenhouse gas (GHG) emissions mitigation solutions present distinct positive and negative potential impacts on biodiversity in Brazil. However, our analysis does not consider the impacts associated with climate change, but only the risks imposed by mitigation policies. Our results indicate that biodiversity loss decreases in the scenarios from (1) to (3), implying that stronger climate change mitigation actions could result in smaller biodiversity losses. We conclude that Brazil has the opportunity to align its biodiversity and climate goals through nature-based solutions (NBS), such as forest conservation, restoration, pasture recovery, and the use of crop-pasture and agroforestry systems.

Comparative analyses of solar photovoltaic, wind turbine, and solar photovoltaic and wind turbine hybrid systems: Case study of Thailand

Thailand is rapidly moving toward sustainable electricity generation by employing renewable energy systems, particularly solar photovoltaic systems and wind turbines. While various aspects of renewable energy resources in Thailand have been represented in the literature, the modeling of renewable energy systems, particularly hybrid solar photovoltaic and wind energy systems, have rarely been studied. To cover this gap, four renewable energy modeling tools, SAM, PVsyst, HOMER, and RETScreen, were used to model solar photovoltaic systems (PVS), wind turbine systems (WTS), and solar photovoltaic-wind turbine hybrid systems (PVWHS) for residential applications in Thailand. The developed models were validated to ensure their accuracy by comparing the estimated annual electricity generation to the actual energy yield from the real systems. The PVS modeling results from SAM, PVsyst, HOMER, and RETScreen contained errors of 3.6, 6.9, 5.5, and -3.9%, respectively. The WTS modeling results from HOMER and RETScreen had 15.7% and 18.1% deviations from the real system, respectively. The simulation results show that a 6.6 kW PVS is the most economical system in Chaiyaphum with high solar irradiance whereas a PVWHS (6.05 PVS and 1 kW WTS) is the best system in Sa Kaeo with a high wind speed and average solar irradiance. The PVS, WTS, and PVWHS simulations were also performed in seven different regions across Thailand; Chanthaburi, Nakhon Si Thammarat, Kanchanaburi, Ubon Ratchathani, Samutprakan, Chiang Rai, and Maha Sarakham. It was found that only Chanthaburi with a 7.7 m/s average wind speed is suitable to install PVWHS while PVS is the most economical system for the rest of the locations. A sensitivity analysis was conducted to create a diagram that can help to identify the most economically suitable system with respect to solar irradiance and wind speed. The results indicate that PVWHS is a viable option for some regions in Thailand.

Comparative assessment of multi-objective optimization of hybrid energy storage system considering grid balancing

With the accelerated transition towards affordable and clean energy sources, the energy sector is undergoing a structural transformation that has resulted in a further increase in the complexity of energy system planning with rapid changes in techno-economic, environmental, reliability and social constraints. This signifies the consideration of purpose-driven multi-objective functions depending upon the functionality and applicability of the model. However, most of the studies adopt conventional bi-objective optimization either involving techno-economic, reliability and grid balancing parameters but there is a lack of comparative assessment of multi-objective optimization sizing for grid-interactive hybrid renewable energy system (HRES) consisting of short and long-term, battery and pumped hydro storage (PHS), energy storage systems (ESS). This study presents a comparative multi-objective framework to assess bi- and tri-objective function sizing techniques under grid balancing and non-balancing modes, to understand the scope and adaptivity of the modeling process for large-scale grid-interactive HRES. The analysis of results shows that the non-balancing mode underestimates the cost of energy (COE) by 18–30% compared to the grid balancing mode due to smaller decision variable space while long-term ESS dominance is vital for the reduction of grid burden compared to short-term ESS. In terms of configuration, a hybrid ESS system, 0.22 MWh battery, 18.1 MWh PHS, and 5.4 MW PV capacity, is the best optimal configuration in grid balancing mode with the COE, EEI and EII equal to 0.09 $/kWh, 7.5% and 10.5% respectively, whereas higher grid energy mismatch is induced by non-balancing mode with the overestimation of EEI and EII indexes up to 30% and 33% respectively. The environmental analysis shows that the carbon emissions avoided (CEA) are underestimated by 59.1% with the non-consideration of grid balancing. This signifies that the adaptive optimization model improves the design and planning process of grid-interactive HRES by capturing larger uncertainties related to COE, grid balancing, and CEA with changes in the system and ESS sizing. Overall, this analysis provides a purpose-driven perspective to energy modelers and policymakers for the energy system modeling process of grid-interactive HRES.