Energy System Model Research Articles

Modeling of the temperature field in the soil massive for different operating modes of the ground source heat pump

The paper considers modeling the influence of the operating modes of the ground source heat pump for heating and air conditioning systems using geothermal source of energy from borehole on the temperature of the soil formation around the borehole with the account of climate conditions of Ukraine. The proposed methodology of modelling of the ground source heat pump with the account of reversible direction of heat flow for heating and cooling work modes contributes to the field of energy system models. The ultimate goals are to obtain criterion dependencies for calculating the operating parameters of a geothermal borehole and to develop a methodology for designing geothermal boreholes taking into account their long-term operation in a cyclic mode. The idea behind the method allows high spatial and temporal resolution as well as the inclusion of the technical details of the power system and formation the temperature of the soil massive around the borehole for the climate conditions of the Ukraine and South-East Regions of Europe. The novelty of this method is the usage of a parametric approach is chosen to analyze different reversible operating modes of the ground source heat pump for heating and cooling. This provides insights on the systematic effects of ground source heat pump operation with regeneration with limitation on reaching negative temperatures at the bottom of the borehole, which can lead to ice formation, an increase in the volume of soil massive during freezing and destruction of pipelines. The article describes the change in the temperature of the soil mass under the operating conditions of a heat pump in two modes - stationary and with alternating "heat supply - cooling (air conditioning)" for long-term operation. Based on the obtained results it was revealed that when a geothermal borehole operates with an alternating direction of heat flow (with regeneration), the long-term consequences of operation associated with the deviation of the soil massive temperature from the background value are reduced. The additional novelty aspect of modelling was obtained for the operating parameters of a geothermal source of energy with the account of longterm reversible operating mode of the heat pump. The energy balance model is well suited for the analysis of temperature in the bottom part of the geothermal boreholes was revealed for heating and air conditioning systems due to the non-stationary thermal loads determined by climatic conditions of the Ukraine and South-East Regions of Europe. The obtained results may serve as a new approach to optimization technical and economic indicators of geothermal boreholes during operation of the ground source heat pump operating with the account of reversible direction of heat flow for heating and cooling work modes to have continuous improvement of energy efficiency

A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment

Brazil presents a high share of renewable electricity generation, mainly due to its favorable hydroelectric potential. However, reports estimate that demand will grow substantially in the long term, requiring a significant diversification of the electricity mix. In this context, this paper analyzes how the environmental impacts of electricity generation tend to change over the horizon 2025–2050 in Brazil and identifies improvement opportunities accounting for environmental and economic factors. To do so, the Open Source Energy Modeling System (OSeMOSYS) is applied to estimate the composition of the electricity mix in 2050. Then, life cycle assessment (LCA) is used to evaluate the associated environmental impacts across ten categories. Lastly, hybrid weighted ɛ-constraint multi-objective optimization (MOO) is employed to evaluate which sources can be used to minimize climate change and the levelized cost of electricity while ensuring that other categories are reasonably restrained. The results indicate that the environmental performance of the Brazilian electricity matrix tends to deteriorate substantially, both when analyzing overall impacts and impacts per kWh. Moreover, the proposed MOO approach suggests that some sources, mainly hydropower, onshore wind, and centralized PV present an all-around economic/environmental performance and might be favorable for expanding the power system.

Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.

Life cycle carbon emission calculation model of energy system in public buildings: A case study in Shanghai

Energy consumption and carbon emissions (CE) in the building sector have grown in importance in relation to global climate change for China's sustainable development. Relatively few research have addressed the embodied CE, despite the fact that many have examined the operational CE from energy system operational use. In order to determine the CE of the energy production, transportation, construction, operation, and recycling stages, we therefore put up an integrated model. An inventory analysis of the energy system's life cycle CE, a scenario forecast of various combinations of building energy system equipment, and a case study of the building energy system of a typical office building in Shanghai's service industry. According to the results, the embodied CE emits 38.01 % of its energy, and the operational CE emits 61.99 %. However, we should not underestimate the significance of the impact of the embodied CE. Among all projection scenarios, the suggested CE share is highest (18.40 %) when the system combination of air-source heat pump units, LED lights, and ground-source heat pump units is implemented. In the end, based on the structural analysis of the embodied CE, low-carbon growth strategies and policy recommendations for Shanghai's service sector will be made.

The role of solar district heat in the energy transition of the German heating sector

This study explores the integration of solar district heating (SDH) into German heating networks, focusing on the energy transition in the heating sector. Utilizing energyPRO software, 8410 scenario variations were analysed, considering collector areas ranging from 1000 to 200 000 m2 and thermal storage capacities from 0 to 100 000 m³. The research identifies the most cost-effective SDH system designs, with solar fractions between 11 % and 18 % and collector area/storage ratios between 4 and 6.7, resulting in levelized costs of heat (LCOH) between 45 and 64 €/MWh. The techno-economic potential of SDH in Germany is estimated at 17.6 TWh by using a spatial analysis model. The paper presents sensitivity analyses and preliminary design equations, contributing to a comprehensive understanding of SDH's role in decarbonizing the heating sector. By integrating spatial data analysis with energy system modeling, the study offers a novel methodological approach, providing insights into the technical and economic feasibility of SDH systems in Northern Germany and similar climatic regions. The findings aim to assist policymakers and decision-makers in evaluating the costs and dimensions of SDH systems based on local heat demands and influencing economic and technical factors.

The role of hydropower in decarbonisation scenarios

An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies.

Modeling of an advanced renewable energy system coupled with hydrogen production and liquefaction for sustainable communities

The primary objective of this study is to develop a sustainable and efficient renewable energy-based multigeneration system that is specifically designed to meet diverse energy demands, such as heat, cooling, electricity and hydrogen. The system comprises several subsystems: a digester for biomass processing, a solar energy collection system, a thermal energy storage system, a multistage Brayton cycle, a steam Rankine cycle with reheat, a double-effect absorption system, a sonic hydrogen production unit, and a multistage hydrogen liquefaction system. The methodology involves conducting energy and exergy analyses, economic evaluation, and environmental impact assessment to determine the system's performance and viability. The energy efficiency results show that the Brayton cycle, steam Rankine cycle and liquefaction system have efficiencies of 30.92 %, 30.18 %, and 64.53 %, respectively. The exergy efficiencies for these subsystems are 50.10 %, 67.21 %, and 11.29 %, respectively. The double-effect absorption system exhibits energetic and exergetic coefficients of performance of 1.68 and 0.64, respectively. The overall energy and exergy efficiencies of the system are 36.41 % and 55.74 %, respectively. The economic analysis indicates a significant revenue potential, with the project reaching its break-even point in 2031 and achieving a net present value of $6.60 million. The results of an environmental impact assessment study highlight a reduction in CO2 emissions compared to conventional systems, emphasizing the system's contribution to sustainable energy solutions.

Hydrogen Infrastructure in the Future CO2‐Neutral European Energy System—How Does the Demand for Hydrogen Affect the Need for Infrastructure?

The fast rollout of hydrogen generation, transport, and storage infrastructure has become a top priority of the European Union and its member states. Planning hydrogen infrastructure requires a thorough understanding of the future role of hydrogen in the energy system. At the same time, there is still huge uncertainty about the future demand for hydrogen and its overall role. An energy systems analysis is conducted with high temporal and spatial as well as technological resolution under alternative demand scenarios. An energy system model is used to optimize the entire European energy system with hourly time resolution and high spatial consideration of renewable energy potentials. The hydrogen demand in the five scenarios ranges from about 700 TWh for mainly industrial uses to 2800 TWh in all sectors in the EU27 + UK by 2050. The results show that an integrated European hydrogen system is a robust element of the cost‐optimal system design in all scenarios. This encompasses flexible electrolyzers at the most favorable wind and solar locations, long‐distance hydrogen transport network, large‐scale seasonal underground storage, and electricity generation for peak demand periods. Conclusions about the individual components are provided and high‐resolution data on hydrogen demand are available for future research.

Future energy landscapes: Analyzing the cost-effectiveness of nuclear-renewable integrated energy Systems in Retrofitting of coal power plants

Coal Power Plants (CPPs), despite their substantial contribution to global energy needs, pose significant environmental concerns due to Greenhouse Gas (GHG) emissions. Thus, the world has started thinking of alternative generation sources to replace CPPs. To replace CPPs, some energy generation resources must come into the scenario that can outshine the advantages of CPPs, such as easy availability of fuel, operational safety, and cost effectiveness. Concerning this matter, nuclear-renewable integrated systems can play a vital role as a potential replacement for CPPs. In this study, we systematically explore the transitional approach from CPPs to advanced energy systems and conduct an exhaustive comparative analysis focusing on three proposed energy system models: Greenfield, Coal-to-Nuclear (C2N), and Coal-to-Integrated Energy Systems (C2IES). Before conducting the comparative analysis, we determine the most feasible coal sites from Alaska, our surrogate location for this study, using a GIS-based nuclear reactor siting tool named “Siting Tool for Advanced Nuclear Development (STAND).” To carry out the comparative analysis among the proposed energy models for the selected coal site, we find out the optimal configuration of each system using a robust and recent nature-based metaheuristic optimization algorithm, Mountain Gazelle Optimization (MGO), complemented by another nature-based metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), for validation. The key data used in this study include solar irradiance, temperature, wind speed, load profiles, and comprehensive cost data for each system component. Since the proposed energy models are highly complex and consider several assumptions, the key research findings are strengthened by performing a comprehensive sensitivity analysis. The base case results show that C2IES can reduce the Cost of Energy (COE) by roughly 65 % compared to Greenfield and C2N and ensure the utmost reliability of the energy system. Although this cost-saving margin contrasts in the sensitivity analysis across a range of scenarios, C2IES consistently offers the most cost-effective solution, highlighting its potential for sustainable energy transition.

Modeling hydrogen markets: Energy system model development status and decarbonization scenario results

Hydrogen can be used as an energy carrier and chemical feedstock to reduce greenhouse gas emissions, especially in difficult-to-decarbonize markets such as medium- and heavy-duty vehicles, aviation and maritime, iron and steel, and the production of fuels and chemicals. Significant literature has been accumulated on engineering-based assessments of various hydrogen technologies, and real-world projects are validating technology performance at larger scales and for low-carbon supply chains. While energy system models continue to be updated to track this progress, many are currently limited in their representation of hydrogen, and as a group they tend to generate highly variable results under decarbonization constraints. The present work provides insights into the development status and decarbonization scenario results of 15 energy system models participating in study 37 of the Stanford Energy Modeling Forum (EMF37), focusing on the U.S. energy system. The models and scenario results vary widely in multiple respects: hydrogen technology representation, scope and type of hydrogen end-use markets, relative optimism of hydrogen technology input assumptions, and market uptake results reported for 2050 under various decarbonization assumptions. Most models report hydrogen market uptake increasing with decarbonization constraints, though some models report high carbon prices being required to achieve these increases and some find hydrogen does not compete well when assuming optimistic assumptions for all advanced decarbonization technologies. Across various scenarios, hydrogen market success tends to have an inverse relationship to success with direct air capture (DAC) and carbon capture and storage (CCS) technologies. While most model-scenario combinations predict modest hydrogen uptake by 2050 – <10 million metric tons (MMT) – aggregating the top 10 % of market uptake results across sectors suggests an upper range demand potential of 42–223 MMT. The high degree of variability across both modeling methods and market uptake results suggests that increased harmonization of both input assumptions and subsector competition scope would lead to more consistent results across energy system models. The wide variability in results indicates strongly divergent conclusions on the role of hydrogen in a decarbonized energy future.

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

The Robust Optimization of Low-Carbon Economic Dispatching for Regional Integrated Energy Systems Considering Wind and Solar Uncertainty

In this paper, a two-stage robust optimization approach is employed to address the variability in renewable energy output by accounting for the uncertainties associated with wind and solar energy. The model aims to achieve a balanced system that is both low-carbon and economically efficient while also being resilient to uncertainties. Initially, a regional integrated energy system model is developed, integrating electricity, gas, and heat. The variability of wind and photovoltaic power outputs is represented using a modifiable uncertainty set. A resilient optimal scheduling model is formulated in two stages, with the objective of minimizing costs under worst-case scenarios. This model is solved iteratively through a column and constraint generation approach. Additionally, the scheduling model incorporates horizontal time shifts and vertical complementary substitutions for carbon trading costs and demand-side loads to avoid excessive conservatism and to manage carbon emissions and energy trading in the regional integrated energy system (RIES). Results show that the two-stage robust optimization approach significantly enhances the system’s resilience to risks and minimizes economic losses. The inclusion of carbon trading mechanisms and the demand response prevents the system from becoming overly robust, which could impede economic growth, while also reducing carbon emissions. The proposed method effectively achieves balanced optimal scheduling for a robust, economical, and low-carbon system.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

Quantifying the impact of energy system model resolution on siting, cost, reliability, and emissions for electricity generation

Quantifying the impact of energy system model resolution on siting, cost, reliability, and emissions for electricity generation

Energy and environmental performance from field operation of commercial-scale SOFC systems

Solid Oxide Fuel Cells (SOFCs) are capable of generating electrical and thermal power with very high conversion efficiency and almost no pollutant emissions into the atmosphere. Despite extensive literature on SOFC-based energy system models and experimental testing at the cell and short-stack level, there is currently a lack of performance data for SOFC modules under actual field conditions. To fill this gap, the present work investigates the energy and environmental performance of six SOFC modules, ranging in size from 10 to 60 kW, over thousands of hours of operation. These systems, supplied by the three leading SOFC manufacturers in Europe, have been installed and operated in different non-residential buildings worldwide, as part of the European Comsos project. The aim of this study is to establish a comprehensive set of field operation data to characterise commercial-scale SOFC systems. Specifically, raw data are processed to derived electrical and thermal efficiency maps, degradation rates and pollutant emissions, including particulate matter (PM), nitrogen oxides (NOx) and carbon monoxide (CO). A comparison with competing technologies is also provided to better highlight the potential benefits of adopting SOFC-based cogeneration systems. Values in the range of 51–61% were found for the system-level electrical efficiency under rated conditions. The electrical efficiency also remained consistently high across a wide modulation range (between 50% and 100% of rated power), with peak values reaching 65%. In addition, promising results were obtained for the average percentage loss in electrical efficiency, with a minimum value of 0.7%/1000 h. Regarding the environmental analysis, NOx and CO emissions were analysed at both constant and variable power output, proving to be impressively low across the entire modulation range. The same applies to PM concentrations, which were below ambient level. Overall, SOFCs demonstrated to be one of the best cogeneration solutions for commercial-scale systems (tens to hundreds of kW in size), from both an energy and environmental perspective. However, further reductions in costs and dedicated financial schemes are necessary for a widespread market penetration.

Comparative analysis of Indonesia’s energy system scenarios: Assessing emissions reduction strategies and cost-effectiveness

Abstract This research conducts a comprehensive comparative analysis of energy system scenarios in Indonesia, examining their emissions reduction strategies and cost-effectiveness. The study utilizes the least-cost optimization approach using LEAP (Long-range Energy Alternatives Planning System) and NEMO (Next Energy Modeling system for Optimization). Four distinct scenarios are evaluated: Business as Usual (BAU), Zero Emissions (ZER), Demand Reduction (DMR), and Net Zero Emission (NZE). The findings suggest that while the ZER scenario may achieve significant emission reductions compared to BAU, with an average of 382.9 million tons CO2e/year, it comes at a higher cost per unit of emission mitigated by the average of 170.8 USD/tons CO2e. On the other hand, the DMR scenario, with its focus on demand-side management, offers a more cost-effective approach to emission mitigation, reaching 81.0 USD/tons CO2e. Combining both strategies, the NZE scenario proves to be the most effective in emission mitigation and cost, with 693.7 million tons CO2e/year and 54.0 USD/tons CO2e, respectively. This research seeks to inform Indonesian policymakers, energy planners, and stakeholders in developing sustainable energy systems that effectively reduce emissions while considering economic feasibility.

Comprehensive and open model structure for the design of future energy systems with sector coupling

Energy system modeling supports the identification of the optimal technology mix to achieve decarbonization targets across multiple sectors. Especially when sector coupling is considered for future technology landscapes, the large solution space leads to a complex optimization problem in terms of computational feasibility and data requirements. The authors identify a research gap in developing an open-source model structure with consideration of the relevant future technologies of power, heat, other conversions, transport, and industry defined with a new level of detail in a sector-coupled energy world and in including detailed insights into the accompanying definition process. A strong focus is set on the transparency and reproducibility of the provided open-source structure and its flexible and consistent application to different framework families to foster the ease of applicability of this work. The paper first gives a detailed description of the model base, including an overview of the model frame definition process, the core adjustments to model sector coupling appropriately, and the measures to make the resulting problem computationally feasible. The core result of this work is the presentation of a detailed model structure to model sector coupling for a German energy system, yielding approximately 2000 processes that characterize the heterogeneous and technology-open landscape of existing and possible future technologies across relevant energy sectors. This supports energy system modelers in understanding and reproducing energy system models based on open-source data and thereby tries to accelerate the research on sector coupling and its role in the energy transition.

Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Energy interactions across integrated energy systems constitute crucial means to enhance energy efficiency and match supply and demand. However, the cooperative operation and benefit distribution among multiple integrated energy systems still need in-depth exploration. To fill this gap, this paper presents an asymmetric Nash bargaining optimization model for multiple integrated energy systems considering peer-to-peer trading. First, a scheduling model for multi-integrated energy systems is formulated considering carbon trading. Then, the model is incorporated into Nash bargaining framework and transformed into the alliance cost minimization subproblem and peer-to-peer trading payment bargaining subproblem. The bargaining power factor is introduced to measure the contribution of participants in energy sharing. The alternating direction multiplier method is utilized to handle the proposed model. Finally, a case study is carried out to validate the validity of the proposed strategy. The results show that compared with the independent operation mode, the performances of three integrated energy systems in collaborative operation mode are enhanced by 11.7 %, 9.0 %, and 4.8 % respectively. The distributed algorithm can reduce the computation time by 30 % and obtain highly efficient solutions while protecting private information of each participant. This research provides support and practical tools for conducting peer-to-peer transactions of multiple integrated energy systems.

The competitive edge of Norway's hydrogen by 2030: Socio-environmental considerations

Can Norway be an important hydrogen exporter to the European Union (EU) by 2030? We explore three scenarios in which Norway's hydrogen export market may develop: A Business-as-usual, B Moderate Onshore, C Accelerated Offshore. Applying a sector-coupled energy system model, we examine the techno-economic viability, spatial and socio-economic considerations for blue and green hydrogen export in the form of ammonia by ship. Our results estimate the costs of low-carbon hydrogen to be 3.5–7.3€/kg hydrogen. While Norway may be cost-competitive in blue hydrogen exports to the EU, its sustainability is limited by the reliance on natural gas and the nascent infrastructure for carbon transport and storage. For green hydrogen exports, Norway may leverage its strong relations with the EU, but is less cost-competitive than countries like Chile and Morocco, which benefit from cheaper solar power. For all scenarios, significant land use is needed to generate enough renewable energy. Developing a green hydrogen-based export market requires policy support and strategic investments in technology, infrastructure and stakeholder engagement, ensuring a more equitable distribution of renewable installations across Norway and national security in the north. Using carbon capture and storage technologies and offshore wind to decarbonise the offshore platforms is a win-win solution that would leave more electricity for developing new industries and demonstrate the economic viability of these technologies. Finally, for Norway to become a key hydrogen exporter to the EU will require a balanced approach that emphasises public acceptance and careful land use management to avoid costly consequences.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.