Energy System Model Research Articles

Assessing the implications of hydrogen blending on the European energy system towards 2050

With the aim of reducing carbon emissions and seeking independence from Russian gas in the wake of the conflict in Ukraine, the use of hydrogen in the European Union is expected to rise in the future. In this regard, hydrogen transport via pipeline will become increasingly crucial, either through the utilization of existing natural gas infrastructure or the construction of new dedicated hydrogen pipelines. This study investigates the effects of hydrogen blending in existing pipelines on the European energy system by the year 2050, by introducing hydrogen blending sensitivities to the Global Energy System Model (GENeSYS-MOD). Results indicate that hydrogen demand in Europe is inelastic and limited by its high costs and specific use cases, with hydrogen production increasing by 0.17% for 100%-blending allowed compared to no blending allowed. The availability of hydrogen blending has been found to impact regional hydrogen production and trade, with countries that can utilize existing natural gas pipelines, such as Norway, experiencing an increase in hydrogen and synthetic gas exports from 44.0 TWh up to 105.9 TWh in 2050, as the proportion of blending increases. Although the influence of blending on the overall production and consumption of hydrogen in Europe is minimal, the impacts on the location of production and dependence on imports must be thoroughly evaluated in future planning efforts.

A novel energy management method for multiple residential energy systems with energy exchange

Energy saving and emission reduction are the worldwide outstanding problems in the development of economy and society, which lead to wide attention of residential energy management. This paper presents a novel energy management method for multiple residential energy systems with energy exchange based on action-dependent heuristic dynamic programming (ADHDP). The major contributions are displayed as follows: (1) a novel energy management model is established for multiple residential energy systems with real-time electricity price and time-varying residential loads; (2) a novel ADHDP based energy management method is presented to obtain the energy scheduling scheme of multiple residential energy systems; (3) the continuous control action of the overall system is transformed into discrete control space which avoids training the action network. First, the structure and system models of multiple residential energy systems are introduced, and the energy management problem is formulated. Then, a brief introduction of ADHDP method is provided. Based on this, an energy management method is presented and an effective discretization method of control action is developed for solving the presented energy management problem with less computational burden. Simulation results show that the presented method can reduce the energy cost effectively. Finally, the comparison results verify the superiorities over the traditional energy management methods.

Available Energy Analysis of Electric Unmanned Aerial Vehicle Hybrid Energy System

The issue of energy and power systems is the main challenge faced by electric unmanned aerial vehicles for long endurance. Under the power demand conditions of typical mission profiles of electric drones, this paper establishes an energy balance equation between the hybrid energy system power supply of electric drones and the power demand of drones. Considering the impact of operating temperature of each battery on power generation performance, a heat transfer model for solar cells, fuel cells, and lithium batteries was established. The heat transfer process of hybrid energy systems was analyzed. Based on the heat transfer model of hybrid energy systems, the heat transfer changes of solar cells, fuel cells, and lithium batteries were analyzed, and the heat transfer changes of hybrid energy systems were studied. The heat loss changes of each battery subsystem with operating temperature were given. The results show that the hybrid energy system has the best power generation performance at the reference temperature, and changes in operating temperature will cause thermal losses and reduce power generation.

Pathways to Clean Energy Transition in Indonesia’s Electricity Sector with Open-Source Energy Modelling System Modelling (OSeMOSYS)

Responding to the Paris Agreement and climate change mitigation, Indonesia aims to reach net zero by 2060 or sooner. Due to Indonesia’s dependence on coal and growing consumption, alternative sources of clean energy are imperative for meeting its rising energy needs and reducing energy-related greenhouse gas emissions to achieve the energy transition. This project aims to examine Indonesia’s opportunities and potential to achieve low carbon ambition in the energy sector and identify alternative pathways for the energy transition in Indonesia. In this study, the open-source energy modelling system (OSeMOSYS), which is a long-term energy system modelling tool, is employed to compare electricity generation, investment, and carbon dioxide emissions between business-as-usual and five alternative scenarios. Six scenarios, including business as usual, least-cost, two coal-phrase out and two net zero aligned with national climate targets and optimal scenarios, were simulated across different target years. The results show that the net zero (NZ) scenario is more cost-effective and emits fewer greenhouse gases than the other scenarios in meeting Indonesia’s future energy demand. However, achieving net zero by 2050 (NZ50) results in significantly lower CO2 emissions (10,134 MtCO2), which is less than half of the emissions in the net zero by 2060 (NZ60) scenario (16,849 MtCO2) at a similar cost (6229 and 6177 billion USD, respectively). This paper’s insights emphasise that large-scale renewable energy deployment and coal retirement are critical pathways to reaching carbon neutrality and achieving the energy mix transition.

A linear reduced-order model for the activated sludge process for the integration into a mixed-integer linear energy system optimisation model

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

A Methodological Approach to the Simulation of a Ship’s Electric Power System

Modern ships are complex energy systems containing a large number of different elements. Each of these elements is simulated separately. Since all these models form a single system (ship), they are interdependent. The operating modes of some systems influence others, but at the same time, the work of all the systems should be aimed at fulfilling the basic functions of the ship. The work proposes a methodological approach to combining various systems of ships into a single complex model. This model allows combining models of ship systems of various levels (microlevel, macrolevel, metalevel, megalevel). The work provides examples of models of such multi-level energy systems. These are energy systems composed of an electric generator, a diesel engine, a propeller shaft, and algorithms used for operating the common parts of the ship’s electric power system and a piston wear process. Analytical, structural, numerical, and object-oriented models were made for these objects. Each of these particular models describes a limited class of problems, has characteristic properties, and a mathematical structure. The work shows how particular models can be interconnected using a set-theoretic description. Particular models are combined into macrolevel models, whose output parameters are quantities that are by no means related. The macrolevel models are interrelated using control models. Control models belong to the metalevel and allow for assigning settings and response thresholds to algorithms used in automation systems. Such a model (megalevel model) allows, ultimately, investigating the dynamics of the entire system as a whole and managing it.

The Modeling and Control of (Renewable) Energy Systems by Partial Differential Equations—An Overview

Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors’ best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers.

Economic Model-Predictive Control of Building Heating Systems Using Backbone Energy System Modelling Framework

Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisation horizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks.

Battery electric vehicle energy demand in urban energy system modeling: A stochastic analysis of added flexibility for home charging and battery swapping stations

The successive upsurge in the penetration rate of battery electric vehicles (BEV) in the transport sector stresses electricity grids. Concurrently, batteries in BEVs add flexibility, while the reintroduction of battery swapping stations challenges home charging with regards to added flexibility. To address research gaps regarding the role of BEV charging systems in urban decarbonization pathways, this paper introduces an innovative modeling approach that reflects the implications of home charging and battery swapping stations in urban environments and accounts for the stochasticity of vehicle use.The developed method is demonstrated using a case study that comprises a sample residential area equipped with rooftop PV. The comparison of home charging and battery swapping stations shows that both approaches allow significant increases of self-consumption of locally provided electricity over conventional opportunistic home charging, with battery swapping stations evincing the highest increase in flexibility. This increase in added flexibility might, however, not justify a system-wide introduction of battery swapping stations for BEVs, as required surplus batteries and industry-wide standardization entail added efforts over home charging.Policy recommendations derived by this work support building regulations regarding the installation of charging stations and the utilization of PV rooftop potentials considering the anticipated market penetration of BEVs and the possibility to share electricity within local energy hubs.

Global energy scenarios: A geopolitical reality check

The ongoing Global Energy System Transformation (GEST) has attracted the attention of multiple academic disciplines and practitioners, approaching the process with different analytical and conceptual tools. We explore the ‘integration gap’ that exists between, on the one hand, Energy System Modelling and the stylised scenarios they use, and on the other, energy geopolitics. We consider how these approaches can complement each other to further our understanding of the global energy system’s future. Using a novel qualitative analytical framework, we review the extent to which a range of state-of-the-art global energy scenarios capture and reflect key issues in energy geopolitics in their narratives and model implementation. We find that few scenarios consider geopolitics in any depth. Those that do often treat it as a barrier to decarbonisation efforts that are aligned with the climate objectives of the Paris Agreement. Normative, Paris-aligned scenarios describe smooth processes of change where cooperation and coordination between countries are assumed and where geopolitics is often completely absent. Our findings emphasise the need for a more intricate understanding of the difference between ‘paper transitions’ and the real-world messiness and complexities of GEST, where geopolitics has a dual quality of simultaneously accelerating and hindering the transformation process.

Quantifying the impact of travel time duration and valuation on modal shift in Swiss passenger transportation

Decarbonizing the passenger transportation sector is critical for climate change mitigation. Existing studies on net-zero scenarios using Energy System Optimization Models (ESOM) often overlook non-monetary aspects of consumers' mobility choices but primarily focus on cost aspects. This study incorporates consumers' travel time duration and valuation, and an endogenous modal shift option into the Swiss TIMES Energy system Model (STEM). STEM is applied in a multi-objective optimization framework to quantify the impacts of faster Public Transport (PT) and slower car speeds on modal shifts in the transport sector's transformation. Similarly, we assess scenarios where consumers weigh travel time less, reflecting improved travel productivity. The results show that speed variations on medium- and long-distance trips, which can be interpreted as policies for highway speed limits and more efficient PT, can induce modal shifts towards 5–10% higher PT demand. Its implied secondary effects across the energy system include a reduced need for electrification of heavy-duty trucks by 11% and a decrease in hydrogen demand in road transportation by 34% by 2050. If travelers weigh costs over travel time, PT becomes less competitive against cars. Thus, electric vehicles (EVs) need to play a more dominant role in decarbonization, with a demand increase of 13% in 2040 (+9.2 billion passenger kilometer (bpkm)) and 6% in 2050 (+5.0 bpkm), along with the need for additional 45,000 public chargers of 22 kW size. Policy implications include the emphasis on improved PT speeds, speed limits on highways, needs to achieve more widespread EV adoption, and the need for balancing travelers' decision factors when aiming for reduced transport CO2 emissions.

Beyond fixed-speed pumped storage: A comprehensive evaluation of different flexible pumped storage technologies in energy systems

Traditional fixed-speed pumped storage (PS) has been a reliable measure to provide power system flexibility. However, the increasing need for flexibility of power systems due to adverse environmental events and amplified utilization of renewable sources necessitates the need for modernization of PS units. This paper studies the effect of five PS technologies with different operational flexibility in Qinghai Province's energy system. Firstly, PS models considering the difference in minimum stable generation & pumping, unit efficiency, configurations of turbine & pump are established. Then, the energy mixes, planning capacity, inflow, reservoir operation curve and other data are input into the energy system model. Finally, we quantify the effects of different PS forms on the energy system based on technical, economic, and emission indicators, also identify the optimal PS considering cost preference and emission preference. Results show that the investment costs for various pumped storage technologies depend on the motor type and pump capacity. Fixed-speed pump turbine technologies have a lower cumulative power variation of up to 11,225 GWh and the largest number of start-ups in pumping conditions, at 10,857 times. Increasing operational flexibility stabilizes the reservoir water level, and enhancing pump capacity reduces net load fluctuations for ternary PS and variable-speed quaternary PS. Improving wind-solar curtailment is limited by pump capacity, generating flexibility, and reservoir water level. Systems equipped with fixed-speed PS technologies emit more than carbon emissions other PS technologies. Our research outcomes have the potential to provide valuable insights, enabling stakeholders to optimize the deployment of pumped storage flexibility in low-carbon energy systems, taking into account the energy mix and hydrological conditions that are specific to each region.

Implementation of electric vehicles to support energy conservation and efficiency improvement in the transportation sector in Indonesia

More than 45.7% of Indonesia’s total energy consumption in 2021 came from the transportation sector. The transportation sector needs to improve energy efficiency and apply energy conservation measures in order to reduce energy consumption. The use of electric cars as an environmentally friendly replacement for internal combustion engine (ICE) vehicles is one effort in this direction. Using electric vehicles will reduce greenhouse gas emissions in the transportation sector; however, if fossil fuels continue to dominate the power sector, this will also lead to an increase in global GHG emissions. The power sector must employ NRE as much as possible in order to cut emissions. The energy system model, which is projected to be operational until the year 2100, was created using the LEAP and NEMO tools. Power plants are optimized for electricity delivery using the BAU and NZE scenarios. As a result of the energy system’s optimization, the usage of petroleum products will be reduced by about 778.7 million BOE in 2100, and the transportation sector’s GHG emissions will be reduced by 304.4 million tons of CO2e. By promoting energy efficiency, conservation, and the environment’s impact, the use of electric vehicles can improve Indonesia’s NZE situation in the transportation sector.

Day-ahead economic dispatch of building energy system considering virtual energy storage

For the increasing proportion of building energy consumption in global energy consumption in recent years, this paper proposes a day-ahead economic dispatching method of building energy systems considering virtual energy storage. Firstly, virtual energy storage is modeled according to the thermal inertia of the building. Then, a building energy system model with a distribution grid, photovoltaic generator sets, and heating equipment (heat pump) is constructed. Finally, a day-ahead economic optimal dispatch of the building energy system considering virtual energy storage is modeled to achieve the charging and discharging management of virtual energy storage. Case study results show that under the precondition for meeting the thermal comfort of the occupants, the daily electric power purchase of the building energy system after considering virtual energy storage is reduced by 278.69 kW. The daily comprehensive operating cost is decreased by 8.3%. The validity and reliability of this suggested approach in this paper are proved.

Short-Term Prediction for Indoor Temperature Control Using Artificial Neural Network

Recently, data-based artificial intelligence technology has been developing dramatically, and we are considering how to model, predict, and control complex systems. Energy system modeling and control have been developed in conjunction with building technology. This study investigates the use of an artificial neural network (ANN) for predicting indoor air temperature in a test room with windows on an entire side. Multilayer perceptron (MLP) models were constructed and trained using time series data obtained at one-second intervals. Several subsampling time steps of 1 s, 60 s, 300 s, 600 s, 900 s, 1800 s, and 3600 s were performed by considering the actual operation control mode in which the time interval is important. The performance indices of the neural networks were evaluated using various error metrics. Successful results were obtained and analyzed based on them. It was found that as the multi-step time interval increases, performance degrades. For system control designs, a shorter prediction horizon is suggested due to the increase in computational time, for instance, the limited computing capacity in a microcontroller. The MLP structure proved useful for short-term prediction of indoor air temperature, particularly when control horizons are set below 100. Furthermore, highly reliable results were obtained at multi-step time intervals of 300 s or less. For the multivariate model, both calculation time and data dispersion increased, resulting in worsened performance compared to the univariate model.

The effect of time resolution on energy system simulation in case of intermittent energies

The management and integration of intermittent renewable energy sources, such as wind and solar power, require precise capacity planning due to their variable nature. This study investigated the efficacy of using hourly time resolution in energy system models, a common practice in capacity planning. Concerns have been raised about the ability of hourly data to accurately represent rapid fluctuations in energy production and demand since it inherently constantly under- or overestimates actual real-time conditions. This research compared the outputs of energy models using 60-min resolution data with those utilizing a 1-min resolution benchmark across various dimensions: stability of outputs, temporal performance, geographical performance, impact of starting time shifts in data sampling, and trend effects. Results indicate that models using 60-min resolution data maintain a high level of accuracy, with output deviations of less than 2 % from the benchmark. This finding provides strong support that the current significant number of research studies, based on 60-min resolution data, do not carry potentially biased results due to their time resolution and are suitable for capacity planning decisions, thereby aiding in policy formulation.

Dynamic modeling and techno-economic assessment of hybrid renewable energy and thermal storage systems for a net-zero energy greenhouse in South Korea

Dynamic modeling and techno-economic assessment of hybrid renewable energy and thermal storage systems for a net-zero energy greenhouse in South Korea

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

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 optimising 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 challenge, a novel investment-based optimisation method is proposed. The method involves linear optimisation of the hybrid renewable energy system and subsequent investment optimisation, accounting for diminishing improvements per investment. The results from applying the investment-based optimisation to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, show that improving the discharge efficiency is the most valuable for all technologies. The second most important parameters are the costs of discharge capacity and energy storage capacity, and the least important parameters are the charge capacity cost and charge efficiency. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimisation method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response

Chlor-alkali electrolysis plays a significant role in Germany's electricity demand, with a share of >2%. It offers a promising avenue for leveraging demand response strategies. In times of escalating electricity prices, load shifting can help to maintain economic competitiveness of domestic industries. This study aims to assess the potential for electricity cost savings and related CO2 emission reductions through optimal load shifting practices for chlor-alkali electrolysis, considering both current and projected future electricity prices. The research employs the mixed-integer energy system modeling framework IRPopt to model and optimize a chlorine value chain under various scenarios. A sensitivity analysis is conducted to identify the primary factors driving electricity cost savings. The findings reveal that, based on the 2019 electricity price distribution (before pandemic and energy crisis), load shifting can yield electricity cost savings of 5.8% and CO2 emission reductions of 2.7%. As the share of variable renewable energy sources increases along with other driving factors, the electricity price spread is projected to rise from 28% in 2019 to an average of 87% in 2040 scenarios. Consequently, electricity cost savings rise up to 22% and CO2 emission reductions up to 10%. The primary drivers behind electricity cost savings include electricity price spreads and the utilization and operating range of the electrolyzer. In addition to the business-level advantages, the resulting decrease in residual load and lower CO2 emissions contribute to the integration of variable renewable energy sources and the achievement of decarbonization targets at the economic level.

Beyond dollars and cents: why socio-political factors matter in energy system modeling

Traditional energy system modeling (ESM) has predominantly focused on techno-economic factors such as costs and efficiency. However, this narrow perspective fails to capture the intricate interplay between energy systems and socio-political dynamics that significantly influence their real-world impact. This piece underlines the importance of incorporating socio-political factors into ESM and highlights the need for a more comprehensive approach. We identify several reasons why socio-political factors are often overlooked in ESM, including technical bias, complexity, data standardization challenges, time and resource constraints, and societal biases. These factors must be addressed to improve the relevancy of ESM, reduce social injustice, and promote innovation in the design of future energy systems. Incorporating socio-political factors into ESM offers several benefits. To achieve these benefits, the paper suggests a shift towards a more comprehensive and value-driven approach and offers several options for improving ESM including: (I) recognizing that socio-political factors are just as important as techno-economic factors (II) improving the structure of the modeling process (III) developing new and innovative metrics for socio-political factors (IV) linking different types of models (V) emphasizing the need for interdisciplinarity in ESM. By prioritizing the aspects of the energy system that concern people to the same degree we prioritize cost, we can derive value-driven insights to assist in creating a more sustainable future for all.