Energy System Configuration Research Articles

Evaluation of Technological Configurations of Residential Energy Systems Considering Bidirectional Power Supply by Vehicles in Japan

To reduce CO2 emissions in the residential and transportation sectors, distributed energy technologies, such as photovoltaic power generation (PV), stationary storage batteries (SBs), battery electric vehicles (BEVs), and vehicle-to-home (V2H) systems, are expected to be introduced. The objective of this study was to analyze the impact of the installed capacity of PV and SB, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. Thus, this study developed a model to optimize the technological configuration of residential energy systems, including various vehicle types and driving patterns. The simulation results showed that it is more economically and environmentally efficient to install a BEV and a V2H system in households with longer parking times at the residence and to install an SB in addition to these technologies in households with shorter parking times at the residence. Furthermore, comparing a gasoline vehicle and an SB, the most economical combination, with a BEV and a V2H system and with a BEV, a V2H system, and an SB, estimated the carbon tax rate necessary for cost equivalence. The result indicated that the carbon tax rate needs to be increased from its current level.

Experimental and simulation investigation of a Savonius vertical axis wind turbine for residential applications: a case study in Indonesia

The aim of this study was to investigate several geometric designs to improve the performance of a Savonius vertical axis wind turbine (SVAWT) at low wind speeds. The SVAWT models were numerically tested using ANSYS prediction software to determine the best performance. Furthermore, a SVAWT prototype was experimentally evaluated in subsonic wind tunnels at wind speeds of 1–5 m/s to determine the electrical power produced. The experimental results were used as input parameters for HOMER tools to optimise renewable energy systems and establish the prototype’s techno-economic environmental viability for satisfying the residential demand load. The simulation results showed that the maximum power coefficient obtained was 0.25 for a SVAWT model with a twist angle of 45° at a wind speed of 5 m/s. The experimental results showed that the modified SVAWT produced 494.9 W of electrical power at a wind speed of 5 m/s. The results confirmed that the cost of electrical energy following an initial investment in the optimised renewable energy system configuration (a grid-connected 1-kW SVAWT with a – kW electrical converter and two battery strings) was 0.0963/kWh and 4,425. This setup achieves a 65.8% renewable fraction, cutting CO2 emissions by over 53.71%, fit for residential use.

Community-to-vehicle-to-community (C2V2C) for inter-community electricity delivery and sharing via electric vehicle: Performance evaluation and robustness analysis

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios.

Methods for Coordinating Optimization of Urban Building Clusters and District Energy Systems

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.

Quantifying the carbon footprint of energy storage applications with an energy system simulation framework — Energy System Network

Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

Performance analysis and capacity configuration of building energy system integrated with PV/T technology under different operation strategies

Implementing suitable operation strategies significantly enhances the overall performance of the energy network of solar buildings. Previous studies predominantly concentrated on dispatching energy from standalone photovoltaic or photothermal modules within buildings. The photovoltaic/thermal (PV/T) modules have garnered attention due to their higher energy efficiency. However, the investigations targeting the operation strategies of PV/T modules in building remain scarce. To fill this research gap, this study scrutinized the maximum self-consumption (MSC), time-of-use (TOU), and optimization-based (OPT) strategies within a building energy system integrated with PV/T modules. The investigation encompassed an analysis of overall performance, parameter sensitivity, and optimal energy storage capacity. The main results were: (1) The OPT strategy displayed superior flexibility and lower operation costs compared to other strategies but exhibited lower energy utilization efficiency and greater grid impact. (2) The MSC strategy demonstrated lower sensitivity concerning battery power and charge states when contrasted with other strategies. (3) The optimal capacities for battery and water tank configurations for the MSC, TOU, and OPT strategies were identified as 4 kWh/200 L, 2.5 kWh/200 L, and 4.5 kWh/100 L, respectively. This research aims to provide reference and insights for planning the integration of PV/T modules into building energy systems.

Optimal energy system configuration for zero energy buildings using hybrid thermal-photovoltaic solar collector

Optimal energy system configuration for zero energy buildings using hybrid thermal-photovoltaic solar collector

Application of hybrid renewable energy systems for supplying electricity demand of a water pump station of agricultural plants: a case-based research

Abstract Employing clean energy production in agricultural purposes has attracted the attention of many researchers. One of the challenges is the flexible nature of irrigation of the plants that need to have a changeable power source of pump stations to supply the load demands. Renewable energies are among the best solutions to supply environmentally-friendly power, especially in remote areas where there is no access to the electricity. This study considers the needed water of five different plants, and according to the calculated hydromodule, the capacity of the pump station as well as its monthly and yearly load demands are determined. Based on the calculated load demand, different configurations of hybrid renewable energy systems (HRES) are considered, and the optimum sizes of equipment are determined. The needed load demand is 1463 kWh/day and peak load is 266.16 kW. Based on the suppositions and inputs of this study, results show that the best economic configuration would be PV/wind turbine/diesel generator/battery the way that cost of energy (COE) and net present cost (NPC are 0.28 ($/kWh) and $2.48 million. Also, the best environmentally friendly system (PV/wind turbine/battery) where CO2 emission is zero and RF is 100%, can be obtained with 647 kW PV panels and 106 wind turbines, the way that COE and NPC values are 0.5 ($/kWh) and 4.41 $ million. To reach the best technical system (PV/generator/battery), 514 kW PV, 100 kW diesel generator, and 1816 batteries would have the least unmeted load and excess electricity where COE and NPC values are 0.34 ($/kWh) and 2.98$ million.

Effects of dispatch algorithms and fuel variation on techno-economic performance of hybrid energy system in remote island

A stand-alone hybrid energy system is needed to mitigate the growing demand for future energy and to reduce emissions generated by conventional fuel sources. This work shows and proposes an optimum hybrid energy system configuration with less environmental pollution for a coastal region of Bangladesh with a load demand of 292.20 kWh/d and a peak load of 41.14 kW. This research presents the cost of energy (COE) and net present cost (NPC) for a PV-wind hybrid system with five alternative fuel generator technologies. For each possible configuration, the effects of three alternative dispatch algorithms, load following (LF), cycle charging (CC), and combined dispatch (CD), are examined. All three algorithms were validated using a genetic algorithm (GA), Cuckoo search algorithm (CUSA), Constrained Particle Swarm Optimization (CPSO), Harmony search algorithm (HSA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, a sensitivity analysis is also conducted by utilizing the gasoline price, discount rate, battery cost, PV cost, and inflation rate. The results show that the PV-wind-natural gas-based system provides the minimum COE (0.196 USD/kWh) and NPC (270,483 USD) when the CD algorithm is followed. For the same optimal hybrid energy system, the COE is 0.201 USD/kWh, 0.0998 USD/kWh, 0.101 USD/kWh, 0.101 USD/kWh, and 0.0987 USD/kWh respectively for GA, CUSA, CPSO, HSA, and NSGA-II. However, COE increases by 8 % (0.212 USD/kWh) and 15 % (0.225 USD/kWh) when the CC and LF algorithms are followed. In addition, a comparison of all configurations reveals that the PV-wind-biomass configuration with the CD algorithm excludes biomass generators to lower the COE, making the system emission-free.

Model-based Parametric Study for Comparison of System Configurations and Control of a Hydrogen Hybrid Cargo Vessel

The current state of research in marine energy systems has concentrated on conventional diesel systems, while limited literature is available on the configuration and control of alternative energy sources such as hydrogen hybrid systems, which have attracted increasing interest recently owing to the energy transition. This paper presents a modelling and control study for conceptual retrofitting of a general cargo vessel to a hydrogen-hybrid version. Generic fuel cell, battery, and converter models are used, enabling easy adaptation to various powerplant sizes and ship types. A robustly coordinated Energy Management Strategy (EMS), which can be implemented for different vessel’s power profiles, was developed for power sharing, DC bus voltage control, and battery State of Charge (SoC) regulation. The total installed fuel cell power and battery capacity were heuristically selected from a range of power profiles of the ship. A database of fuel cells with stacks from different manufacturers was created to test different combinations in terms of fuel consumption, cost, and weight, based on the framework of the problem. Uncertainties in terms of fuel prices are presented using normal distribution graphs. The system configurations and control results are presented for one power profile of the vessel and the average fuel costs. It is demonstrated that with the proposed control method, the power losses are less than 1%, the DC bus voltage fluctuations are less than 0.5%, and the battery SoC remains between 35-65% for the entire duration of the analysed power profile. The configuration with eight stacks of 150 kW has the lowest total fuel cost (730 $) with an average difference of 7.1% from the other solutions, and the lowest total weight (10.54 tons) with an average difference of 15.4% from the other configurations. Overall, this study demonstrates the efficient configuration and control of hybrid energy systems using parameterized components.

Integrating fuzzy multi-criteria analysis for sustainable configuration of an on-grid hybrid renewable energy system: case study of Tunisia

Integrating fuzzy multi-criteria analysis for sustainable configuration of an on-grid hybrid renewable energy system: case study of Tunisia

Integrating fuzzy multi-criteria analysis for sustainable configuration of an on-grid hybrid renewable energy system: case study of Tunisia

Integrating fuzzy multi-criteria analysis for sustainable configuration of an on-grid hybrid renewable energy system: case study of Tunisia

Optimal Capacity Configuration of a Low-Carbon Energy System Considering Carbon Capture Technology and Hydrogen-Diversified Utilization Under Multiple Operational Scenarios

Optimal Capacity Configuration of a Low-Carbon Energy System Considering Carbon Capture Technology and Hydrogen-Diversified Utilization Under Multiple Operational Scenarios

Energy, Exergy and Thermoeconomic Analyses on Hydrogen Production Systems Using High-Temperature Gas-Cooled and Water-Cooled Nuclear Reactors

The use of nuclear energy is inevitable to reduce the dependence on fossil fuels in the energy sector. High-temperature gas-cooled reactors (HTGRs) are considered as a system suitable for the purpose of reducing the use of fossil fuels. Furthermore, eco-friendly mass production of hydrogen is crucial because hydrogen is emerging as a next-generation energy carrier. The unit cost of hydrogen production by the levelized cost of energy (LCOE) method varies widely depending on the energy source and system configuration. In this study, energy, exergy, and thermoeconomic analyses were performed on the hydrogen production system using the HTGR and high-temperature water-cooled nuclear reactor (HTWR) to calculate reasonable unit cost of the hydrogen produced using a thermoeconomic method called modified production structure analysis (MOPSA). A flowsheet analysis was performed to confirm the energy conservation in each component. The electricity generated from the 600 MW HTGR system was used to produce 1.28 kmol/s of hydrogen by electrolysis to split hot water vapor. Meanwhile, 515 MW of heat from the 600 MW HTWR was used to produce 8.10 kmol/s of hydrogen through steam reforming, and 83.6 MW of electricity produced by the steam turbine was used for grid power. The estimated unit cost of hydrogen from HTGR is approximately USD 35.6/GJ with an initial investment cost of USD 2.6 billion. If the unit cost of natural gas is USD 10/GJ, and the carbon tax is USD 0.08/kg of carbon dioxide, the unit cost of hydrogen produced from HTWR is approximately USD 13.92/GJ with initial investment of USD 2.32 billion. The unit cost of the hydrogen produced in the scaled-down plant was also considered.

An efficient mixed-variable generation operator for integrated energy system configuration optimization

Energy consumption has skyrocketed as society has progressed, resulting in energy crises and environmental issues. New energy technologies are being developed to address these challenges, and technologies such as the cogeneration of electricity, heating, and cooling are being used to improve energy utilization. The configuration of an energy system has a critical impact on the economics of the system, its ability to supply energy, etc. This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system. The model considers equipment types (categorical variables), equipment quantities (integer variables), and critical parameters (real variables) as optimization variables, where economic performance, the proportion of renewable energy, supply shortage, and dependence on the external power grid are considered unoptimized objectives. This is a multiobjective and mixed-variable problem, and the combination of the two properties poses an excellent challenge for evolutionary algorithms in optimizing the model. Therefore, we propose an efficient generating operator based on a fully connected weighted neural network, called the FCWN operator. This operator overcomes the challenge of the discrete variable space and broken neighborhood relationships in mixed-variable problems. During the algorithm search process, search history information is used to update the fully connected weight network for distribution estimation of the variable space. Offspring are generated based on the fully connected network to improve the algorithm’s search efficiency. In the experimental section, we construct a model with 20 variables and conduct simulation-based configuration optimization for scenarios with 3, 5, and 8 available equipment types. The final obtained Pareto frontier solution set is evaluated using the hypervolume metric, a widely used multiobjective evaluation metric. The Wilcoxon rank sum test on the experimental results shows that the proposed algorithm has better results than other state-of-the-art algorithms at a 95% confidence level.

Hydrogen storage capacity of salt caverns and deep aquifers versus demand for hydrogen storage: A case study of Poland

Geological structures in deep aquifers and salt caverns can play an important role in large-scale hydrogen storage. However, more work needs to be done to address the hydrogen storage demand for zero-emission energy systems. Thus the aim of the article is to present the demand for hydrogen storage expressed in the number of salt caverns in bedded rock salt deposits and salt domes or the number of structures in deep aquifers. The analysis considers minimum and maximum hydrogen demand cases depending on future energy system configurations in 2050. The method used included the estimation of the storage capacity of salt caverns in bedded rock salt deposits and salt domes and selected structures in deep aquifers. An estimation showed a large hydrogen storage potential of geological structures. In the case of analyzed bedded rock salt deposits and salt domes, the average storage capacity per cavern is 0.05–0.09 TWhH2 and 0.06–0.20 TWhH2, respectively. Hydrogen storage capacity in analyzed deep aquifers ranges from 0.016 to 4.46 TWhH2. These values indicate that in the case of the upper bound for storage demand, there is a need for the 62 to 514 caverns, depending on considered bedded rock salt deposits and salt domes or the 9 largest analyzed structures in deep aquifers. The results obtained are relevant to the discussion on the global hydrogen economy, and the methodology can be used for similar considerations in other countries.

An Optimization Ensemble for Integrated Energy System Configuration Strategy Incorporating Demand–Supply Coordination

As one representative smart energy infrastructure in smart cities, an integrated energy system (IES) consists of several types of energy sources, thus making more complicated coupling connections between the supply and demand sides than a power grid. This will impact when allocating different energy sources to ensure the appropriate energy utilization in the IES. With this motivation, an IES energy configuration optimization strategy based on a multi-model ensemble is proposed in this paper. Firstly, one coupling model is constructed to assess the underlying collaborative relationships between two sides for a renewable-energy-connected IES. Next, the independent component analysis (ICA) method is implemented for noise reduction in massive heterogeneous input databases, which can effectively improve the computing efficiency under such high-dimensional data conditions. Also, the self-adaptive quantum genetic model (SAQGM) is built for subsequent configuration optimization. Specifically, the quantum bit representation is incorporated to reduce computation complexity in multi-states scenarios, the double-chain formation of chromosomes is deployed to diminish the uncertainty when encoding, and the dynamic adaptation quantum gate is established to successively amend parameters. Finally, an empirical case study is conducted which can demonstrate the benefits of this strategy in terms of feasibility, efficiency, and economy.

Impacts of digitalization and societal changes on energy transition: a novel socio-techno-economic energy system model

The increased diffusion of information and communication technologies (ICTs) impacts daily life and economic growth. It introduces new social practices for households and business models for companies that influence society and energy infrastructure development. A framework capable of quantifying and analyzing the impact of digitalization on achieving energy and climate targets, with a focus on behavioral changes induced by ICT, is currently lacking. In this paper, a new framework is developed that is technology-rich and captures the preferences and behaviors of households and firms in the energy system to assess sustainable energy system configurations that are technically and socially feasible. The framework is designed and demonstrated for Switzerland. We find, for example, that teleworking in Switzerland reduces commuting demand by 10%, and the savings in transport expenses can favor the investment in efficient and clean residential technologies to compensate for the increased residential energy demand due to working at home. This manuscript contributes to the growing literature of suitable frameworks and case studies to account for the co-evolution of society and energy systems in achieving the transition to low-carbon economies.

Optimal configuration of double carbon energy system considering climate change

How to achieve the “double carbon” goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the “double carbon” goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China’s “double carbon” goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively.