Simulation Of Energy Systems Research Articles

Design of a positive energy district: A Nigerian case study

The potential of possible solutions for the design and implementation of a 50-house Positive Energy District (PED) in Southern Nigeria is presented in this study. Using a transient energy systems simulation software (TRNSYS 18), the energy demand of a typical building is estimated. Different passive design options considering building materials, window sizes, building orientation, roof ventilation are studied to determine the most energy efficient building envelope. TRNSYS and PVSyst (another energy simulation software), are also used to simulate the dynamics of a positive energy building and operation of solar energy systems used for electricity generation, domestic hot water heating and space cooling for Lagos weather conditions. In terms of electric energy balance, the total annual electricity demand is 598,000 kWh while the electricity generated by PV is 728,600 kWh in a year. As regards to site energy balance, annually, the district exports more electricity than it imports. The difference is 110,200 kWh. These results show that in such climatic conditions, despite economic poverty, it is possible to implement the modern PED principles. However, first, it is necessary to reduce energy consumption as much as possible, including solar passive solutions to the building architecture.

Numerical simulation of a hybrid energy system proposed for low carbon data center

Low-carbon energy application for both large grid and distributed energy consumers is an urgent problem to be solved. Data centers (DC) are the typical distributed large-size energy consumers, and the application of renewable energy and energy storage is a promising solution for data center decarbonization. This paper developed a complete hybrid energy system (HES) simulation process for data center applications. The photovoltaic (PV) cell, hydrogen electrolysis, hydrogen storage, proton exchange membrane fuel cell (PEMFC), lithium battery (BAT), grid, and data center load are considered in the HES. A one-dimensional two-phase flow PEMFC model is developed. The detailed mass transfer process inside the PEMFC cell is modeled. The PEMFC model-solving algorithm is developed and integrated into the HES simulation program. The energy management system (EMS) based on a logical flow chart energy management strategy is integrated by editable subfunction. The HES simulation program is coded and implemented based on the MATLAB-SIMULINK environment. The yearly HES performance simulations are conducted and analyzed for a 200 kW data center in Xi’an with different HES parameters and an appropriate HES parameter arrangement is provided. The main contribution of this work is the development of an editable HES simulation program that is extensible for data center applications.

Preface

With the rapid progress of science and technology, the research and application in the fields of energy technology and materials science are increasingly in-depth, and experts and scholars around the world are committed to promoting the innovation and development in these areas. It is against this background that we welcomed the highly anticipated 2024 8th International Conference on Energy Technology and Materials Science (ICETMS 2024). The conference was successfully held in Chongqing, China from May 31st to June 2nd, 2024, attracting the participation of almost 100 scholars, researchers and industry experts from all over the world. The convening of this conference has benefited from the active participation and enthusiastic support of experts and scholars around the world. We received a large number of high-quality paper submissions, covering New and Renewable Energy Technologies, Traditional Energy Transformation and Cleaner Production, Energy Storage Technologies and Battery Materials, Energy Security and Sustainable Development, Energy System Simulation and Optimization, and many other aspects. After a rigorous review process, excellent papers from different countries and regions were finally accepted, which fully demonstrated the latest research results and innovative ideas in these domains. In the course of the conference, we invited well-known experts and scholars at home and abroad to give keynote speeches and reports, and their wonderful speeches provided valuable academic inspiration and reflection for the participants. Among these, with the research Industrial Fabrication of Electrocatalysts for Green Hydrogen Production, Prof Hui Pan (University of Macau, China) introduced the design, synthesis and possible industrial production of novel electrocatalysts for oxygen and hydrogen evolution reactions (HER and OER). And his research demonstrated that multi-metal compound systems were catalytically active for OER and HER in electrochemical energy devices, which may find practical application in hydrogen-energy technologies. At the same time, we also organized a number of group discussions, providing participants with more in-depth communication and interaction opportunities. The successful holding of this conference not only promoted the academic exchanges and cooperation in the fields of energy technology and materials science, but also injected new vitality and impetus into the development of these domains. Finally, we would like to express our heartfelt thanks and high respect to all the experts and scholars who participated in this conference. At the same time, we would also like to thank the organizing committee for its hard work and selfless dedication, which has provided a strong guarantee for the success of the conference. We look forward to continuing to work with experts and scholars in the coming days to promote the development and progress of energy technology and materials science! List of Committee Member is available in this Pdf.

Application of hybrid chaotic particle swarm optimization and slime mould algorithm to optimally estimate the parameter of fuel cell and solar PV system

Through the use of fuel cells and photovoltaic (PV) solar cells, green energy has become one of the most popular developments in electricity generation in recent years. Accurate modeling of fuel cell and PV cell is critical in the simulation and analysis of energy systems that use both sources. Due to the complexity and nonlinearity of these hybrid systems, the model must be optimized under different operating conditions. In this article, a novel metaheuristic efficient hybrid algorithm is proposed for this purpose. A Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA) has been proposed to estimate the PV/fuel cell system parameters. The hybrid HCPSOSMA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. The problem formulation is based on minimizing the Root Mean Square Error (RMSE) between the calculated values and measured values for both the triple-diode PV model and the proton exchange membrane (PEM) fuel cell. The results show that the hybrid algorithm outperforms other algorithms in determining the best PV/PEMFC parameters that produce accurate V–I and P–I characteristic curves and the RMSE is about 4 × 10–12. Finally, non-parametric tests based on Friedman's ranking, Wilcoxon's rank sum test and Kruskal-Wallis test are implemented to prove the superiority of the proposed hybrid algorithm in the parameter estimation.

Characterizing Supply Reliability Through the Synergistic Integration of VRE towards Enhancing Electrification in Kenya

Decentralized electrical power systems, driven by variable renewable energy sources such as solar PV and wind, have the potential to provide accessible and sustainable energy, contributing to the realization of a zero-carbon transition. However, these sources are susceptible to extreme weather conditions, presenting a challenge to the reliability of the power system. With abundant resources and a significant rural population lacking access to electricity, Africa has emerged as a key area for research on variable renewable energy-based electricity generation. Despite this focus, there remains a substantial gap in understanding at regional-scale the potential and variability of solar and wind power across various time scales, as well asthe impact of available resource synergy. Thisstudy aims to bridge this knowledge gap by conducting comprehensive simulations of hybrid wind and solar energy systems, both on-grid and off-grid, across 20 geographically diverse locations in Kenya. Using high-resolution hourly time step data, we examine the effect of resource complementarity on system reliability at varying time scales: daily, monthly and annually. The study findings shows the available VRE resource exhibit moderate tendency for complementarity, and optimizing their deployment can reduce hourly variability by 20%, significantly enhancing supply reliability, especially in the northern and eastern regions.

Analysis of Power-to-gas-to-X systems with metal hydride storage based on coupled electrochemical and thermodynamic simulation

Power-to-gas-to-X systems consisting of photovoltaic cells, proton-exchange membrane electrolysis, hydrogen storage based on metal hydrides, proton-exchange membrane fuel cells and buffer batteries could be used to meet heat and electricity demands of homes, businesses, or small districts. The actual size of the individual components and their interplay have to be optimized for the technical and economic feasibility of the overall system. A simulation-based optimization workflow would be a suitable way to accomplish this task, but there are hardly any tools that can simultaneously simulate power, fluid and heat flows of such systems and efficiently perform their optimization. In this paper, a multiphysical energy system simulation and optimization tool is introduced which models electrochemical and thermodynamic processes simultaneously, including modern equations of state and an own numerical solver for the arising differential–algebraic system of equations, and provides new methods for the calibration of parameters of the metal hydride storage, proton-exchange membrane electrolyzer and fuel cell as well as a metamodel-based approach for sizing optimization. As a demonstrator for the novel tool, a simulation model of a hydrogen lab is successfully set up based on experimental results. The novel tool is able to extract polarization and jump curves of the fuel cell, determine a first temperature and pressure dependency of the efficiency of the electrolysis coupled with the metal hydride storage and speed up sizing optimization through metamodeling by a factor 262.1 at 4.9% and 32.7 at 3.3% accuracy.

Optimizing the integration of renewable energy sources, energy efficiency, and flexibility solutions in a multi-network pharmaceutical industry

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, these are also the goals of the EU founded Horizon Europe FLEXIndustries project. This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies were investigated. The outcomes of each scenario identify a set of 500 Pareto optimal solutions, obtained through 40,000 simulations. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants.

Computational fluid dynamics based multi-species transport simulation of auxiliary energy systems for friction stir welding of dissimilar materials

This research compares auxiliary energy-assisted friction stir welding (FSW) techniques with conventional FSW when joining dissimilar materials. Specifically, it conducts numerical modeling and experimental validation for the effectiveness of plasma-assisted FSW and induction-assisted FSW for DH36 steel and 6061-T6 aluminum alloy. Fully coupled 3D computational fluid dynamics (CFD) models, incorporating the multi-species transport method, were developed, where the species mass fractions of the workpieces are transported through diffusion, convection, and reaction sources for individual species. Based on the temperature validation, the dedicated heat flux based on the rectangular heat flux and Gaussian heat flux distribution were considered for induction coil and plasma arc heating on the DH36 steel side, respectively. The established conventional and auxiliary energy-assisted FSW models were validated against experimentally observed temperature fields and the joints’ material features. Results indicate that the assistance of plasma and induction auxiliary energy sources increased the temperature field, strain rate, and flow velocity without forming stagnant zones on the steel side caused by reduced dynamic viscosity. In plasma arc-assisted FSW, the steel could not extrude effectively from the base steel sheet due to deficient heat and flow velocity input; therefore, defect-prone coarse steel fragments were blended with the Al matrix. In induction-assisted FSW, the uninterrupted steel layer was extruded from the steel side and placed on the Al side, which was caused by enhanced heat build-up and flow velocity. Moreover, induction-assisted FSW achieved symmetric material flow on both advancing and retreating sides, resulting in defect-free welds.

A novel standalone hybrid renewable energy systems onboard conventional and autonomous tugboats

The recent surge in fuel costs and stringent gas emission regulations mandated by the International Maritime Organization (IMO) has prompted research into integrating renewable energy and energy management strategies (EMS) onboard ships. This study illustrates the computation, simulation, and optimization of a hybrid renewable energy system (HRES) to ensure continuous power supply for auxiliary loads and critical systems on both conventional and fully autonomous tugboats. The primary aim is to design an optimal HRES with minimal annualized cost of system (ACS) and a higher proportion of renewable energy while using an artificial bee colony (ABC) algorithm. Validation of optimization outcomes is conducted using particle swarm optimization (PSO), genetic algorithm (GA), and Hybrid Optimization of Multiple Energy Resources (HOMER) Pro. The HRES incorporates diesel generators (Gensets), photovoltaic (PV) arrays, vertical axis wind turbines (VAWT), and battery banks. The optimal HRES configuration for both conventional and fully autonomous tugboats is found to be Genset/PV/VAWT/Battery. We observe that the ABC algorithm exhibits superior convergence, reliability, cost-effectiveness, renewable energy fraction, and reduced carbon emissions compared to alternative algorithms. Results of robustness tests suggest that the shipload variation, fuel prices, temperature fluctuations, wind speed and solar irradiance along the navigation route have significant impact on the optimal HRES configuration. Ultimately, we conclude that the fully autonomous tugboat demonstrates superior performance in terms of costs, carbon dioxide emissions, and renewable energy fraction compared to its conventional counterpart.

The long term price elastic demand of hydrogen – A multi-model analysis for Germany

Hydrogen and its derivatives are important components to achieve climate policy goals, especially in terms of greenhouse gas neutrality. There is an ongoing controversial debate about the applications in which hydrogen and its derivatives should be used and to what extent. Typically, the estimation of hydrogen demand relies on scenario-based analyses with varying underlying assumptions and targets. This study establishes a new framework consisting of existing energy system simulation and optimisation models in order to assess the long-term price-elastic demand of hydrogen. The aim of this work is to shift towards an analysis of the hydrogen demand that is primarily driven by its price. This is done for the case of Germany because of the expected high hydrogen demand for the years 2025–2045. 15 wholesale price pathways were established, with final prices in 2045 between 56 €/MWh and 182 €/MWh. The results suggest that – if climate targets are to be achieved - even with high hydrogen prices (252 €/MWh in 2030 and 182 €/MWh in 2045) a significant hydrogen demand in the industry sector and the energy conversion sector is expected to emerge (318 TWh). Furthermore, the energy conversion sector has a large share of price sensitive hydrogen demand and therefore its demand strongly increases with lower prices. The road transportation sector will only play a small role in terms of hydrogen demand, if prices are low. In the decentralised heating for buildings no relevant demand will be seen over the considered price ranges, whereas the centralised supply of heat via heat grids increases as prices fall.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

Two-phase flow thermo-hydro-mechanical modeling for a water flooding field case

Simulation of subsurface energy system involves multi-physical processes such as thermal, hydraulical, and mechanical (THM) processes, and requires a so-called THM coupled modeling approach. THM coupled modeling is commonly performed in geothermal energy production. However, for hydrocarbon extraction, we need to consider multiphase flow additionally. In this paper, we describe a three-dimensional numerical model of non-isothermal two-phase flow in the deformable porous medium by integrating governing equations of two-phase mixture in the porous media flow in the reservoir. To account for inter-woven impacts in subsurface conditions, we introduced a temperature-dependent fluid viscosity and a fluid density along with a strain-dependent reservoir permeability. Subsequently, we performed numerical experiments of a ten-year water flooding process employing the open-source parallelized code, OpenGeoSys. We considered different well patterns with colder water injection in realistic scenarios. Our results demonstrate that our model can simulate complex interactions of temperature, pore pressure, subsurface stress and water saturation simultaneously to evaluate the recovery performance. High temperature can promote fluid flow while cold water injection under non-isothermal conditions causes the normal stress reduction by significant thermal stress. Under different well patterns the displacement efficiency will be changed by the relative location between injection and production wells. This finding has provided the important reference for fluid flow and induced stress evolution during hydrocarbon exploitation under the environment of large reservoir depth and high temperature.

International Conference on Modelling, Simulation, and Optimization of Energy Systems

International Conference on Modelling, Simulation, and Optimization of Energy Systems

A novel dynamic simulation strategy for regional integrated energy system considering coupling components failure

Dynamic simulation of regional integrated energy systems (RIES) is beneficial for improving stability and safety of RIES. However, it is hard to implement a dynamic simulation due to the RIES's complexity caused by the coupled multiple energy sources, especially considering coupling components failure. To address this issue, a novel fault-driven co-simulation strategy for coupled heat-gas-electric system under different states is developed in this study. This strategy includes two steps: (1) the RIES is firstly decomposed into different modules and construct corresponding sub-models; and then (2) the solution sequence of these sub-models is determined according to the system states. Additionally, the strategy also considers the transmission delay of gas and heat. Two typical components failures (combined heat and power unit failure and gas-fired generator unit failure) at a typical industrial park's RIES are selected to validate the dynamic simulation strategy. And results indicate that such strategy can obtain accurate dynamic behavior of RIES after coupling components failures occurs, such as the flow fluctuation of the gas pipeline that lasts for 179 s and the temperature reduction process of the heat node that lasts for more than 35000 s. Therefore, this study is instructive for further development of RIES technology.

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.

Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models

As energy system design moves to more complex methods of optimisation including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilised to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilising both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. It is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

Sizing and Simulation of a Photovoltaic Hybrid Energy System and Generator for the Electricity Supply of the Residence of the Governor of Mamou, Guinea

Sizing and Simulation of a Photovoltaic Hybrid Energy System and Generator for the Electricity Supply of the Residence of the Governor of Mamou, Guinea

Wind energy systems and power quality: matrix versus two-level converters

This paper presents data of the actual installed electric power capacity for the renewable energy power systems in Portugal. Also, this paper presents a contribution consisting in a modulation strategy for simulation of a wind energy system with different topologies for power converters, consisting on a matrix and a two-level converter. Although more complex, this modulation strategy is justified for more accurate results.

Thermoeconomic simulation of a complex energy system for performance analysis and prediction in deep peak shaving

The establishment of a cost model for complex energy systems based on thermoeconomics can provide technical and economic evaluation indicators for complex energy systems. At the same time, to integrate more renewable energy into the grid, complex energy systems must participate in deep peak shaving. To evaluate the technical-economic performance of complex energy systems involved in deep peak shaving, a novel thermoeconomic cost construction method is proposed based on the production structure diagram, using the gas-steam combined cycle system as an example. these can effectively avoid the derivation error of high-dimensional models, improve the modeling and calculation speed, and obtain the variation trend of system thermoeconomics cost under load changes and operating parameter changes. The results show that the external and internal factor can change the power generator thermoeconomic cost. the power generation cost of the system increases with increasing natural gas price and environmental temperature When the compressor pressure ratio increases, the power generation cost of the system also increases. At 100% load, the power generation cost reaches its lowest value when the exhaust temperature is equal to 615?C.