Optimization Of Energy Systems Research Articles

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Carbon and economic prices optimization of a solar-gas coupling energy system with a modified non-dominated sorting genetic algorithm considering operating sequences of water-cooled chillers

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field.

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

Application of Renewable Energy in Green Buildings and Energy Consumption Optimization

INTRODUCTION: With the increasing global awareness of sustainable development and environmental protection, green building has become one of the important development directions in the construction industry. The application of sustainable type energy in the construction industry is of great significance in reducing building energy consumption and environmental pollution. This study aims to explore the application of sustainable types of energy and conduct research on energy consumption optimization. OBJECTIVE: The aim of this study is to analyze the current situation of the application of sustainable types of energy in the construction industry, to explore its impact on the energy consumption of buildings, and to propose corresponding optimization strategies in order to achieve the goal of sustainable development of green buildings in China. METHODS: This study adopts a combination of literature review and case study; firstly, a literature review on the application of sustainable types of energy, sorting out its technical characteristics and application effects; then, several typical cases are selected to analyze its energy application and energy consumption in buildings; finally, relevant strategies and suggestions for optimizing the energy consumption are put forward by combining the results of the literature review and the case study. RESULTS: Through the literature review and case analysis, it is found that sustainable types of energy, such as solar energy and wind energy, have been widely used in buildings and achieved certain energy-saving effects. However, there are also some problems, such as inefficient energy utilization and high cost. To address these problems, this study proposes a series of optimization strategies, including suggestions for optimizing energy system design, improving energy utilization efficiency, and reducing energy costs. CONCLUSION: This study concludes that the application of sustainable types of energy in green buildings is an important way to optimize building energy consumption and sustainable development. Through measures such as optimizing energy system design and improving energy utilization efficiency, building energy consumption can be further reduced, environmental pollution can be reduced, and the development of the construction industry can be promoted. However, further research and practice are still needed to continuously improve relevant technologies and policies to promote the application and development of sustainable types of energy in buildings.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

A Framework for Modeling and Optimization of Data-Driven Energy Systems Using Machine Learning

A Framework for Modeling and Optimization of Data-Driven Energy Systems Using Machine Learning

Optimization of off-grid renewable energy systems using a hybrid version of golden search algorithm

Currently, the development of HRESs (hybrid renewable energy systems) in remote areas is of great importance and popularity. However, measuring and optimizing the capacity of these systems faces a difficult challenge. Multiple works had been reported in the literature to optimize such systems, all of which aim to achieve an optimal configuration with minimum annual net cost. Therefore, the significance of providing off-grid electrification to remote areas through HRESs can be highlighted as a crucial case for sustainable growth. Accordingly, the study proposes a modified metaheuristic approach, known as the Hybrid Golden Search Algorithm (HGSA), for long-term application planning and optimization of the off-grid HRES. The aim of this algorithm is to minimize the amount of net cost which is used annually; to reduce the probability of power supply interruption. In order to assess the effectiveness of the proposed algorithm, a simulation study over a long period on a remote area was conducted. From the results, increasing the reliability level from 1 % to 3 % causes a decrease in the total net annual cost by around 7.3 % under the proposed HGSA and also results in a reduction in component size (around 6 % and 21 % reductions for the wind turbine area and storage tanks, respectively). Further, the HGSA technique obtains the lowest value of fitness function for the hybrid system at a reliability level of 3 %, which is 31,539,810$. This result demonstrates that the efficiency of HGSA outperforms Fuzzy Logic and Optimization, Artificial Bee Colony (ABC), and GSA techniques. Based on this, the proposed HGSA could lead to more promising results than the other comparative algorithms. Hence, the proposed HGSA can be a valuable tool for long-term application planning and optimization of off-grid HRES, which can contribute significantly to achieving sustainable development goals.

Open energy system modelling for low-emission hydrogen roadmap planning: The case of Colombia

Climate change is driving the need for new approaches to energy production and utilization globally, contributing to the mitigation of greenhouse gas (GHG) emissions. Low-emission hydrogen is recognized as a key energy vector for decarbonization, and numerous countries are currently evaluating strategies and roadmaps for its integration. Colombia, with its abundant renewable resources, is poised as a potential player in hydrogen production. This study presents a techno-economic assessment of hydrogen pathways and their cross-sectoral interactions within the Colombian energy system. By employing an open energy system optimization framework (OSeMOSYS), we modelled a detailed supply-demand hydrogen chain and analysed a series of scenarios from 2021 to 2050, including a sensitivity analysis. Our results show that hydrogen pathways will require 2–6.4 Mt/year in hydrogen production, 8–27 GW in new installed capacity, and US$50–80 billion in investments, with favourable impacts in GHG mitigation, energy imports, and socioeconomic benefits. We use these findings to suggest an update to the targets of the current National Hydrogen Roadmap focused on a higher ambition to deploy a full hydrogen economy. Our open methodology provides a valuable tool for conducting further assessments in developing countries interested in defining low-emission hydrogen roadmaps.

Optimal scheduling of multi-regional energy system considering demand response union and shared energy storage

In the current context of the scarcity of fossil energy and the large-scale development and utilization of new energy sources, the power system is developing in the direction of multi-source synergy and interconnection. Demand response technology and energy storage technology have become important adjustment means of integrated energy system because of their efficient coordination ability and flexible adjustment ability. However, in the system where multiple energy systems operate cooperatively, there are still some limitations in time and capacity scales. Therefore, in order to enhance the demand-side response capability in multi-energy systems and give full play to the function of energy storage power stations, this paper proposes an optimal scheduling model for multi-area energy systems that considers joint demand response and shared energy storage. First, the system energy coupling matrix is constructed based on energy hubs, and the modeling framework for joint demand-side response and shared energy storage is established. Second, the multi-objective optimization problem is solved using NSGA-II algorithm solution with the optimization objectives of minimizing the total operating cost of the system and maximizing the net environmental impact. Finally, the simulation analysis is carried out. The simulation results show that the addition of joint demand response and shared energy storage can guide the scheduling optimization of multiple energy sources in each region in time and space, and realize the energy complementarity and mutual assistance of multi-regional energy systems. Compared with the traditional multi-regional energy system optimization scheduling scheme, the operation cost of the scheme is reduced by 4.2 %, and the environmental protection is improved by 41.9 %, which proves the feasibility of the optimal scheduling model proposed in this paper.

Determination of optimal energy system and level for growing pigs.

This study mainly evaluated the responses in growth performance of growing pigs to different energy systems and energy levels in diets. Subsequently, we compared the nutrient digestibility and digestible nutrient concentrations of each energy level diet. In experiment 1, a total of 144 growing pigs with an average initial body weight (BW) of 26.69 ± 7.39 kg were randomly allotted to six dietary treatments (four pigs/pen; six replicates/treatment) according to a 2 × 3 factorial arrangement resulting from two energy systems (metabolizable energy [ME] and net energy [NE]) and three energy levels (low [LE], recommended [C], and high energy [HE]). Pigs were fed the experimental diets for 6 weeks and were allowed free access to feed and water during the experimental period. In experiment 2, 12 growing pigs with an average initial BW of 27.0 ± 1.8 kg were randomly allotted to individual metabolism crates and fed the six diets in a replicated 6 × 6 Latin square design. The six dietary treatments were identical to those used in the growth trial. Pigs were fed their respective diets at 2.5 times the estimated energy requirement for maintenance per day, and this was divided into two equal meals provided twice per day during the experimental period. Differences in energy systems and energy levels had no significant effect on the growth performance or nutrient digestibility (except acid-hydrolyzed ether extract [AEE]) of growing pigs in the current study. However, the digestible concentrations of ether extract, AEE, and acid detergent fiber (g/kg dry matter [DM]) in diets significantly increased (p < 0.05) with increasing energy levels. Additionally, there was a tendency (p = 0.09) for an increase in the digestible crude protein content (g/kg DM) as the energy content of the diet increased. Consequently, differences in energy systems and levels did not affect the BW, average daily gain, and average daily feed intake of growing pigs. This implies that a higher variation in dietary energy levels may be required to significantly affect growth performance and nutrient digestibility when considering digestible nutrient concentrations.

Complementary assessment and design optimization of a hybrid renewable energy system integrated with open-loop pumped hydro energy storage

The intermittent nature of renewable energy resources is a major obstacle in utilizing such resources for power generation despite their abundance. In this paper, a dynamic analysis of an integrated system based on photovoltaic (PV) panels and wind turbines coupled with pumped hydro energy storage is presented. When the power generation exceeds the power demand, surplus electricity is used to pump the water to the upper reservoir. The natural inflow of the river obviates the necessity for oversized reservoirs and saves costs. A recently introduced multi-objective equilibrium optimizer algorithm is developed to find the optimal size of the integrated hybrid energy system. The objectives are to minimize the levelized cost of electricity and maximize the reliability of the proposed system. The results show that the correlation between solar and wind power generation in the selected location is mildly strong with a value of 0.3. The total energy generation from hydro, solar, and wind resources are 77,508.7 kWh, 103,323 kWh, and 47,375.4 kWh, respectively. Comparing the results with other algorithms indicates that the equilibrium optimizer has a better performance in finding the optimal design.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Reinforcement Learning Techniques in Optimizing Energy Systems

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

Power sector decarbonisation in developing and coal-producing countries: A case study of Indonesia

Global efforts to transition away from coal within a decade encounter significant challenges, particularly in developing and coal-producing countries, e.g. Indonesia. Most current studies do not discuss the impact of coal phase-out on the upstream sector, which is important for coal-producing countries. This study aims to analyse Indonesia's power sector decarbonisation impact on the power and fossil upstream sectors. This analysis used VEDA-TIMES energy system optimisation using three scenarios: least-cost system (BAU), decarbonisation without coal phase-out (PA 1.5), and decarbonisation with coal phase-out (PA 1.5 PO). In all cases, decarbonisation will reduce the amount of coal used in energy systems. However, in a technology-agnostic transition, coal still constitutes 16% of total capacity in 2060. By this time, the coal-fired power generating assets have also transitioned to high efficiency, low emissions (HELE) technologies. CCS plays an important role in Indonesia's decarbonisation. A key implication of the coal phase-out scenario is the stranding of $9 bn worth of existing assets and an increased role for renewable energy and imported natural gas in 2035–2050, posing a notable threat to gas security within the industrial sector. In addition, the potential loss due to untapped coal reserves is estimated at $63 bn, while the cumulative net loss in the coal upstream sector amounts to $799 bn (0.26% of Indonesia's GDP and 3.7% of the coal-producing GDRP). Nevertheless, the total investment and electricity production costs exhibit negligible differences in the rapid VRE expansion, but it will reduce total investment by 24% in the low VRE buildrate.

REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION

Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.&#x0D; Keywords: Role, AI, Energy, Efficiency, Optimization.

Numerical assessment and optimization of photovoltaic-based hydrogen-oxygen Co-production energy system: A machine learning and multi-objective strategy

Green hydrogen production technology based on photovoltaic (PV), battery energy storage system (BESS) and proton exchange membrane (PEM) water electrolysis plays a crucial part in the transition process of energy to zero carbon technology. But few studies have focused on achieving all-day continuous hydrogen production, which is the key to large-scale hydrogen utilization. In this study, we developed an off-grid PV-BESS-PEM system for hydrogen and oxygen production. Firstly, the PV, BESS and PEM water electrolysis components were modeled and model validation was performed. Secondly, an energy management strategy (EMS) is proposed to achieve the goal of continuous and stable hydrogen production. Then, a surrogate model between design variables and optimization objectives is established based on machine learning methods, and a multi-objective optimization algorithm is used to drive the optimization process. Finally, a case study of the PV-BESS-PEM system is presented based on solar irradiation data in Lhasa, where there is sufficient solar resources and oxygen demand. The results demonstrate that the EMS can satisfy the purpose of all-day continuous and stable hydrogen production. The number of cells in the electrolyzer and the water temperature have a significant effect on the hydrogen production and the electrolysis efficiency. And the optimal system has increased the hydrogen production by 16.80 % and the electrolysis efficiency of PEM by 12.08 %, respectively. Herein, the methodological framework is expected to provide theoretical guidance for large-scale water electrolysis hydrogen production applications based on solar energy.

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

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