Integration Of Renewable Resources Research Articles

Synergistic integration of control equipment and renewable resources for improved self-healing in smart distribution networks

Synergistic integration of control equipment and renewable resources for improved self-healing in smart distribution networks

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.

Experimental Study on Phase Change Energy Storage Flooring for Low-Carbon Energy Systems in Grassland Pastoral

Phase change energy storage technology enhances the integration of renewable resources into low-carbon energy systems for grassland pastoral settlements, further addressing the balance between energy needs and environmental sustainability. This study examines a heating system using an experimental platform in an environmental chamber, where the thermal storage and release processes of phase change energy storage flooring were monitored. The results revealed that phase change energy storage flooring exhibits higher heat transfer efficiency and faster heating rates. Under 40 °C heating conditions, the heating rate of the thermal storage layer increased by 12.5% within 1 h. The flooring also demonstrated superior heat release performance, with the peak heat flux of the thermal storage layer delayed by 15 min. Higher heating temperatures shortened the heating time and extended the heat release duration of the phase change energy storage flooring. Under 45 °C heating conditions, the heat transfer efficiency of the surface temperature of the thermal storage layer increased by 38% within 1 hour and by 24.7% over 4 h. In addition, energy consumption in different tests was analyzed, and thermal conductivity was discussed according to the heat transfer model. Phase change energy storage flooring, when coupled with the abundant solar energy resources available in grassland pastoral areas, presents a viable option for the construction of low-carbon energy systems in grassland pastoral settlements.

Optimal operation for district cooling systems coupled with ice storage units based on the per-unit value form

The integration of non-dispatchable renewable resources into the power system, as part of efforts to mitigate carbon emissions and achieve carbon neutrality, presents a significant problem in maintaining the balance between generation and consumption within multi-energy complementary systems. The widespread adoption of district cooling systems (DCS) with ice storage units has proven to be a highly effective approach for enhancing flexibility by changing the operating schedule or adopting different control strategies to participate in demand response, reducing load peaks or peaking and filling to improve system economy. However, the nonlinearity and the presence of multiple variables with different orders of magnitude of the DCS model pose a significant challenge for arithmetic operations well. The present study establishes models based on per-unit value for DCS coupled with ice storage units, and employes several objective functions to validate the selection of base values, demonstrating the superiority of the calculation process for per-unit value model. Simulation results show that, the adoption of per-unit value models of DCS significantly reduce the calculation time, particularly for large systems where it would be possible to have a reduction in time-consuming by more than 68%. Additionally, this paper suggests an operational strategy for real DCS that incorporates demand response by per-unit value models, demonstrating the efficacy of the proposed approach in achieving a reduction of 11.17% in operating costs, while simultaneously enhancing flexibility to meet the load demands of multi-energy complementary systems.

A Fault Restoration Method for DC-interconnected Distribution Systems with Renewable Resources Considering Multiple Fault Scenes

Background:: The distribution systems are gradually transformed into active systems with the large-scale integration of renewable resources, which brings greater potential to the fault recovery of the distribution networks and higher requirements for the power scheduling capability under fault conditions. Power electronic devices such as soft open points (SOPs) can break the radial constraints of the traditional distribution network, provide cross-station power support in fault scenarios, and enhance the resilience of the distribution networks. Methods:: This paper focuses on the distribution systems with multi-transformer flexible interconnection, and a multi-device coordinated fault restoration method is proposed. Firstly, the characteristics of different transformer fault scenes and the fault restoration process coordinated by the bus-bar switches and SOPs in each scene are sorted out. Secondly, an optimization model of the fault restoration for DC-interconnected distribution system is established considering the network operation and the equipment constraints in the fault restoration process. Result:: The proposed model aims at the maximization of the weighted load restoration amount, and is transformed into a second-order cone programming model to be solved quickly. Lastly, the effectiveness of the proposed method is verified in a typical flexible interconnection distribution system. Conclusion:: The simulation results prove that it can effectively improve the capability of preserving power supply for important loads after transformer failure.

Thermo-economic analysis of an integrated combined heating, cooling, and power unit with dish collector and organic Rankine cycle

In recent years, the integration of renewable resources into simultaneous production systems has resulted in a substantial reduction in fuel consumption when compared to the conventional methods. This study focuses on the design and modeling of a CCHP (combined cooling, heating, and power) system for the campus of Faculty of New Sciences and Technologies, University of Tehran. The system harnesses the primary energy and heat derived from a dish collector and natural gas, while electricity generation is facilitated by a Brayton cycle. Additionally, two organic Rankine cycles recover the waste heat and generate supplementary electricity downstream of the system. The total efficiency of this system attained 79%, and the total exergy efficiency of the CCHP reached 48%. When examining the economic sector, noteworthy savings can be observed, compared to a centralized power generation system. This can be attributed to a 37% reduction in fuel consumption, resulting from the adoption of the dish collector as the primary supplier. Overall, this is a novel setup that involves two stages of ORC, along with a dish collector and combustion chamber that supply the microturbine. The proposed system in this article could be a guide for novel commercial and residential systems in the future.

Achieving Carbon Mitigation with Economic Benefits through High-Resolution Analysis of Renewable Resource Integration in Global Coastal Cities.

Coastal regions, home to more than half of the global population and contributing over 50% to the global economy, possess vast renewable resources, such as seawater and solar energy. The effective utilization of these resources, through the seawater-cooled district cooling system (SWDCS), seawater toilet flushing (SWTF), and rooftop solar photovoltaic system (RTPV), has the potential to significantly reduce carbon emissions. However, implementing these technologies in different geographic contexts to achieve the desired carbon and economic outcomes at the city level lacks a clear roadmap. To address this challenge, we comprehensively analyzed 12 coastal megacities worldwide by integrating geospatial building data. Our study evaluated the potential energy savings, carbon mitigation, and levelized carbon abatement costs (LCACs) from a life cycle perspective. The results revealed that using seawater and solar energy within urban boundaries can reduce electricity consumption from 1 to 24% across these cities. The spatial distribution of the LCAC for seawater-based systems exhibited more variation compared to the RTPV. By applying specific LCAC thresholds ranging from 0 to 225 USD/tCO2e, all cities could achieve both carbon reductions and economic benefits. These thresholds resulted in up to 80 million tonnes of carbon emission reductions and 5 billion USD of economic benefits, respectively. Our study provides valuable insights into integrating renewable resource systems, enabling coastal cities to achieve carbon and economic advantages at the city scale simultaneously.

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

Electromagnetic Waves: Exploring The Fundamental Properties and Impact on Modern Technology and Society

This paper thoroughly investigates electromagnetic waves (EM), highlighting their fundamental characteristics, applications in electrical energy transmission, and mobile phone technology. The study begins by dissecting EM waves’ wave-particle duality and spectrum range, establishing a foundation to understand their significant role in modern technology and society. A key focus is placed on their efficiency in electrical energy transmission, especially relevant in the context of increasing energy demands and the integration of renewable resources for sustainable development. The paper also examines how electromagnetic waves have revolutionized mobile phone technology, encompassing aspects like wireless connectivity, GPS, and near-field communication, profoundly influencing global communication and information exchange. Despite these advancements, the paper acknowledges challenges like health and environmental risks associated with electromagnetic pollution. These complexities underscore the need for balanced advancement in electromagnetic wave applications and technology.

Enhancing Virtual Inertia Control in Microgrids: A Novel Frequency Response Model Based on Storage Systems

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today’s microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions.

Integration of renewable resources into the electricity energy matrix. Practical case applied to a small rural municipality

Climate change is a global problem, which is being mitigated over the last decades. To this end, several regulations are being developed to make an energy transition towards renewable resources. In this framework, more and more energy communities are being created in Europe, with the aim of reducing energy consumption based on fossil fuels and promoting the use of renewable energies. This work proposes the design and management of renewable resources to introduce them into the energy matrix. This energy matrix based on renewable energies aims to cover the energy demand of a small rural municipality in the Valencian Community (Spain), for which the annual consumption is known. The results show the possibility of achieving a high percentage of self-consumption in rural municipalities, minimising dependence on the general electricity grid.

Network for the Large-Scale Integration of Renewable Energies in Electrical Systems (RIBIERSE-CYTED, 723RT0150): Results for 2023

The RIBIERSE-CYTED network, i.e., the network for the large-scale integration of renewable energies in electrical systems (723RT0150) (2023-2026) is a hub for researchers and technologists belonging to Ibero-American universities, companies, and local administrations. This network promotes cross-training, mobility between centers, and the dissemination and implementation of technical and training activities aimed at analyzing and developing opportunities for the maximal integration of renewable resources, seeking a more sustainable energy model while allowing for the increased use of renewable resources and electric mobility (e-mobility).

A Barrier-Certificated Reinforcement Learning Approach for Enhancing Power System Transient Stability

Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. This paper aims to provide a general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.

Energy optimization of an electrical supply network by the integration of renewable source Case study: ADE - Adrar

This research lies within the scope of environmental research for seeking solutions for the optimizations of energy in the electrical supply network ADE- Adrar (Algérienne des Eaux d’Adrar/Algeria). Indeed, the principal aiming of this research is the integration of renewable resources (energy independence and sustainable development) that it pushes us to consider from now on the energy problem not only according to the economic point of view, but also according to an ecological point of view (reduction of CO2 emission). This with us encouraged to develop our systems of energy on the basis of distributed generation on a large scale including/understanding renewable energy and the high-output energetic solutions.

Day Ahead Solar Generation Forecasting in Smart Grids to Minimize Electricity Bill

Abstract: In order to meet the increasing growth of energy demand, integration of renewable resources into residential applications appears to be a viable solution. In the proposed model,a residential house is considered where the consumer is able to generate his own energy from a microgrid consisting of solar panels and wind turbines. In this study, an optimization method is employed to minimize the overall electricity bill of a residential home over a periodof 24 hours. In smart grids, energy management is imperative in reducing the electricity cost of consumers under a real time pricing approach. Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is one of the most popular techniques of deep learning. This technique predicts day ahead solar irradiation and wind speed data. We also consider an energy storage system (ESS) for efficient energy utilization. Optimization is done and from the obtained results a substantial reduction in electricity bill is observed

Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

In an electric power system operation, the main goal of economic dispatch (ED) is to schedule the power outputs of committed generating units efficiently. This involves consideration of relevant system equality and inequality constraints to meet the required power demand at the lowest possible operational cost. This is a challenging optimization problem for power system operators that can be dealt with efficient meta-heuristic algorithms. This article uses a recent meta-heuristic approach named the generalized normal distribution optimization (GNDO) algorithm to achieve near-optimal solutions. The efficacy of the proposed GNDO algorithm is validated through experimentation on three distinct test power system networks: one with three thermal units, the second one with six thermal-unit, and the third one with ten thermal units. The algorithm's performance is also assessed on a power network with renewable energy sources. All analyses of the four test cases are conducted on the MATLAB/SIMULINK platform. Finally, this article also compares the obtained results with other literature-reported strategies, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bald eagle search (BES) algorithm. It is evident from the simulated cases that the employed GNDO algorithm exhibits superior performance for two cases and competitive performance for the remaining cases in achieving the lowest operation costs and power losses.

Simulation and study of the simultaneous use of geothermal energy and flue gas waste energy in an innovative combined framework for power, chilled water, and fresh water generation

The integration of renewable energy sources, such as solar, biomass, and geothermal, with other technologies and energy production cycles holds the potential to significantly enhance power generation processes' efficiency. A novel trigeneration structure is introduced that harnesses geothermal energy and waste energy from flue gases. The proposed system comprises several key components, like combined flash and binary geothermal system, a water desalination unit, a Kalina power and cooling cycle, and organic Rankine cycles. The system is analyzed from various perspectives, encompassing energy, exergy, economic, and environmental viewpoints. This assessment allows for a holistic understanding of the trigeneration structure's performance, considering its energy efficiency, exergetic effectiveness, economic viability, and environmental impact. By combining elements, the trigeneration system aims to optimize the utilization of geothermal energy while efficiently converting waste energy from flue gases into useful power. The implementation and evaluation of this trigeneration structure offer promising prospects for sustainable and more effective power generation, promoting the integration of renewable resources with existing energy production technologies. The potential benefits of this approach could lead to a more environmentally friendly and economically viable solution for meeting our energy demands.

Estimation of Energy Consumption for Concentrate Process of Tungsten Ore towards the Integration of Renewable Energy Sources in Mongolia

It is important to estimate the energy required in ore processing to select the most affordable and efficient energy system for the integration of renewable resources into the mining industry. In the present work, the energy consumption for the concentrate of tungsten ore in Mongolia was theoretically predicted based on operational variations (particle size and the hardness of the tungsten ore) and different equipment. The energy was in the range from 0.48 to 1.32 kWh/t for the crushing stage, and a cone crusher was more suitable than a jaw crusher due to the particle size of feed material and product. The required energy in the grinding stage was from 6.22 to 11.88 kWh/t using a SAG mill or from 3.04 to 7.39 kWh/t using a ball mill. The further separation by a flotation consumed 4.83 kWh/t or by a shaking table consumed 1.29 kWh/t. The maximum energy consumption per hour for the whole process was estimated to be 2–3 MW, which was better to integrate with a hybrid renewable energy system. The sizing method Power Pinch Analysis was used to estimate the electric supply based on the combination of wind, biomass and solar resources, which was sufficient for the demand from the predicted range of energy.

Analysis of fuel cell integration with hybrid microgrid systems for clean energy: A comparative review

Microgrids have received a lot of attention in the past few decades and researchers are evaluating the integration of renewable resources especially fuel cells to overcome the energy crisis. This review article aims to provide an in-depth analysis of fuel cells, including the technical complexities and challenges encountered in integration with microgrid systems. Additionally, it explores combined heat and power systems that leverage waste heat recovery. The findings indicated that the solid oxide fuel cells (SOFCs) have extensive applicability in various microgrid systems, including grid-connected, backup, and stand-alone setups. These systems can achieve an efficiency of 95% when combined with the heat and power technique. Electricity generation capacity can be attained up to 100 MW using SOFC-based microgrid systems and generates an average of 33.6 kWh utilizing 1-kg hydrogen. In conclusion, this article provides valuable insights for researchers related to the challenges and future directions in fuel cell integrated microgrids.

An Economic Dispatch for a Shared Energy Storage System Using MILP Optimization: A Case Study of a Moroccan Microgrid

Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green & Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix.