Energy Performance Of System Research Articles

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.

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Water resources are crucial for social and economic development, human needs, and food production. However, with water scarcity on the rise, minimizing waste during pumping is vital. Using DC motors for pumping offers a solution for sustainable and efficient water management. This study explores intelligent control strategies to enhance energy efficiency in water pumping systems, comparing Advanced Fuzzy Logic and PID Controllers through simulation. The research aims to optimize the control approach for water pumping operations, improving energy efficiency and system performance. Simulation results indicate that both controllers effectively regulate water pumping operations. The Advanced Fuzzy Logic Controller offer better response to setpoints, with minimal overshoot 0,7%, fast settling time 0.052 Seconds, Rise Time 0.048 Seconds, Time Peak 0.05 Seconds and no steady-state error, Compared to PID Controller with Overshoot 10%, Settling Time 0.23 Seconds, Rise Time 0.048 Seconds, Time Peak 0.078 Second and no steady-state error Moreover, the Advanced Fuzzy Logic Controller performs overall better performance in terms of energy efficiency and robustness compared to the PID controller. The findings suggest that the energy efficiency of water pumping systems can be significantly increased by utilizing Advanced Fuzzy Logic Controller approaches, specifically the Advanced Fuzzy Logic Controller. This study contributes to developing control methods for sustainable water management systems with potential uses in agriculture, irrigation, and water distribution.

Comparative analysis of space layouts and facade systems for office retrofit design in hot climate

ABSTRACT Worldwide, office buildings account for almost one-third of global energy consumption. Therefore, the energy retrofit design of office buildings could reduce total energy consumption. Considering the benefits of advanced facade systems, retrofit facade design is an effective strategy for energy conservation while improving daylight performance, occupant well-being, and productivity. This paper compares the daylight and energy performance of single and double-skin facade systems used in a typical office building in a hot climate with different space layouts. The main objective is to optimize the daylight and energy performance of various facade systems in combination with a wide range of design variables. Simulations were conducted in DesignBuilder software using a multi-objective optimization approach. Eleven design variables and 103 simulations were considered with the objective of enhancing daylight performance while reducing cooling and lighting loads. Based on the results, single-skin facade configurations could enhance daylight performance significantly compared to double-skin facades, while double-skin facades performed better by reducing the risk of glare and annual energy consumption. Using single and double-skin facade systems could reduce annual total energy consumption by 50% and 60% compared to the existing building.

An Efficient and Interpretable Stacked Model for Wind Speed Estimation Based on Ensemble Learning Algorithms

Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models’ estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively.

Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

Energy efficiency analysis of novel combined open absorption multifunctional heat pump and FlashME desalination system with a compressed air dryer

Energy intensive thermal desalination and compressed air-drying processes coupled with low grade heat recovery units can significantly contribute to carbon and energy footprint reduction. In this paper, a novel combined system based on an open absorption multifunctional heat pump and FlashME desalination system with a compressed air dryer is proposed to recover the latent heat from flue gas and efficiently utilize it to drive the desalination process for seawater treatment and supply the compressed dry air. The energy performance of the combined system is analyzed under the effect of various key operational parameters by simulating the validated process model. The results of energy analysis indicate that the novel system can recover the latent waste heat from flue gas with an efficiency of 83.02 % and supply the compressed dry air at low humidity of 1.41 g/kg by operating at a thermal COP of 1.95. Furthermore, the system can efficiently utilize the recovered low-grade heat to produce freshwater at a recovery rate of 23.14 %. The findings of the critical performance analysis show that the drying, and latent heat recovery performance of the coupled system is dependent mainly on the inlet parameters of the spray solution and the flue gas humidity. However, the water recovery of FlashME is totally dependent on heat capacity and the regeneration pressure such that the performance ratio increases from 1.51 to 4.03 by operating with hot seawater of 60–80 °C, when the regeneration pressure is raised up to 53.48 kPa. Moreover, the coupled system configured in parallel double stage can lower 11.43–37.78 % regeneration load along with 54.24 % improvement in distillate productivity when the operating pressure is raised up to 800 kPa and the rise in airflow rate in dryer can decrease the 10.41–52.45 % regeneration load on external heat source.

Evaluating the impact of virtual energy storage under air conditioning and building coupling on the performance of a grid-connected distributed energy system

Energy storage technologies are vital in improving the operation performance of grid-connected distributed energy systems. The adjustability of indoor temperature and the thermal inertia of buildings can form an excellent virtual energy storage. However, there are few studies on the impact of this virtual energy storage on the operation performance of grid-connected distributed energy systems. Therefore, this study first calculates the equivalent thermal resistance and thermal capacitance of a building by using EnergyPlus and establishes the first-order thermodynamic load calculation model. Subsequently, the system dynamic optimal scheduling model, considering the virtual energy storage, was developed based on the first-order thermodynamic load calculation model. The Gurobi nonconvex solver is used to optimize the model, and the results are compared with the optimal scheduling results without considering the virtual energy storage. The results indicate that, guided by time-of-use electricity pricing, the virtual energy storage effectively reduces the air conditioning load during high and peak tariff periods while increasing it during valley tariff periods. This change in air conditioning load leads to an increase in grid power consumption during valley tariff periods. However, it also improves the system's operation efficiency and ultimately reduces the total grid power and gas consumption. The lower energy consumption makes the primary energy saving rate and carbon dioxide emission reduction rate of Scenario 2 greater than zero compared to Scenario 1 and increases with the increase of renewable energy penetration of the integrated power system. Furthermore, affected by the difference in the proportion of grid power consumption under time-of-use electricity pricing and the total energy consumption, the system's operating cost in Scenario 2 is less than in Scenario 1. The reduction rates in summer and winter typical days are 1.95 % and 6.48 %, respectively. Therefore, fully utilizing the virtual energy storage under air conditioning and building coupling can reduce the operating cost, primary energy consumption, and carbon dioxide emissions of grid-connected distributed energy systems.

A multi-stage interval optimization approach for operation of the smart multi-carries energy system considering energy prices uncertainty

A multi-stage interval optimization approach for operation of the smart multi-carries energy system considering energy prices uncertainty

Artificial Intelligence for Thermal Energy Storage Enhancement: A Comprehensive Review

Abstract Thermal energy storage (TES) plays a pivotal role in a wide array of energy systems, offering a highly effective means to harness renewable energy sources, trim energy consumption and costs, reduce environmental impact, and bolster the adaptability and dependability of power grids. Concurrently, artificial intelligence (AI) has risen in prominence for optimizing and fine-tuning TES systems. Various AI techniques, such as particle swarm optimization, artificial neural networks, support vector machines, and adaptive neurofuzzy inference systems, have been extensively explored in the realm of energy storage. This study provides a comprehensive overview of how AI, across diverse applications, categorizes, and optimizes energy systems. The study critically evaluates the effectiveness of these AI technologies, highlighting their impressive accuracy in achieving a range of objectives. Through a thorough analysis, the paper also offers valuable recommendations and outlines future research directions, aiming to inspire innovative concepts and advancements in leveraging AI for TESS. By bridging the gap between TES and AI techniques, this study contributes significantly to the progress of energy systems, enhancing their efficiency, reliability, and sustainability. The insights gleaned from this research will be invaluable for researchers, engineers, and policymakers, aiding them in making well-informed decisions regarding the design, operation, and management of energy systems integrated with TES.

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

THE IMPACT OF ALBEDO, CLOUD COVERS AND GLOBAL SOLAR RADIATION ON GLOBAL WARMING USING UYO COUNTY DATA, NIGERIA

Ways of countering the causes global warming is the world involve all researchers. The impact of albedo, cloud covers and global solar radiation on global warming have been studied using atmospheric data from Uyo. The data was obtained from NIMET. The outcomes were achieved using Microsoft Excel and SPSS packages. The result obtained showed that albedo varies directly with cloud covers and indirectly with the surface temperature and global solar radiation. The mean values of albedo, cloud cover and global solar radiation are 0.5823 (about 58), 72.25 and 15.22358 MJmday. The challenge of low performance of the solar energy systems from April to October is possible. Uyo may be seen as an unvegetated land, devoid of snow according to this research outcome. Making Uyo a fully forested land, global solar radiation would increase, leading to a decrease in cloud cover. This practice would add to the possible way of resolving the challenge of global warming.

Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

The port microgrid cluster, which integrates berth allocation and energy scheduling for joint optimization, is a highly interconnected system of logistics and energy closely coupled microgrids. It can reduce port load fluctuations and ensure the stable and reliable operation of the port energy system. A port berth allocation and microgrid cluster joint optimization scheduling method based on master-slave game is proposed to address issues such as large fluctuations in current port loads, vulnerability of port power supply system to the impact of vessel loading and unloading operations, and conflicts of interest among various participants in the power market. To ensure power quality while achieving peak shaving and valley filling, a two-stage optimization model for microgrid cluster was constructed, enabling joint optimization of berth allocation and energy scheduling. In the first stage, considering the impact of random events during vessel navigation, berth allocation is conducted with the objectives of maximizing peak shaving and valley filling rates and minimizing overall costs, thereby achieving preliminary load leveling while ensuring power supply system quality. In the second stage, dispatching is performed for power generation equipment and adjustable loads, aiming to further achieve peak shaving and valley filling while maintaining the economic viability of the microgrids. Addressing conflicting interests among stakeholders in the port's electricity market, a master-slave game strategy has been established. This strategy aims to balance the interests between the electricity trading agent and the microgrid aggregator composed of multiple microgrid models. By solving the second-order partial derivatives of each optimization variable with respect to the objective function, we demonstrate the existence and uniqueness of the equilibrium solution in the master-slave game. In response to the challenge of difficulties in solving the multi-objective optimization model due to conflicting objective functions, a Dung Beetle Optimization (DBO) algorithm incorporating non-dominated sorting strategy and crowding distance was introduced. This resulted in a novel multi-objective optimization algorithm termed Non-Dominated Sorting Dung Beetle Optimizer (NSDBO). The NSDBO algorithm was demonstrated to possess favorable characteristics in terms of distribution, convergence, and uniformity. Using a northern Chinese port as an example, berth allocation and energy scheduling for three port areas were jointly optimized, and various scenarios were compared. The results indicated that, compared to Scenario 1, which did not consider random events, the proposed joint optimization method increased the peak shaving and valley filling rate by 8.46 %, reduced network losses by 42 kW, and increased the revenue of the microgrid aggregation merchant by 2.11 %. Compared to Scenario 2, which only considered energy scheduling, the joint optimization increased the peak shaving and valley filling rate by 36.89 %, reduced network losses by 18 kW, and increased the revenue of the microgrid aggregation merchant by 2.29 %. Compared to Scenario 3, which was individually optimized, the joint optimization increased the peak shaving and valley filling rate by 48.42 %, reduced network losses by 24 kW, and increased the revenue of the microgrid aggregation merchant by 2.73 %. This validates the superiority of the proposed joint optimization method in mitigating the impact of random events, reducing network losses, stabilizing port load fluctuations, and enhancing the revenue of the microgrid cluster.

Optimal design and analysis of a grid‐connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar

AbstractThis study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond.

Optimal operation of multiple energy system considering load management approach and uncertainty

In recent years, the advancement of technology has led to the development of energy networks' infrastructure, resulting in the transformation of conventional networks into smart networks. By incorporating communication equipment, each component of the network can now exchange information with other components, creating an environment where innovative ideas can be implemented. One such idea is the utilization of multi-carrier energy systems, also known as Energy Hubs. Integrating energy hubs into traditional networks used to be a challenging task. However, with the expansion and enhancement of smart networks, the potential for incorporating and locating energy hubs within the power grid has significantly increased. In these systems, the focus shifts from optimizing a single energy carrier to ensuring the optimal operation of a system that encompasses multiple energy carriers, such as natural gas, electric grid, and thermal energy. Energy hubs act as intermediaries between energy supply companies and various consumers, including residential, commercial, and industrial sectors. These energy suppliers can encompass different energy networks, such as electricity, gas, and local heating. The integration of residential and commercial energy hubs has become a crucial aspect of modern energy systems, particularly in the context of load response applications. This paper proposes a novel approach for effectively utilizing residential and commercial energy hubs, taking into account the time of use (ToU) load response program. In the proposed system, the inherent uncertainties of the system are addressed using the two-point estimation method of the model. To achieve the desired objective, the Cuckoo optimization algorithm, a well-established meta-heuristic algorithm for solving optimization problems, is employed. This algorithm aids in determining the optimal scheduling of energy resources while considering ToU tariffs and load management plan signals. The results demonstrate that the proposed approach can significantly reduce energy costs for the energy system.

Reliability evaluation of non-isolated high gain interleaved DC-DC converter

A high gain DC-DC converter is the crucial part in renewable energy systems (RES) and in electric vehicular systems. The reliability of those high-gain converters needs to be assessed for the long-term operation of renewable energy systems. This article presents the reliability analysis of non-isolated high gain interleaved DC-DC converter. The analysis primarily relies on calculating the mean time between failures (MTBF). Based on military handbook (MIL-HDBK-217) criteria, the reliability calculation is performed. Stress factors and predicted failure rate for each component of presented converter is evaluated and tabulated. Reliability evaluation is performed for 1.5 kW hardware prototype. Based on reliability evaluation results, a reliable converter with better operating life time has been introduced.

Thermal Comfort, Daylight, and Energy Performance of Envelope-Integrated Algae-based Bioshading and Static Shading Systems through Multi-objective Optimization

Energy-efficient envelopes integrated with different shading devices, as noticeable passive design strategies, have been of great interest for research studies. However, there is a lack of attention on optimizing envelope-integrated novel algae-based bioshading systems (ABBS) in comparison with other shading devices to enhance buildings’ sustainability-oriented performances. Integrating microalgae culture system with building façade as a state-of-the-art bioshading system is among recent developments in high-performance architecture. Although, challenges/limitations concerning different performances of this technology have not been extensively addressed. The present study conducts a multi-objective optimization framework to investigate the Thermal Comfort Percentage (TCP), Useful Daylight Illuminance (UDI), and Energy Usage Intensity(EUI) in a school building to evaluate the relationship between the performance objectives and design variables (shading characteristics, window-to-wall ratio (WWR)) through comparing different static shading systems and ABBS in the hot (BSh) and cold semi-arid (BSk) climates. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was also applied to determine the best design options within Pareto frontiers. Results demonstrated that horizontal louver by 128.52% TCP, 15.10% UDI improvement in south-oriented façade in BSk, and overhang with fin(OF) by 18.99% EUI reduction in southern facade in BSh climates contribute the most to enhance the objective metrics. Also, ABBS was not a stand-first system to enhance all the objective metrics compared to the other shading systems in none of the examined climates. Sensitivity analysis indicated that WWR has the most significant positive impact on EUI compared to the other studied metrics in almost all design options.

Projected wind and solar energy potential in the eastern Mediterranean and Middle East in 2050

The ongoing energy transition from conventional fuels to renewable energy sources (RES) has given nations the potential to achieve levels of energy self-sufficiency previously thought unattainable. RES in the form of utility-scale solar and wind energy are currently the leading alternatives to fossil-fuel generation. Precise location siting that factors in efficiency limitations related to current and future climate variables is essential for enabling the green energy transition envisioned for 2050. In this context, understanding and mapping the intermittency of RES provides insights to energy system operators for their seamless integration into the grid. The Eastern Mediterranean and Middle East (EMME) region has the potential to harness vast amounts of RES. The scarcity of observations from weather station networks and the lack of private sector incentives for transitioning to RES mean that relevant, supporting weather and climate studies have been limited. This study employs the Weather Research and Forecasting model with Chemistry to estimate the RES technical potential of EMME countries and map the hourly generation profiles per source and country, simulated for the reference year 2015 and considering future conditions. The findings indicate that by 2050, seven countries within the region could transform into net energy exporters, while the remaining nine might remain reliant on energy imports or fossil fuels. Egypt emerges as a “powerhouse”, potentially enjoying a potential surplus energy generation of 76 GW per hour, whereas the United Arab Emirates may face an annual deficit of 955 TWh. Further, we derived the hourly generation profiles for wind and solar during different seasons. Four dominant patterns were identified. We find a complementary relationship for six countries, and for four countries, a substitute relationship between solar and wind energy generation. Greece stands out with a near-constant wind energy source, which would facilitate its integration into the national grid.

Optimal control of a heat pump-based energy system for space heating and hot water provision in buildings: Results from a field test

This paper presents and discusses results from a field test that uses model predictive control (MPC) to optimise the operation of a multi-source heat pump-based energy system for space heating and hot water provision in a building. The objective of the optimisation is to minimise the electricity consumption of the heat pump using flexibility from the heat sources, the space heating and domestic hot water tanks while ensuring end-user comfort and system constraints. The field test was performed in the context of the RES4BUILD project. The integrated system consists of photovoltaic-thermal (PVT) collectors, an advanced vapour-compression multi-source heat pump and water buffer tanks for space heating and hot water needs. The heat pump utilises a solar buffer, a borehole thermal energy storage (BTES) and air as heat sources. The solar buffer is supplied with heat from the PVT collectors. The optimal operation of the system is obtained by monitoring and controlling the interactions between the different system components using MPC, taking into account the weather and heat load forecasts. Results have shown that MPC has a potential and added value for the optimal operation of multi-source heat pumps in real-life while considering system constraints and user behaviour to ensure thermal comfort. However, significant effort and expert knowledge are needed to develop the sufficiently accurate system models required by this control approach. The outcomes and conclusions of this work are therefore a basis for further development of such control approaches to improve their replicability and feasibility on a large scale, considering the diverse nature of energy system components in buildings.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.