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1
Khan, Kamil, Ahmad Kamal, Abdul Basit, Tanvir Ahmad, Haider Ali, and Anwar Ali. "Economic Load Dispatch of a Grid-Tied DC Microgrid Using the Interior Search Algorithm." Energies 12, no. 4 (February 16, 2019): 634. http://dx.doi.org/10.3390/en12040634.
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This paper presents the effectiveness of the interior search algorithm in economic power scheduling of a grid-tied DC microgrid with renewable generation (wind and photovoltaic) and battery energy storage. The study presents the modelling and simulation of various DC/DC converters for tracking maximum power from wind and photovoltaic sources and the bidirectional power flow of battery energy storage. The DC microgrid and its controllers were modelled and simulated in MATLAB/Simulink. The generating units were dispatched economically using the interior search algorithm with the objective to minimize the operating cost of the microgrid. The simulated results verify the effectiveness of the interior search algorithm as the daily cost of microgrid operation was reduced by 11.25%.
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Jia, Chun Xia, Yi Ping Guo, and Shu Long Teng. "Technical and Economic Analysis of BIPV Project in a University Campus of Beijing." Advanced Materials Research 450-451 (January 2012): 1477–81. http://dx.doi.org/10.4028/www.scientific.net/amr.450-451.1477.
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The campus building-integrated photovoltaic project is introduced in the paper. The installed gross power of the PV system is 470 KW, and its generation index is 173.7 KWh/m2.Compared with the traditional municipal power supply, the unit incremental cost of photovoltaic is 53.5 RMB/W and the cost effectiveness ratio is 1.75 RMB/KWh. However utilization of PV system will save fossil energy, lower pollutions and greenhouse gases obviously.
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Wang, Chen, Fu Yang, Xiuqiang Chen, Houming Song, and Zhihua Li. "Multi-object optimal configuration of energy storage-photovoltaic capacity in AC/DC active distribution network." Journal of Physics: Conference Series 2260, no. 1 (April 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2260/1/012041.
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Abstract The AC/DC active distribution network is considered as an efficient way to achieve intelligent use of electricity. However, the economical configuration of distributed generation is critical for system economic operation. In the power distribution network, the economic operation mode of the photovoltaic power generation, storage battery and load are established firstly. Considering the cost of equipment installation, replacement, operation and maintenance, power purchase cost, and power sales profit, the use life of battery and security constraint, a multi-object economics evaluation function is then established. By optimizing the install capacity of distributed power generation, the operating economy of the distribution network is significantly improved. Finally, using the operating data of the partner-type intelligent power generation system in Lianyungang, the total cost of the operating state is calculated to determine the optimal capacity allocation scheme of the system, and the effectiveness of the proposed capacity allocation method is verified.
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Hsieh, Wei Lin, Chia Hung Lin, Chao Shun Chen, Cheng Ting Hsu, Chin Ying Ho, and Hui Jen Chuang. "Optimal Penetration of Photovoltaic Systems in Distribution Networks." Applied Mechanics and Materials 479-480 (December 2013): 590–94. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.590.
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The penetration level of a PV system is often limited due to the violation of voltage variation introduced by the large intermittent power generation. This paper discusses the use of an active power curtailment strategy to reduce PV power injection during peak solar irradiation to prevent voltage violation so that the PV penetration level of a distribution feeder can be increased to fully utilize solar energy. When using the proposed voltage control scheme for limiting PV power injection into the study distribution feeder during high solar irradiation periods, the total power generation and total energy delivered by the PV system over a 1-year period are determined according to the annual duration of solar irradiation. With the proposed voltage control to perform the partial generation rejection of PV systems, the optimal installation capacity of PV systems can be determined by maximizing the net present value of the system so that better cost effectiveness of the PV project and better utilization of solar energy can be obtained.
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Yoza, Akihiro, Kosuke Uchida, Atsushi Yona, and Tomonobu Senju. "Optimal Operation Method of Smart House by Controllable Loads based on Smart Grid Topology." International Journal of Emerging Electric Power Systems 14, no. 5 (August 7, 2013): 411–20. http://dx.doi.org/10.1515/ijeeps-2012-0059.
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Abstract From the perspective of global warming suppression and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all electrification apartment house or residence such as DC smart house have increased in recent years. However, due to fluctuating power from renewable energy sources and loads, supply–demand balancing fluctuations of power system become problematic. Therefore, “smart grid” has become very popular in the worldwide. This article presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuations. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuations, it is possible to reduce the maximum electric power consumption and the electric cost. This system consists of photovoltaics generator, heat pump, battery, solar collector, and load. In order to verify the effectiveness of the proposed system, MATLAB is used in simulations.
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LEE, Donggil, Seongjae JEONG, Seonghun KIM, Pyungkwan KIM, and Yongsu YANG. "Analysis of Cost Effectiveness on Fishing Trip Cost by Adopting Photovoltaic Power Generation System in a Small Fishing Vessel." JOURNAL OF FISHRIES AND MARINE SCIENCES EDUCATION 29, no. 5 (October 31, 2017): 1470–79. http://dx.doi.org/10.13000/jfmse.2017.29.5.1470.
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Hsu, Cheng-Ting, Roman Korimara, and Tsun-Jen Cheng. "Cost-Effectiveness Analysis of a PVGS on the Electrical Power Supply of a Small Island." International Journal of Photoenergy 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/264802.
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This paper presents a feasibility study of a large simulated stadium-scale photovoltaic generation system (PVGS) on a small island. Both the PVGS contribution to the energy demand on the island and its financial analysis were analysed in this study. The maximum allowable PVGS installation capacity is obtained by executing load flow analysis without violating the voltage magnitude and voltage variation ratio limits. However, the estimated power generation of PVGS is applied to know its impact on the power system according to the hourly solar irradiation and temperature. After that, the cost-benefit analysis of payback years (PBY) and net present value (NPV) method is derived considering the cash flow from utilities annual fuel and loss saving, the operation and maintenance (O&M) cost, and the capital investment cost. The power network in Kiribati (PUB DNST) is selected for study in this paper. The simulation results are very valuable and can be applied to the other small islands for reducing the usage of fossil fuel and greenhouse gas emissions.
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Melo, Gustavo Costa Gomes de, Igor Cavalcante Torres, Ícaro Bezzera Queiroz de Araújo, Davi Bibiano Brito, and Erick de Andrade Barboza. "A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application." Sensors 21, no. 9 (May 10, 2021): 3293. http://dx.doi.org/10.3390/s21093293.
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Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.
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Pan, Tingzhe. "A Novel Coordinated Control System to Reactive Power Compensation of Photovoltaic Inverter Clusters." International Transactions on Electrical Energy Systems 2022 (October 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/6396345.
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With the development of new energy, a cost-effective reactive power compensation scheme is essential to the voltage stability of the power system for small-capacity distributed generation. This paper proposes a coordinated control scheme of inverter cluster which is based on the reactive power support capability of the photovoltaic inverter. Moreover, by using power angle vectors, a reactive power distribution algorithm is proposed to solve the poor power quality of the point of common coupling connecting source and load in the distributed generation station. Simulations verify the performance of the algorithm is better than the conventional static capacity distribute algorithm and dynamic residual margin distribute algorithm. Finally, the effectiveness of the reactive power compensation scheme and distribution strategy for improving power quality and regulation ability proposed in this paper is verified by operation experiments in the actual power station.
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Ananthu, Durga Prasad, Neelshetty K., and M. Venkateshkumar. "Artificial intelligent controller-based energy management system for grid integration of PV and energy storage devices." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 617. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp617-628.
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In the modern world, photovoltaic (PV) energy generation is becoming more prevalent and cost-effective. To address climate change, many countries have prioritised photovoltaics and made significant investments in energy generation. Because of its non-linear nature, solar energy generation is extremely difficult. This is completely dependent on the solar radiation and the outside temperature. The maximum power generation of a PV system in non-linear weather circumstances and the grid integration of PV with power management are discussed in this article. Artificial intelligence (AI) is vital for improving the energy output of PV systems across a wide range of environmental conditions because traditional controllers do not aid a solar system in producing the maximum energy. The grid integration of PV and EMS (energy management systems) was covered in the later part of this article. In this paper, artificial intelligence is used to provide customers with continuous power through a battery system, which plays a critical role in energy management. Furthermore, the suggested model was simulated in Matlab and its performance was evaluated under various operational scenarios. To demonstrate the effectiveness of the proposed system, the results are compared to IEEE 519.
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Liu, Ximu, Mi Zhao, Zihan Wei, and Min Lu. "Economic Optimal Scheduling of Wind–Photovoltaic-Storage with Electric Vehicle Microgrid Based on Quantum Mayfly Algorithm." Applied Sciences 12, no. 17 (August 31, 2022): 8778. http://dx.doi.org/10.3390/app12178778.
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The effectiveness of energy management systems is a great concern for wind–photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind–photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit’s operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system’s cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function
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Wai, Rong-Jong, and Pin-Xian Lai. "Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data." Energies 15, no. 10 (May 23, 2022): 3838. http://dx.doi.org/10.3390/en15103838.
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In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of data transmission cost, it brings more challenges to the solar PV power generation forecast. Because power operators usually need real-time solar PV power generation as a basis for the power dispatch, but considering the cost of communication, they cannot always provide corresponding historical power generation data in real time. In this study, an intelligent solar PV power generation forecasting mechanism combined with weather information is designed to cope with the issue of the absence of real-time power generation data. Firstly, the Pearson correlation coefficient analysis is used to find major factors with a high correlation in relation to solar PV power generation to reduce the computational burden of data fitting via a deep neural network (DNN). Then, the data preprocessing, including the standardization and the anti-standardization, is adopted for data-fitting or real-time solar PV power generation data to take as the input data of a long short-term memory neural network (LSTM). The salient features of the proposed DNN-LSTM model are: (1) only the information of present solar PV power generation is required to forecast the one at the next instant, and (2) an on-line learning mechanism is helpful to adjust the trained model to adapt different solar power plant or environmental conditions. In addition, the effectiveness of the trained model is verified by six actual solar power plants in Taiwan, and the superiority of the proposed DNN-LSTM model is compared with other forecasting models. Experimental verifications show that the proposed forecasting model can achieve a high accuracy of over 97%.
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Siwiec, Dominika, and Andrzej Pacana. "Model of Choice Photovoltaic Panels Considering Customers’ Expectations." Energies 14, no. 18 (September 20, 2021): 5977. http://dx.doi.org/10.3390/en14185977.
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Photovoltaic electricity generation is key to achieving deep decarbonization with a high degree of electrification. It is predicted that the energy sector will reduce carbon dioxide by producing electricity mainly from photovoltaic (PV) power. Although dynamic development of the implementation of photovoltaic panels has been observed, their choice considering customer specificity is still a problem. Therefore, the purpose of this study is to propose the model of choice photovoltaic panels considering customers’ expectations. It can support the choice of a photovoltaic panel of a certain quality (satisfaction of concrete customer) in combination with the cost of its purchase. The proposed model includes acquiring and then processing customers’ expectations into technical criteria, while simultaneously considering the weighting of these criteria. It is realized in a standardized way, i.e., the zero-unitarization method (MUZ), after which normalized values of the quality of the photovoltaic panels’ criteria are obtained. In turn, the quality of these products is estimated by the weighted sum model (WSM) and then integrated with purchase cost in qualitative cost analysis (AKJ). As a result, using the scale of relative states, it is possible to categorize customer satisfaction from indicating qualitative cost and selecting the photovoltaic panel expected by customers (the most satisfactory). The effectiveness of the model was demonstrated by a sensitivity analysis, after which the key PV criteria were indicated. The proposed model is intended for any entity who selects a photovoltaic panel for customers. The computerization of calculations may contribute to its utilitarian dissemination.
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Pan, Yen, Wang, Sun, Huang, Hwang, Liu, Chu, and Hoe. "A Misalignment Optical Guiding Module for Power Generation Enhancement of Fixed-Type Photovoltaic Systems." Micromachines 10, no. 10 (October 11, 2019): 687. http://dx.doi.org/10.3390/mi10100687.
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: This study presents a misalignment light-guiding module to increase the effectiveness of absorbing light. For a general fixed-type photovoltaic (PV) panel, the misalignment light decreases the efficiency of the system. A solar tracking system was installed for obtaining higher power generation. However, the cost of the PV system and maintenance was 5–10 times higher than the general type. In this study, this module is composed of an array of misalignment light-guiding units that consist of a non-axisymmetric compound parabolic curve (NACPC) and a freeform surface collimator. The NACPC efficiently collects the misalignment light within ±30° and guides the light to the collimator. The light has a better uniformity and smaller angle at the exit aperture. The simulation results show that the optical efficiency of the unit was above 70% when the misalignment angle was smaller than 20°. The experimental results show that the power generation of the light-guiding unit was 1.8 times higher than the naked PV panel.
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Menezes, Roberto Felipe Andrade, Guilherme Delgado Soriano, and Ronaldo Ribeiro Barbosa de Aquino. "Locational Marginal Pricing and Daily Operation Scheduling of a Hydro-Thermal-Wind-Photovoltaic Power System Using BESS to Reduce Wind Power Curtailment." Energies 14, no. 5 (March 6, 2021): 1441. http://dx.doi.org/10.3390/en14051441.
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The Daily Operation Scheduling (DOS) gets new challenges while a large-scale of renewable energy is inserted into the power system. In addition to the operation, the power variability of these sources also causes a problem in the hourly pricing, represented here by Locational Marginal Pricing (LMP). Therefore, new applications, such as energy shifting, offer greater efficiency to the system, minimizing the negative effects caused by wind power curtailment (WPC). This paper shows the LMP formation in the DOS of the hydro-thermal-wind-photovoltaic power system with a battery energy storage system and the reduction of WPC. Here, the wind and photovoltaic power plants are designed to be dispatched, not mandatory, to be able to cut the generation, and the insertion of Distributed Generation is considered. Moreover, to solve the DOS problem, the interior-point method is used. Additionally, the DC optimal power flow, used to represent the DOS in addition to the representation of the electric grid, is modeled with an iterative approach. The analysis is made in an IEEE 24-bus system with data from Brazil. Lastly, the results of simulations are presented and discussed, demonstrating the effectiveness of the optimization to reduce the WPC, the total operation cost, and to provide the LMP curve.
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Li, Rui, and Peng Li. "CHP Microgrid Optimized Operation Based on Bacterial Foraging Optimization Algorithm." Advanced Materials Research 981 (July 2014): 668–72. http://dx.doi.org/10.4028/www.scientific.net/amr.981.668.
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CHP system with Energy saving, environmental protection, economic and other characteristics,have good prospects for the development and application value.This paper directe a micro grid system consisted by photovoltaic cells, wind turbines, fuel cells, microturbines, auxiliary boilers, thermal energy storage systems and batteries and heat load and electrical load.Considering various distributed power generation costs, environmental costs and micro-grid equipment maintenance costs,To meet the constraints of micro-grid operation, optimization of the different micro-grid distributed power and energy storage system power output, make the system's total operating costs are minimized.This paper analyzes the economic and environmental of micro-grid optimal operation characteristics, given a model of CHP micro-grid.For the cost of power generation and emissions of different weights, using bacterial foraging optimization(BFO) algorithm,through a numerical example verified the Correctness and effectiveness of mathematical model and optimization algorithm .
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Boyekin, Tahsin, and İsmail Kıyak. "Technoeconomic Performance Analysis of Solar Tracking Methods for Roof-Type Solar Power Plants and Electric Vehicle Charging Stations." International Journal of Photoenergy 2021 (April 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/6681084.
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In building integrated photovoltaic (BIPV) solar energy projects, cost effectiveness, durability, and long-term reliability are among the criteria that should be taken into consideration as well as the gain in electricity generation efficiency. Also, in a study, it is stated that a dual-axis solar tracking system occupies approximately 100% more space than a single-axis system and 160% more than a fixed-angle system. It has been observed that most of the studies that are mounted on the building and include a tracking system are small-scale experimental studies. The aim of this article is to present a systematic analysis with a low investment cost, a low operating cost, and high reliability, in a real application especially for roof applications in buildings. Three buildings in the same location and with the same roof area were selected. Photovoltaic power plants with 23.68 kW power were installed; these panels had three types: fixed-angle, manually controlled, and single-axis solar tracking systems. The energy generation system is connected to the network with a double-sided meter, and there is a double-sided energy flow. The energy produced is used to meet the energy needs of the vehicle charging station and common areas of the buildings. Although the single-axis tracking system is 27.85% more efficient than other energy generation methods, the manually adjusted method has proven to have the shortest amortization time. The study also presents shading, which is a serious problem in large-scale roof projects, and the area covered by the module per unit watt produced.
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Rana, Md Masud, Akhlaqur Rahman, Moslem Uddin, Md Rasel Sarkar, SK A. Shezan, C. M. F. S. Reza, Md Fatin Ishraque, and Mohammad Belayet Hossain. "Efficient Energy Distribution for Smart Household Applications." Energies 15, no. 6 (March 13, 2022): 2100. http://dx.doi.org/10.3390/en15062100.
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Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically.
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Shimoji, Tsubasa, Hayato Tahara, Hidehito Matayoshi, Atsushi Yona, and Tomonobu Senjyu. "Comparison and Validation of Operational Cost in Smart Houses with the Introduction of a Heat Pump or a Gas Engine." International Journal of Emerging Electric Power Systems 16, no. 1 (February 1, 2015): 59–74. http://dx.doi.org/10.1515/ijeeps-2014-0137.
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Abstract Due to the concerns of global warming and the depletion of energy resources, renewable energies such as wind generation (WG) and photovoltaic generation (PV) are gaining attention in distribution systems. Efficient electric equipment such as heat pumps (HP) not only contribute low levels of carbon to society, but are also beneficial for consumers. In addition, gas instruments such as the gas engine (GE) and fuel cells (FC) are expected to reduce electricity cost by exhaust heat. Thus, it is important to clarify which systems (HP or GE) are more beneficial for consumers throughout the year. This paper compares the operational cost for the smart house between using the HP and the GE. Current electricity and gas prices are used to calculate the cost of the smart house. The system considered in this research comprises a PV, battery, solar collector (SC), uncontrolled load and either an HP or a GE. In order to verify the effectiveness of the proposed system, MATLAB is used for simulations.
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Gamil, Mahmoud M., Makoto Sugimura, Akito Nakadomari, Tomonobu Senjyu, Harun Or Rashid Howlader, Hiroshi Takahashi, and Ashraf M. Hemeida. "Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs." Energies 13, no. 14 (July 16, 2020): 3666. http://dx.doi.org/10.3390/en13143666.
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Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.
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Kiptoo, Mark Kipngetich, Oludamilare Bode Adewuyi, Mohammed Elsayed Lotfy, Theophilus Amara, Keifa Vamba Konneh, and Tomonobu Senjyu. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning." Future Internet 11, no. 10 (October 22, 2019): 219. http://dx.doi.org/10.3390/fi11100219.
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The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.
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Ma, Junchao, Xiaoming Huang, Boliang Lou, Xiulin Xiao, Yinlong Fan, and Dan Zhou. "Adaptive Voltage Control of Distribution Network with High Proportion PV." E3S Web of Conferences 252 (2021): 01033. http://dx.doi.org/10.1051/e3sconf/202125201033.
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With the increase of grid-connected PV capacity, voltage regulation at point of common coupling by controlling the reactive power injected into the grid is available. This paper presents an adaptive voltage control strategy for distribution network with high proportion PV system. The PI gain of the voltage controller is automatically adjusted by the extremum seeking algorithm to dynamically respond to the changes of the network. The PI gains are updated online through the minimization of a cost function, which represents the voltage controller performance. Finally, a distribution network model of 5 MW photovoltaic power generation system is built in MATLAB / Simulink to verify the effectiveness of the proposed control strategy.
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Sankar, R. S. Ravi, S. V. Jayaram Kumar, and K. K. Deepika. "Flexible Power Regulation of Grid-Connected Inverters for PV Systems Using Model Predictive Direct Power Control." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 1, 2016): 508. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp508-519.
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<p>This paper presents a Model Predictive Direct Power Control (MPDPC) strategy for a grid-connected inverter used in a photovoltaic system, as found in many distributed generating installations. The controller uses a system model to predict the system behavior at each sampling instant. Using a cost function, the voltage vector with least power ripple is generated. The resultant voltage vector is applied during the next sampling period which gives flexible power regulation. The effectiveness of the proposed MPDPC strategy is verified using MATLAB/ SIMULINK. </p>
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Riaz, Muhammad, Aamir Hanif, Haris Masood, Muhammad Attique Khan, Kamran Afaq, Byeong-Gwon Kang, and Yunyoung Nam. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer." Sustainability 13, no. 23 (December 3, 2021): 13382. http://dx.doi.org/10.3390/su132313382.
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A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.
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Gao, Ren, Juebo Wu, Wen Hu, and Yun Zhang. "An Improved ABC Algorithm for Energy Management of Microgrid." International Journal of Computers Communications & Control 13, no. 4 (July 25, 2018): 477–91. http://dx.doi.org/10.15837/ijccc.2018.4.3143.
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Microgrids are an ideal way of electricity generation, distribution, and regulation for customers by means of distributed energy resources on the community level. However, due to the randomness of photovoltaic and wind power generation, it is a crucial and difficult problem to achieve optimal economic dispatch in microgrids. In this paper, we present an optimal economic dispatch solution for a microgrid by the improved artificial bee colony (ABC) optimization, with the aim of satisfying load and balance demand while minimizing the cost of power generation and gas emission. Firstly, we construct a mathematical model according to different characteristics of distributed generation units and loads, and improve the performance of global convergence for ABC in order to fit such model. Secondly, we explore how to solve the optimal economic dispatch problem by the improved ABC and give the essential steps. Thirdly, we carry out several simulations and the results illustrate the benefits and effectiveness of the proposed approach for optimal economic dispatch in microgrid.
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Qin, Yunfu, Hongyu Lin, Zhongfu Tan, Qingyou Yan, Li Li, Shenbo Yang, Gejirifu De, and Liwei Ju. "A Dispatching Optimization Model for Park Power Supply Systems Considering Power-to-Gas and Peak Regulation Compensation." Processes 7, no. 11 (November 4, 2019): 813. http://dx.doi.org/10.3390/pr7110813.
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To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of a park power system with P2G was built. Secondly, a dispatching optimization model for the park power supply system was constructed with a peak regulation compensation mechanism. Finally, the effectiveness of the model was verified by a case study. The case results show that with the integration of P2G and the marketized peak regulation compensation mechanism, preferential power energy storage followed by gas storage had the best effect on the park power supply system, which minimized the clean energy curtailment to 11.18% and the total cost by approximately $120.190 and maximized the net profit by approximately $152.005.
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Park, Woan-Ho, Hamza Abunima, Mark B. Glick, and Yun-Su Kim. "Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy." Energies 14, no. 19 (September 23, 2021): 6038. http://dx.doi.org/10.3390/en14196038.
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The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid
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Ali Khan, Muhammad Yasir, Haoming Liu, Zhihao Yang, and Xiaoling Yuan. "A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies." Energies 13, no. 16 (August 13, 2020): 4185. http://dx.doi.org/10.3390/en13164185.
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The installation of photovoltaic (PV) system for electrical power generation has gained a substantial interest in the power system for clean and green energy. However, having the intermittent characteristics of photovoltaic, its integration with the power system may cause certain uncertainties (voltage fluctuations, harmonics in output waveforms, etc.) leading towards reliability and stability issues. In PV systems, the power electronics play a significant role in energy harvesting and integration of grid-friendly power systems. Therefore, the reliability, efficiency, and cost-effectiveness of power converters are of main concern in the system design and are mainly dependent on the applied control strategy. This review article presents a comprehensive review on the grid-connected PV systems. A wide spectrum of different classifications and configurations of grid-connected inverters is presented. Different multi-level inverter topologies along with the modulation techniques are classified into many types and are elaborated in detail. Moreover, different control reference frames used in inverters are presented. In addition, different control strategies applied to inverters are discussed and a concise summary of the related literature review is presented in tabulated form. Finally, the scope of the research is briefly discussed.
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Nishimwe H., Leon Fidele, and Sung-Guk Yoon. "Combined Optimal Planning and Operation of a Fast EV-Charging Station Integrated with Solar PV and ESS." Energies 14, no. 11 (May 28, 2021): 3152. http://dx.doi.org/10.3390/en14113152.
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Sufficient and convenient fast-charging facilities are crucial for the effective integration of electric vehicles. To construct enough fast electric vehicle-charging stations, station owners need to earn a reasonable profit. This paper proposed an optimization framework for profit maximization, which determined the combined planning and operation of the charging station considering the vehicle arrival pattern, intermittent solar photovoltaic generation, and energy storage system management. In a planning horizon, the proposed optimization framework finds an optimal configuration of a grid-connected charging station. Besides, during the operation horizon, it determines an optimal power scheduling in the charging station. We formulated an optimization framework to maximize the expected profit of the station. Four types of costs were considered during the planning period: the investment cost, operational cost, maintenance cost, and penalties. The penalties arose from vehicle customers’ dissatisfaction associated with waiting time in queues and rejection by the station. The simulation results showed the optimal investment configuration and daily power scheduling in the charging station in various environments such as the downtown, highway, and public stations. Furthermore, it was shown that the optimal configuration was different according to the environments. In addition, the effectiveness of solar photovoltaic, energy storage system, and queue management was demonstrated in terms of the optimal solution through a sensitivity analysis.
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Smadi, Issam A., and Ahmad AL-Ramaden. "An algorithm to extract the maximum power from the PV-based generation systems under non-uniform weather." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 2 (June 1, 2022): 1129. http://dx.doi.org/10.11591/ijpeds.v13.i2.pp1129-1139.
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This paper presents a fast and simple algorithm to extract the maximum power under non-uniform weather from the photovoltaic (PV) based generation systems. The proposed algorithm’s three stages are the scanning stage, the tracking stage, the detecting and avoiding the hidden points stage. The hidden points are caused by a transition between the global maximum power point (GMPP) and a local maximum power point (LMPP) when the partial shading conditions (PSCs) are changed. This transition cannot be observed by monitoring only the power difference of the PV generation system. Simulation results with comparisons to other algorithms developed for global maximum power point tracking (GMPPT) under PSCs are provided to clarify and show the effectiveness of the proposed GMPPT algorithm. The average tracking speed of the proposed algorithm is two times faster than the compared MPPT algorithms, with about 2% more power generated with no additional cost. Moreover, the proposed GMPPT algorithm is implemented in real-time using National Instruments (NI) CompactRIO in field-programmable gate array (FPGA) mode to confirm the applicability of the proposed work.
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Cortes-Vega, David, Hussain Alazki, and Jose Luis Rullan-Lara. "Current Sensorless MPPT Control for PV Systems Based on Robust Observer." Applied Sciences 12, no. 9 (April 26, 2022): 4360. http://dx.doi.org/10.3390/app12094360.
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Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.
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Cortes-Vega, David, Hussain Alazki, and Jose Luis Rullan-Lara. "Current Sensorless MPPT Control for PV Systems Based on Robust Observer." Applied Sciences 12, no. 9 (April 26, 2022): 4360. http://dx.doi.org/10.3390/app12094360.
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Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.
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Naji Alhasnawi, Bilal, Basil H. Jasim, and M. Dolores Esteban. "A New Robust Energy Management and Control Strategy for a Hybrid Microgrid System Based on Green Energy." Sustainability 12, no. 14 (July 16, 2020): 5724. http://dx.doi.org/10.3390/su12145724.
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The recent few years have seen renewable energy becoming immensely popular. Renewable energy generation capacity has risen in both standalone and grid-connected systems. The chief reason is the ability to produce clean energy, which is both environmentally friendly and cost effective. This paper presents a new control algorithm along with a flexible energy management system to minimize the cost of operating a hybrid microgrid. The microgrid comprises fuel cells, photovoltaic cells, super capacitors, and other energy storage systems. There are three stages in the control system: an energy management system, supervisory control, and local control. The energy management system allows the control system to create an optimal day-ahead power flow schedule between the hybrid microgrid components, loads, batteries, and the electrical grid by using inputs from economic analysis. The discrepancy between the scheduled power and the real power delivered by the hybrid microgrid is adjusted for by the supervisory control stage. Additionally, this paper provides a design for the local control system to manage local power, DC voltage, and current in the hybrid microgrid. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses load fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses’ daily electrical load demand. Also, the control of the studied hybrid microgrid is designed as a method to improve hybrid microgrid resilience and incorporate renewable power generation and storage into the grid. The simulation results verified the effectiveness and feasibility of the introduced strategy and the capability of proposed controller for a hybrid microgrid operating in different modes. The results showed that (1) energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption, and (2) the newly developed energy management system proved to provide more robust and high performance control than conventional energy management systems. Also, the results demonstrate the effectiveness of the proposed robust model for microgrid energy management.
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Babatunde, Olubayo M., Josiah L. Munda, and Yskandar Hamam. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation." Energies 13, no. 10 (May 15, 2020): 2488. http://dx.doi.org/10.3390/en13102488.
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The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.
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Jha, Nishant, Deepak Prashar, Mamoon Rashid, Zeba Khanam, Amandeep Nagpal, Ahmed Saeed AlGhamdi, and Sultan S. Alshamrani. "Energy-Efficient Hybrid Power System Model Based on Solar and Wind Energy for Integrated Grids." Mathematical Problems in Engineering 2022 (February 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/4877422.
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Global energy needs have risen in recent years, and traditional energy sources such as fossil fuels are no longer viable. To meet the growing electricity demand, attention has moved to renewable energy sources such as solar and wind energy. Furthermore, the development of clean energy is vital for combating climate change. Various studies have shown the effectiveness of using hybrid systems (combination of solar photovoltaic and wind energy systems) for generating power. However, a significant amount of energy gets wasted. To prevent the wastage of energy, a dual-energy generation system for integrated grids has been suggested in this paper. The load data have been collected from various regions in Rajasthan, India. An optimal grid system configuration is designed using net present cost and cost per unit of energy. Other factors such as the tilt angle of PV array optimization, wind energy, and inverter optimization have also been used for increasing the reliability and stability of the system. Sensitivity analysis has been performed to analyze the effective variations of the capital costs on the developed system economy. The results obtained from the simulations show that the overall costs of generating electricity from diesel systems and nongrid-based systems have been reduced to 20% at 10% annual capacity shortage allowance. Upon cost analysis, we found that the total cost for installing the suggested system is 49,500 USD, whereas for other systems, the costs came out as 66,000 USD, 56,500 USD, and 56,300 USD, respectively.
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Rauf, Abdul, Ali T. Al-Awami, Mahmoud Kassas, and Muhammad Khalid. "Optimal Sizing and Cost Minimization of Solar Photovoltaic Power System Considering Economical Perspectives and Net Metering Schemes." Electronics 10, no. 21 (November 7, 2021): 2713. http://dx.doi.org/10.3390/electronics10212713.
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In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia’s Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations.
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Shi, Zhong, Zhijie Wang, Yue Jin, Nengling Tai, Xiuchen Jiang, and Xiaoyu Yang. "Optimal Allocation of Intermittent Distributed Generation under Active Management." Energies 11, no. 10 (September 30, 2018): 2608. http://dx.doi.org/10.3390/en11102608.
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In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.
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Wang, Yang, Fengyun Chen, Wen Xiao, and Zhengming Li. "Operation Optimization of DC Distribution Network with BSS Based on GA-WDO Hybrid Algorithm." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 7 (November 4, 2020): 1087–96. http://dx.doi.org/10.2174/2352096513999200422142041.
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Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.
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Antonijević, Vladimir, and Željko Đurišić. "Električni automobil sa integrisanim fotonaponskim sistemom." Energija, Ekonomija, Ekologija 22, no. 1-2 (2020): 160–66. http://dx.doi.org/10.46793/eee20-1-2.160a.
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- The paper deals with the modeling and analysis of the effects of integrating photovoltaic systems into the body elements of electric vehicles. The aim of this paper was to examine the capacities, opportunities and effects of local electricity generation in real electric cars. Given the complex geometry of the vehicle and its mobility, this task posed a relatively complex engineering challenge. A mathematical model in MATLAB software has been developed that enables the estimation of the time diagram of photovoltaic panels production integrated into moving objects of complex geometry. Based on real irradiation measurement data for several locations in Serbia, analyses and calculations of the energy balances of electric vehicles with and without integrated photovoltaic panels in the car body were carried out. The results of the paper show the cost effectiveness for the application of this solution in the automotive industry. In addition to the local production of clean energy, the effects of increasing vehicle autonomy and longer range, less frequent visit to the charging station, less dependency and less impact of vehicles on power system are also achieved.
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Abbas, Ahmed S., Ragab A. El-Sehiemy, Adel Abou El-Ela, Eman Salah Ali, Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish. "Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation." Applied Sciences 11, no. 2 (January 15, 2021): 774. http://dx.doi.org/10.3390/app11020774.
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In recent years, with the widespread use of non-linear loads power electronic devices associated with the penetration of various renewable energy sources, the distribution system is highly affected by harmonic distortion caused by these sources. Moreover, the inverter-based distributed generation units (DGs) (e.g., photovoltaic (PV) and wind turbine) that are integrated into the distribution systems, are considered as significant harmonic sources of severe harmful effects on the system power quality. To solve these issues, this paper proposes a harmonic mitigation method for improving the power quality problems in distribution systems. Specifically, the proposed optimal planning of the single tuned harmonic filters (STFs) in the presence of inverter-based DGs is developed by the recent Water Cycle Algorithm (WCA). The objectives of this planning problem aim to minimize the total harmonic distortion (THD), power loss, filter investment cost, and improvement of voltage profile considering different constraints to meet the IEEE 519 standard. Further, the impact of the inverter-based DGs on the system harmonics is studied. Two cases are considered to find the effect of the DGs harmonic spectrum on the system distortion and filter planning. The proposed method is tested on the IEEE 69-bus distribution system. The effectiveness of the proposed planning model is demonstrated where significant reductions in the harmonic distortion are accomplished.
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Shi, Tao, Ruan-Ming Huang, and Cang-Bi Ding. "Research on the Optimal Configuration of Regional Integrated Energy System Based on Production Simulation." Processes 8, no. 8 (July 25, 2020): 892. http://dx.doi.org/10.3390/pr8080892.
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This paper is focused mainly on the production simulation method of a regional integrated energy system under random scenarios for optimal configuration. First, the cooling, heating, and electric load demand of the regional integrated energy system is described quantitatively in the form of time series, as well as the power characteristics of renewable energy, such as wind power and photovoltaic power generation. Then, a typical scenario set of regional integrated energy system configurations considering the random probability characteristics is established through scene clustering. Second, considering the power output characteristics and cost factors of different types of distributed energy, the corresponding technical and economic quantitative model is established. Third, a multi-objective production simulation model of a regional integrated energy system considering configuration constraints and operation constraints is proposed with economic and environmental protection as the main objectives. Finally, the accuracy and effectiveness of the above methods are verified based on a case study of actual engineering.
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Das, Soumya, Pradip K. Sadhu, Suprava Chakraborty, Malayendu Saha, and Moumita Sadhu. "Life cycle economic analysis of stand-alone solar pv system in India – a relative study." World Journal of Engineering 12, no. 1 (February 1, 2015): 37–44. http://dx.doi.org/10.1260/1708-5284.12.1.37.
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In this paper, life cycle economic analysis (LCEA) of stand-alone solar photovoltaic (PV) modules is performed. It is tested for their commercial prospects in remote regions of India, which do not have a direct access of grid supply. Availability of grid supply depends on the population density. Solar PV technology is one of the first among several renewable energy technologies that have been adopted worldwide for meeting the basic needs of generation of electricity particularly in remote areas. Overall lifetime expenditures related to the power projects are analyzed and compared with the help of net present worth (NPW) theory. In the context of a developing country like India, it is found that the cost effectiveness of conventional or ‘green’ power driven sources depends on kW rating of generators and daily demand of consumers. The demand coverage, which would determine the commercial viability of renewable and non-renewable sources is calculated considering the practical power rating of generators available in the local market. This study is intended to assist planning of financial matters with regard to installing small to medium scale electric power generation using solar PV module in remote areas of India.
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Xu, Weifeng, Bing Yu, Qing Song, Liguo Weng, Man Luo, and Fan Zhang. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability." Energies 15, no. 24 (December 19, 2022): 9639. http://dx.doi.org/10.3390/en15249639.
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The integration of renewable resources with distribution networks (DNs) is an effective way to reduce carbon emissions in energy systems. In this paper, an economic and low-carbon-oriented optimal planning solution for the integration of photovoltaic generation (PV) and an energy storage system (ESS) in DNs is proposed. A convolutional neural network (CNN)-based prediction model is adopted to characterize the uncertainties of PV and load demand in advance. Then, taking the lowest total economic cost, the largest carbon emission reduction, and the highest system power supply reliability as the optimization objectives, the optimal distribution network planning model is constructed. The improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the optimization model, and the effectiveness of the proposed solution is confirmed through a comparative case study on the IEEE-33 bus system. Simulation results show that the proposed solution can better maintain the balance between economic cost and carbon emissions in DNs.
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Madasamy, P., Rajesh Verma, A. Rameshbabu, A. Murugesan, R. Umamageswari, Josiah Lange Munda, C. Bharatiraja, and Lucian Mihet-Popa. "Neutral Point Clamped Transformer-Less Multilevel Converter for Grid-Connected Photovoltaic System." Electronics 10, no. 8 (April 19, 2021): 977. http://dx.doi.org/10.3390/electronics10080977.
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Transformer-less (TL) inverter topologies have elicited further special treatment in photo-voltaic (PV) power system as they provide high efficiency and low cost. Neutral point clamped (NPC) multilevel-inverter (MLI) topologies-based transformer-less are being immensely used in grid-connected medium-voltage high-power claims. Unfortunately, these topologies such as NPC-MLI, full-bridge inverter with DC bypass (FB-DCBP) suffer from the shoot-through problem on the bridge legs, which affect the reliability of the implementation. Based on the previous above credits, a T type neutral point clamped (TNP)—MLI (TNP-MLI) with transformer-less topology called TL-TNP-MLI is presented to be an alternate which can be suitable in the grid-connected PV power generation systems. The suggested TL-TNP-MLI topologies free from inverter bridge legs shoot-through burden, switching frequency common-mode current (CMC), and leakage current. The control system of the grid interface with hysteresis current control (HCC) strategy is proposed. The effectiveness of the proposed PV connected transformer-less TNP-MLI topology with different grid and PV scenario has been verified through the MATLAB/Simulink simulation model and field-programmable gate area (FPGA)-based experimental results for a 1.5 kW system.
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Nematollahi, Amin Foroughi, Hossein Shahinzadeh, Hamed Nafisi, Behrooz Vahidi, Yassine Amirat, and Mohamed Benbouzid. "Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches." Applied Sciences 11, no. 9 (May 1, 2021): 4156. http://dx.doi.org/10.3390/app11094156.
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In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.
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Iqbal, Sajjad, Amin, Haroon, Liaqat, Khan, Waseem, and Shah. "Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer." Applied Sciences 9, no. 23 (December 1, 2019): 5226. http://dx.doi.org/10.3390/app9235226.
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The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
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Barbaric, Marina, and Drazen Loncar. "Energy management strategies for combined heat and electric power micro-grid." Thermal Science 20, no. 4 (2016): 1091–103. http://dx.doi.org/10.2298/tsci151215081b.
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The increasing energy production from variable renewable energy sources such as wind and solar has resulted in several challenges related to the system reliability and efficiency. In order to ensure the supply-demand balance under the conditions of higher variability the micro-grid concept of active distribution networks arising as a promising one. However, to achieve all the potential benefits that micro-gird concept offer, it is important to determine optimal operating strategies for micro-grids. The present paper compares three energy management strategies, aimed at ensuring economical micro-grid operation, to find a compromise between the complexity of strategy and its efficiency. The first strategy combines optimization technique and an additional rule while the second strategy is based on the pure optimization approach. The third strategy uses model based predictive control scheme to take into account uncertainties in renewable generation and energy consumption. In order to compare the strategies with respect to cost effectiveness, a residential micro-grid comprising photovoltaic modules, thermal energy storage system, thermal loads, electrical loads as well as combined heat and power plant, is considered.
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Liu, Xin, Hong-Kun Chen, Bing-Qing Huang, and Yu-Bo Tao. "Optimal Sizing for Wind/PV/Battery System Using Fuzzy c-Means Clustering with Self-Adapted Cluster Number." International Journal of Rotating Machinery 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5142825.
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Integrating wind generation, photovoltaic power, and battery storage to form hybrid power systems has been recognized to be promising in renewable energy development. However, considering the system complexity and uncertainty of renewable energies, such as wind and solar types, it is difficult to obtain practical solutions for these systems. In this paper, optimal sizing for a wind/PV/battery system is realized by trade-offs between technical and economic factors. Firstly, the fuzzy c-means clustering algorithm was modified with self-adapted parameters to extract useful information from historical data. Furthermore, the Markov model is combined to determine the chronological system states of natural resources and load. Finally, a power balance strategy is introduced to guide the optimization process with the genetic algorithm to establish the optimal configuration with minimized cost while guaranteeing reliability and environmental factors. A case of island hybrid power system is analyzed, and the simulation results are compared with the general FCM method and chronological method to validate the effectiveness of the mentioned method.
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Bu, Ling, Shengjiang Quan, Jiarong Han, Feng Li, Qingzhao Li, and Xiaohong Wang. "On-Site Traversal Fractional Open Circuit Voltage with Uninterrupted Output Power for Maximal Power Point Tracking of Photovoltaic Systems." Electronics 9, no. 11 (October 29, 2020): 1802. http://dx.doi.org/10.3390/electronics9111802.
Full textAbstract:
The fractional open-circuit voltage (FOCV) method is commonly adopted to track maximal power point of photovoltaic systems due to easy implementation and cost-effectiveness. However, the FOCV method is confronted with unstable output power and limited tracking accuracy. This paper proposes a novel on-site traversal FOCV method with uninterrupted output power and increased tracking accuracy through simulation and experimental verifications. Each solar cell is connected with a bypass diode and switching circuitry, so that specific solar cell can be traced and measured consecutively for determining its maximal power point (MPP). MATLAB/Simulink simulation results show that, in the time-varying irradiance case, the proposed method achieves a low ripple factor of 0.13% in 11–13 h and 0.88% in 9–15 h, under the typical 24 h irradiance curve. In the spatial-varying irradiance case, the accuracy of the proposed method reaches 99.85%. Compared with other FOCV methods, like pilot cell and semi pilot cell methods, the proposed method is of higher accuracy with a limited ripple effect. Experimental results show that this method can effectively trace different output performance of specific solar cell while generating stable output voltage with a low ripple factor of 1.55%, proving its compatibility with distributed sensing and applicability in smart photovoltaic systems.
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Pierdicca, Roberto, Marina Paolanti, Andrea Felicetti, Fabio Piccinini, and Primo Zingaretti. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images." Energies 13, no. 24 (December 9, 2020): 6496. http://dx.doi.org/10.3390/en13246496.
Full textAbstract:
Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient.