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Optimal Operation Control of Microgrid Connected Photovoltaic-Diesel Generator Backup System Under Time of Use Tariff
摘要： With the exponentially increasing demand of electrical energy, many developing countries are struggling to provide electricity to the end-users. This challenge has mainly strained traditional power systems. To mitigate against this strain, governments are diversifying and liberalizing the energy market to meet future energy demand. In addition, end-users have several alternatives to reduce electricity cost at demand side. This can be realized either by using ef?cient devices or incorporating renewable energy sources while scheduling their powers economically. Within this framework, microgrids are considered as ef?cient power systems to exploit renewable energy sources with demand side management program. Nowadays, microgrids which can harness photovoltaic solar source at low cost are becoming an attractive option for reduction in electricity cost at demand side. To ensure uninterrupted power supply, diesel generators are often used as backup energy systems in most large-scale or industrial applications in Kenya. Therefore, this paper proposes a constrained optimal operation control strategy of microgrid connected photovoltaic with diesel generator backup system related to commercial and industrial (C&I) setups in Kenya. Particularly, the objective function simultaneously aims at reducing energy purchased from utility grid and the fuel consumption cost of the conventional diesel generator. The constraints related to control variables are taken in the context of C&I in Kenya. The optimal operation control is carried out using FMINCON interior-point algorithm, and two scenarios are analysed. The ?rst scenario is carried out by considering the intermittent mode from 7:00 hrs to 18:00 hrs, while the second scenario is considered in the intermittent connected mode. A case study is done based on the daily load pro?le of the Engineering workshops at Jomo Kenyatta University of Agriculture and Technology (JKUAT) located at ?1.099? latitude and 37.014? longitude. The optimal operation control has shown great bene?ts in terms of energy saving, cost saving as well as daily revenue. The daily energy saving is increased up to 52.1%, the daily cost saving is 20%, and daily energy sold is found to be 142.4 kWh which can generate daily income of $17.
关键词： Renewable energy,Levelized cost of energy,Optimization algorithm,Demand side management,Open-loop optimal control
更新于2025-09-23 15:21:01
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[Advances in Intelligent Systems and Computing] Knowledge Engineering and Management Volume 214 || Knowledge-Based Decision System for Sizing Grid-Connected Photovoltaic Arrays
摘要： In this paper we have developed a methodology and a numerical model for sizing photovoltaic (PV) arrays for grid-connected power consumers. The decision variables proposed are the PV contribution to the power demand and a transfer factor. The latter is a new index that we have de?ned, and it constitutes key knowledge for decision making when aiming to reduce power transfers between a local power system and the electric grid. The developed model generates days of solar irradiance and calculates PV power output for commercial PV modules. Sizing diagrams are presented that illustrate how the PV capacity, for a speci?c location and a local power demand, affects the PV contribution to the demand and the power trades with grid. For the particular simulation conditions studied, the sizing diagrams show PV contribution saturation levels and global transfer factor minimums.
关键词： Photovoltaic solar power,Renewable energy,Optimization,Sizing
更新于2025-09-23 15:21:01
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Optical Communications and Networks
摘要： Due to energy and throughput constraints of visual sensing nodes, in-node energy conservation is one of the prime concerns in visual sensor networks (VSNs) with wireless transceiving capability. To cope with these constraints, the energy ef?ciency of a VSN for a given level of reliability can be enhanced by recon?guring its nodes dynamically to achieve optimal con?gurations. In this paper, a uni?ed framework for node classi?cation and dynamic self-recon?guration in VSNs is proposed. The proposed framework incorporates quality-of-information (QoI) awareness using peak signal-to-noise ratio-based representative metric to support a diverse range of applications. First, for a given application, the proposed framework provides a feasible solution for the classi?cation of visual sensing nodes based on their ?eld-of-view by exploiting the heterogeneity of the targeted QoI within the sensing region. Second, with the dynamic realization of QoI, a strategy is devised for selecting suitable con?gurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacri?cing the expected information retrieval reliability. The robustness of the proposed framework is evaluated under various scenarios by considering: 1) target QoI thresholds; 2) degree of heterogeneity; and 3) compression schemes. From the simulation results, it is observed that for the second degree of heterogeneity in targeted QoI, the uni?ed framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability.
关键词： visual sensor networks,dynamic reconfiguration,node,3D field-of-view modelling,classification,energy optimization,quality-of-information,reliability analysis
更新于2025-09-23 15:19:57
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Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization
摘要： The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back-up unit (storage). Uncertain solar irradiance and wind speed are simulated via log-normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed-rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.
关键词： power system planning,optimal power flow,solar energy,wind energy,optimization
更新于2025-09-12 10:27:22
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[IEEE 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Paris, France (2018.10.14-2018.10.17)] 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Energy Consumption Optimization through Dynamic Simulations for an Intelligent Energy Management of a BIPV Building
摘要： Having 6500 integrated sensors and a wide network of embedded systems, the ADREAM building can be categorized as one of the prototype smart buildings of France. As needs for intelligent management of energy are growing, the associated project provides a multidisciplinary platform of experimentation, developing solutions for efficient Energy Networks, HVAC Systems, Photovoltaics, and Smart Grids. This paper provides the overview of the project along with three different modeling techniques, simulating the thermal behavior of the building and the functioning of the different energy systems. A thermal model of the building was developed and calibrated for energy consumption analysis and prediction, using the software Pleiades+Comfie. A “black box” model was developed using artificial neural networks for the simulation of energy system parameters and the exploration of efficient control strategies. The last method provides the overview of developing an all-inclusive physical model with equivalent Matlab/Simulink for simulating the global functioning of the building and its HVAC systems. The conclusion addresses the utility of exploring and combining different types of models for optimizing the energy management of Smart Buildings.
关键词： Simulink,Energy Optimization,Model Calibration,Building Simulation,Smart Building,Pleiades + Comfie,Energy Management
更新于2025-09-04 15:30:14
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AIP Conference Proceedings [Author(s) 6TH INTERNATIONAL CONFERENCE ON PRODUCTION, ENERGY AND RELIABILITY 2018: World Engineering Science & Technology Congress (ESTCON) - Kuala Lumpur, Malaysia (13–14 August 2018)] - Design and implementation of solar powered stirling engines: Review
摘要： Growing energy consumption leads to discovery of new technology to accommodate global energy demand. Stirling Engine is one of the traditional engine which can harvest solar energy with minimal modification on the configuration. This paper covers literature review on Solar Powered Stirling Engine technology. The development of traditional Stirling Engine will be discussed on earlier part. The second part covers the integration of solar power with Stirling Engine and application of this combined system in industry. There were many researches and studies carried out previously on the development and application of traditional Stirling Engine and solar powered Stirling Engine. This paper is focus on optimization of performance of Solar Powered Stirling which will provide comprehensive details on the factor needed to be optimized in order to develop all rounded Solar Stirling Engine for future studies.
关键词： Stirling Engine,solar energy,optimization,Solar Powered Stirling Engine
更新于2025-09-04 15:30:14
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Uncertain Analysis of a Stationary Solar Compound Parabolic Concentrator PV Collector System Using Fuzzy Set Theory
摘要： The uncertain analysis of fixed solar compound parabolic concentrator (CPC) collector system is investigated for use in combination with solar PV cells. Within solar CPC PV collector systems, any radiation within the collector acceptance angle enters through the aperture and finds its way to the absorber surface by multiple internal reflections. It is essential that the design of any solar collector aims to maximize PV performance since this will elicit a higher collection of solar radiation. In order to analyze uncertainty of the solar CPC collector system in the optimization problem formulation, three objectives are outlined. Seasonal demands are considered for maximizing two of these objectives, the annual average incident solar energy and the lowest month incident solar energy during winter; the lowest cost of the CPC collector system is approached as a third objective. This study investigates uncertain analysis of a solar CPC PV collector system using fuzzy set theory. The fuzzy analysis methodology is suitable for ambiguous problems to predict variations. Uncertain parameters are treated as random variables or uncertain inputs to predict performance. The fuzzy membership functions are used for modeling uncertain or imprecise design parameters of a solar PV collector system. Triangular membership functions are used to represent the uncertain parameters as fuzzy quantities. A fuzzy set analysis methodology is used for analyzing the three objective constrained optimization problems.
关键词： uncertain analysis,solar CPC PV collector system,fuzzy set theory,solar energy,optimization
更新于2025-09-04 15:30:14