1,721,026 research outputs found
Load modeling and scheduling optimization for energy sharing in prosumers network
No full textIn the smart home scenario, the deployment of smart meters, smart sensors and home energy management systems (HEMSs) is expected to enable the optimal and dynamic schedule of the domestic energy devices according to different objectives (e.g. self-consumption maximization, peak-period consumption avoidance and emission reduction), while keeping satisfied end-users comfort constraints. However, despite interesting results can be achieved at single-house level, only HEMSs interaction under new business models (e.g. P2P markets) are expected to unleash most of the untapped value. For these reasons, we aim to develop a HEMS model with the right compromise between accuracy and computational burden, suitable to investigate the HEMS performances both at single house level, as in this work, and at community level through a multi-agent simulation framework for future researches. In order to find realist optimal schedule from an end-user perspective, we incorporate detailed sub-models to optimize the trade-off between cost and comfort with a Mixed Integer Linear Problem (MILP) formulation. We consider wet appliances (dish washer, washing machine and tumble dryer), thermal appliances, an energy storage system (ESS) and PV modules. Reported results on the scheduling optimization show the potentiality of the proposed approach
An Overview of Optimization Methods for Home Energy Management Systems
No full textEnergy Management System (EMS) is a major component of a smart grid and significant for the operational qualification. Controlling the residential grid, resulting into improving cost, emission, and comfort. The study aims to investigate the optimization methods used in Home Energy Management System(HEMS) and evaluate their effectiveness. The paper dis-cusses the architecture of HEMS, which is classified into three layers: physical, communication, and software. The physical layer comprises measurement systems and sensors for data acquisition, including smart meters, IoT sensors and smart appliances. The communication layer facilitates the interconnection between the control systems, central platforms, and smart devices. The software layer comprises the algorithmic and programming parts to optimize the system. Culminate into evaluating the different optimization methods used in HEMS, including mathematical, meta-heuristic, and artificial intelligence models
Fuel cell characteristic curve approximation using the Bezier curve technique
Full text linkAccurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bezier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12Wstack, NedStackPS6, and 250Wproton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein-Bezier function and de Casteljau's algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bezier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bezier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques
Comparative Analysis of Wireless Protocols in Smart Home Energy Management Systems
No full textThis paper presents a comprehensive comparative analysis of various wireless protocols employed in smart home energy management systems. As the proliferation of smart home technologies continues, the efficient and reliable management of energy consumption becomes crucial. Wireless communication protocols, such as Zigbee, Thread, Z-Wave, Wi-Fi, Bluetooth, and LoRa, play a pivotal role in ensuring seamless connectivity among smart devices. This study evaluates these protocols based on key performance metrics including energy efficiency, range, data throughput, scalability, and security. This comparative analysis aims to guide system designers in selecting the most appropriate wireless protocol to optimize energy management in smart homes, thereby enhancing sustainability and user experience. Comparing the aforementioned protocols evolves into an outcome that demonstrates a solution for smart home communication systems, such as Thread, ZigBee, and Z-Wave. These 3 protocols are precisely designed for home communication and controlling local network systems
Metaswarm: Enhanced evolutionary algorithms for electromagnetic optimization
No full textHere some variations of the basic Particle Swarm algorithm (a relatively novel approach for global stochastic optimisation) are proposed in order to increase the efficiency of the search over the solution space with a negligible overhead in the algorithm complexity and speed. The resulting algorithms have been first compared in terms of capability and speed of convergence by their application to a test function, then the resulting best technique has been used for the design of a microwave filter
A Cost-Effective Fuzzy-based Demand-Response Energy Management for Batteries and Photovoltaics
No full textDistributed energy sources play an essential role in microgrids to meet the load demand. To maintain power stability in a grid-connected microgrid, a fuzzy logic-based management system has been designed in this work for a photovoltaic (PV) and battery-based microgrid feeding power to the load in the MultiGood MicroGrid Lab in Politecnico di Milano. For this system, a design based on 34 rules has been implemented for effective power management considering the electricity tariff in Milan, Italy. The main goal of this design is to minimize the power bought from the main grid during peak hours throughout the day under high-load demand while maintaining a stable power supply to the load and keeping the batteries within safe limits. The controller rules and membership functions are optimized to meet the designed criteria of this system which has been implemented using the fuzzy logic toolbox in MATLAB (2020b) and tested through simulations in MATLAB/Simulink (2020b) environment. Lastly, a cost analysis of the power bought from the grid with the designed fuzzy-based EMS has been performed which shows minimum power intake from the main grid while maintaining the state of charge of BESS in safe limits
Implementation of Nonlinear Controller to Improve DC Microgrid Stability: A Comparative Analysis of Sliding Mode Control Variants
Full text linkElectricity generation from sustainable renewable energy sources is constantly accelerating due to a rapid increase in demand from consumers. This requires an effective energy management and control system to fulfil the power demand without compromising the system’s performance. For this application, a nonlinear barrier sliding mode controller (BSMC) for a microgrid formed with PV, a fuel cell and an energy storage system comprising a battery and supercapacitor working in grid-connected mode is implemented. The advantages of the BSMC are twofold: The sliding surface oscillates in the close vicinity of zero by adapting an optimal gain value to ensure the smooth tracking of power to its references without overestimating the gains. Secondly, it exhibits a noticeable robustness to variations and disturbance, which is the bottleneck of the problem in a grid-connected mode. The stability of the presented controllers was analyzed with the Lyapunov stability criterion. Moreover, a comparison of the BSMC with sliding mode and supertwisting sliding mode controllers was carried out in MATLAB/Simulink (2020b) with real PV experimental data. The results and the numerical analysis verify the effectiveness of the BSMC in regulating the DC bus voltage in the presence of an external disturbance under varying conventional load and environmental conditions
Stability Analysis and Optimal Energy Management of a Stand-Alone Hybrid Micro-Grid
No full textThis paper presents the analysis of different spinning reserve calculation and allocation methods applied to an islanded hybrid power plant supplying the city of Garowe, in the North of Somalia. The preliminary analysis of the partially stochastic power flows, related to renewable energy system generators and load demand, allows to set minimum requirements to ensure the grid stability. Three criteria for the calculation of the spinning reserve and for its allocation on the generators involved in primary and secondary frequency regulation are here proposed and analyzed
Different Approaches to Multi-Objective Sparse Array Problem with Social Network optimization
No full textMulti-objective problems with two or more conflicting objectives are very common in every engineering fields, also for antenna optimization. Evolutionary optimization Algorithms are important tools due to their effectiveness, flexibility and applicability especially for multi-objective problems because they can provide directly the non-dominated set. Among Evolutionary Algorithms, Social Network optimization (SNO) shows very good optimization performance.In this paper three different approaches for solving a multiobjective problem are tested with SNO: the first one is the weighted sum method, the second is the epsilon-constrained method and the third one is the simultaneous search with a multiobjective implementation of SNO. The analysed application is the design of a sparse-array antenna
Modelling and Parameters Extraction of Flexible Amorphous Silicon Solar Cell a-Si:H
No full textAbstract: The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques
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