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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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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Sams??
摘要： This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Sams? (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master’s office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a na?ve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the na?ve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the na?ve approach.
关键词： Energy management,Renewable energy,On-line scheduling,Microgrid,Optimization algorithm,Demand side management,Model predictive control,Energy storage
更新于2025-09-23 15:21:01
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A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms
摘要： The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.
关键词： partial shading,global optimization,series–parallel,deterministic optimization algorithm,implicit model solution,metaheuristic optimization algorithm,photovoltaic array
更新于2025-09-23 15:19:57
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A Novel Modified Robust Load Frequency Control for Mass-Less Inertia Photovoltaics Penetrations via Hybrid PSO-WOA Approach
摘要： The high penetrations of distributed energy resources (DERs) leads to severe problems such as reducing system inertia and increasing the frequency deviations from the nominal value. The main target of this study is to enable modern photovoltaics (PVs) with large penetration amounts to participate effectively in the load frequency control in the interconnected power systems, in which frequency and tie-line power sharing deviations exist. In this research, a model for the solar PV is developed to help study the heavy penetration of the solar PVs within interconnected power systems. Secondly, a time domain objective function based on the norm of the area control error is formulated. Thirdly, in order to tune the PI controllers, a combined meta-heuristic algorithm based on particle swarm optimization and whale optimization algorithm (PSO-WOA) is developed and compared with the individual PSO and WOA controllers. From the studied scenarios, the developed combined scheme outperforms the other algorithms in terms of system performance indices. Therefore, the developed PSO-WOA approach is plausible and straightforward to solve many engineering problems as it benefits from the exploitation characteristics of the conventional PSO and the exploration features of the WOA.
关键词： robust control,frequency deviations (Df),virtual generator,mass-less inertia,load frequency control (LFC),whale optimization algorithm (WOA)
更新于2025-09-23 15:19:57
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Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems
摘要： This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.
关键词： voltage profile,harmonic distortions,power losses,coyote optimization algorithm,solar photovoltaic distribution generation units,radial distribution systems
更新于2025-09-23 15:19:57
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Switched Capacitor-Coupled Inductor DCa??DC Converter for Grid-Connected PV System using LFCSO-Based Adaptive Neuro-Fuzzy Inference System
摘要： In this paper, the Levy °ight-based chicken swarm optimization (LFCSO) is proposed to follow the highest power of grid-joined photovoltaic (PV) framework. To analyze the grid-associated PV framework, the characteristics of current, power, voltage, and irradiance are determined. Because of the low yield voltage of the source PV, a big advance up converter with big productivity is required when the source PV is associated with the matrix power. A tale great advance up converter dependent on the exchanged capacitor and inductor is illustrated in this paper. The LFCSO algorithm with the adaptive neuro-fuzzy inference system is used to generate the control pulses of the transformer-coupled inductor DC–DC converter-less switched capacitor. While using the switched capacitor-coupled inductor, the voltage addition is expanded in the DC–DC converter and the power of PV is maximized. Here, the normal CSO algorithm is updated with the help of Levy °ight functions to generate optimal results. To get the accurate optimal results, the output of the proposed LFCSO algorithm is given as the input of the ANFIS technique. After that, the optimal results are generated and they provide the pulses for the system. The working guideline is analyzed and the voltage addition is derived with the utilization of the proposed technique. From that point forward, it predicts the exact maximum power of the converter according to its inputs. Under the variety of solar irradiance and partial shading conditions (PSCs), the PV system is tested and its characteristics are analyzed in di?erent time instants. The proposed LFCSO with ANFIS method is actualized in Simulink/MATLABstage, and the tracking executing is examined with a traditional method such as genetic algorithm (GA), perturb and observe (P&O) technique–neuro-fuzzy controller (NFC) and fuzzy logic controller (FLC) technique.
关键词： irradiance,Levy °ight-based chicken swarm optimization algorithm,partial shading conditions,transformer-less switched capacitor-coupled inductor DC–DC converter voltage,current,power,grid-connected PV,GA-NFC and FLC methods
更新于2025-09-23 15:19:57
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Highly Efficient (110) Orientated FA‐MA Mixed Cation Perovskite Solar Cells via Functionalized Carbon Nanotube and Methylammonium Chloride Additive
摘要： Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic e?ciency of structures during their service periods. Selecting the appropriate optimization algorithm is the key to solving structural optimal design problems. In this study, a new global optimization idea is proposed and named the moving baseline strategy. A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal value. The proposed strategy is a simple but e?ective, general, and stable algorithm that can be used to solve constrained and unconstrained structural optimization problems. Di?erent from traditional gradient-based, stochastic and heuristic algorithms, the developed algorithm provides a completely new idea to solve global or local optimization problems. Some unconstrained and constrained numerical benchmark examples are used to test the proposed methodology. In addition, structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure (X-37B) are utilized to verify the e?ectiveness of the developed strategy in addressing structural design optimization problems in engineering.
关键词： Constrained optimization,Optimization algorithm,Moving baseline strategy,Structural design,Global optimization
更新于2025-09-19 17:13:59
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Grasshopper optimization algorithm utilized Xilinx controller for maximum power generation in photovoltaic system
摘要： A novel Grasshopper Optimization (GOA) based Xilinx System Generator (XSG) controller is proposed for the purpose of evaluating the Maximum Power Point Tracking in the grid integrated Photovoltaic (PV) power generation system. The proposed controller functions with the assistance of the GOA algorithm and the XSG procedures. The innovation of the controller is intended to collect the maximum power produced from the PV array in accordance with the solar irradiance and temperature of the array. The proposed GOA algorithm based XSG controller is achieved by maximum power from the PV array, it is necessary to adjust the switching signal for the voltage source inverter (VSI). The proposed PV structure is elegantly designed in the mighty platform of the MATLAB/Simulink and the switching schemes are produced in accordance with the XSG controller. At last, the output response of the presented GOA based XSG controller is analyzed and compared with conventional techniques such as the Particle Swarm Optimization (PSO) and the Artificial Bee Colony (ABC) algorithms.
关键词： Voltage source inverter,Maximum power point tracking,Artificial bee colony,Particle swarm optimization,Photovoltaic array,Grasshopper optimization algorithm,Xilinx system generator controller
更新于2025-09-19 17:13:59
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Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition
摘要： Because of dust, trees, high buildings in the surrounding area, partial shading conditions (PSC) occur in photovoltaic (PV) systems. This condition affects the power output of the PV system. Under PSC there is a global maximum power point (GMPP) besides there are a few local maximum power points (LMPP). This condition makes the maximum power point tracking (MPPT) procedure a challenging task. In order to solve this issue, soft computing techniques such as gray wolf optimization (GWO), particle swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are implemented. However, the performance of MPP trackers still needs to be improved. The main contribution of this paper is improving the tracking speed by implementing BOA to the MPPT of the PV system under PSC. Thus, in real-time applications a promising alternative presented to the literature to improve the performance of the PV systems under variable PSC because of its fast tracking speed. PV system consists of PV array, boost converter and load are modeled and simulated in MATLAB/Simulink. BOA algorithm is implemented for three different insolation scenarios on the PV array. The results of the BOA algorithm verified by a comparative analysis with PSO-GSA and GWO algorithms. The results show that BOA can give high accuracy and better tracking speed than these algorithms in recent literature.
关键词： Partial shading,energy,MPPT,butterfly optimization algorithm,solar,photovoltaic
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： Takagi-Sugeno fuzzy model,Fuzzy system identi?cation,ant colony optimization algorithm (ACO),fuzzy c-regression model
更新于2025-09-19 17:13:59