An optimal sizing framework for autonomous photovoltaic/hydrokinetic/hydrogen energy system considering cost, reliability and forced outage rate using horse herd optimization

The components outage of an energy system weakens its operation probability, which can affect the sizing of that system. An optimal sizing framework is presented for an autonomous hybrid photovoltaic/hydrokinetic/fuel cell (PV/HKT/FC) system with hydrogen storage to supply an annual load demand with forced outage rate (FOR) of the clean production resources based on real environmental information such as irradiance, temperature, and water flow. The sizing problem is implemented with the objective of cost of energy (COE) minimization and also satisfying probability of load supply (PLS) as a reliability constraint. The FOR effect of the photovoltaic and hydrokinetic resources is evaluated on the hybrid system sizing, energy cost, reliability, and also storage contribution of the system. Meta-heuristic horse herd optimization (HHO) algorithm with perfect capability on exploration and exploitation phases is used to solve the sizing problem. The results proved that the PV/HKT/FC configuration is the optimal option to supply the demand of an autonomous residential complex with the minimum COE and maximum PLS compared with the other system configurations. The results demonstrated the overlap of hydrogen storage with clean production resources to achieve an economic-reliable power generation system. The findings indicated that the COE is increased and the PLS is decreased due to the FOR increasing because of reducing the generation resources operational probability. The results demonstrated that the hydrogen storage level is increased with FOR increasing to maintain the system reliability level. Also, the sizing results indicated that the FOR of the hydrokinetic is more effective than the photovoltaic resources in increasing the system cost and undermining the load reliability. In sizing of the hybrid PV/HKT/FC system, the COE is obtained 1.57 $/kWh without considering the FOR and is achieved 1.66 and 1.63$/kWh considering the FOR (8%) for the hydrokinetic and photovoltaic resources, respectively. Moreover, the results cleared that the HHO is superior in comparison with particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimizer (GWO) in the PV/HKT/FC system sizing with the lowest COE and higher reliability

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks

The selection process of conductor size in radial distribution network is very essential issue to improve the performance of the network. The true conductor size selection leads to less power loss, achieve improvement of the bus voltage profile and obtain reduction in the annual operating cost of the system. This paper proposes a novel approach based on crow search algorithm (CSA) for optimal selection of the conductors in a radial distribution network. The CSA is a recent meta-heuristic algorithm which is based on the intelligent behavior of crows. The objective function presented in our work is the sum of conductor capital cost and the conductor energy loss cost. The proposed constraints are bus voltage limits and the current capacity of the conductor. The independent variables are the type and the size of conductor such that minimizing the proposed objective function. The proposed approach is applied on two different network topologies, the first one is 16-bus system and the second is large scale system of 85-bus system. A sensitivity analysis of the CSA controlling parameters are also studied for 16-bus system. The obtained results via CSA are compared to previous works’ results; the CSA results encourage the usage of the proposed approach in optimal selecting the conductor type and size in the distribution network

A New Study of Maximum Power Point Tracker Techniques and Comparison for PV Systems

The maximum power point tracker techniques vary in many aspects as simplicity, digital or analogical implementation, sensor required, convergence speed, range of effectiveness, implementation hardware,popularity, cost and in other aspects. This paper presents in details comparative study between two most popular  algorithm  technique  which  is  incremental  conductance  algorithm  and  perturb  and  observe algorithm.  Two  different  converters  buck  and  cuk  converter  use  for  comparative  in  this  study. Few comparisons such as efficiency, voltage, current and power output for each different combination have been recorded. Multi changes in irradiance, temperature by keeping voltage and current as main sensed parameter been done in the simulation. Matlab simulink tools have been used for performance evaluation on energy point. Simulation will consider different solar irradiance and temperature variations

Optimal PI microcontroller-based realization for technical trends of single-stage single-phase grid-tied PV

The paper introduces optimal PI controllers for a single-phase single-stage PV grid-tied inverter. In the proposed model, a time domain objective function based on the integral square error (ISE) of the current and voltage errors is formulated in order to obtain the PI controllers in an offline manner. The performance of the developed controllers is simulated via MATLAB/SIMULINK whereas a prototype was implemented to verify the effectiveness of the developed controllers. The developed controllers enable the developed inverter to provide active power to the utility grid with reactive power compensation. From the simulated and experimental results, the developed controller had better performance over different operating conditions. Besides, it is simple, low cost, and attractive for households’ energy production. Keywords: Grid connected photovoltaic inverters, Power control, Phase shift, Reactive power injectio

Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper