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

Multiobjective and Simultaneous Two-Problem Allocation of a Hybrid Solar-Wind Energy System Joint with Battery Storage Incorporating Losses and Power Quality Indices

In this paper, a multiobjective and simultaneous two-problem allocation of a hybrid distributed generation (HDG) system comprises of solar panels, wind turbines, and battery storage is proposed in a 33-bus unbalanced distribution network which can decrease total losses and improve power quality (PQ). The PQ indices are defined as voltage swell, total harmonic distortion, voltage sag, and voltage unbalance. In this study, the two problems of hybrid system design and its allocation in the distribution network are solved simultaneously. In the allocation problem, the HDG is placed ideally in the network to reduce energy losses and enhance PQ indices. The HDG is measured to minimize the cost of energy generation, including the initial investment, maintenance, and operation costs. The decision variable including the size of HDG components and its location is optimally determined via escaping bird search (EBS) algorithm which is inspired by the maneuvers of the swift bird to avoid predation. The results cleared that the proposed methodology using the wind and solar resources integrated with battery storage reduced the losses, voltage swell, total harmonic distortion, voltage sag, and voltage unbalance by 34.31%, 49.60%, 0.25%, 40.19%, and 2.18%, respectively, than the base network via the EBS and the results demonstrated the better network performance using all renewable resources against wind or solar application only. The outcomes demonstrated the superiority of the EBS in achieving the highest improvement of the different objectives compared with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO). Moreover, the superior capability of the EBS-based methodology is proved in comparison with previous studies

Spotted hyena optimizer algorithm for capacitor allocation in radial distribution system with distributed generation and microgrid operation considering different load types

In this paper, the optimal allocation of constant and switchable capacitors is presented simultaneously in two operation modes, grid-connected and islanded, for a microgrid. Different load levels are considered by employing non-dispatchable distributed generations. The objective function includes minimising the energy losses cost, the cost of peak power losses, and the cost of the capacitor. The optimization problem is solved using the spotted hyena optimizer (SHO) algorithm to determine the optimal size and location of capacitors, considering different loading levels and the two operation modes. In this study, a three-level load and various types of loads, including constant power, constant current, and constant impedance are considered. The proposed method is implemented on a 24-bus radial distribution network. To evaluate the performance of the SHO, the results are compared with GWO and the genetic algorithm (GA). The simulation results demonstrate the superior performance of the SHO in reducing the cost of losses and improving the voltage profile during injection and non-injection of reactive power by distributed generations in two operation modes. The total cost and net saving values for DGs only with the capability of active power injection is achieved 105,780 $and 100,560.54$, respectively and for DGs with the capability of active and reactive power injection is obtained 89,568 $and 76,850.46$, respectively using the SHO. The proposed method has achieved more annual net savings due to the lower cost of losses than other optimization methods

Meta-heuristic matrix moth-flame algorithm for optimal reconfiguration of distribution networks and placement of solar and wind renewable sources considering reliability

This paper presents optimal multi-criteria reconfiguration of radial distribution systems with solar and wind renewable energy sources using the weight factor method while considering reliability. Minimizing the power loss, improving the voltage profile and stability of the system, as well as, enhancing the reliability are the main objective functions of the problem to address. The reliability index is assumed as the energy not-supplied (ENS) of the end-users. Optimized variables of the problem include opened lines of the system in the reconfiguration process to maintain the radial structure of the network along with finding the optimal place and size of photovoltaic (PV) systems and wind turbine (WT) units in the distribution system, which are determined based on a new meta-heuristic called moth–flame optimization (MFO) algorithm. Simulations for different scenarios are performed utilizing reconfiguration and placement of renewable sources on an IEEE 33-bus radial distribution system. Obtained results in solving the problem indicate the superiority of the presented method compared with some methods in the literature. Furthermore, the results showed that the combined method as the reconfiguration and WT placement simultaneously bring the best performance for the network with lower power loss, improved voltage profile and stability, and enhanced reliability. Moreover, the results showed that considering reliability helps significantly reduce the energy not-supplied of the customers and supply their maximum load demand

Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001–2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications

Multi-criteria optimal design of hybrid clean energy system with battery storage considering off- and on-grid application

This paper examines the optimal design of a hybrid photovoltaic-wind generator system with battery storage (PV-wind-battery) with off-grid and on-grid operation modes. The objective of the study is to supply annual load demand considering environmental emissions and energy generation cost, as well as the cost of load losses. The decision variables include the optimal size of photovoltaic and wind resources, transfer of the power of the inverter to the load, battery storage bank size, and photovoltaic panel angle. The angle is determined optimally using a meta-heuristic algorithm, i.e., the spotted hyena optimisation (SHO), considering minimisation of the objective function and satisfying the constraints. The objective function is defined as the sum of net present cost (NPC) of the system components and load losses and also cost of emissions (TNPC). The constraints are a maximum and minimum size of the system components and also reliability constraint as load interruption probability (LIP). In this study, first, an off-grid PV-wind-battery system was designed without considering emissions; then, an on-grid PV-wind-battery system was designed considering emissions. It was then optimized using SHO. The simulation results indicated that the PV-wind-battery and wind-battery combinations are the best and worst system combinations, respectively, in terms of TNPC and reliability indices. Additionally, the performance of the SHO algorithm was compared with that of the particle swarm optimisation (PSO), and the results proved the superiority of SHO in system design with a lower cost and better reliability indices. Moreover, the results cleared that the TNPC and LIP achieved 1.286 M$and 0.13$ and 0.19%. The obtained results showed that reliability can be improved by purchasing power from the network and considering minimisation of emissions in the on-grid operation mode. The impact of increasing the maximum available power of the network (MAPnet) was examined on the hybrid PV-wind-battery system designing. The results confirmed that the system reliability was improved and TNPC decreased by increasing the MAPnet, and vice versa

Stochastic-Metaheuristic Model for Multi-Criteria Allocation of Wind Energy Resources in Distribution Network Using Improved Equilibrium Optimization Algorithm

In this paper, a stochastic-meta-heuristic model (SMM) for multi-criteria allocation of wind turbines (WT) in a distribution network is performed for minimizing the power losses, enhancing voltage profile and stability, and enhancing network reliability defined as energy not-supplied cost (ENSC) incorporating uncertainty of resource production and network demand. The proposed methodology has been implemented using the SMM, considering the uncertainty modeling of WT generation with Weibull probability distribution function (PDF) and load demand based on the normal PDF and using a new meta-heuristic method named the improved equilibrium optimization algorithm (IEOA). The traditional equilibrium optimization algorithm (EOA) is modeled by the simple dynamic equilibrium of the mass with proper composition in a control volume in which the nonlinear inertia weight reduction strategy is applied to improve the global search capability of the algorithm and prevent premature convergence. First, the problem is implemented without considering the uncertainty as a deterministic meta-heuristic model (DMM), and then the SMM is implemented considering the uncertainties. The results of DMM reveal the better capability of the IEOA method in achieving the lowest losses and the better voltage profile and stability and the higher level of the reliability in comparison with conventional EOA, particle swarm optimization (PSO), manta ray foraging optimization (MRFO) and spotted hyena optimization (SHO). The results show that in the DMM solving using the IEOA, traditional EOA, PSO, MRFO, and SHO, the ENSC is reduced from $3223.5 for the base network to$632.05, $636.90,$638.14, $635.67, and$636.18, respectively, and the losses decreased from 202.68 kW to 79.54 kW, 80.32 kW, 80.60 kW, 80.05 kW and 80.22 kW, respectively, while the network minimum voltage increased from 0.91308 p.u to 0.9588 p.u, 0.9585 p.u, 0.9584 p.u, 0.9586 p.u, and 0.9586 p.u, respectively, and the VSI improved from 26.28 p.u to 30.05 p.u, 30.03 p.u, 30.03 p.u, 30.04 p.u and 30.04 p.u; respectively. The results of the SMM showed that incorporating uncertainties increases the losses, weakens the voltage profile and stability and also reduces the network reliability. Compared to the DMM, the SMM-based problem is robust to prediction errors caused by uncertainties. Therefore, SMM based on existing uncertainties can lead to correct decision-making in the conditions of inherent-probabilistic changes in resource generation and load demand by the network operator

Stochastic-Metaheuristic Model for Multi-Criteria Allocation of Wind Energy Resources in Distribution Network Using Improved Equilibrium Optimization Algorithm

In this paper, a stochastic-meta-heuristic model (SMM) for multi-criteria allocation of wind turbines (WT) in a distribution network is performed for minimizing the power losses, enhancing voltage profile and stability, and enhancing network reliability defined as energy not-supplied cost (ENSC) incorporating uncertainty of resource production and network demand. The proposed methodology has been implemented using the SMM, considering the uncertainty modeling of WT generation with Weibull probability distribution function (PDF) and load demand based on the normal PDF and using a new meta-heuristic method named the improved equilibrium optimization algorithm (IEOA). The traditional equilibrium optimization algorithm (EOA) is modeled by the simple dynamic equilibrium of the mass with proper composition in a control volume in which the nonlinear inertia weight reduction strategy is applied to improve the global search capability of the algorithm and prevent premature convergence. First, the problem is implemented without considering the uncertainty as a deterministic meta-heuristic model (DMM), and then the SMM is implemented considering the uncertainties. The results of DMM reveal the better capability of the IEOA method in achieving the lowest losses and the better voltage profile and stability and the higher level of the reliability in comparison with conventional EOA, particle swarm optimization (PSO), manta ray foraging optimization (MRFO) and spotted hyena optimization (SHO). The results show that in the DMM solving using the IEOA, traditional EOA, PSO, MRFO, and SHO, the ENSC is reduced from $3223.5 for the base network to$632.05, $636.90,$638.14, $635.67, and$636.18, respectively, and the losses decreased from 202.68 kW to 79.54 kW, 80.32 kW, 80.60 kW, 80.05 kW and 80.22 kW, respectively, while the network minimum voltage increased from 0.91308 p.u to 0.9588 p.u, 0.9585 p.u, 0.9584 p.u, 0.9586 p.u, and 0.9586 p.u, respectively, and the VSI improved from 26.28 p.u to 30.05 p.u, 30.03 p.u, 30.03 p.u, 30.04 p.u and 30.04 p.u; respectively. The results of the SMM showed that incorporating uncertainties increases the losses, weakens the voltage profile and stability and also reduces the network reliability. Compared to the DMM, the SMM-based problem is robust to prediction errors caused by uncertainties. Therefore, SMM based on existing uncertainties can lead to correct decision-making in the conditions of inherent-probabilistic changes in resource generation and load demand by the network operator