• Energy Research

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

In this paper, a methodology of sizing optimization of a stand-alone hybrid PV/wind/battery power generation system (HPWGS) is proposed. The main objective of this optimization is to minimize the total annual cost (TAC) of the hybrid system considering power balanced constraint and determination of the optimal number of wind turbines, PV panels, and batteries. For this purpose, a new meta-heuristic nature-inspired algorithm, called Grey Wolf Optimizer is utilized. The obtained results show that the proposed methodology finds optimal sizing of the HPWGS easily with fast convergence and lower cost. Comparing the results of this new method with the well-known published works in the literature, the superior capabilities of this proposed method are demonstrated. Moreover, the results show that the reliability characteristics are weakened with decreasing the component availability, and the supply reliability of the HPWGS is improved by increasing the inverter efficiency while the TAC is decreased. Furthermore, the HPWGS can cover the load demand variations with high reliability.

Abstract In this paper, an optimized stand-alone hybrid energy system consists of photovoltaic (PV) arrays, wind turbines (WT), and battery (BA) storage (HPV/WT/BA) presented with the objective of total net present cost (TNPC) minimization subjected to loss of energy probability (LOEP) considering the effect of interest rate (IR) changes. The hybrid system optimization is done based on real annual data of irradiance, wind speed, temperature and demand of a remote site. The main goal of the optimization is optimal sizing of the hybrid system components as the number of PVs, WTs, and batteries and transferred power to load by the inverter with minimizing the TNPC and satisfying the LOEP. A new method named improved grasshopper optimization algorithm (IGOA) based on a nonlinearly decreasing inertia weight strategy is applied for the determination of optimal sizing of the system with the lowest TNPC and best LOEP. Energy management is adapted with the operation of the hybrid system based on load following strategy (LFS). The results showed that the proposed methodology based IGOA finds the HPV/BA system as an optimal combination to supply the site demand with the lowest TNPC and better reliability. The superiority of the IGOA is confirmed in comparison with conventional GOA and well-known particle swarm optimization (PSO) methods in achieving an optimal solution with lower cost and higher reliability. The results of considering IR changes effect are cleared that the LOEP and cost of energy (COE) are increased by 0.56% and 5.37%, respectively and TNPC is decreased by 1.66% due to 1% increasing the IR in optimization of the HPV/BA system. So, the results of interest rate changes indicate a significant effect on the system cost and reliability. Moreover, with increasing the storage capital cost, the TNPC and COE are increased and the reliability level is dropped.

Abstract In this paper, optimal designing and energy management of hybrid photovoltaic/wind/fuel cell (PV/WT/FC) system is presented with cost of hybrid system life span (CHSLS) minimizing and considering loss of load interruption probability (LOLIP) for Recreational Center of Gonbad (RCOG), a remote area region in Iran country using actual irradiance and wind speed data of this region. A meta-heuristic improved sine–cosine algorithm (ISCA) is used based on nonlinearly decreasing inertia weight strategy (NDIWS) for improving global and local search of the conventional SCA to find the optimal combination of hybrid system. In designing problem the optimal size of hybrid system components is determined using ISCA considering CHLS and LOLIP to satisfy the RCOG load demand in most cost-effective way. The performance of the proposed ISCA is compared with conventional SCA and well-known PSO methods in different combinations and under LOLIP changing conditions. Simulation results showed that the ISCA finds easily the optimal combination as PV/WT/FC system with lower CHSLS and better LOLIP. Also WT/FC system is not economical due to high CHSLS for RCOG. The results demonstrated that the average cost of per kW RCOG load supply using the PV/WT/FC for maximum LOLIP of 1% is equal to 0.87$ and for maximum LOLIP of 5% is 0.76$. The results showed the contribution and management of PV, WT and fuel cell together as PV/WT/FC is made a reliable and cost-effective energy system to supply the remote area application like RCOG. Moreover superiority of the ISCA than the SCA and PSO is proved with better convergence speed and accuracy. Also, the effect of some effective factors on the system designing is evaluated.

Abstract The use of multi-level inverters is increasing in different structures, high power and medium power applications due to advantages such as low switching losses, harmonic distortion and electromagnetic interference at the output which could be used in microgrid systems. A microgrid can be defined as groups of renewable energy sources such as photovoltaic and wind turbine i.e. The switching technique for inverter control plays a significant role in reducing or eliminating the harmonics of inverter output voltage and reducing the switching losses. To minimize the distortion of the output voltage of the cascaded H bridge multi-level inverter due to low-order harmonics, an optimization method used for frequency selection, i.e. the carrier wave amplitude in the SPWM strategy within this study. The proposed method is called OSPWM, which employs a new optimization method based on the Salp swarm algorithm. The proposed method applied to a cascade H bridge five-level inverter. The simulation results show the reduction of the low-frequency harmonics amplitude and THD output voltage by optimizing the OSPWM carrier wave parameters with the optimization algorithm. The proposed method also compared with the classical SPWM method.

In this paper, optimal allocation of a distributed thyristor-controlled series compensator (DTCSC) in a power system is presented to minimize overload, voltage deviations, and power losses while improving system reliability. The decision variable was defined as the optimal reactance of the DTCSC in the power system, which was determined using a new meta-heuristic algorithm named the improved equilibrium optimization algorithm (IEOA). A nonlinear inertia weight reduction strategy was used to improve the performance of traditional EOA in preventing premature convergence and facilitate a quick global optimum solution. The effect of system critical line outage was evaluated for each of the considered goals. To evaluate the effectiveness of the proposed methodology, IEOA capability was compared with particle swarm optimization (PSO) and manta ray foraging optimizer (MRFO) methods. Simulations were carried out considering different scenarios on 14- and 118-bus test systems. The results showed that, for all scenarios, optimal allocation of DTCSC could result in a significant reduction in overloading, voltage deviation of network buses, as well as power losses under the condition of line outage, due to the optimal injection of reactive power. In all investigated scenarios, our results attested to the superiority of the IEOA over the traditional EOA, PSO, and MRFO in achieving a better value for the objective function. In addition, the results showed that improving reliability in the objective function could eliminate overloading, and hence, introduce further improvement in each of the objectives.

This work-study deals with the economic planning of an island sustainable system that consists of a wind turbine, battery, combined heat and power system, and thermal storage to meet electrical and thermal loads at the same time. The approach attempts to find minimum capital cost, repair and maintenance cost, and operating cost, as well as minimize the pollution of these elements. It is also constrained by the operation-planning model of the mentioned resources and storage, provided that a wind turbine and combined heat and power system have priority in electrical load supply, a combined system of heat and power has priority in thermal load supply, and batteries and storage devices have the task of filling the gap between supply and demand profiles. Uncertainty parameters of the scheme include electrical and thermal load and power output of the wind system. The scheme adopts the unscented transformation method to provide proper modeling of the mentioned uncertainties. Moreover, the essence of the problem is integer nonlinear programming, which uses the combined gray wolf optimization and honey bee mating optimization algorithm to achieve the desired solution. Simultaneous supply of electrical and thermal loads, modeling of uncertainties, and adoption of a combined solver in the proposed scheme are among the contributions. Finally, by implementing this scheme on data from the city of Espoo, Finland, the numerical results indicate a reduction of 10%–11% in the planning cost of the proposed scheme compared to a design that only supplies electricity.

Reconfiguration of the distribution network to determine its optimal configuration is a technical and low-cost method that can improve different characteristics of the network based on multi-criteria optimization. In this paper reconfiguration of unbalanced distribution networks is presented with the objective of power loss minimization, voltage unbalance minimization, voltage sag improvement, and minimizing energy not supplied by the customers based on fuzzy multi-criteria approach (FMCA) using new improved corona-virus herd immunity optimizer algorithm (ICHIOA). The voltage unbalances and voltage sag is power quality criteria and also the ENS refers to the reliability index. Conventional CHIOA is inspired based on herd immunity against COVID-19 disease via social distancing and is improved using nonlinearly decreasing inertia weight strategy for global and local exploration improvement. The methodology is implemented as single and multi-objective optimization on 33 and 69 bus IEEE standard networks. Moreover, the performance of the ICHIOA in problem-solving is compared with some well-known algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth flame optimizer (MFO), ant lion optimizer (ALO), bat algorithm (BA) and also conventional CHIOA. The simulation results based on the FMCA showed that all criteria are improved with reconfiguration due to compromising between them while in single-objective optimization, some criteria may be weakened. Also, the obtained results confirmed the superiority of the ICHIOA in comparison with the other algorithms in achieving better criteria with lower convergence tolerance and more convergence accuracy. Moreover, the results cleared that the ICHIOA based on FMCA is capable to determine the best network configuration optimally to improve the power loss, voltage sag, voltage unbalance, and ENS in different loading conditions.

The unbalanced and nonlinear characteristic of many loads connected to the power system can cause power quality problems, which in turn affects other consumers. Electric arc furnaces (EAF) are among the loads that cause major problems for system power quality. The use of static var compensator (SVC) is a way to reduce power quality problems caused by EAF. Since precise quantification of the SVC susceptance function can greatly influence the optimization of the reactive power, in this paper proposed using of a fuzzy logic controller to optimize the susceptance function to further improve the voltage profile and reduce SVC capacitance. The results from simulations show that the SVC based fuzzy method optimally determines the coefficient of the susceptance function. The results show that the proposed method with the proposed method has good performance in reduction of harmonic currents and improving the voltage profile of the system. So, the results clear that in case of EAF load, the SVC is capable of rapidly compensating its effect and improve the system power quality. By maintaining the bus voltage and eliminating voltage oscillations, the active power received is quickly stabilised and power oscillations are removed. Thus, power quality problems of EAF application is eliminated with application of optimized SVC based fuzzy method. In addition, the performance of the proposed method compared to mathematical methods in previous studies has been evaluated, which presents superiority of the proposed method in reducing voltage oscillations and reducing harmonic currents.