Loss Of Power Supply Probability Research Articles

A Techno-Economic Optimal Evaluation and Feasibility Analysis of Hybrid Microgrid Electrification for Smart Cities in Tamil Nadu

The cost-effectiveness of the hybrid design of microgrids for determining if a hybrid MG system is necessary is taken into consideration as the study focuses on improving power access in an optimal and economical manner. Both the cost of electricity (COE) and the loss of power supply probability (LPSP) of the hybrid MG design are optimized. The hybrid microgrid design including the photovoltaic (PV)-wind systems with battery storage and diesel generators are modeled and optimized for technical and economic feasibility using MATLAB. The energy management technique is applied between hybrid energy sources and loads. For this hybrid microgrid energy management system, an optimisation approach called spider monkey optimization (SMO) is employed. Using their distinct fission and fusion techniques, spider monkeys always engage in natural foraging behavior. Here, a variety of common constraints related to PV, wind, energy storage elements, inverters, and diesel generators with a variety of cost functions and dimensions are resolved by the proposed optimization method. The SMO optimization method is used to find a utilitarian optimal hybrid energy system configuration with a minimum COE, minimum LPSP, and leading renewable factor to meet the customer load throughout the entire time horizon.

Operational strategy and capacity optimization of standalone solar-wind-biomass-fuel cell energy system using hybrid LF-SSA algorithms

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system.

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.

The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant

Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios.

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF.

An Effective Optimal Economic Sustainable Clean Energy Solution With Reduced Carbon Capturing/Carbon Utilization/ Carbon Footprint for Grid Integrated Hybrid System

The integration of conventional sources with the grid has many challenges, like carbon emission, optimal cost of the system, and power quality issues. All these shortcomings create a non-sustainability in the environment, which is of great concern. In order to overcome such issues, a hybrid system is designed that is composed of various components or sources like wind energy, solar photovoltaic energy, thermal energy, and battery energy storage with the purpose of providing an environmentally friendly, economically viable, sustainable, and reliable solution. The objective is to reduce the carbon capture, carbon utilization, and carbon footprint. The carbon footprint is measured as the optimal difference between carbon capture (CC) and its utilization (CU), and carbon emission is represented as loss of carbon emission (LCE). Another objective is to reduce the optimal size of components, the distortion level, and the optimal cost in terms of loss of cost of energy (LCOE) for the various values of loss of power supply probability (LPSP). All the above objectives are accomplished by designing a nonlinear multi-objective problem. The designed nonlinear multi-objective function is based on a hybrid hysteresis fuzzy algorithm. The proposed algorithm is a combination of both fuzzy logic controllers and the hysteresis band method. The effectiveness of the proposed topology is tested on an IEEE standard 9 bus system. It is observed that a nonlinear hybrid hysteresis fuzzy algorithm provides a reliable and sustainable solution for optimal cost with a reduced effective carbon footprint and minimal distortion by maintaining the proper balance between carbon capturing and carbon utilization. The average values of LCOE,LCE, CU, and CC with the proposed method for various LPSP are found to be 0.5926 $/KWh, 70.64 g CO2/kWh, 89 g CO2/kWh, and 159 g CO2/kWh, which are the least among all methods.

Short- and long-duration cooperative energy storage system: Optimizing sizing and comparing rule-based strategies

Reasonable configuration of energy storage equipment could solve the mismatch problem between load demand and renewable power output. The energy storage devices could be classified into short-duration and long-duration storage according to the operation timescale. Short- and long-duration cooperative energy storage is a promising trend because of its complementary advantages. This work focuses on the systems of photovoltaics and wind farms combined with energy storage components, such as batteries, thermal energy storage (TES), and hydrogen energy storage (HS). The optimal design parameters are obtained by multi-objective optimization with the objective of levelized cost of energy and loss of power supply probability. The techno-economic performance of different short- and long-term cooperative energy storage systems are compared. The influence of rule-based strategies on the system performance is investigated. Results indicate that the system with batteries and TES has great competitiveness from an economic perspective, and the system with batteries and HS has a lower potential energy waste probability when the power-supply reliability is extremely high. The recommended discharging priority of the system with batteries and TES is TES first and then batteries, which could completely achieve power supply reliability.

Demand response-fuzzy inference system controller in the multi-objective optimization design of a photovoltaic/wind turbine/battery/supercapacitor and diesel system: Case of healthcare facility

One of the most common causes of power outages in developing countries is a global mismatch between supply and demand. The effects of this phenomenon are especially devastating in the healthcare sector. This paper describes the management of the loads' operation using Demand Response-Fuzzy Inference System Controller (DR-FIS) for the sizing optimization of photovoltaic/wind turbine/battery/supercapacitor and photovoltaic/wind turbine/battery/diesel generator systems operating autonomously in a health center in northern Cameroon using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) methods. The assessment criteria for this optimization are Loss of Power Supply Probability (LPSP), Net Present Cost (NPC), Cost of Energy (COE), Total Greenhouse gases Emission (TGE), Wasted Energy (WE), and Renewable Generation (REG). Implementing a Demand Response-Fuzzy Inference System controller (DR-FIS) has allowed significant energy savings (15.4130% reduction in energy demand) and increased worldwide supply–demand adequacy. This study highlights the techno-economic and environmental significance of using a supercapacitor (SC) as a backup in contrast to a diesel generator (DG), as well as the validation of its compatibility with storage batteries because of the provision of a robust energy management approach. Finally, in this study, MOGA results in better results than MOPSO after evaluating the outcomes of the various multi-objective optimization methods. This strategy enabled the determination of the ideal configuration for the studied Healthcare Center's power supply. This configuration includes the Demand Response-Fuzzy Inference System. It consists of 20 solar panels (PV), 02 wind turbines (WT), 04 batteries (BT), and 07 supercapacitors (SC) for a COE of 0.1691 $/kWh, a NPC of 1.1808e + 03 $, a TGE of 439.7901 Kg, a WE of 4.0066e + 03 Kwh, 100% REG and unfortunately 0.9858 % LPSP.

A Bottom-Up Approach to PV System Design for Rural Locality Electrification: A Case Study in Burkina Faso

This work evaluates the performance of optimal hybrid PV/battery and PV/diesel generator renewable energy systems for a remote village in Burkina Faso. Based on socioeconomic data and the household sample survey, a technoeconomic simulation and optimization model of electrical loading are presented. Ant colony optimization (ACO) and the loss of power supply probability (LPSP) algorithms were used for the search of the optimal hybrid power system. For the selected village location, the results have shown that the hybrid PV/battery system represents the best renewable energy solution due to abundant solar irradiation and carbon emission free compared to the conventional diesel generator (DG) or PV/DG system. To reach the estimated load power demand of 2150 kWh for the studied location, optimized PV/battery configuration sizing required 650 PV modules of 250 W and 715 batteries of 300 Ah. The economical evaluation reveals a cost investment of about 1,293 025.7 USD for a lifetime of 25 years in comparison of that of PV/DG and DG systems, which are 1,088 701.9 USD and 1,682 850.6 USD, respectively. However, environmental and atmospheric pollution is minimized with a saving of more than 17943 tons of CO2. Therefore, the production of electricity from the PV/battery system leads to better competitiveness reliability for a socioeconomic development of studied remote villages.

Performance analysis of hydrogen supply using curtailed power from a solar-wind-storage power system

A solar-wind system including solar and wind power plants, electric and thermal storage devices, and hydrogen supply devices is constructed to discuss the feasibility of combined power and hydrogen supply. On the premise that over 90% of the power demand is economically met, the installed capacities of hydrogen supply devices are optimized under different conditions to obtain the highest reliability and lowest cost of hydrogen supply. The stable and typical seasonal fluctuating load demands of power and hydrogen are considered. The influences of hydrogen production technology on system energy utilization is analyzed. Results show that when the loss of hydrogen supply probability (LHSP) is required to be 0.1, 0.05 or even 0, the hydrogen supply cost is as low as 7.49 $/kg, 10.76 $/kg and 17.33 $/kg. The fluctuating power and hydrogen supply curves with better coincidence with weather distribution, higher power supply reliability requirement and less hydrogen demand are conducive to achieve lower hydrogen supply cost under the same reliability. When LHSP is required to be 0.1, the hydrogen production technology can recover 12.6%–26.1% of curtailed photovoltaic and wind power with the loss of power supply probability (LPSP) of 0.1, and 10.6%–21.3% of curtailed power with LPSP of 0.05.

Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

Realistic performance evaluation and optimal energy management of a large-scale bifacial photovoltaic system

This paper introduces an optimal energy management and a comprehensive innovative comparison between on-grid monofacial and bifacial solar photovoltaic systems in terms of energy production, reliability, economic feasibility, ecological impact, footprint area, and other performance indicators. This is to satisfy the load demand of Al-Husainya city, Jordan. Realistic measured data are obtained from authoritative companies in Jordan. This includes ambient temperature, solar irradiance, and a utility-scale load demand. The optimal sizing and energy management strategy are achieved using Marine Predators optimization algorithm. This is done by minimizing Loss of Power Supply Probability. The decision variable is the number of photovoltaic modules. The unit investigation shows that the maximum power of the bifacial panel is increased compared with a monofacial module. So, this will highly affect system size and hence reliability, footprint required area, cost and ecological indicators are impacted too. Results show that the minimized values of the objective function of Loss of Power Supply Probability for monofacial and bifacial systems are 0.3534 % and 0.2511 %, respectively. The number of monofacial and bifacial panels are 12,940 and 11,584 respectively. In addition, the area required to install the bifacial array is decreased by 10.4791 % compared with the monofacial array. Finally, four additional optimization algorithms were used to validate and compare with the obtained results using the Marine Predators algorithm. The methodology described in this paper, which adopts the bifacial photovoltaic technology, is recommended and it can be applied anywhere to get highly reliable, feasible, and more environmentally friendly solar energy systems.

Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm

Recently, renewable energy resources (RESs) have been utilized to supply electricity to remote areas, instead of the conventional methods of electrical energy production. In this paper, the optimal design of a standalone hybrid RES comprising photovoltaic (PV), wind turbine (WT), and biomass sources as well as an energy storage system, such as a hydro-pumped storage system, is studied. The problem of the optimal sizing of the generating units in the proposed energy system is formulated as an optimization problem and the algorithms heap-based optimizer (HBO), grey wolf optimizer (GWO), and particle swarm optimization (PSO) are applied to achieve the optimal sizing of each component of the proposed grid-independent hybrid system. The optimization problem is formulated depending on the real-time meteorological data of the Ataka region on the Red Sea in Egypt. The main goal of the optimization process is to minimize the cost of energy (COE) and the loss of power supply probability (LPSP), while satisfying the constraints of system operation. The results clarify that the HBO algorithm succeeded in obtaining the best design for the selected RE system with the minimum COE of 0.2750 USD/kWh and a net present cost (NPC) of USD 8,055,051. So, the HBO algorithm has the most promising performance over the GWO algorithm in addressing this optimization problem.

Optimal Configuration Planning of Multi-Energy Systems using Optimization-based Deep Learning Technique

There has been discovered that the freestanding hybrid renewable resource, which combines energy sources like solar, wind, etc., is an efficient substitute for supplying electricity to isolated places that are not connected to utility grids. The appropriate supply of electricity is subject to several restrictions, including variations in peak load, power outage supply, and other factors like swings. This study suggests an effective strategy for managing and sizing hybrid renewable energy resources to get around the drawbacks. First, the prediction of weather and loading demand is carried out using Bi-Directional Gated Recurrent Unit (Bi-GRU) network, in which past data are utilized to predict the uncertainties in load. This is done to assist the right sizing of resources for that purpose, a Long Short-Term Memory (LSTM) is used. Furthermore, the Search and Rescue Optimization Algorithm (SRO) employed for optimizing the parameter of power management. Utilizing MATLAB R2020a, the proposed model is tested by simulating it and comparing its performance to that of other models using metrics like variation rate, battery energy, Loss of Power Supply Probability (LPSP), forecasting error, net present cost, and cost of energy.

Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems

This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations.

Optimal sizing of RES and BESS in networked microgrids based on proportional peer‐to‐peer and peer‐to‐grid energy trading

AbstractNetworked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed “proportional trading method” via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability.

An effective design of hybrid renewable energy system using an improved Archimedes Optimization Algorithm: A case study of Farafra, Egypt

The Improved Archimedes Optimization Algorithm (IAOA) is presented and applied to design a hybrid renewable energy system (HRES) for a microgrid system in the Farafra region of Egypt. The studied microgrid consists of three scenarios based on PV panels, wind turbine systems, diesel generators, and a battery energy storage system (BESS). The objective is to minimize the design function of the net present cost (NPC) that englobes all expenses during the project lifetime, respecting three constraints: the renewable fraction index, loss of power supply probability, and availability. The simulation results are compared with several known algorithms, such as the original Archimedes optimization algorithm (AOA), artificial electric field algorithm (AEFA), Equilibrium optimizer (EO), Grey Wolf optimizer (GWO), and Harris Hawks Optimization (HHO) algorithms. The results prove the ability of the proposed algorithm to solve the problem design, and they also demonstrate its superior efficiency to competing algorithms. The best-found HRES is the PV panel/wind turbine/diesel generator/battery storage system HRES, the NPC is $187,181, equivalent to cost energy (LCOE) of 0.213 $/kWh. The constraints are respected, the reliability is approximately 5%, the renewable fraction index is close to 90%, and the availability is approximately 100%. From the results, it is observed that the synergy of PV and wind systems is mandatory in such areas, and the battery also plays an important role in managing and arranging the energetic flow in HRES systems. The benchmark functions are tested using 23 functions, which proved that the IAOA performed better than the original AOA algorithm.

Investigation of a reliable and sustainable stand-alone hybrid energy system for freshwater supply: a case study

ABSTRACT Hybrid renewable energy system-based reverse osmosis desalination has been identified as a reliable and sustainable solution for supplying potable water to the off-grid communities. In this respect, this study proposes a stand-alone hybrid energy system comprising solar PV, wind turbine, diesel generator, and a battery bank to power a reverse osmosis desalination plant. The proposed hybrid system is optimised using a novel metaheuristic optimisation technique named salp swarm algorithm (SSA) to minimise the three objective functions: levelised cost of energy (LCOE, $/kWh), life cycle emissions (LCE, CO2 kg-eq/yr.), and the excess energy (EE, kWh/yr.). Moreover, the performance of the novel algorithm is scrutinised using the benchmark solutions achieved from the widely utilised non-dominated sorting genetic algorithm II (NSGA-II) and particle swarm optimisation (PSO) techniques. In addition, the study also investigates the effects of water demand and the loss of power supply probability on the techno-economic performance. The results reveal that SSA outperforms NSGA-II and PSO algorithms towards achieving Pareto optimality hence provides financially rewarding solutions. The LCOE for the optimal HES is 0.179 $/kWh, and the LCE is 5921 CO2 kg-eq/yr. with an EE of 402 kWh/yr., whereas the cost of water for this system is 1.37 $/m3.

Techno-economic and environmental design of hybrid energy systems using multi-objective optimization and multi-criteria decision making methods

Reliable, affordable and sustainable supply of energy is of paramount importance. This can be achieved by designing and implementing hybrid renewable energy systems, also known as micro/smart-grids. The main goal of this study is the techno-economic and environmental multi-objective optimization of a decentralized energy system to provide electrical and thermal loads of a large energy consuming complex with daily average electric and thermal loads of 38.7 and 99.4 MWh, respectively. Pareto-optimal mix of several renewable and non-renewable technologies including photovoltaic (PV), wind turbine (WT), two types of combined heat and power technologies, namely micro-gas turbine (MGT) and fuel cell (FC), grid power, electrolyzer, hydrogen tank (Htank), battery and inverter is investigated. For this purpose, different configuration of these units were simulated in HOMER software, and the exact Pareto set was found by developing a MATLAB code. A total of 1945 optimal non-dominated designs including 11 stand-alone and 41 grid-connected configurations were found, in which the dominant configuration was PV/FC/Electrolyzer/Htank/Battery/Inverter/20%Grid. The Pareto sets included systems with a wide variety of characteristics; the range (average) of net present cost (NPC), loss of power supply probability (LPSP) and carbon dioxide emissions (CE) were 26.9–44.0 (36.1) M$, 0–10 (5.7)% and 8800.9–13649.2 (9790.4) tons/yr for off-grid scenario and 17.2–37.5 (27.2) M$, 0–10 (4.3)% and 11215.1–17968.2 (13476.1) tons/yr for on-grid scenario, respectively. The TOPSIS combined with weighting methods was introduced to choose final design among non-dominated solutions set. At last, sensitivity analysis revealed that the most significant variables on the NPC of the selected micro-grids are real interest rate, investment costs, electric and thermal loads and level of irradiation, respectively.

Experimental validation of an AI-embedded FPGA-based Real-Time smart energy management system using Multi-Objective Reptile search algorithm and gorilla troops optimizer

This paper proposes an AI-embedded FPGA-based Smart Energy Management System (SEMS) that ensures intelligent, secure, consistent, and synchronous energy management in an isolated microgrid. The proposed techno-economic SEMS comprises two levels of control to achieve optimal management and operation for an isolated microgrid. The first level adopts the use of the FPGA as a central controller, which is characterized by its high processing speed and small settling time. The second level aims to develop a coordinated operation strategy based on the optimal operation and management of an isolated microgrid in order to optimize the coordinated use of backup sources. An efficient multi-objective optimization problem for optimal operation and management of the microgrid is formulated. Two multi-objective optimization algorithms namely, Gorilla Troops Optimizer (GTO) and Reptile Search Algorithm (RSA) are applied to solve the optimization problem. The three main objectives considered in this study are to minimize the operating costs, the loss of power supply probability (LPSP), and the surplus power consumed by the dummy load. The results prove the superiority of the RSA algorithm in achieving the goals of the objective functions. Within 100 min of the experimental testing, it achieves the lowest operating cost 166.2423 $. The cost savings reach about 6.467 % when using the RSA, while it is 6.0363 % when using the GTO. The developed SEMS reduces the wasted power in the dummy load. In addition, it achieves the lowest value of LPSP about zero, which is considered the best value as it ensures continuous supply.