Loss Of Power Supply Probability Research Articles

Optimal capacity and operation strategy of a solar-wind hybrid renewable energy system

A hybrid renewable energy system, including photovoltaic (PV) plant, wind farm, concentrated solar power (CSP) plant, battery, electric heater, and bidirectional inverter, is proposed. The optimal combination of power plants and energy storage devices, and their optimal capacities are obtained by the multi-objective optimization algorithm. A superior operation strategy of the system, which consists of multiple energy storage technologies and flexible power supplies, is proposed. Results show that the PV plant with an inverter can generate power at the lowest cost but with poor reliability. The combination of the CSP plant and PV plant is an effective way to improve power generation reliability economically. When low investment costs of PV plant, battery, and inverter are adopted, the battery is preferentially integrated into PV plant to reduce the loss of power supply probability (LPSP) to a certain level. But it still cannot meet the high power generation reliability requirements. The CSP plant is still an essential technology to further significantly reduce LPSP with good economic performance. The operation strategy in which the power cycle takes precedence over the battery to supplement the power shortage is recommended. The levelized cost of energy (LCOE) of the system with power cycle output priority is 8.36% lower than that of the system with battery output priority when the LPSP is 1.34%.

Designing framework of hybrid photovoltaic-biowaste energy system with hydrogen storage considering economic and technical indices using whale optimization algorithm

In this study economic, reliable and environmentally friendly designing of a hybrid photovoltaic-biowaste-fuel cell (PV-Biowaste-FC) system based on hydrogen storage energy is presented using whale optimization algorithm (WOA) considering the availability of components for 20 years useful lifespan of the project. The WOA is a robust meta-heuristic method for solving optimization problems with high convergence speed and accuracy. The fuel cell system includes an electrolyzer, a hydrogen storage tank, and a fuel cell stack. The objective function is defined as minimization of total net present cost (TNPC) include investment, maintenance and repair and replacement cost and reliability constraint is considered as loss of power supply probability (LPSP). The optimization variables include the area occupied by photovoltaic (PV) panels, number of biowaste units, electrolyzers, hydrogen storage tanks, fuel cells, and inverters that are optimally determined by the WOA considering the TNPC and LPSP. To validate the WOA method in the PV-Biowaste-FC designing, its performance is compared with the particle swarm optimization (PSO) method. Simulations have been carried out for different scenarios including designing of Biowaste-FC and PV-Biowaste-FC system, evaluating the effect of PV, Biowaste and inverter availability and also effect of investment cost of FC on the system designing. Simulation results show that the hybrid system with the participation of all resources and hydrogen storage system as the PV-Biowaste-FC presents the minimum TNPC (2.820 M$) and the lower LPSP (0.0029) compared to the other combinations. Also, the cost of energy (COE) value for the hybrid PV-Biowaste-FC system is obtained at 0.5238 $/kWh. The proposed method for designing the hybrid system provides better performance than the PSO in achieving less TNPC and better reliability with higher convergence speed and accuracy. Further, the effects of the availability of PV units, biowaste, and inverter on the system designing are evaluated. According to the results, decreasing the availability of the components increases the TNPC and weakens the reliability indices.

A peer-to-peer energy trading for a clustered microgrid – Game theoretical approach

This paper proposes a peer-to-peer energy trading system based on multi-objective game-theoretic optimisation for a clustered microgrid to find suitable sizes of distributed generations, including energy storage systems. The chosen architecture includes three different microgrids, and each microgrid can be a combination of solar panels, wind turbines and batteries, to meet the requirements of their residential loads. In the clustered microgrid, all microgrids are connected with one another, and also with the main grid, and they have the ability to perform both peer-to-peer and peer-to-grid energy trading based on load requirements and to reach the optimum value of the outlined objective function. To formulate a multi-objective function, two different criteria’ the annual profit and the loss of power supply probability are considered, and the Nash equilibrium-based game theory technique is modelled for clustered microgrids using a particle swarm optimisation algorithm to obtain suitable sizes of the players and payoff values. In the end, for each microgrid, suitable sizes of solar panels, wind turbines and batteries are proposed to meet the residential load requirements in the grid-connected mode. The outcomes of the multi-objective function are analysed and compared for two energy trading schemes, including peer-to-grid and peer-to-peer. The trends of objective functions and sizes of the players are analysed to verify the design of the proposed architecture.

Amulti‐objectiveapproach for the optimal design of a standalone hybrid renewable energy system

Dependency on alternative energy sources has increased due to the depletion of fossil fuel and their costs. Even though the usage of renewable energy sources (RESs) has significantly reduced these problems, there are quite a few challenges like reliability, lower efficiency, and high cost which can be overcome by the integration of multiple RES to meet the energy demand. In this study, a multi-objective approach is proposed to get the best optimal sizing configuration of a standalone hybrid renewable energy system (HRES) by choosing the minimization of the total cost of the system and loss of power supply probability. In this regard, wind turbines, solar photovoltaic (PV), battery storage systems, biomass, and diesel generators are used to design HRES. The proposed approach deals with the intermittent nature of wind and solar PV power generation using Weibull distribution to model wind speed and solar irradiance. The normal distribution is used to model the load demand. The backtracking search algorithm capable of solving single-objective optimization problems is modified to handle the multi-objective optimization problem and to find the optimal sizes of wind turbines, solar PV, battery storage system, diesel, and biomass generators for efficient HRES performance.

Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

This study investigates the use of division algorithms to optimize the size of a desalination system integrated with a microgrid based on a wind turbine plant and the battery storage to supply freshwater based on cost, reliability, and energy losses. Cumulative exergy demand is used to identify and minimize the energy losses in the optimized system. Division algorithms are used to overcome the drawback of low convergence speed encountered by the well-known method genetic algorithm. The findings indicated that there is a positive relationship between cost, cumulative exergy, and reliability. More specifically, when the loss of power supply probability is 10%, compared to when it is 0%, the total cumulative exergy demand and total life cycle cost are reduced by 34.76% when the battery is full and 45.44% when the battery is empty and there is a 44.43% decrease in total life cycle cost, respectively. However, the more reliable system, the less exergy is lost during the production of 1 m3 freshwater by desalination integrated into wind turbine plant.

Feasibility study of wind turbine system integrated with insulated Greenhouse: Case study in Tunisia

The current study highlights the importance of intelligent control for Insulated Greenhouse (IG) with the exploitation of wind energy in order to minimize energy consumption. The experiment of the IG has been conducted with and without a Fuzzy Logic Controller (FLC). Firstly, an innovative dynamic model is established and experimentally validated to be then controlled via a FLC to attain a favorite microclimate into the IG. Secondly, a Wind Turbine System (WTS), supported by a Permanent Magnet Synchronous Generator (PMSG), was proposed to cover the power required by the studied IG. The proposed configuration is simulated under MATLAB, and the results showed the reliability of the established thermodynamic model of the IG to anticipate the indoor temperature and humidity with a good agreement. Besides, the performance of the model is indicated by a small Root Mean Squared Error (RMSE) equal to 0.17 for the temperature and 0.08 for the humidity, whereas the Coefficient Of Determination (R2) is equal to 0.92 for the temperature and 0.82 for the humidity. Further, the ability of the proposed WTS to satisfy 100% of the required power is also proved and justified with 99.96% of Renewable Factor (RF) and 0.03 of Loss of Power Supply Probability (LPSP).

Assessment and multi-objective optimization of an off-grid solar based energy system for a Conex

In this study, an off-grid PV system is optimized to supply a Conex electricity demand in the top ten earthquake-prone cities using mixed-integer linear programming techniques. The stand-alone photovoltaic system is designed by a photovoltaic array, a cooling/heating system, battery banks, an inverter, and a charge controller. For determining the optimum size and specifications of the system components such as PV panel, HVAC coefficient of performance, by considering two objectives of the study, a mixed-integer linear programming method is used. These conflicting objectives are the probability of lack of power and total cost of the system. The weighted factor method is utilized, and final optimized systems are achieved using MATLAB 2019b. Using the weighted factor method, several optimum solutions, in which the importance of objectives are different from each other, are obtained for each case concerning objectives. The suggested model is optimized for ten earthquake-prone cities globally, while it can be utilized for any location. The Pareto frontiers are presented to show the trade-offs between two objective functions. The average cost of off-grid PV system supplied electrical power is from about 1000$ for lima (subtropical desert climate) to 4000$ for Tokyo and Osaka (humid subtropical). Analysis of obtained results demonstrates that the system is suitable for all of the considered cities. It can supply the load demand of an off-grid Conex with a loss of power supply probability as low as 1%.

Reliability Evaluation of Renewable Power Systems through Distribution Network Power Outage Modelling

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more faithful in reproducing the dispersion of outage events. Using these models, the effectiveness and performance of the PV-battery system is verified on a large number of simulated outage scenarios, to estimate the real performance of the optimized design. It leads to a more accurate evaluation of the behavior of a renewable power system to a weak and unreliable electrical grid.

Impact of solar tracker and energy storage system on sizing of hybrid energy systems: A comparison between diesel/PV/PHS and diesel/PV/FC

Design of renewable energy-based hybrid systems has become a challenging task especially for electrification to off-grid regions. In the design problem, it is important to economically investigate utilization of energy storage systems (ESSs) and solar tracker. This paper focuses on optimal size of hybrid energy systems which make use of different types of tracking systems. For this aim, two hybrid energy systems, diesel generator/photovoltaic/pumped hydro storage (diesel/PV/PHS) and diesel generator/photovoltaic/fuel cell (diesel/PV/FC), have been designed with respect to different tracking systems (fixed, single-axis and double axis). In order to find optimum design of the hybrid systems, two important objectives, total net present cost (TNPC) and reliability index expressed by loss of power supply probability (LPSP) are minimized in a multi-objective framework. With respect to complexity of this optimization problem, a multi-objective efficient approach is used to solve the problem. Simulation results show that compared to FC when PHS is employed as energy storage system, TNPC of the hybrid system decreases. Moreover, using a solar tracker in diesel/PV/PHS is not cost-effective in comparison with fixed PV panels.

A Comparative Design of a Campus Microgrid Considering a Multi-Scenario and Multi-Objective Approach

This article proposes a plan to replace real-time power with constant power from the grid to reduce costs and reduce the impact of the micro-grid on the main grid at the same time. Most of the peak electricity consumption periods of universities or some enterprise institutions are during the daytime. If solar energy can be used reasonably at this time, it can provide a good guarantee of peak power. In this study, a grid-linked solar-plus-storage micro-grid was used to supply power to a university located in Okinawa, Japan. The non-dominated sorting genetic algorithm II (NSGA-II) was used to optimize the model size, and the loss of power supply probability (LPSP), life cycle cost (LCC), and waste of energy (WE) were taken as the optimization indicators. For this study, three scenarios were considered where the first scheme (Case 1) was a comparison scheme, which used a PV battery and real-time power from the infinity bus. Both the second and third cases used constant power. While Case 2 used constant power throughout the year, Case 3 used daily constant power. The optimal solutions for the power supply units were grouped into three cases where Case 1 was found to be the most expensive one. It was found that the costs of Cases 2 and 3 were 62.8% and 63.3% less than Case 1. As a result, the waste of energy was found to be more significant than Case 1: 70 times and 60 times, respectively. On the contrary, Case 1 had 15.2% and 16.7% less carbon emissions than Case 2 and Case 3, respectively. This article put forward the idea of constant power supply growth at the financial markets, which breaks the traditional way in which the power supply side follows the user’s consumption. While reducing costs, it reduces the impact on large-scale power grids and can also ensure the reliability of campus microgrids.

A Solution to the Problem of Electrical Load Shedding Using Hybrid PV/Battery/Grid-Connected System: The Case of Households’ Energy Supply of the Northern Part of Cameroon

A techno-economic study of a hybrid PV/Battery/Grid-connected system for energy supply is carried out in this paper to respond to the problem of electrical load shedding. An optimal design of the system is realized thanks to a double-objective optimization based on a proposed operational strategy of the system and on Firefly Algorithm (FA). The system is designed for household energy supply in three different towns of the northern part of Cameroon. For different LPSP (Loss of Power Supply Probability), the double objective simulation determines the optimal configurations of the system with their related cost. The optimal and reliable PV/Battery subsystem configuration corresponding to LPSP of 0% obtained for one household is composed for the towns of Maroua and Garoua by 8 PV modules and a battery capacity of 11.304 kWh with 1-day autonomy. For the town of Ngaoundéré, it is composed by 10 PV modules and battery capacity of 11.304 kWh with 1-day autonomy. The related investment costs corresponding to these optimal configurations are USD 6225.6 for Maroua and Garoua and USD 7136.6 for Ngaoundéré. The great proportion of the monthly energy demand consumed by the load is provided by the PV/Battery system. The monthly PV/Battery energy represents 60.385% to 72.546% of the load consumed in Maroua, 58.371% to 71.855% of the load consumed in Garoua, and 61.233% to 74.160% of the load consumed in Ngaoundéré. The annual main grid energy consumed for one household is 1299.524 kWh in Maroua, 1352.818 kWh in Garoua, and 1260.876 kWh in Ngaoundéré. Moreover, the annual PV/Battery energy consumed for one household is 1580.730 kWh in Maroua, 1527.815 kWh in Garoua, and 1619.530 kWh in Ngaoundéré. Thus, the PV/Battery system, by reducing the grid energy consumption, acts as the principal source of energy of the whole system. The time the PV/Battery/Grid-connected system needs to be economically more advantageous than the electric grid without blackouts is 17 years for Maroua and 18 years for both Garoua and Ngaoundéré. It is demonstrated in this paper that the hybrid PV/Battery/Grid-connected system is an effective solution for electrical load shedding in sub-Saharan zones. This system is very useful for grid energy consumption reduction. For a long-term investment, the PV/Battery/Grid-connected system is more economically advantageous than the main grid alone.

Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals

The Egyptian irrigation system depends mainly on canals that take water from the River Nile; nevertheless, the arid climate that dominates most of the country influences the high rate of water losses, mainly through evaporation. Thus, the main objective of this study is to develop a practical approach that helps to accommodate solar photovoltaic (PV) panels over irrigation canals to reduce the water evaporation rate. Meanwhile, a solar PV panel can contribute effectively and economically to an on-grid system by generating a considerable amount of electricity. A hybrid system includes a solar PV panel and a diesel generator. Several factors such as the levelized cost of energy (LCOE), total net present cost, loss of power supply probability, and greenhouse gas emissions should be considered while developing a technoeconomically feasible grid-connected renewable integrated system. A mathematical formulation for the water loss was introduced and the evaporation loss was monthly estimated. Thus, this study also aims to enhance an innovative metaheuristic algorithm based on a cuckoo search optimizer to show the way forward for developing a technoeconomic study of an irrigation system integrated with an on-grid solar PV panel designed for a 20-year lifespan. The results are compared using the mature genetic algorithm and particle swarm optimization to delimit the optimal size and configuration of the on-grid system. The optimal technoeconomic feasibility is connected to the graphical information system to delimit the optimal length and direction of the solar PV accommodation covering the canals. Finally, based on the simulated results, the optimal sizing and configuration of the irrigation-system-integrated on-grid solar PV accommodation have less impact on the LCOE without violating any constraint and, at the same time, generating clean energy.

Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia

Hybrid microgrids are presented as a solution to many electrical energetic problems. These microgrids contain some renewable energy sources such as photovoltaic (PV), wind and biomass, or a hybrid of these sources, in addition to storage systems. Using these microgrids in electric power generation has many advantages such as clean energy, stability in supplying power, reduced grid congestion and a new investment field. Despite all these microgrids advantages, they are not widely used due to some economic aspects. These aspects are represented in the net present cost (NPC) and the levelized cost of energy (LCOE). To handle these economic aspects, the proper microgrids configuration according to the quantity, quality and availability of the sustainable source of energy in installing the microgrid as well as the optimal design of the microgrid components should be investigated. The objective of this paper is to design an economic microgrid system for the Yanbu region of Saudi Arabia. This design aims to select the best microgrid configuration while minimizing both NPC and LCOE considering some technical conditions, including loss of power supply probability and availability index. The optimization algorithm used is Giza Pyramids Construction (GPC). To prove the GPC algorithm’s effectiveness in solving the studied optimization problem, artificial electric field and grey wolf optimizer algorithms are used for comparison purposes. The obtained results demonstrate that the best configuration for the selected area is a PV/biomass hybrid microgrid with a minimum NPC and LCOE of $319,219 and $0.208/kWh, respectively.

Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics

Finding the optimal size of a hybrid renewable energy system is certainly important. The problem is often modelled as an multi-objective optimization problem (MOP) in which objectives such as annualized system cost, loss of power supply probability etc. are minimized. However, the MOP model rarely takes the load characteristics into account. We argue that ignoring load characteristics may be inappropriate when designing HRES for a place with intermittent high load demand. For example, in a training base the load demand is high when there are training tasks while the demand decreases to a low level when there is no training task. This results in an interesting issue, that is, when the loss of power supply probability is determined at a specific value, say 15%, then it is very likely that most of loss of power supply would occur right in the training period which is unexpected. Therefore, this study proposes a constraint multi-objective model to deal with this issue—in addition to the general multi-objective optimization model, the loss of power supply probability over a critical period is set as a constraint. Correspondingly, the non-dominated sorting genetic algorithm II with a relaxed epsilon constraint handling strategy is proposed to address the constraint MOP. Experimental results on a real world application demonstrate that the proposed model and algorithm are both effective and efficient.

Optimal structure design of a PV/FC HRES using amended Water Strider Algorithm

A techno-economic study has been proposed for an off-grid combined renewable energy system (HRES) by photovoltaic (PV) and fuel cell (FC) systems. The proposed HRES has been studied to provide electricity for a remote area in Jiaju Tibetan Village, Danba, Sichuan Province China The main idea is formulated according to the Total Annual Cost (TAC). The study uses a new improved metaheuristic, called Amended Water Strider Algorithm (AWSA) to optimal estimation of the photovoltaic panels’ number, electrolyzer, fuel cells, and hydrogen storage tanks to get the minimum value of the overall net present value (ONPV). For increasing the system efficiency, the loss of power supply probability (LPSP) has been considered. Sensitivity analysis is used on the changing of the proposed HRES according to Levelized cost of energy (LCOE). Also, a comparison of the algorithm with several techniques from the literature is performed to illustrate its effectiveness. The results showed that the proposed method has further proper results than the other algorithms.

A Comprehensive Assessment of Large-scale Battery Integrated Hybrid Renewable Energy System to Improve Sustainability of a Smart City

ABSTRACT Hybrid renewable energy systems (HRES) must be leveraged to an extent that curtailment of fossil fuel does not hinder economic growth. However, owing to their intermittency, HRES must be supported with energy storage systems (ESS) that are quite expensive. Hence, optimal sizing of such systems is advisable to improve utilization and efficiency. In this paper, a comprehensive assessment and optimal sizing of HRES, consisting of floating and rooftop solar energy, wind energy systems, and battery-energy storage system (BESS) is presented and evaluated for a smart city, Visakhapatnam, India. Open-source tools such as web-based Bhuvan GIS, the polygon tool in Google Earth and Sketch Up are used identify the potential areas and for a realistic assessment of these renewable energy technologies. It is estimated that the proposed location has a potential of 105 MW of floating solar with 20% utilization of water bodies, 260 MW of rooftop bifacial PV system with an assumed acceptance rate of 5%. In addition, 62 MW of wind energy is estimated over shorelines and hilltops. With the upper limits of HRES, various multi-objective optimization techniques, differential evolution, mutation-based PSO, Quantum-behaved PSO, and mutation-based QPSO, are applied to minimize reliability indices like Levelized cost of energy (LCoE), loss of power supply probability (LPSP), and Life Cycle Emissions (LCE). Sensitivity analysis is performed to assess the impact of techno-economic changes, such as load appreciation, damage to batteries, and price changes. The results show that the proposed hybrid structure helps in combating carbon emissions with LPSP in acceptable limits of 0.285% to 0.3% while LCoE is in between 0.9 $/kW and 0.11 $/kW.

Multi-objective capacity optimization of a hybrid energy system in two-stage stochastic programming framework

Hybrid energy system is one main mean to deal with the energy crisis and its capacity optimization is a key to obtain an economical and reliable supply of energy. In this paper, the capacity optimization of a novel hybrid system composed of wind turbine, concentrated solar plant and electric heater is modeled as a multi-objective two-stage stochastic problem, where capacity optimization minimizing the life cycle cost (LCC) and energy management strategy optimization minimizing the loss of power supply probability (LPSP) are integrated. The scenario-based approach is applied to reflect the random characteristics of wind and solar resources. The Pareto set of the problem is obtained by non-dominated sorting genetic algorithm (NSGA-II), whose effectiveness is validated by algorithm comparisons with multi-objective particle swarm optimization (MOPSO) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Furthermore, techno-economic comparisons with a reference hybrid energy system without electric heater are performed to investigate economic benefits of the electric heater. The comparison results show that the electric heater is beneficial for a lower LCC which is reduced by 1.21%, 1.34%, 1.68%, 3.14%, 4.94% and 4.55% respectively when the LPSP is 0%, 1%, 2%, 3%, 4% and 5%.

A novel optimal planning methodology of an autonomous Photovoltaic/Wind/Battery hybrid power system by minimizing economic, energetic and environmental objectives

ABSTRACT Generated electricity from renewable sources such as solar panels and wind turbines is considered, until now, as clean and nonpolluting energy. However, these systems are responsible and directly tied to greenhouse gases (GHG) emissions when considering their different steps of manufacturing, transportation, operation, maintenance, and decommissioning. This paper describes a new sizing optimization methodology of a stand-alone hybrid Photovoltaic/Wind/Battery system, minimizing the Levelized Cost of Energy (LCOE), the Loss of Power Supply Probability (LPSP), and the Equivalent Carbon Dioxide (CO2-eq) life cycle emission. An elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to solve this constrained nonlinear multi-objective optimization problem taking the expected photovoltaic peak power, wind turbine output power, batteries’ energy capacity as decision variables and embodied carbon dioxide per unit of electricity consumed as a constraint. Different combinations of PV/Wind/Battery systems are optimized and compared to identify the cost-effective, reliable, and environmentally friendly optimal architecture. Finally, a sensitivity analysis is applied to the proposed algorithm; only the batteries’ state of charge (SOC) setpoint is considered to examine its impact on the system sizing procedure. The proposed algorithm is used for optimal planning of a stand-alone hybrid renewable power system expected to be installed in Borj Cedria Science and Technology Park (latitude = 36.71ºN, longitude = 10.42ºE). Simulation results proved the effectiveness of the proposed method to achieve economic, energetic, and environmental objectives.

Electric System Cascade Extended Analysis for optimal sizing of an autonomous hybrid CSP/PV/wind system with Battery Energy Storage System and thermal energy storage

The key core contribution of the paper is to solve the problem of optimal sizing of hybrids systems with multiple storage facilities and minimizing the Levelised cost of electricity under the premise of ensuring the load supply. To achieve this, we have developed a new extended algorithm based on the Power Pinch Analysis (PPA) and the Modified Electric System Cascade Analysis (MESCA) which we previously used to size hybrid systems in previous papers [1,2].Meanwhile, The ESCEA is used in the research to obtain the optimal combination of the contributions (%) of each type of generator adopted in an autonomous hybrid system (photovoltaic panels PV, wind turbines generators WT, and a concentrated solar power system CSP), with a thermal energy storage system (TES) to store heated fluid simultaneous with batteries for storing electrical energy (BESS).The ESCEA algorithm applied in a case study with data from the city of Oujda with an electrical load of 50 MWe indicated that it is possible to achieve 0.18 $/kWh of the Levelized Cost of Electricity (LCOE) for 0% Loss of Power Supply Probability (LPSP) with specific contributions of each generator type.

An artificial intelligence approach to optimization of an off-grid hybrid wind/hydrogen system

Off-grid electrification of remote areas using a hybrid renewable energy scheme is a requirement to achieve the goals of sustainable development. However, the optimization and sizing for the capacity of such systems are challenging. In this regard, this study targets an improved optimization algorithm with high efficiency for optimization and long-term capacity planning of an off-grid hybrid renewable energy scheme composed of wind, fuel cell, and hydrogen storage schemes. The suggested methods are three improved versions of the global dynamic harmony search to do pitch adjustment mechanism. The objective function of this study is to reduce the total net annual cost of the system and the loss of power supply probability to a minimum. The performance of this hybrid system is examined via a simulation study, which had been performed on a remote area located in eastern Iran over a long period. The results of the three improved proposed algorithms are compared with the original global dynamic harmony search algorithm. Also, sensitivity analysis is proposed to showcase the influence of uncertainties on the system and input parameters on the algorithm. The simulation results indicate that three improved versions of the global dynamic harmony search algorithm find more promising results than the original algorithm, and confirm the superior accuracy, convergence speed, and robustness of the global dynamic harmony search-II. Also, reliability level and iteration values have a considerable impact on the total net annual cost of the optimal hybrid energy system based on wind and hydrogen energy.