Loss Of Load Probability Research Articles

Streamlining Smart Grids Reliability Assessment: An Innovative Mapping Approach

Assessing smart grid reliability, considering both cyber and physical components, typically involves a mapping step, escalating complexity and computational overhead. This paper presents a pioneering mapping approach that redefines the fundamental paradigm of smart grid reliability assessment. By leveraging a defined interconnection matrix, the method optimizes computational efficiency, curtailing the array of potential system states. Evaluation employing key performance metrics - Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS) - quantitatively demonstrates the superiority of our approach. Also, we explore the ramifications of integrating a dynamic thermal rating (DTR) in the process of reliability assessment, augmenting component safety through permissible enhancements in their ratings. Results underscore a notable reduction in total system states, from 221 to 214 for the bus topology and from 222 to 216 for the ring topology. Moreover, the analysis reveal substantial enhancements (Up to 43.58% ) in reliability indices upon consideration of the DTR system.

Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.

Design and optimisation of hybrid solar PV energy system for rural areas in the Narmada Valley, India

This study aims to design and select an optimal hybrid photovoltaic (PV) energy system for rural areas in the Narmada Valley (21.68°N, 74.92°E), Madhya Pradesh (MP) state, India. Two case studies in Narmada Valley (farmhouse and healthcare) were analysed for technical, economic, environmental, and reliability parameters, using MATLAB/Simulink and HOMER Pro software. The result indicates that the PV/BB/DG farmhouse energy system has capital cost of ₹2643200 (CC), an operating cost (OC) of ₹955763, fuel cost (FC) of ₹7240725, net present cost (NPC) of ₹3496135, an annualized cost (AC) of ₹257246 and cost of energy (COE) of ₹12.05. The PV/BB/DG healthcare energy system is CC ₹528640, OC ₹221191, FC ₹1979132, NPC ₹802055, AC ₹53213 and COE ₹12.34. The failure rate, expected energy not supplied (EENS) and loss of load probability (LOLP) of the farmhouse hybrid PV/BB/DG system are 0.0501/yrs, 0.0927 hrs/yrs, 0.4356 kWh/hrs and 1.05822E-05, respectively. The healthcare hybrid PV/BB/DG system failure rate (0.0117199), interruption duration (0.09009), system unavailability (2.16 hours.), EENS (0.234234 kWh/yrs), and LOLP (1.02842E–05) are all much lower than those of other configurations. Hybrid solar PV/BB/DG energy systems provide reliable, cost-effective, reduced environmental impact, sustainability, and job creation in rural areas.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

System-level optimisation of hybrid energy powered irrigation system

Renewable energy-powered irrigation systems have emerged as sustainable solutions, particularly for farmers in off-grid areas. While existing research often highlights tank storage-based systems as the most cost-effective option, large-scale deployment of water tanks incurs significant costs and maintenance challenges. Additionally, there is limited research on the feasibility and optimisation of battery-based irrigation systems, which are often deemed costly despite their potential benefits. This study addresses this gap by identifying the optimal storage solution for hybrid energy-powered irrigation systems through a system-level optimisation model. The model evaluates the suitability of three storage options: direct-coupled water tank storage, battery-coupled storage, and a hybrid battery-tank storage system. Optimisation criteria include life cycle cost (LCC), loss of power supply probability (LPSP), and loss of load probability (LOLP), ensuring a comprehensive assessment of both cost and reliability. Results indicate that the hybrid battery-tank storage system is the most reliable, followed by battery-only storage, while tank-only storage, despite its lower initial cost, poses scalability and maintenance challenges. The LCC over a 25-year project lifetime is £31 k for battery-tank, £26 k for battery-only, and £23.3 k for tank-only systems. Despite the lower cost of tank storage, its complexity and maintenance make it the least preferred option for large-scale systems.

Frequency optimisation and performance analysis of photovoltaic-battery water pumping system

Photovoltaic-battery water pumping systems (PVBWPSs) can provide fresh water and irrigation in off-grid areas. Previous research has focused on direct current (DC) voltage versus frequency to control the speed of a pump. However, the use of photovoltaic (PV) modules with batteries to create a high-performance hybrid system with fixed and variable frequencies of supply power remains challenging, particularly in an off-grid water pumping system with limited power and water supplies. Based on a conventional frequency conversion mode and power balance, this work addresses fixed and variable frequencies under changing solar irradiance conditions for a PV system and a PV system combined with a battery (PVB) mode to improve energy utilisation. According to DC power balance and centrifugal pump theories, a mathematical model of the power supply frequency in the PVBWPS is presented, as well as the loss of load probability (LLP) and pumping coefficient (Cp), through which the performance metrics are obtained. The formulated models are validated through the experimental PVBWPS, which includes 2.19 kWp PV modules, a 9.6 kWh battery bank, and a 0.75 kW centrifugal pump. The experimental results show that the root mean square error (RMSE) and maximum relevant error (RE) of the frequency are 0.14 Hz and 0.74 %, respectively. Consequently, the output performances are revealed via software simulation. The calculated results indicate that the maximum pumping volume for fixed-power-frequency operation is 48 Hz, which is 27.56 m3 on a sunny day and 17.63 m3 on a cloudy day. On a rainy day, the maximum pumping volume is 3.27 m3 at 41 Hz. Similarly, the Cp values reach maxima of 2.51 m3/kWh and 2.11 m3/kWh at 48 Hz in both sunny weather and cloudy weather, respectively, while on rainy days, the Cp peaks at 0.77 m3/kWh at 41 Hz. Moreover, every 1 Hz increase in the fixed frequency mode leads to a rise in the LLP, while the minimum change is at 46–48 Hz for cloudy and rainy days. Furthermore, the simulations revealed that for variable frequency control, the volume of water pumped in the PVB mode reached 40.19 m3, 29.36 m3, and 15.11 m3, which are increased by 4.91 %, 21.83 % and 103.09 % compared with the variable frequency PV mode, and 45.83 %, 66.53 % and 362.08 % higher than in PV fixed frequency mode, respectively. Compared with the PV mode, the system weighted efficiency of the variable-frequency PVB mode is increased by 2.06 %, 4.98 %, and 8.36 % under three weather conditions. This work provides critical theoretical guidelines for the design and operation of high-performance PVBWPs.

Overvoltage risk regulation strategy with distributed energy application in a distribution network based on the Stackelberg game

Along with the increasing low-carbon demand of the power system, the access of a high percentage of renewable energy resources to the distribution network has a large impact on the voltage fluctuation of the system and reduces the operational reliability. In this paper, we consider utilizing the reactive capacity of distributed resources to participate in system voltage regulation to reduce node loss of load probability (LOLP) caused by node overvoltage faults and propose an overvoltage risk regulation strategy for the interaction between distribution network operators (DSOs) and distributed users in the framework of the Stackelberg game. First, the nodes are clustered and analyzed based on the two-dimensional indexes of node voltage regulation ability, and different voltage regulation compensation tariffs are assigned. Second, the cost-benefit model of voltage regulation for the leader and follower sides and the node LOLP model are constructed to measure the reliability of the system. The Stackelberg game is used to co-optimize the two parties’ compensation tariffs and voltage regulation strategies. The optimal solution of voltage regulation under the equilibrium of the game is obtained by solving using the particle swarm optimization (PSO) algorithm. Based on the IEEE-33 node system, a case study is carried out to verify that the proposed overvoltage risk regulation strategy can maximize the benefits of the regulator participants while enhancing the operational reliability of the system.

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

Integration of Different Storage Technologies towards Sustainable Development—A Case Study in a Greek Island

The necessity for transitioning to renewable energy sources and the intermittent nature of the natural variables lead to the integration of storage units into these projects. In this research paper, wind turbines and solar modules are combined with pumped hydro storage, batteries, and green hydrogen. Energy management strategies are described for five different scenarios of hybrid renewable energy systems, based on single or hybrid storage technologies. The motivation is driven by grid stability issues and the limited access to fresh water in the Greek islands. A RES-based desalination unit is introduced into the hybrid system for access to low-cost fresh water. The comparison of single and hybrid storage methods, the exploitation of seawater for the simultaneous fulfillment of water for domestic and agricultural purposes, and the evaluation of different energy, economic, and environmental indices are the innovative aspects of this research work. The results show that pumped hydro storage systems can cover the energy and water demand at the minimum possible price, 0.215 EUR/kWh and 1.257 EUR/m3, while hybrid storage technologies provide better results in the loss of load probability, payback period and CO2 emissions. For the pumped hydro–hydrogen hybrid storage system, these values are 21.40%, 10.87 years, and 2297 tn/year, respectively.

Reliability based computational model for stochastic unit commitment of a bulk power system integrated with volatile wind power

Reliable and economic operation of transmission systems is one of the most onerous problems faced by the grid operators as a penetration rate of wind energy is on rise. It is a crucial carbon-free source and alleviates dependency upon the conventional resources, though its stochastic and unpredictable nature can cause the risk of random component failures. Therefore, reliability evaluation of the system under wind power is of paramount importance and is suggested as the proposed study. A Reliability based Stochastic Unit Commitment (RSUC) model is proposed for a bulk power system integrated with volatile wind power. It is formulated using Mixed Integer Linear problem considering networks and spinning reserve constraints. The volatility of wind speed is modeled by generating different scenarios using K-means clustering approach with computation of correlation among them using autocorrelation factor and their transition probabilities is proposed using Markov Chain (MC). Further, the system's reliability is evaluated using Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). The proposed approach is investigated on the IEEE 24 Reliability Test System (RTS) under the various overloading and random component outage conditions. Further, its results effectiveness is compared and validated with the other existing approaches.

Optimal selection and analysis of microgrid energy system using Markov process

This study investigated the reliability of a microgrid (MG) energy system using the Markov process reliability method. Twenty possible MG configurations based on photovoltaic (PV), wind turbine generator (WTG), biomass generator (BMG), battery bank (BB), diesel generator (DG) and an electric vehicle (EVs) have been considered. This study also selects an optimal configuration based on reliability indices (failure rate, repair time, unavailability, expected energy not supplied (EENS) and loss of load probability (LOLP)), annually load demand, availability of the system and its components. The results show that the hybrid PV/WTG/BMG/BB/EVs/DG microgrid system has the lowest system failure rate (0.01012 /year), repair time (1.1858 hrs), unavailability (0.0120 hrs/year), EENS (4.5 kWh/year) and LOLP (1.36986E-06 /year). This MG configuration has an availability of 99%. The PV array (97.55%) and the changeover switch (99.5%) are the parts of the MG system that are the most reliable. The least stable elements of an MG system are battery (63.64%), diesel generator (80%) and biomass generator (83.89%). PV (62%) and WTG (28% of total) are the main contributors to electricity generation, with the battery, electric vehicles, and diesel generator acting as backup sources. Under different weather and load conditions, the hybrid PV/WTG/BMG/EVs/BB/DG microgrid was found to be a reliable and efficient system for remote areas. This research carries a significant impact for policymakers, government, energy planners, and researchers working on stand-alone microgrid systems as it provides valuable insights into optimizing system configuration for reliability and efficiency.

A new approach for reliability assessment of the smart microgrids using k-shortest path algorithms

The reliability of cyber-physical microgrids (MGs) is crucial in the development of smart grids. The reliability of MGs can be affected by cyber network failures, which have a significant impact on the physical components. Since MGs have interdependent cyber and physical elements, a smart MG can be viewed as a system of n dependent two-mode components. This paper proposes an approach to finding the k most likely configurations of the system. The method involves three phases. Firstly, the multi-mode model is obtained for physical components, considering the operation and topology of the cyber network. Then, the problem is transformed to finding the k most likely state of a system consisting of n independent multi-mode components. In the second phase, a transformation using new transformed metrics is applied. This results in the problem being converted to finding the k shortest path, which can be solved using efficient algorithms. Finally, the states are evaluated using a DC load flow, and reliability indices such as loss of load probability (LOLP) and expected energy not supplied (EENS) are calculated. Moreover, we have incorporated the dynamic thermal rating (DTR) system into our proposed model, addressing the safe enhancement of system component ratings. The results indicate that the most probable states of the system are related to the failure of distribution generators. The most severe events occur due to failure in the cyber network, and cyber network malfunction has a higher effect on EENS compared to LOLP. Additionally, we observe a significant enhancement in reliability indices when considering the DTR system over the static thermal rating (STR) system. This approach is efficient in reliability calculation using fewer system states.

Impact analysis of electricity pricing on the reliability of integrated Nepal power system

Abstract Time of Use of Electricity Pricing (TOU) strategy is adopted by many countries to reduce the peak load and minimize the cost of Electricity. In this research, the effect of the TOU tariff system on the reliability of the Integrated Nepal Power System (INPS) has been investigated. Implemented TOU is based on three time periods: Peak time, Flat time, and Valley time (PFV). The moving variable method is used to find the optimal PFV period in a day. Particle swarm optimization (PSO) is used to find optimal prices corresponding to the given periods, where two objective functions are converted to a single objective function. Prices are modeled with the demand response and a new load pattern of consumers after TOU has been obtained. Power plants of INPS are modeled into four states model and the Monte-Carlo simulation is used to find the generation adequacy indices like Loss of Load Probability (LOLP), Loss of Load Expected (LOLE), and Loss of Energy Expected (LOEE) before and after the implementation of the TOU. The risk level of INPS is reduced by 37% and peak load is reduced by 4.2% after the implementation of TOU. It is found that the reliability of INPS can be improved with the help of TOU

Reliability Analysis of PTMG System Operation PT. Pertamina EP. Bunyu Based On Load Loss Probability Index

Power plants are equipment used to generate electricity by converting an energy into electrical energy. In power plants, it is necessary to pay attention to the reliability of the generating system in meeting the needs of a load. The reliability of the generating system can be described by the plant reliability index. To pay attention to the reliability of the generating system, an analysis is needed that is used to evaluate the reliability index of the plant, one of which is Loss of Load Probability (LOLP). This study presents the calculation of the LOLP value at the PT Pertamina EP Bunyu PLTMG in 2022. In calculating the LOLP value, namely by using Microsoft Exel and Matrix laboratory simulation. PT Pertamina EP Bunyu PLTMG has 4 generating units with each capacity having a power of 1000 KW. The duration of disruption of the generating unit affects the Forced Outage Rate (FOR) value which will be used to find the probability of each combination of generating units. From this probability value, the LOLP value is obtained. At the PT Pertamina EP Bunyu PLTMG, the LOLP value is 0.00005579646% with an outage probability of 0.0203657079 days/year. While the standard set by PT PLN (Persero) in the 2018-2027 PLN RUPTL of LOLP is smaller than 0.274% or equivalent to a probability of outage of 1 day / year, it can be said that the reliability index of the power plant at PT Pertamina EP Bunyu PLTMG in 2022 is in the reliable category.

Reliability Analysis of Biomass Power Plant Using Loss of Load Probability Index at PT. Tanjung Buyu Perkasa Plantation

The main function of a power generation system is to provide reliable electrical energy. This study aims to determine the reliability of Biomass Power Plants at PT. Tanjung Bayu Perkasa Plantation (PT. TBPP) in the years 2020, 2021, and 2022 using the Loss of Load Probability (LOLP) index calculation method. LOLP is a reliability index that indicates the possibility that the power plant cannot meet the demand. The LOLP of biomass power plants at PT. TBPP in 2020 amounted to 1.2769 days per year. However, the LOLP in 2020 does not comply with the standards set by the Republic of Indonesia Minister of Energy and Mineral Resources Decree in 2018. In contrast, in 2021, it was 0.53403 days per year, and in 2022, it was 0.41748 days per year, both of which meet the standards of the Republic of Indonesia Minister of Energy and Mineral Resources Decree in 2018. The LOLP is affected by the Forced Outage Rate (FOR) and demands exceeding the capacity. Based on the LOLP of biomass power plants at PT. TBPP in 2020, 2021, and 2022, it can be concluded that the reliability of biomass power plants at PT. TBPP is at a higher level of reliability compared to the micro-hydro power plant in Pantai Baru Pandasimo with a LOLP of 51.3627 days per year and the steam power plant at Pertamina EP Asset IV Field Sukowati with LOLP of 4.259535 days per year. The recommendation for future research is to explore ways better to optimize boiler operations in steam power plants at PT. TBPP.

Standalone power system with photovoltaic and thermoelectric installations for power supply of remote monitoring and control stations for oil pipelines

During the operation of oil pipelines, accidents with oil spills may occur, which can cause severe damage to the ecological situation in the region and disrupt many processes in nature. The oil pipeline’s remote monitoring and control systems (RMCS) are used to prevent such situations. In areas remote from the centralized power supply, it is necessary to use a standalone power system (SPS) to provide electricity to RMCS facilities. Examples of such facilities are remote monitoring and control (RMC) stations, including systems for monitoring the state of the oil pipeline and its parameters, leak detection systems, and the pipeline’s cathodic protection. The paper proposes a methodology for sizing SPS with a photovoltaic plant, an on-pipe thermoelectric generator, and batteries for the power supply of the RMC station. The idea of the work is to use energy sources that are most efficient at opposite seasons of the year. Particle swarm optimization is used to optimize the sizing of SPS components. The technical optimization criterion is the loss of load probability. The economic optimization criterion is the levelized cost of electricity. The impact of the oil temperature at the point of the RMC station on the efficiency of the thermoelectric generator is estimated. The calculations carried out in this work were made for the climate region of the Vankorsk oil and gas field in the north of Russia. The methodology proposed in this paper can also be used for northern territories, such as Canada, Norway, and the USA.

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.

A Multi-Objective Time – Series Optimization for Optimum Planning Design of Integrative Power System with the Effects of Multi-Dimensional Sources of Uncertainty

Time series aggregation (TSA) procedures are used to simplify data entry and enhance solution efficiency. This study suggested a two-step multi-objective optimization framework for TSA strategy determination. TSA methodologies for optimum MES design and operation were used to create the multi-energy system’s preliminary design. In the second step, the system’s total annual cost, as well as loss of load probability (LSLP), were used as objectives, or the optimal combination of the several objectives was found optimal TSA strategy for the MES’s structure and performance. The ideal energy-storage MES design proved the method’s efficacy. TSA techniques were compared and analyzed for architectural schemes & system performance variations. The findings show that the initial full-time series data may be used to verify the viability of the best MES design schemes and quantify the departure of outcomes from alternative TSA procedures. LSLP decrease raises expenses. The TAC of the framework improves from $9.79 × 107 to $1.56 × 108 when the LSLP reduces to 7.90% to zero in the numerous grids method (TG1) with 730-time steps (TS). To fulfill energy system demand, the minimal LSLP approach must be chosen. The best TSA tactics for MES design may be chosen based on unique needs.

A Novel Renewable Smart Grid Model to Sustain Solar Power Generation

The stability performance of smart grid power systems is critical and requires special attention. Additionally, the combination of Battery Energy Storage (BES) systems, Solar Photovoltaic (SPV), and wind systems in the intelligent grid model provides utilities with excellent efficiency and dependability. However, a coordination grid with PV and other resources frequently results in severe issues, such as outages or power disruptions. A power outage in the grid might result in a power loss in the delivery system. As a result, the distributed grid model’s dependable performance is intended for integrated wind energy, SPV arrays, and BE systems. This paper proposes a renewable intelligent grid model to sustain solar power generation. The model incorporates a boost converter to optimize the performance of solar panels by converting the DC power generated by the panels into AC power for use in the grid. The boost converter is optimized using a novel Horse Herd Optimization Algorithm (HOA) method. In this case, the HOA method is used to optimize the control parameters of the boost converter, such as the duty cycle and the inductor and capacitor values. According to the final results, the proposed method has reduced the Total Harmonic Deformation (THD) and power loss. Additionally, the proposed method outperformed existing strategies related to the Expected Energy Not Supplied (EENS), Loss of Load Probability (LOLP), and Loss of Load Expected (LOLE), indicating the sustainability of power generation.

EVs’ Integration Impact on the Reliability of Saudi Arabia’s Power System

This paper investigates the effect of involving electric vehicles (EVs) in the load profile on the generation system. The impact was studied from a reliability perspective on Saudi Arabia’s total generation system capacity as one source supplying the expected load for the year 2030. The EV load profile was then added. The outcomes were examined considering the gradual penetration percentage to the total load. The reliability indices measured are the loss of load probability (LOLP) and the expected energy not supplied (EENS). The results show that the estimated generation system of Saudi Vision 2030 will not withstand the estimated number of EVs without negatively impacting reliability. Similarly, the reliability assessment was conducted for the central region considering EVs in Riyadh City to verify Saudi Vision 2030. The results show that EV integration will greatly affect the electrical network’s reliability. Furthermore, a sensitivity analysis was conducted for Saudi Arabia and the central region to assess the generation system better. The study shows that investing in the generation infrastructure is essential to handle EV growth for the upcoming years. The work introduced in this paper will also help decision-makers make appropriate planning decisions in the future.