Loss Of Load Expectation Research Articles

Optimal sizing of energy resources considering configuration of energy storage systems in a microgrid

This paper studies the critical topic of optimal sizing of energy resources, focusing specifically on the configuration of storage system solutions within a microgrid framework. A microgrid is a localized energy system that can operate independently or in conjunction with the main power grid, and it is increasingly recognized for its potential to enhance energy resilience, efficiency, and sustainability. In this study, we examine a microgrid that integrates three key components: photovoltaic (PV) systems, battery storage, and fuel cell (FC) systems. Each of these technologies plays a vital role in the overall energy management strategy of the microgrid. In addition to installation costs, we also focus on minimizing net present costs (NPC), which encompass the total cost of ownership over the lifespan of the energy systems, including initial capital expenditures, operational and maintenance costs, and any potential revenue from energy sales or savings. By carefully analyzing the trade-offs between different system configurations, we seek to identify the most economically viable options. Another critical metric we consider is the loss of load expectation (LOLE), which quantifies the reliability of the energy supply. LOLE represents the expected number of hours per year during which the energy demand exceeds the available supply. By minimizing LOLE, we enhance the reliability and stability of the microgrid, ensuring that it can meet the energy needs of its users even during periods of high demand or low generation. Through a comprehensive analysis of these factors, this paper aims to provide valuable insights into the optimal sizing of energy resources within microgrids. By leveraging advanced modeling techniques and optimization algorithms, we seek to identify configurations that not only reduce costs but also enhance the overall performance and reliability of hybrid energy systems. Ultimately, our findings will contribute to the ongoing efforts to develop sustainable and resilient energy solutions that can meet the challenges of a rapidly changing energy landscape. The teaching-learning-based optimization (TLBO) metaheuristic algorithm is employed for configuring microgrids. Results from the algorithm demonstrate that TLBO offers a more effective resource configuration than alternative approaches.

Evaluation of reliability and resilience in wind integrated power systems using 80 meter mast measurements

Renewable energy sources, especially wind turbine generators, are increasingly considered viable alternatives to conventional electric power generation due to their sustainability and positive environmental impact. The rising penetration of wind power highlights the importance of developing widely applicable methods to evaluate the concrete benefits of incorporating wind turbines into traditional power systems. This work proposes an approach for assessing the reliability and resilience of power systems incorporating wind power. Evaluating the impact of wind energy generation on system reliability involves analyzing metrics such as Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). Additionally, a conceptual framework is developed which is aimed at enhancing the evaluation of power system resilience, when subjected to severe wind events. To evaluate the impact of wind storms on network resilience, a Monte Carlo Optimal Power Flow approach is utilized. The Average Energy Not Supplied (AENS) is used as the resilience metric and is computed for different rated speeds of wind turbines. The proposed methods for evaluating the reliability and resilience of the power systems are tested using the IEEE-RTS 24 bus system. Real-time wind resource data measured at an 80-meters mast at Paradeep location in the Odisha state on the east coast of India are used to evaluate the indices.

Quantifying Peak Load-Carrying Capability: A Comprehensive Reliability Analysis of Grid-Connected Hybrid Systems

Energy leaders around the world are constantly looking into feasibility and opportunities in renewable energy to diversify their energy sources. This study examines the reliability of a grid-connected microgrid consisting of solar energy, wind energy, and storage batteries to supply the required load and share the surplus with the grid. As the reliability of each component separately has an impact on system reliability, in this study, the loss of load expectation (LOLE) technique was used to estimate the peak load-carrying capability (PLCC) of the systems and the duration of outages as a means of analyzing the reliability of these systems and selecting the optimal combination among the cases. Moreover, this study used the load data of the area under study as the primary load and considered the grid as a secondary load to share the surplus after fulfilling the demand requirements. Furthermore, ten cases of grid-connected system configurations were considered to conduct this research, incorporating various combinations of solar panels, wind turbines (WTs), and batteries. The results revealed that, while maintaining an acceptable risk level represented by an LOLE of 0.1 days per year, the WT (850 MW) case emerged as the leading power producer compared to the other cases. It was able to produce 840.245 MW and 818.345 MW as the total power produced and the amount of surplus power that will be delivered to the grid after meeting the primary load needs in the area under study, respectively. This analysis can be informative for administrators in charge of planning and policy-making, helping them to take appropriate action.

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

A Long-Term Power Supply Risk Evaluation Method for China Regional Power System Based on Probabilistic Production Simulation

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated.

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

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

PurposeThe purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.Design/methodology/approachThis paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.FindingsThe proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.Originality/valueWhat sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Development of new reliability metrics for microgrids: Integrating renewable energy sources and battery energy storage system

Microgrids (MGs) are gaining popularity due to their ability to provide reliable and resilient power supply, especially when integrated with renewable energy sources (RESs) and battery energy storage systems (BESS). Reliability is a critical factor for MG owners and policy makers. However, existing reliability indices such as loss of load expectation (LOLE) and expected energy not supplied (EENS) may not offer a comprehensive understanding of MG reliability and resiliency. To address this gap, this paper proposes three new indices for MGs to provide supplementary information on the performance of RESs: the Microgrid Resiliency Index (MRI), the Microgrid Renewable Energy Availability Index (MREAI), and the Microgrid Renewable Energy Energy Index (MREEI). The MRI assesses the MG’s ability to recover from interruptions, providing insights into its resiliency. The MREAI and MREEI offer additional information beyond LOLE and EENS, specifically highlighting the contribution of RESs to the availability and energy losses in the MG. These indices enable a more comprehensive assessment of MG reliability. The formulation for calculation of the indices are provided and applied to a MG with integrated solar photovoltaic (PV), wind turbine (WT), and BESS components. To evaluate the effectiveness of the proposed indices in providing supplementary information on resiliency and the contribution of RESs, various scenarios are examined. These scenarios include the impact of using BESS, changes in RES availability, and variations in RES output. The results demonstrate that the proposed indices effectively capture the contribution of RES to MG reliability and offer valuable insights for decision-making related to RES installation. Also, by integrating BESS, the contribution of RESs to outage hours and lost energy is effectively decreased from 89.41% and 87.41% to 32.69% and 28.94% respectively. Additionally, results highlight the substantial enhancement in the resiliency of the MG, which witnessed an impressive 68.43% improvement.

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.

Potential Impacts of Climate Change on Renewable Energy and Storage Requirements for Grid Reliability and Resource Adequacy

Abstract This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources and storage requirements for grid reliability and resource adequacy. Climate change models and available regional data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed within a specific U.S. southwest service area as a case study. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources. Results for the extreme climate change scenario show that the projected wind power may decrease by ∼13% due to projected decreases in wind speed. Projected solar power may decrease by ∼4% due to decreases in irradiance and increases in temperature. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance among electrical load, generation, and storage was calculated throughout the year. The annual loss of load expectation (LOLE) was found to increase from ∼0 days/year to a median value of ∼2 days/year due to potential reductions in renewable energy resources caused by climate change and decreased battery performance. Significantly increased battery storage was required to reduce the LOLE to desired values of 0.2 days/year.

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.

Modeling demand flexibility impact on the long-term adequacy of generation systems

This paper proposes a novel probabilistic model for quantifying the impact of demand flexibility (DF) on the long-term generation system adequacy via Sequential Monte Carlo Simulation (SMCS) method. Unlike load shedding, DF can be considered an important instrument to postpone bulk consumption from periods with limited reserves to periods with more generating capacity available, avoiding load shedding and increasing the integration of variable renewable generation, such as wind power. DF has been widely studied in terms of its contribution to the system’s social welfare, resulting in numerous innovative approaches ranging from the flexibility modeling of individual electric loads to the definition of aggregation strategies for optimally deploying this lever in competitive markets. To add to the current state-of-the-art, a new model is proposed to quantify DF impact on the traditional reliability indices, such as the Loss of Load Expectation (LOLE) and the Expected Energy Not Supplied (EENS), enabling a new perspective for the DF value. Given the diverse mechanisms associated with DF of different consumer types, the model considers the uncertainties associated with the demand flexibility available in each hour of the year and with the rebound effect, i.e., the subsequent change of consumption patterns following a DF mobilization event. Case studies based on a configuration of the IEEE-RTS 79 test system with wind power demonstrate that the DF can substantially improve the reliability indices of the static and operational reserve while decreasing the curtailment of variable generation cause by unit scheduling priorities or by short-term generation/demand imbalances.

Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications

High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.

The effect of short term storage operation on resource adequacy

The potential contribution of short term storage technologies such as batteries to resource adequacy is becoming increasingly important in power systems with high penetrations of Variable Renewable Energy Sources (VRES). However, unlike generators, there are multiple ways in which storage may be operated to contribute to resource adequacy. We investigate storage operational strategies which result in the same amount of Expected Energy Not Served (EENS) but differing Loss of Load Expectation (LOLE) to investigate the range of LOLE possible and what factors affect this range. A case study of a Belgium-like power system using an economic dispatch model, typical of state-of-the-art adequacy assessments, results in a LOLE ranging between 2 and 6 h/yr, with the difference decreasing for greater storage duration and increasing for higher installed capacities of storage. Capacity Credits (CCs), which give the relative contribution of a resource to system adequacy, may also be affected by storage operation and the CC of storage is shown to differ by up to 30% depending on the operation and how the CC is calculated. Given these findings, it is recommended that modellers be explicit and transparent about the storage operation they assume in adequacy assessments and capacity credit calculations.

Efficient Energy Management and Reliability Assessment by Optimal Placement of Renewable Energy Sources with Pump Storage Plant

Deteriorating environment conditions due to global warming and depleting fossil fuels, has forced mankind to shift towards renewable energy sources (RES). The integration of RES in existing power system is challenging due to its intermittent nature. This study formulates novel sensitivity index i.e. nodal price of energy index (NPEI) to assist the location of sensitive buses for optimal placement of RES. Firstly, the proposed approach subjects power system to (N-1) contingency including both loss of generator and line outage. The NPEI is computed for every bus in various contingency conditions. The most severe contingency is identified based on highest value of sensitivity index in peak load period. The buses with highest values of NPEI is the optimal location for RES placement in the most critical contingency scenario. Secondly, the proposed approach focuses on efficient and cost-effective energy management by optimally scheduling disparate energy generation sources. The system incorporates pumped storage plant (PSP) based energy storage devices to effectively utilize wind and solar energy. The objective of this study is to minimize the total generation cost which includes the generation cost associated with conventional generators, its emission cost and valve-point loading effect, generation cost of RES, and the cost associated with energy supplied by PSP. The partial shading of PV modules is also implemented and its impact on system parameters are observed. The penetration of RES and energy storage effects system reliability hence, the proposed approach computes reliability indices expected energy not supplied (EENS) and loss of load expected (LOLE) for both presence and absence of RES and PSP. Finally, the proposed approach results in optimal placement of RES and optimal energy scheduling of disparate energy sources along with analyzing its effect on system reliability. The study is performed on IEEE 30 bus system.

A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method’s effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.

Reliability Analysis and Economic Prospect of Wind Energy Sources Incorporated Microgrid System for Smart Buildings Environment

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

Optimal Loss of Load Expectation for Generation Expansion Planning Considering Fuel Unavailability

In generation expansion planning, reliability level is the key criterion to ensure enough generation above peak demand in case there are any generation outages. This reliability criterion must be appropriately optimized to provide a reliable generation system with a minimum generation cost. Currently, a method to determine an optimal reliability criterion is mainly focused on reserve margin, an accustomed criterion used by several generation utilities. However, Loss of Load Expectation (LOLE) is a more suitable reliability criterion for a generation system with a high proportion of renewable energy since it considers both the probabilistic characteristics of the generation system and the entire load’s profile. Moreover, it is also correlated with the reserve margin. Considering the current fuel supply situation, a probabilistic model based on Bayes’ Theorem is also proposed to incorporate fuel supply unavailability into the probabilistic criterion. This paper proposes a method for determining the optimal LOLE along with a model that incorporates fuel supply unavailability into consideration. This method is tested with Thailand’s Power Development Plan 2018 revision 1 to demonstrate numerical examples. It is found that the optimal LOLE of the test system is 0.7 day/year, or shifted to 0.55 day/year in the case of considering the fuel supply unavailability.

Impact Assessment of Diverse EV Charging Infrastructures on Overall Service Reliability

A higher penetration of EVs may pose several challenges to the power systems, including reliability issues. To analyze the impact of EVs on the reliability of power systems, a detailed EV charging infrastructure is considered in this study. All possible charging locations (home, workplace, public locations, and commercial fast chargers) and different charging levels (level 1, level 2, and DC fast charging) are considered, and seven charging infrastructures are determined first. Then, the reliability impact of each charging infrastructure is determined using the two widely used reliability indices, i.e., the loss of load expectation (LOLE) and the loss of energy expectation (LOEE). The impact of mixed charging infrastructure portfolios is also analyzed by considering two different cases, which included the equal share of all charging infrastructure and charging infrastructure share based on consumer preferences. The performance is analyzed on a well-known reliability test system (Roy Billinton Test System) and different penetration levels of EVs are considered in each case. Test results have shown that fast-charging stations have the worst reliability impact. In addition, it was also observed that mixed charging portfolios have lower reliability impacts despite having a fair share of fast-charging stations.