Probabilistic estimation of capacity value of photovoltaic system (original) (raw)
Related papers
Capacity value of photovoltaic systems and their impacts on power system reliability
2017 North American Power Symposium (NAPS), 2017
This paper investigates the various aspects of the capacity value of photovoltaic systems (PV-systems) and the effect of their penetration levels on power system reliability. Unlike wind power, the available output power of PV-systems can be classified into two specific time periods: certainly unavailable during the night and uncertainly available during the day. In other words, during the night, the output power is zero while during the day the output power experience uncertainties due to, among others, clouds and humidity. Therefore, this mixed behavior cannot be modeled as a stochastic variable during all time periods. The output power of PV-systems is clustered into bands and convolved with the output power of the conventional generators to determine the reliability indices and the capacity value. The IEEE RTS is used to demonstrate the effects of different strategies in calculating the capacity values of PVsystems. The results not only show that the capacity value is different for the two case scenarios but also the it decreases in a semi-linear manner as the size of the PV-system increases. Sequential Monte Carlo simulation is utilized to validate the results.
Solar Power Capacity Value Evaluation-A Review
Solar Power Capacity Value Evaluation-A Review, 2013
With the increase in renewable generation in power systems, it is critical to accurately determine the capacity value of renewables during generation planning to maintain system reliability. This review paper is intended for readers who are seeking to understand the basics of reliability evaluation and gives an insight on the factors that affect the capacity value of solar resources. The capacity value estimation methods are discussed briefly, followed by a discussion of the factors that may affect the capacity value of solar resources. The methodology explained here is applicable to any renewable generation. However, the focus of the discussion is on the factors that affect the capacity value of solar resources. Also, the impact of input data on the solar capacity value is included. Potential future work is also included.
Modeling and evaluating the capacity credit of PV solar systems using an analytical method
2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
This paper introduces an analytical method to calculate the capacity credit of PV system in a manner that considers both the effect of input uncertainty and system components avaliability. The intermittency of the input source and system components availability were modeled separately and then convolved to construct a single capacity outage table. The discrete convolution method has been used in this work to build a generation model in the form of a capacity outage probability table. The proposed method is applied on the RBTS. The obtained results are compared to the results obtained by Monte Carlo simulation. The proposed method reduces the complexity of calculating the capacity credit of PV system considering the effect of system components availability.
Applied Sciences, 2017
The contribution of solar power in electric power systems has been increasing rapidly due to its environmentally friendly nature. Photovoltaic (PV) systems contain solar cell panels, power electronic converters, high power switching and often transformers. These components collectively play an important role in shaping the reliability of PV systems. Moreover, the power output of PV systems is variable, so it cannot be controlled as easily as conventional generation due to the unpredictable nature of weather conditions. Therefore, solar power has a different influence on generating system reliability compared to conventional power sources. Recently, different PV system designs have been constructed to maximize the output power of PV systems. These different designs are commonly adopted based on the scale of a PV system. Large-scale grid-connected PV systems are generally connected in a centralized or a string structure. Central and string PV schemes are different in terms of connecting the inverter to PV arrays. Micro-inverter systems are recognized as a third PV system topology. It is therefore important to evaluate the reliability contribution of PV systems under these topologies. This work utilizes a probabilistic technique to develop a power output model for a PV generation system. A reliability model is then developed for a PV integrated power system in order to assess the reliability and energy contribution of the solar system to meet overall system demand. The developed model is applied to a small isolated power unit to evaluate system adequacy and capacity level of a PV system considering the three topologies.
Effect of Integrating PV Generation on the Load Carrying Capability of Bangladesh Power System
International Journal of Energy and Power by Science and Engineering Publishing Company, 2014
The scarcity of traditional energy sources (i.e. oil, coal, natural gas etc.) lead to think of integrating the renewable energy sources in the power system network. Among all the renewable energy sources (i.e. wind, water, solar etc.), PV cell generators are considered as one of the most reliable ones due to handle the system load with reliably and less uncertainty. In this respect, an important reliability index of the system is the load carrying capability (LCC) to evaluate the impact of added PV cell generating unit for satisfying the system load growth. This paper presents an effective simulation method for the assessment of reliability index, load carrying capability (LCC) to measure the effect of additional PV cell power generator that is applied to Bangladesh Power System (BPS) by considering the uncertainty of different FORs and also its effect on system stability by calculating the loss of load probability (LOLP). For this reason, generator model and load model are used to analyse the relevant available generator data and load data of National Load Dispatch Centre (NLDC) of BPS for the year of 2012. In addition, availability of PV cells is calculated from its reliability model. BPS has eighty six generators and a total installed capacity of 6545 MW. The maximum demand of BPS is approximately 5700 MW. The analysis relevant data of the generators and hourly load profiles are collected from NLDC of Bangladesh and the reliability index LCC is calculated for the additional renewable power generation of 500 MW of different FORs. Furthermore, the effect of different uncertainty of the additional PV cells generation of 500 MW is analysed in the test simulated MATLAB environment that is depicted graphically. Finally, LOLP of different FORs is calculated and graphically shown the relation between percentage loading of added generator and LOLP with different uncertainty.
Modeling the output power of PV farms for power system adequacy assessment
2015 North American Power Symposium (NAPS), 2015
This paper presents a method to model the expected output power of PV systems and to evaluate their effect on power system reliability. Grid level PV systems are usually constructed from a large number of electronic components and converters. Modeling of these systems in power system reliability is a complex task due to the dependency of the output power on an intermittent source (solar) and the availability of a large number of system components. An analytical method to construct a capacity outage probability table (COPT) that captures both the intermittency of the input source and component failures was proposed to model PV systems in power system adequacy assessment. The intermittency of the input source and component availabilities were modeled separately and then convolved to construct a single COPT. The resulted COPT can be viewed as a single PV system with multi-derated states. System reliability indices were evaluated using discrete convolution method. Monte Carlo simulation was used to validate the results of the proposed method. The proposed method was implemented on the IEEE-RTS and Roy Billinton test system (RBTS) with a wind farm of 30 MW. The obtained results were very close to those obtained using Monte Carlo simulation with less computation effort.
Reliability evaluation of a solar photovoltaic system with and without battery storage
2015 Annual IEEE India Conference (INDICON), 2015
Solar energy is considered as one of the major renewable energy sources and electricity generated from photovoltaic system involves zero green house gas emission and zero dependence on fossil fuel. However, the use of solar energy suffers from the challenge of ensuring steady flow of electric power particularly during the periods of low solar radiation. This has caused system designers to look into the reliability aspects of solar photovoltaic systems. One way of minimizing the impact of irregular power supply is by the inclusion of a storage unit so that the surplus energy generated during period of high solar radiation can be stored and utilized later during periods when solar radiation is low or absent. But, storage systems using batteries are an expensive proposition. It is of interest to system design engineers to examine just how much is gained in terms of reliability of power delivery at the cost of hardware failure of panel for solar PV systems operated with and without battery storage. The present paper attempts to investigate the changes in system reliability of a solar photovoltaic system under the cases of operation with and without adequate battery storage. Reliability estimation through Loss of load probability index is carried out using Monte Carlo Technique.
Incorporating large photovoltaic farms in power generation system adequacy assessment
Recent advancements in photovoltaic (PV) system technologies have decreased their investment cost and enabled the construction of large PV farms for bulk power generations.The output power of PV farms is affected by both failure of composed components and solar radiation variability. These two factors cause the output power of PV farms be random and different from that of conventional units. Therefore, suitable models and methods should be developed to assess different aspects of PV farms integrationinto power systems, particularly from the system reliability viewpoint. In this context a reliability model has been developed for PV farms with considering both the uncertainties associated with solar radiation and components outages. The proposed model represents a PV farm by a multi-state generating unit which is suitable for the generation system assessment. Utilizing the developed reliability model, an analytical method is proposed for adequacy assessment of power generation systems including large PV farms. Real solar radiation data is used from Jask region in Iran which are utilized in the studies performed on the RBTS and the IEEE-RTS. Several different analyses are conducted to analyze the reliability impacts of PV farms integration and to estimate the capacity value of large PV farms in power generation systems.
Energies, 2020
PV hosting capacity (PVHC) analysis on a distribution system is an attractive technique that emerged in recent years for dealing with the planning tasks on high-penetration PV integration. PVHC uses various system performance indices as judgements to find an available amount of PV installation capacity that can be accommodated on existing distribution system infrastructure without causing any violation. Generally, approaches for PVHC assessments are implemented by iterative power flow calculations with stochastic PV deployments so as to observe the operation impacts for PV installation on distribution systems. Determination of the stochastic PV deployments in most of traditional PVHC analysis methods is automatically carried out by the program that is using random selection. However, a repetitive problem that exists in these traditional methods on the selection of the same PV deployment for a calculation was not previously investigated or discussed; further, underestimation of PVHC ...