A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function

The current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder.Comment: Reliability Engineering & System Safety (2012) 1-2

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

A multi-state model for the reliability assessment of a distributed generation system via universal generating function

International audienceThe current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

International audienceThe purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout

OPERATIONAL RELIABILITY AND RISK EVALUATION FRAMEWORKS FOR SUSTAINABLE ELECTRIC POWER SYSTEMS

Driven by a confluence of multiple environmental, social, technical, and economic factors, traditional electric power systems are undergoing a momentous transition toward sustainable electric power systems. One of the important facets of this transformation is the inclusion of high penetration of variable renewable energy sources, the chief among them being wind power. The new source of uncertainty that stems from imperfect wind power forecasts, coupled with the traditional uncertainties in electric power systems, such as unplanned component outages, introduces new challenges for power system operators. In particular, the short-term or operational reliability of sustainable electric power systems could be at increased risk as limited remedial resources are available to the operators to handle uncertainties and outages during system operation. Furthermore, as sustainable electric power systems and natural gas networks become increasingly coupled, the impacts of outages in one network can quickly propagate into the other, thereby reducing the operational reliability of integrated electric power-gas networks (IEPGNs). In light of the above discussion, a successful transition to sustainable electric power systems necessitates a new set of tools to assist the power system operators to make risk-informed decisions amid multiple sources of uncertainties. Such tools should be able to realistically evaluate the hour- and day-ahead operational reliability and risk indices of sustainable electric power systems in a computationally efficient manner while giving full attention to the uncertainties of wind power and IEGPNs. To this end, the research is conducted on five related topics. First, a simulation-based framework is proposed to evaluate the operational reliability indices of generating systems using the fixed-effort generalized splitting approach. Simulations show improvement in computational performance when compared to the traditional Monte-Carlo simulation (MCS). Second, a hybrid analytical-simulation framework is proposed for the short-term risk assessment of wind-integrated power systems. The area risk method – an analytical technique, is combined with the importance sampling (IS)-based MCS to integrate the proposed reliability models of wind speed and calculate the risk indices with a low computational burden. Case studies validate the efficacy of the proposed framework. Third, the importance sampling-based MCS framework is extended to include the proposed data-driven probabilistic models of wind power to avoid the drawbacks of wind speed models. Fourth, a comprehensive framework for the operational reliability evaluation of IEPGNs is developed. This framework includes new reliability models for natural gas pipelines and natural gas-fired generators with dual fuel capabilities. Simulations show the importance of considering the coupling between the two networks while evaluating operational reliability indices. Finally, a new chance-constrained optimization model to consider the operational reliability constraints while determining the optimal operational schedule for microgrids is proposed. Case studies show the tradeoff between the reliability and the operating costs when scheduling the microgrids

Photovoltaic Systems: Forecasting for Demand Response Management and Environmental Modelling to Design Accelerated Aging Tests

abstract: Distributed Renewable energy generators are now contributing a significant amount of energy into the energy grid. Consequently, reliability adequacy of such energy generators will depend on making accurate forecasts of energy produced by them. Power outputs of Solar PV systems depend on the stochastic variation of environmental factors (solar irradiance, ambient temperature & wind speed) and random mechanical failures/repairs. Monte Carlo Simulation which is typically used to model such problems becomes too computationally intensive leading to simplifying state-space assumptions. Multi-state models for power system reliability offer a higher flexibility in providing a description of system state evolution and an accurate representation of probability. In this study, Universal Generating Functions (UGF) were used to solve such combinatorial problems. 8 grid connected Solar PV systems were analyzed with a combined capacity of about 5MW located in a hot-dry climate (Arizona) and accuracy of 98% was achieved when validated with real-time data. An analytics framework is provided to grid operators and utilities to effectively forecast energy produced by distributed energy assets and in turn, develop strategies for effective Demand Response in times of increased share of renewable distributed energy assets in the grid. Second part of this thesis extends the environmental modelling approach to develop an aging test to be run in conjunction with an accelerated test of Solar PV modules. Accelerated Lifetime Testing procedures in the industry are used to determine the dominant failure modes which the product undergoes in the field, as well as predict the lifetime of the product. UV stressor is one of the ten stressors which a PV module undergoes in the field. UV exposure causes browning of modules leading to drop in Short Circuit Current. This thesis presents an environmental modelling approach for the hot-dry climate and extends it to develop an aging test methodology. This along with the accelerated tests would help achieve the goal of correlating field failures with accelerated tests and obtain acceleration factor. This knowledge would help predict PV module degradation in the field within 30% of the actual value and help in knowing the PV module lifetime accurately.Dissertation/ThesisMasters Thesis Industrial Engineering 201