Sequential Monte Carlo Simulation Research Articles

Stochastic Diffusion Process-based Multi-Level Monte Carlo for Predictive Reliability Assessment of Distribution System

Reliability assessment of electrical distribution systems is an important criterion to determine system performance in terms of interruptions. Probabilistic assessment methods are usually used in reliability analysis to deal with uncertainties. These techniques require a longer execution time in order to account for uncertainty. Multi-Level Monte Carlo (MLMC) is an advanced Monte Carlo Simulation (MCS) approach to improve accuracy and reduce the execution time. This paper provides a systematic approach to model the static and dynamic uncertainties of Time to Failure (TTF) and Time to Repair (TTR) of power distribution components using a Stochastic Diffusion Process. Further, the Stochastic Diffusion Process is integrated into MLMC to estimate the impacts of uncertainties on reliability indices. The Euler Maruyama path discretization applied to evaluate the solution of the Stochastic Diffusion Process. The proposed Stochastic Diffusion Process-based MLMC method is integrated into a systematic failure identification technique to evaluate the distribution system reliability. The proposed method is validated with analytical and Sequential MCS methods for IEEE Roy Billinton Test Systems. Finally, the numerical results show the accuracy and fast convergence rates to handle uncertainties compared to Sequential MCS method.

Interval Reliability Evaluation of a Hybrid Energy Generation System With Energy Storage

To deal with the uncertainties of wind power and load residing in the power supply reliability model, an interval reliability evaluation method is proposed by combining the wind power generation and energy storage system (ESS). Firstly, the interval power supply reliability evaluation model, which belongs to an interval mixed integer program (IMIP), is established based on the interval variables. Secondly, the IMIP model is transformed into the deterministic optimization model under two extreme circumstances by utilizing the possibility degree theory of interval numbers. The maximum power supply probability, considering the wind power interval to meet the load demand interval, is sought by optimizing outputs of the ESS and generators, i.e., the upper boundary of the load shedding is the smallest. Finally, the states of wind turbines and generators are generated based on sequential Monte Carlo simulation, and the reliability of the hybrid energy generation system is evaluated by calculating the loss of load expectation, expected energy not supplied, and maximum power supply probability, which provides a basis for establishing interval optimal allocation model of energy storage. IEEE RTS-24 test system is utilized to verify the performance of the proposed method, and the model is solved by the CPLEX 12.7 solver. The simulation results demonstrate the effectiveness and applicability of the proposed method.

Reliability evaluation of generating systems considering aging processes

Equipment aging has been a major concern among electric utilities’ planners, since quality of service can be put at risk. This paper proposes a new method that allows the effects of the aging of generators to be properly included into the reliability analysis of power systems. First, a new model for the failure rate capable of representing the stages of normal operation and wear-out, replacing the adoption of constant failure rates, is described. The effects of repairable and non-repairable (i.e., catastrophic) failures on the reliability assessment of generation systems are also discussed. The proposed method is built from the previous models using a sequential Monte Carlo simulation and variance reduction techniques. To validate the proposed method, the IEEE Reliability Test System is used with modifications in which different ages are defined for existing generating units that are progressively aging over the years. Several tests are described together with strategies for adopting preventive refurbishments and the corresponding results are duly discussed.

Reliability assessment of island multi‐energy microgrids

Multi-energy microgrids could result in more flexibility and increase reliability by interconnecting networks. Electricity and gas networks exhibit very different dynamic behaviours in response to a fault or failure. Gas networks have built-in energy storages that can continue to provide a reliable supply if gas inputs to the system are compromised. This study presents a novel reliability assessment method applied to multi-energy microgrids; the method combines an incidence matrix analysis that identifies the connectivity between sources and load points with a sequential Monte Carlo simulation and generation adequacy evaluation. A case study is conducted by using an electricity-gas microgrid. The electricity network is a multi-sourced grid, whereas the gas network is supplied by a biogas plant. The linepack (gas stored along the pipelines) is modelled to account for the slower gas dynamics. The proposed method is evaluated on a real-world electricity distribution network in Austria. The results indicate the reliability benefits of forming a multi-energy microgrid.

Power Management of AC/DC Hybrid Distribution Network With Multi-Port PET Considering Reliability of Power Supply System

In the current distribution network, photovoltaic, wind power, energy storage, and other distributed energy are widely connected, and the proportion of generalized DC load is rapidly increasing. With the development of power electronics technology, using multi-port power electronic transformer (PET) to achieve high-efficiency access of large-capacity AC/DC source and load is the current research hotspot, and AC / DC hybrid power supply is inevitable. The introduction of a large number of power electronics and the flexible coordinated control and complementary fault-tolerant advantages of PET bring challenges to the operation and maintenance management of the AC/DC hybrid power system. In this paper, the structure of the AC/DC hybrid power system with the multi-port PET cluster is introduced. Based on the idea of hierarchical and partitioned control, a three-layer and multi-time scale control mode of integrated automation system, PET cluster and PET controller is proposed; Aiming at the problem of reliability evaluation, a sequential Monte Carlo simulation method is proposed to simulate the AC/DC hybrid power system with the multi-port PET cluster. The influence of multi-port PET parallel cluster mode and port capacity limitation on the reliability of the power system is analyzed, and the effectiveness of the model and algorithm is verified. Finally, an example is given to verify that the proposed structure is conducive to improving the utilization level of renewable energy in the distribution network.

A multi-state reliability evaluation model of CSP plants considering partial function failure

• The proposed multi-state model prevents the underestimation of reliability level caused by two-state failure model. • The proposed model can accurately evaluate the reliability index, which better reflects the multiple operation states. • Different TES strategies will lead to different operation decisions and reliability results of CSP plants. • The SF size and the TES capacity are positively correlated with the system reliability level. But the system reliability index tends to be stable when the SF size and the TES capacity are large. This paper proposes a multi-state reliability evaluation model for the concentrated solar power (CSP) plant considering partial function failure. Firstly, the component functions and components’ consequences of random failure of CSP plants are analyzed. Core components affecting the output of CSP plants are selected. The CSP plant is divided into three subsystems: the heat collection, the heat exchange, and the power generation subsystems, according to the component function. Each subsystem is equivalent to a two-state component. Thus, an 8-state Markov model of CSP plant is established. The CSP plant can be operated in a partial function failure state as thermal energy storage (TES) or generation, depending on the functions of solar-heat and heat-electricity conversion. On this basis, the 8-state model can be simplified to a 4-state model by combining the partial function failure states of the same effect. Finally, the 4-state model is applied to the sequential Monte Carlo simulation for the reliability evaluation of power systems containing CSP plants. Two TES strategies are considered in the simulation: One is based on solar field (SF) average output, another is designed to satisfy the load preferentially. The proposed model is tested on the modified RBTS/IEEE RTS systems to verify the effectiveness.

Reliability Analyses of Wide-Area Protection System Considering Cyber-Physical System Constraints

Wide-area measurement, protection and control systems link the cyber power system with the physical power system to realize a cyber-physical power system (CPPS). This paper proposes a reliability modeling and assessment method for the wide-area protection system (WAPS). The proposed WAPS model takes into account the decision accuracy of protection algorithms with limited available data at the control center, and combines it with the tripping reliability of protection devices in corresponding substations to form the operation accuracy of WAPS. In particular, the directional comparison algorithm used in decision-making is functionally divided into three modules, namely the initiation module, the fault area positioning module, and the faulted element recognition module. The proposed reliability models are established based on the Permutation and Combination method under the assumption that the data acceptance rate of the control center is d. The sequential Monte-Carlo simulation is used to solve the proposed reliability indices. Finally, the WAPS reliability assessment is presented for the IEEE 14-bus system and the key influencing factors are identified and discussed.

Energy supply reliability assessment of the integrated energy system considering complementary and optimal operation during failure

Integrated energy system (IES) is an effective solution for energy and environment problems. In view of the difficulty of traditional reliability assessment methods to reasonably and effectively assess the reliability of the IES, an energy supply reliability assessment method based on sequential Monte Carlo simulation is proposed in this study. The optimal operation of the system is realised by mobilising the multi-energy complementary characteristics during device failure. The reliability of the system under three different operation objectives is compared and analysed. Three new reliability indices are proposed, which take into account the supply of multiple energy loads and the differences among different energy. Finally, through the analysis of energy supply reliability under different objectives, the reasonability of the proposed indices and the flexible scheduling of energy flow under different objectives are verified; therefore, the IES can realise adaptive and targeted operation strategy during device failure. Also, the influence of different device faults on the system's energy supply reliability is ranked, which is of great significance to find the weak parts of the system and provide a reference for the system to improve energy supply reliability.

SSO risk evaluation for grid-connected PMSG-based wind farm based on sequential Monte Carlo simulation

In a grid-connected wind farm based on permanent magnet synchronous generators (PMSGs), the wind speed and the number of operating PMSGs are the two most important influencing factors with stochastic nature on subsynchronous oscillation (SSO) from the point view of the farm. This paper proposes a method of unstable SSO risk evaluation for grid-connected PMSG-based wind farm based on sequential Monte Carlo simulation (SMCS). The determination of critical wind speed (CWS) of SSO and the sequential simulation strategy of wind speed states and PMSG states in a wind farm at the same wind speed (S-WF) as well as in a wind farm at different wind speeds (D-WF), are studied. Five indices evaluating the expectation, duration, frequency and energy loss of SSO risk are proposed. Moreover, a strategy to reduce SSO risk by adjusting the cut-in wind speed is discussed. The effectivenesses of the discussed issues in the paper are proved by the case studies of a 750-PMSG wind farm based on the actual wind speed data collected.

Integrated energy system reliability evaluation based on sequential Monte Carlo simulation and fault recovery optimization

In this paper, an integrated energy system (IES) reliability assessment method based on sequential Monte Carlo simulation and fault recovery optimization is proposed considering the flexibility of dispatch and the complementary of multi-energy in IES. The reliability evaluation indices of IES are established considering the energy supply and thermal load delay characteristics. An IES failure recovery optimization model is established in which the shutdown loss and the minimum operating cost under the failure scenario are considered comprehensively. CPLEX software is applied to solve the proposed problem in order to obtain the electric and thermal load reduction power and component equipment output. Based on the principle of sequential Monte Carlo simulation, an IES reliability assessment method and process are designed. The analysis of calculation examples shows that the use of failure recovery optimization strategies and consideration of thermal load delay characteristics can effectively improve the reliability of IES.

Operation Strategies for Coordinating Battery Energy Storage with Wind Power Generation and Their Effects on System Reliability

The variability of wind power generation requires the allocation of a flexible energy reserve which is capable of compensating for possible imbalances between the load and generation. To reduce the variability of wind power generation and loss of load in generation deficit, we propose operation strategies for coordinating battery energy storage with wind power generation. The effects of the operation strategies on system reliability are evaluated by the developed computation model that represents the main aspects and operation limitations of the batteries. The performance evaluation of the power system is based on the composite reliability indices of loss of load probability (LOLP) and expected energy not supplied (EENS), which is calculated through sequential Monte Carlo simulation. Tests are performed by the developed model with a tutorial system consisting of five busbars and the IEEE RTS system. The results show that the use of large-scale batteries is an alternative to physically guarantee the wind power plants and to act as an operation reserve to reduce the risk of loss of load.

Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.

Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.

Quantifying Contribution of DER-Integrated EV Parking Lots to Reliability of Power Distribution Systems

In the future smart cities, parking lots (PLs) can accommodate hundreds of electric vehicles (EVs) at the same time. This trend creates an opportunity for PLs to serve as a potential flexibility resource, considering growing penetration of EVs and integration of distributed energy resources DER (such as photovoltaic and energy storages). Given this background, this paper proposes a comprehensive evaluation framework to investigate the potential role of DER-integrated PLs (DPL) with the capability of vehicle-to-grid (V2G) in improving the reliability of the distribution network. For this aim, first, an overview for the distribution system with DPLs is provided. Then, a generic model for the available generation capacity (AGC) of DPLs with consideration of EV scheduling strategy is developed. On the above basis, an iterative-based algorithm leveraging sequential Monte Carlo simulation is presented to quantify the contribution of DPLs to the reliability of the system. In order to verify the effectiveness of the proposed method, a series of numerical studies are carried out. The simulation results show that the integration of DPLs with the V2G capability could help to improve the reliability performance of distribution grid to a great extent and reduce the adverse impact incurred by EV accommodation, if utilized properly.

Critical Component Identification and Reliability Enhancement of AC Metro Traction Substation using FTA and Sequential Monte Carlo Simulation

Critical Component Identification and Reliability Enhancement of AC Metro Traction Substation using FTA and Sequential Monte Carlo Simulation

A Cyber-Insurance Scheme for Water Distribution Systems Considering Malicious Cyberattacks

As one of the national critical infrastructures, the water distribution system supports our daily life and economic growth, the failure of which may lead to catastrophic results. Besides the uncertainty from the system component failures, cyberattacks are vital to the secure system operation and have great impacts on the reliability of the water supply service. Malicious attackers may intrude into the supervisory control and data acquisition (SCADA) system of pump stations in the water distribution networks and interrupt the water supply to the customers. Cyber insurance is emerging as a promising financial tool in system risk management. In this paper, cyber insurance is proposed for the cyber risk management of the water distribution system. A semi-Markov process (SMP) model is devised to model the cyberattacks against pump stations in the water distribution system. Both the impacts of the independent cyber risks in the individual distribution network and the correlated cyber risks shared across different water distribution networks are evaluated and modeled. A sequential Monte Carlo Simulation (MCS) based algorithm is developed to evaluate the system loss. Cyber insurance premiums for the water distribution networks are designed based on the actuarial principles and potential system losses. Case studies are also performed on multiple representative water distribution networks, and the results demonstrate the validity of the proposed cyber insurance model.

Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems

This paper proposes a comprehensive methodological framework to investigate the potential role of the grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) capability in improving the reliability of supply in future distribution networks. For this aim, we first develop an empirical model for describing the energy demand of electric vehicles (EVs) and their resultant available generation capacity (AGC) that can be utilized for BSS operation. Then, on this basis, a quantitative method to quantify the effect of grid-connected BSS on distribution system reliability is proposed. In order to capture the uncertainties associated with EV users’ behaviors, Latin Hypercube Sampling (LHS) methods were utilized to obtain the time series of the BSS traffic flow and initial State of Charge (SOC) of each EV battery, according to the probability distribution of corresponding uncertain factors whose statistics are obtained from real-world historical data. Compared with existing works in this research field, the main contributions of this paper are threefold. (i) A comprehensive and efficient method to assess the reliability benefits of BSS with an explicit consideration of BSS characteristics (including physical structure, charging strategy, and swapping model) is proposed, which is in contrast to most of the extant studies that only focus on the EV fast-charging paradigm and thus provide a practical tool to analyze the potential value of BSS resources in future distribution systems. (ii) The randomness of EV user behaviors in BSS operation is explicitly modeled and considered. (iii) The LHS-based sequential simulation is used to improve the accuracy and convergence performance of the evaluation, as compared to the traditional Sequential Monte Carlo Simulation (SMCS) method. To verify the effectiveness of the proposed approach, numerical studies are conducted based on a modified IEEE 33-bus distribution network. The simulation results show that with V2G capabilities, BSS can improve reliability to a certain extent and reduce the adverse impact on the reliability of the distribution network. In addition, EV resources should be orderly managed and exploited; otherwise, uncoordinated charging activities could impose a negative impact on the reliability performance of distribution networks. Finally, it is also shown that under the same sampling time, LHS-based sequential simulation could be better than SMCS in the accuracy and convergence speed of the procedure.

A digital twins concept model for integrated maintenance: a case study for crane operation

The paper presents an Integrated Maintenance Decision Making Model (IMDMM) concept for cranes under operation especially into the container type terminals. The target is to improve cranes operational efficiency through minimizing the risk of the Gantry Cranes Inefficiency (GCI) results based on the implementation of the Digital Twins concept for maintenance purposes. The proposed model makes a joint transportation process and crane maintenance scheduling, relevant to assure more robust performances in stochastic environments, as well as to assess and optimize performances at different levels, from components and transport device to production systems (container terminal). The crane operation risk is estimated with a sequential Markov chain Monte Carlo simulation model and the optimization model behind of IMDMM is supported through the Particle Swarm Optimization algorithms because the objective function a non-linear stochastics problem with bounded constrains. The developed model allows the container terminal operators (management process) to obtain a maintenance schedule that minimizes the GCI (holistic indicator), as well as establishing the desired level of risk. The paper demonstrates the effectiveness of the proposed maintenance decision making concept model for cranes under operation using data from of a real container terminal (case study).

A Computational Modeling Study of COVID-19 in Bangladesh.

.The COVID-19 pandemic has spread globally. Only three cases in Bangladesh were reported on March 8, 2020. Here, we aim to predict the epidemic progression for 1 year under different scenarios in Bangladesh. We extracted the number of daily confirmed cases from March 8 to July 20, 2020. We considered the suspected-infected-removed (SIR) model and performed a maximum likelihood-based grid search to determine the removal rate (ɣ). The transmission was modeled as a stochastic random walk process, and sequential Monte Carlo simulation was run 100 times with bootstrap fits to infer the transmission rate (β) and Rt. According to the simulation, the (real) peak daily incidence of 3,600 would be followed by a steady decline, reaching below 1,000 in late January 2021. Thus, the model predicted that there would still be more than 300 cases/day even after a year. However, with proper interventions, a much steeper decline would be achieved following the peak. If we apply a combined (0.8β, 1.2ɣ) intervention, there would be less than 100 cases by mid-October, only around five odd cases at the beginning of the year 2021, and zero cases in early March 2021. The predicted total number of deaths (in status quo) after 1 year would be 8,533 which would reduce to 3,577 if combined (0.8β, 1.2ɣ) intervention is applied. We have also predicted the ideal number of tests that Bangladesh should perform and based on that redid the whole simulation. The outcome, though worse, would be manageable with interventions according to the simulation.

Assessment of the Capacity Credit of Renewables and Storage in Multi-Area Power Systems

Capacity credit (CC) can be defined as the capacity of conventional generators that can be replaced by renewable energy sources (RES) and/or other resources such as energy storage without reducing system reliability. Conventional approaches for calculating CC typically treat the power system as a single area without considering transfer constraints and reliability of interconnectors. However, in multi-area power systems locational aspects are key to assess tradeoffs and synergies arising from transmission, storage and RES in providing adequacy of supply. In this work, we propose a new methodology to quantify the CC of RES and storage in a multi-area power system. Due to the large computational burden brought about by composite system adequacy evaluation, a new accelerated sequential Monte Carlo simulation strategy and a new adaptive sampling approach are specifically developed to achieve computation efficiency. The proposed methodology is tested by demonstrating the impact of interconnectors' transfer constraints and availability on RES-storage CC in the case of the Australian National Energy Market (NEM) multi-area power system, with applications to wind, solar, and pumped hydro storage plants. The results from several realistic NEM case studies highlight how the proposed model can inform strategic, reliability-aware integrated system planning of large-scale interconnected low-carbon power systems.