A Monte Carlo Simulation Platform for Studying Low Voltage Residential Networks

Reliability Evaluation of Active Distribution Networks Considering Customer Satisfaction

The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy, and the RUL estimated from the data is less reliable. Estimating the probabilistic RUL by quantifying the uncertainty in the predictive model parameters using the noisy data increases confidence in the predicted values. In this work, Bayesian inference is used to inversely quantify the uncertainty in the model parameters of the predictive damage model in adhesively bonded joints, and quantified parameter uncertainty is represented by a probability distribution function (PDF). It is difficult to obtain a closed-form solution for the RUL from the PDF of model parameters due to the nonlinearity of the damage models. Instead, statistical simulations are used to generate the model parameter samples from the PDF, and the RUL is estimated from the samples. In the statistical simulations, the predictive damage model is evaluated for each parameter sample. Using a physics-based model as the predictive model increases computational time, which makes the statistical techniques to be computationally expensive. It is essential to reduce the computational time to estimate the RUL in a feasible time. In this work, real-time probabilistic RUL estimation is demonstrated in adhesively bonded joints using the Sequential Monte Carlo (SMC) sampling method and cloud-based computations. The SMC sampling method is an alternative to traditional Markov Chain Monte Carlo (MCMC) methods, which enables generating statistical parameter samples in parallel. The parallel computational capabilities of the SMC methods are exploited by running the SMC simulation on multiple cloud calls. This approach is demonstrated by estimating fatigue RUL in the adhesively bonded joint. The accuracy of probabilistic RUL estimated by SMC is validated by comparing it with RUL estimated by the MCMC and the experimental values. The SMC simulation is run on the cloud and the computational speedup of the SMC is demonstrated.

Cyber-enabled power system places higher requirements on the reliability of communication infrastructure. This paper proposes an approach to calculate reliability of distribution networks in the presence of communication media. Communication failure is incorporated into sequential Monte Carlo simulation using a uniform probability distribution function. A straightforward matrix representation is developed to analyze the impact of fault on each load point. Several scenarios are discussed to illustrate the impact of potential communication failures. For each scenario energy not supplied is calculated for Bus 2 of RBTS. The results show the necessity of evolving well-established reliability calculation methods.

Due to the polymorphic uncertainties in microgrids (MGs), prohibitive computational burden is produced in reliability assessment. In this work, a novel sequential sampling algorithm (NSSA) compatible with sequential Monte Carlo (SMC) simulation is developed to overcome the computational burden. First, optimal probability density functions (PDFs) of random variables are worked out based on variation method. Then, optimal PDFs are employed to chronologically simulate the random states of microturbine (MT), photovoltaics (PV) and time varying load with improved computational efficiency. Therefore, the convergence of reliability assessment is accelerated accordingly. A series of case studies have been conducted, and the computational results show that NSSA provides a favorable sampling efficiency and adaptability to system conditions in reliability assessment of MGs. At last, based on optimal PDFs produced by NSSA, dominant joint PDF (DJ-PDF) is defined and employed to quantify the contributions of different scenarios to the reliability indices. Case studies have confirmed that DJ-PDF can provide detailed information for scenario-based reliability analysis.

Any disturbances may lead to a broad range of transmission line overloads, threating the reliability and security of the network. This paper introduces a network reliability evaluation methodology based on sequential Monte Carlo (SMC) simulation, which helps to assess the impact of transmission line overloads on network reliability and plant ageing. A fault chain search model is implemented via AC power flow to capture cascading failures as a result of severe line overloads. The methodology also integrates a detailed electro-thermal modelling of overhead lines (OHLs) considering their key design and environmental properties to capture the risks of thermal ageing during emergency overload operations. A cost/worth analysis is performed to quantify the value of extensive emergency ratings on network performance against the risk of plant ageing and cascading failures. An application of the methodology is demonstrated on both IEEE 14-bus and RTS-96 network to test its scalability.

In reliability studies of isolated energy supply systems for residential buildings, supply failures due to insufficient generation are generally analysed. Recent studies conclude that this kind of analysis makes it possible to optimally design the sizes of the elements of the generation system. However, in isolated communities or rural areas, it is common to find groups of dwellings in which micro-renewable sources, such as photovoltaic (PV) systems, can be installed. In this situation, the generation and storage of several houses can be considered as an interconnected system forming a cooperative microgrid (CoMG). This work analyses the benefits that sharing two autonomous installations can bring to each one, from the point of view of reliability. The method consists of the application of a random sequential Monte Carlo (SMC) simulation to the CoMG to evaluate the impact of a simple cooperative strategy on the reliability of the set. The study considers random failures in the generation systems. The results show that the reliability of the system increases when cooperation is allowed. Additionally, at the design stage, this allows more cost-effective solutions than single sizing with a similar level of reliability.

This paper presents the simple design of a grid-tied single-phase with distributed rooftop photovoltaic (PV) non-uniformly location and ratings. All the inclusion components in the developed scheme are estimated and defined as the inevitability of low voltage (LV) residential network. This developed scheme is purposed for allocating AC and DC load, which are divided into four steps: the sized determination of PV inverter (1-5kW), the selection of PV array, the size determination of battery and the selection of other supporting components. The purposed configuration consists of modeling the system with non-uniform distributions of rooftop PVs, modeling the rooftop PVs based on their injected active and reactive power, and finally the inclusion of battery storage, based on its state of charge (SOC). Due to test the configuration, several cases are built in the MATLAB platform. Simulation results have been generated and analyzed for an unbalanced three-phase residential feeder which is populated with rooftop PVs and battery storage (BS). The simulation results show that the unbalanced reduction due to the coordinate of PVs and BS that provided educated energy storage when the unequal loadings are there, have significant effect toward the anxiety of the distribution network are successfully done.

Despite the recent advances in planning with MDPs, the problem of generating good policies is still hard. This paper describes a way to generate policies in MDPs by (1) determinizing the given MDP model into a classical planning problem; (2) building partial policies off-line by producing solution plans to the classical planning problem and incrementally aggregating them into a policy, and (3) using sequential Monte-Carlo (MC) simulations of the partial policies before execution, in order to assess the probability of replanning for a policy during execution. The objective of this approach is to quickly generate policies whose probability of replanning is low and below a given threshold.We describe our planner RFF, which incorporates the above ideas. We present theorems showing the termination, soundness and completeness properties of RFF. RFF was the winner of the fully-observable probabilistic track in the 2008 International Planning Competition (IPC-08). In addition to our analyses of the IPC-08 results, we analyzed RFF's performance with different plan aggregation and determinization strategies, with varying amount of MC sampling, and with varying threshold values for probability of replanning. The results of these experiments revealed how they impact the time performance of RFF to generate solution policies and the quality of those solution policies (i.e., the average accumulated reward gathered from the execution of the policies).

Optimal integration of demand response programs and electric vehicles in coordinated energy management of industrial virtual power plants

Guest Editorial

Both Federal and California state policymakers areincreasingly interested in developing more standardized and consistentapproaches to estimate and verify the load impacts of demand responseprograms and dynamic pricing tariffs. This study describes a statisticalanalysis of the performance of different models used to calculate thebaseline electric load for commercial buildings participating in ademand-response (DR) program, with emphasis onthe importance of weathereffects. During a DR event, a variety of adjustments may be made tobuilding operation, with the goal of reducing the building peak electricload. In order to determine the actual peak load reduction, an estimateof what the load would have been on the day of the event without any DRactions is needed. This baseline load profile (BLP) is key to accuratelyassessing the load impacts from event-based DR programs and may alsoimpact payment settlements for certain types of DR programs. We testedseven baseline models on a sample of 33 buildings located in California.These models can be loosely categorized into two groups: (1) averagingmethods, which use some linear combination of hourly load values fromprevious days to predict the load on the event, and (2) explicit weathermodels, which use a formula based on local hourly temperature to predictthe load. The models were tested both with and without morningadjustments, which use data from the day of the event to adjust theestimated BLP up or down.Key findings from this study are: - The accuracyof the BLP model currently used by California utilities to estimate loadreductions in several DR programs (i.e., hourly usage in highest 3 out of10 previous days) could be improved substantially if a morning adjustmentfactor were applied for weather-sensitive commercial and institutionalbuildings. - Applying a morning adjustment factor significantly reducesthe bias and improves the accuracy of all BLP models examined in oursample of buildings. - For buildings with low load variability, all BLPmodels perform reasonably well in accuracy. - For customer accounts withhighly variable loads, we found that no BLP model produced satisfactoryresults, although averaging methods perform best in accuracy (but notbias). These types of customers are difficult to characterize withstandard BLP models that rely on historic loads and weather data.Implications of these results for DR program administrators andpolicymakersare: - Most DR programs apply similar DR BLP methods tocommercial and industrial sector customers. The results of our study whencombined with other recent studies (Quantum 2004 and 2006, Buege et al.,2006) suggests that DR program administrators should have flexibility andmultiple options for suggesting the most appropriate BLP method forspecific types of customers.

Two demand response (DR) programs, namely, load reduction demand response (LRDR) program and load transfer demand response (LTDR) program are both incorporated into the day-ahead scheduling in this paper. The LRDR participants provide load curtailment in energy scheduling and reserve capacity in spinning reserve scheduling while the LTDR customers participate in energy scheduling by providing load shift resources. Compared to basic DR models, the proposed models of LRDR and LTDR are extended with a refined description by adding various constraints in the offer package. The actual operation characteristics of DR resources are reflected in the models. Besides, the day-ahead scheduling mode with DR is studied. Finally, a co-optimized day-ahead energy/reserve scheduling model integrated with DR which simultaneously schedules energy and reserve is established. The effectiveness of the proposed day-ahead scheduling model is verified on a modified IEEE 118-bus system.

Distributed and controllable energy storages are commonly used to provide specific benefits to help the operation of electric power networks. The major benefits may include peak load reduction, renewable energy intermittent problem mitigation, as an emergency/stand by power supply, and provide auxiliary services to networks etc. Recently, more and more homeowners install various sizes of solar power systems to generate clean, low emission, and free electricity for their own homes; meanwhile, the “community solar” may thus formed in a certain of electric power area. To raise the utilization of solar power and to well manage the power generation and load demand in these areas, community energy storage has been considered as a feasible solution. In this work, a low-voltage residential electric network that integrates solar power generation and community energy storage is investigated, and analysis of load electricity consumption under different scenarios and voltage fluctuation problems are discussed by OpenDSS time-series simulation. Finally, the benefit on electricity-saving of the studied system can be presented.

Load profile mining using directed weighted graphs with application towards demand response management

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.