Modeling Photovoltaic Generation Uncertainties for Monte Carlo Method based Probabilistic Load Flow Analysis of Distribution Network

Palahalli, Harshavardhan; Maffezzoni, P.; Gruosso, Giambattista

doi:10.1109/upec49904.2020.9209825

Cited by 12 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods consider probability distributions of uncertain parameters, such as renewable energy generation and load variations, to evaluate the probabilities of different system states and associated risks. Monte Carlo simulation provides a straightforward approach to estimating the probability distributions of system variables by generating random samples from input distributions and simulating power load under various scenarios [32,33]. Polynomial chaos expansion represents uncertain parameters as random variables and approximates the solution using orthogonal polynomials [34].…”

Section: Related Workmentioning

confidence: 99%

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Tang,

Zhang,

et al. 2024

Energies

View full text Add to dashboard Cite

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

show abstract

Section: Related Workmentioning

confidence: 99%

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Tang,

Zhang,

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…As mentioned before, the Weibull distribution function is used to model the uncertainty of wind unit generation, and the normal distribution function is used to model the uncertainties of phPV [33] unit generation and MG load. Uncertainty of the occurrence of emergencies is also considered in two ways: Event mode and non‐event mode.…”

Section: Case Studymentioning

confidence: 99%

Modelling of demand response programs in energy management of combined cooling, heat and power‐based microgrids considering resiliency

Azarinfar

Khosravi

Ranjkeshan

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

Demand‐Side Management (DSM) Strategic plan of International Energy Agency states that the first option in all energy policies to achieve sustainable, reliable, and economic systems is DSM activities such as demand response programs (DRP). Thereby, these programs must also be considered in energy management of microgrids (MGs) and their operation and resiliency optimization. Energy management in MGs is carried out with different objectives such as reducing operation costs and enhancing the resilience response. In the first approach, the microgrid operator attempts to minimize the energy cost supplied by available resources in normal conditions. On the other hand, the goal of improving microgrid resilience response is to reduce the energy outages and load interruptions in emergencies such as occurrence of floods, earthquakes, and hurricanes. Naturally, in the second approach, the generation share of the microgrid resources itself in supplying the required demand is more; because the microgrid and main network connection will be lost in this approach. Since these two approaches may lead to different strategies in microgrid energy management, a compromise must be reached between them. In this paper, DRP, storage devices, renewable resources, such as wind and photovoltaic (PV) units, are modelled to manage energy in the MGs equipped with Combined Cooling, Heat, and Power to minimize the operation costs and enhance the resilience response. Three case studies are considered. The first case deals with the management of energy and structure in MGs without considering resilience objective function and responsive loads. In the second case, the resilience objective function is considered in the problem, and in the third study, responsive loads were also taken into account. The results show that the total objective function is reduced by considering the resilience objective function in case two. Also, the use of responsive loads in the third case also reduces more the total costs of MGs.

show abstract

“…The loads are classified as spot loads and distributed loads. More details on the network and its elements can be found in [14,21]. The network is modified by adding two single-phase PV generators to phase B of bus 3 and bus 4, and four single-phase loads connected to buses 3, 4, 9 and 11, as shown in Figure 5.…”

Section: Ieee 13 Bus Test Feedermentioning

confidence: 99%

“…When the measurement data of a PV system are analyzed, the PDF of the active power delivered by the PV system shows a complex shape, and it does not fit any standard statistical distribution. In this case, a large set of PV samples for MC simulations were generated by determining the empirical cumulative distribution function (CDF) of measurement and using an inverse sampling method that matched the original distribution [14].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks

2021

View full text Add to dashboard Cite

Deterministic load flow analyses of power grids do not include the uncertain factors that affect the network elements; hence, their predictions can be very unreliable for distribution system operators and for the decision makers who deal with the expansion planning of the power network. Adding uncertain probability parameters in the deterministic load flow is vital to capture the wide variability of the currents and voltages. This is achieved by probabilistic load flow studies. Photovoltaic systems represent a remarkable source of uncertainty in the distribution network. In this study, we used a Gaussian copula to model the uncertainty in correlated photovoltaic generators. Correlations among photovoltaic generators were also included by exploiting the Gaussian copula technique. The large sets of samples generated with a statistical method (Gaussian copula) were used as the inputs for Monte Carlo simulations. The proposed methodologies were tested on two different networks, i.e., the 13 node IEEE test feeder and the non-synthetic European low voltage test network. Node voltage uncertainty and network health, measured by the percentage voltage unbalance factor, were investigated. The importance of including correlations among photovoltaic generators is discussed.

show abstract

Modeling Photovoltaic Generation Uncertainties for Monte Carlo Method based Probabilistic Load Flow Analysis of Distribution Network

Cited by 12 publications

References 17 publications

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Modelling of demand response programs in energy management of combined cooling, heat and power‐based microgrids considering resiliency

Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks

Contact Info

Product

Resources

About