Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

[1]  Mohsen Kalantar,et al.  Multi-objective scheduling of electric vehicles considering wind and demand uncertainties , 2015, 2015 Smart Grid Conference (SGC).

[2]  Tian Pau Chang,et al.  Investigation on Frequency Distribution of Global Radiation Using Different Probability Density Functions , 2010 .

[3]  John Peirson,et al.  Non‐Linearities in Electricity Demand and Temperature: Parametric Versus Non‐Parametric Methods , 1997 .

[4]  Sergio Rivera,et al.  Despacho de energía óptimo en múltiples periodos considerando la incertidumbre de la generación a partir de fuentes renovables en un modelo reducido del sistema de potencia colombiano , 2018 .

[5]  A. Medina-Santiago,et al.  Adaptive Model IoT for Monitoring in Data Centers , 2020, IEEE Access.

[6]  Ameena Saad Al-Sumaiti,et al.  Enabling Electricity Access: A Comprehensive Energy Efficient Approach Mitigating Climate/Weather Variability-Part II , 2019 .

[7]  Fushuan Wen,et al.  Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization , 2012, IEEE Transactions on Industrial Informatics.

[8]  Pieter Cabus,et al.  River flow prediction through rainfall–runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium , 2008 .

[9]  S. Wȩglarczyk,et al.  Kernel density estimation and its application , 2018 .

[10]  Roberto Montanari.  Criteria for the economic planning of a low power hydroelectric plant , 2003 .

[11]  A. Abouabdellah,et al.  Modeling and forecasting energy demand , 2016, 2016 International Renewable and Sustainable Energy Conference (IRSEC).

[12]  Mohammed Hassan Ahmed,et al.  Residential Load Management Under Stochastic Weather Condition in Developing Countries , 2014 .

[13]  Ahmed Al-Durra,et al.  Optimal power tracking of PMSG based wind energy conversion systems by constrained direct control with fast convergence rates , 2020, International Journal of Electrical Power & Energy Systems.

[14]  N. Mujere.  Flood Frequency Analysis Using the Gumbel Distribution , 2011 .

[15]  Abhijit R. Abhyankar,et al.  Real-Time Economic Dispatch Considering Renewable Power Generation Variability and Uncertainty Over Scheduling Period , 2015, IEEE Systems Journal.

[16]  Ángel Sánchez,et al.  A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles , 2019 .

[17]  Ameena Saad Al-Sumaiti,et al.  Stochastic PV model for power system planning applications , 2019, IET Renewable Power Generation.

[18]  Sergio Rivera,et al.  Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by Monte Carlo simulation , 2019, International Journal of Power and Energy Conversion.

[19]  Julio Andre BERNAL-RUBIANO.  Mathematical Uncertainty Cost Functions for Controllable Photo-Voltaic Generators considering Uniform Distributions , 2019 .

[20]  Ameena Saad Al-Sumaiti,et al.  Enabling Electricity Access: Revisiting Load Models for AC Grid Operation-Part I , 2019 .