Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

https://ideas.repec.org/a/gam/jeners/v12y2019i20p3816-d274620.htmlThe proposed study presents a novel probabilistic method for optimal allocation of spinning reserves taking into consideration load, wind and solar forecast errors, inter-zonal spinning reserve trading, and demand response provided by consumers as a single framework. The model considers the system contingencies due to random generator outages as well as the uncertainties caused by load and renewable energy forecast errors. The study utilizes a novel approach to model wind speed and its direction using the bivariate parametric model. The proposed model is applied to the IEEE two-area reliability test system (RTS) to analyze the influence of inter-zonal power generation and demand response (DR) on the adequacy and economic efficiency of energy systems. In addition, the study analyzed the effect of the bivariate wind prediction model on obtained results. The results demonstrate that the presence of inter-zonal capacity in ancillary service markets reduce the total expected energy not supplied (EENS) by 81.66%, while inclusion of a DR program results in an additional 1.76% reduction of EENS. Finally, the proposed bivariate wind prediction model showed a 0.27% reduction in spinning reserve requirements, compared to the univariate model

APPLICATION OF PROBABILISTIC METHODS FOR EFFECTIVE AND RELIABLE OPERATION OF ELECTRICAL AND ELECTROMECHANICAL SYSTEMS

This PhD thesis presents novel system control methods that can be utilized for effective and reliable operation of electric grids and passenger elevators. First of all, this study introduces a new spinning reserve allocation optimization technique that takes into account load and renewable power generation, inter-zonal conventional power generating capacity and demand response. Using the bivariate Farlie-Gumbel-Morgenstern probability density function, the framework presented in this thesis utilizes a new method to simulate the power generation of wind farms. In addition, the presented framework uses a Bayesian Network (BN) algorithm to fine-tune the spinning reserve allocation based on previous hours' actual unit commitment, as well as the hour and day types. The model proposed in this study has been tested on the IEEE Two-Area Reliability Test System (RTS) to quantify the effect of the bivariate wind prediction model and the Bayesian network-based Reserve Allocation Adjustment Algorithm (RAAA) on reliability and cost-effectiveness of the power grid. The findings show that combining a bivariate wind forecast model with RAAA improves power grid stability by 2.66 percent while lowering overall system running costs by 1.12 percent. Secondly, the present work introduces an algorithm with an objective of optimal dispatching control of passenger lifts. The algorithm utilizes the data received from video cameras and dispatches the elevator cars based on the passenger count. The proposed algorithm utilizes the information on the number of people and dispatches the lifts with an objective to move the maximum number of passengers to the desired building levels within the minimum amount of time. In addition, the algorithm considers each person's size and whether or not they have luggage. To account for uncertainty in image acquisition, the algorithm assigns the probability weights to the number of people who are waiting for a lift and riding the lifts. The main purpose of the algorithm is to minimize the following performance metrics: average travel time (ATT), average journey time (AJT) and average waiting time (AWT). The suggested algorithm works well in situations of limited traffic sizes, according to a test case scenario conducted on a ten-story office building having four elevator cars (less than 200 people). In a scenario with large up-peak high intensity traffic, the proposed algorithm primarily underperforms. The proposed algorithm's best output was seen in situations with random inter-floor passenger movement. In scenarios of changing traffic intensity and size ATT increased by 39.94 percent and 19.53 percent, respectively

CAMERA-DRIVEN PROBABILISTIC ALGORITHM FOR MULTI-ELEVATOR SYSTEMS

A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively