Coping with wind power uncertainty in Unit Commitment: A robust approach using the new hybrid metaheuristic DEEPSO

Abstract

The uncertainty associated with the increasingly wind power penetration in power systems must be considered when performing the traditional day-ahead scheduling of conventional thermal units. This uncertainty can be represented through a set of representative wind power scenarios that take into account the time-dependency between forecasting errors. To create robust Unit Commitment (UC) schedules, it is widely seen that all possible wind power scenarios must be used. However, using all realizations of wind power might be a poor approach and important savings in computational effort can be achieved if only the most representative subset is used. In this paper, the new hybrid metaheuristic DEEPSO and clustering techniques are used in the traditional stochastic formulation of the UC problem to investigate the robustness of the UC schedules with increasing number of wind power scenarios. For this purpose, expected values for operational costs, wind spill, and load curtailment for the UC solutions are compared for a didactic 10 generator test system. The obtained results shown that it is possible to reduce the computation burden of the stochastic UC by using a small set of representative wind power scenarios previously selected from a high number of scenarios covering the entire probability distribution function of the forecasting uncertainty.

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

“…. , b n } [50][51][52]. The main idea underlying DEEPSO movement is briefly illustrated in Figure 2.…”

“…Components X and X guarantee that a suitable extraction really happens, considering macrogradient points in a descending direction depending on the structured comparison of and . In this sense, component is assumed to be as , and component is sampled from the set of best ancestors from the swarm of n particles, that is, , , … , [50][51][52]. The main idea underlying DEEPSO movement is briefly illustrated in Figure 2.…”

“…The proposed HWDA approach was performed on a standard PC equipped with an Intel Core i7-3537U, 2 GHz CPU and 4 GB of RAM with Windows 10 and the MATLAB ® 2016a platform. The authors used the ANFIS and WT structure functions available in MATLAB toolboxes, while DEEPSO was programmed from scratch in MATLAB considering the information available in [49][50][51][52]. …”

Despacho económico en microredes con penetración de energía renovable usando algoritmo de punto interior y restricciones lineales

“…La principal desventaja de este tipo de energía es la aleatoriedad de la velocidad del viento y la radiación solar que hace que no sea certero programar con antelación un despacho económico. Es entonces necesario utilizar algoritmos rápidos que programen un despacho económico óptimo cuando hay una alta incertidumbre de las inyecciones de energía por parte de los paneles solares y los aerogeneradores eólicos, y así se permitan simular una alta cantidad de posibles escenarios [1], [2].…”

“…De esta manera el problema corresponde a una optimización de una función objetivo y unas restricciones que se deben cumplir, para los diferentes escenarios aleatorios de las potencias inyectadas renovables [1], [2], [3], [4]. En el presente escrito se propone el uso de la función f mincon de M atlab para realizar el despacho económico de un sistema de potencia con penetración de energías renovables.…”