Summary:
The rising interest in energy storage systems (EES), due to their ability to mitigate power fluctuations, enhance system flexibility and improve the reliability of variable renewable technologies. However, this interest has raised concerns regarding the development of operation strategies to optimize the use of batteries [1]. In particular, batteries have a limited lifespan due to the degradation process that takes place during the charge and discharge cycles, resulting in a decrement in their energy storage capacity [2]. Therefore, most of their operating costs are related to this degradation process, making it necessary to include these costs in the economic dispatch models of electricity generation. With the increase in the use of EES, models with different complexities have been developed in the literature to optimize the EES operation. These models analyze battery performance from two different perspectives, leading to empirical and fundamental models. Empirical models are based on experimental data and designed for specific EES applications [3]. The development of these applications has emphasized the importance of accurately considering battery degradation in EES. However, empirical models have their limitations [1]. Fundamental models are typically based on the design and resolution of mathematical optimization models to represent the batteries’ degradation when considering as input the main technical data provided by the batteries’ manufacturer (such as maximum storage energy and the maximum charge capacity) [4]. They typically represent the degradation cost in the objective function as a way to model the replacement cost of new batteries purchases needed due to this degradation. However, these models usually optimize batteries’ operation in a two-stage process for the medium-term. In the first stage, the charge and discharge cycles and its depth are counted by means of an algorithm. In the second stage the obtained results from the algorithm are included as an input in an economic dispatch, repeating the whole iterative process until some convergence criterion is satisfied. However, stagnation and suboptimality of the final decisions can be observed [1]. A new linear mathematical model to optimize the operation of EES for the medium term, including degradation costs, has been developed in this paper. The main contribution is that the model is able to quantify computational efficiencies and representation capacities of the charge and discharge cycles when modelling them in an endogenous way. Thus, the model represents charge and discharge cycles as variables in the economic dispatches without the need of two-stage processes. The case studies show the model accuracy and the computational complexity compared to the ones found in literature.
Spanish layman's summary:
En este artículo se muestra un nuevo problema de programación matemático lineal para optimizar la operación de baterías eléctricas a medio plazo, representando los costes de degradación. El modelo representa los ciclos de carga y descarga como decisiones del despacho económico de mínimo coste.
English layman's summary:
A new linear mathematical model to optimize the operation of storage technologies for the medium term, including degradation costs, has been developed in this paper. The model represents charge and discharge cycles as variables in the cost minimum economic dispatch.
Keywords: batteries, aging, economic dispatch, energy storage, mathematical programming
Registration date: 20/06/2023
IIT-23-140WP
