## 1 Introduction
Some studies have been conducted in order to focus on the optimal scheduling and operation of these ESSs in the power systems. In [10], the authors proposed comprehensive planning of ESSs, so that ESSs can mitigate WT output power fluctuation by using variable-interval optimization along with fuzzy controlling approaches. They also considered different characteristics of ESSs, including economic costs, state-of-health, and energy capacity for effective contribution. One of the applications of ESSs is the frequency regulation owing to their fast controlling abilities [11; 12; 13; 14]. In addition to the mentioned application of ESSs, high penetration of RESs may result in voltage instability of the grids. From this perspective, they generally can be used to moderate voltage instability [15]. In these studies, researchers have not investigated the market-based operation or control of ESSs in order to achieve the maximum monetary incomes. In comparison to [11; 12; 13; 14], this study purposes a market-based operation of ESSs in which the ESSs minimizes day-ahead operation cost of the prosumer. A different study presented an economical approach to define a policy for electricity pricing, which leads to the optimal charge and discharge of ESSs so that a metaheuristic algorithm is used for day-ahead scheduling of multicarrier energy networks [16]. However, they have neglected weather variability in the proposed method, which may affect the optimal result of the system. In another study [17], researchers have proposed an optimum operational strategy for ESSs in order to maximize the level of profit from the South Korean demand response (DR) program. The fact is that ESSs are deployed in order to reduce peak load demand and make a useful contribution to grid reliability and stability simultaneously. It is argued that generating profit from ESSs may be difficult with the present DR program. Nevertheless, this study does not give any strategy for considering RESs in the proposed method. In fact, integration of RESs and ESSs would increase the level of profits from DR programs. Furthermore, some authors did not consider any specific factor for ESS aging. However, they verify that the proposed methods may be feasible in the near future by changing the conditions of the current DR. In [18], a two-phase ESS scheduling model has been introduced. In the first stage of the proposed model, ESS reduces peak load, and in the second stage, electricity trading is performed, which results in a minimization of the overall operating cost of the system by the use of the remaining capacity. Besides, this paper has presented some machine learning techniques for a load prediction. However, the authors indicated that in order to have a better prediction, it is advised to consider variables related to weather, namely ambient temperature, and other environmental factors. In a recent study [19], optimal scheduling and operation of ESSs by considering the corrective operation of ESS has been outlined in prosumer energy market where the objective is to achieve maximum profits of the prosumer. It is important to note that no weather predictions have been considered for RESs productions in their work. In fact, all their assumptions are generally based on historical data. In addition, several studies in recent years have worked on the optimal operation of PHEVs in different networks and markets [20; 21; 22]. Due to the high capital cost of ESSs, and a limited number of charge cycles, many new types of studies have deployed battery depreciation models in their studies [21; 22; 23]. In this paper, enhanced scheduling of the prosumer in a day-ahead electricity market has been proposed, which can benefit from RESs and ESSs simultaneously. Due to the high dependency of RESs upon weather conditions, they are assumed as intermittent energy sources. As a result, a feed-forward artificial neural network (FF-ANN) has been introduced in order to predict day-ahead weather conditions instead of using historical data for PV and WT productions.