Over the last two decades, using renewable energy sources (RESs) such as wind turbines (WTs) and photovoltaic (PV) systems has increased considerably due to some environmental concerns and their unique features. From this perspective, traditional centralized power generations are replaced with modern forms of decentralized power generation, which can change consumption and production patterns pragmatically. In the modern electricity market, a new expression has been recently introduced to present a bilateral role. In fact, prosumers are regarded as an entity consumes electrical energy and reserve the capability to generate electricity in a reasonable way. It is often argued that market reform and regulatory support promote electricity market and ensure the promising profits to individual prosumers. In addition, the distribution system operator (DSO) encourages prosumers to achieve benefits and reduction in maintenance and expansion cost of equipment. In the U.S. and some European countries, the development of this kind of market has led to achieving a great balance between supplies and demand as well [1
Recently, Iran’s electricity market has depicted some developments in the increasing use of renewable energy sources as well as cost adjustment by the integration of prosumers and consumers in a prosumer market. These electricity markets have been established for prosumer aggregators, namely smart cities, zero-carbon building, off-grid islands, or residential and commercial aggregators. It is important to note that residential buildings play an active role in this market because they can be equipped with modern energy technologies such as plug-in hybrid electric vehicles (PHEVs). Besides, they can also supply energy for consumers through the nearest aggregator [6
]. In fact, among different types of electric vehicles (EVs; such as a PHEV, fuel cell electric vehicle (FCEV) [7
], and fully electric vehicle (FEV)), PHEV is the most prominent EV in Iran. Therefore, we have considered PHEV in our model [8
Moreover, there is an ability to deploy load management and minimize energy consumption in order to achieve some economic benefits. Due to the high penetration of RESs and dynamic behavior of their electricity production during different days, a variable amount of electricity can be achieved in different hours of a specific day [9
]. Although solar and wind generations are highly dependent on weather conditions, there may be insufficient renewable outputs energy during adverse weather conditions. As a result, decision-makers of this residential building intend to sell extra electricity to the consumers at a reasonable price. Similarly, consumers are also mainly motivated by the prices that are more affordable than utility rates. Since the electricity prices in Iran are based on the progressive tax scheme, where prices rise with more electricity consumption rate, consumers will avoid paying extra cost by a proper interacting with the prosumers.
The presence of local energy management systems (EMSs) seems essential in order to manage some energy storage systems like stationary batteries (SBs) and PHEVs in a sensible way. Energy storage systems (ESSs), along with other applications, are deployed to increase revenues and improve the operation of RESs efficiently. The primary purpose of taking advantage of energy management systems (EMS) in the current study is to reduce the incoming power from the utility at peak hours and providing extra-generated power for contracted consumers. As a result, an advanced charge and discharge schedule appears to be necessary, particularly for residential buildings, which have been contracted to some consumers with a specific amount of power during scheduled periods like peak hours. Likewise, this is more favorable to store generated electricity from RESs to support contracted consumers instead of purchasing from the network during peak hours.
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
]. 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
], 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
]. 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
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. However, considering a weather predicting module in the EMS of the prosumer was neglected in recent studies [18
]. As a matter of fact, neglecting uncertainties of weather parameter in the day-ahead operation of the prosumer would result in the inaccurate operation cost of the prosumer. In addition, some recent studies in the field of prosumer scheduling have neglected to consider a proper depreciation cost in the optimization model [20
]. This paper tries to fill such a knowledge gap by regarding the depreciation cost of both SB and PHEV in the optimization model based on daily depth of charge (DOD) reduction because of the limited lifetime of the batteries. In the final step, mixed-integer linear programming (MILP) has also been used to minimize the operation cost of the prosumer in a fair way.
In this paper, the authors introduced a day-ahead optimization for the prosumer by considering weather predictions for PV and WT output power. Linear regression results for predicted and real weather data achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. According to the results, solar irradiance and temperature were accurately predicted, however, due to high hourly intermittency of wind speed, it was not properly predicted. The operation cost of the prosumer by using the predicted data had shown a minor difference (US$0.031) with the operation cost of the system with real weather data. In order to investigate the performance of ESS, the depreciation cost was proposed in the optimization model, and the prosumer interaction with the market was analyzed. ESSs reduced the operation cost of the prosumer by optimal charge and discharge cycles. Moreover, the depreciation cost of ESS in the objective function improved the daily operation cost of the prosumer by US$0.8647. Due to a high variability of wind speed, MLP-ANN could not accurately predict the hourly wind speed. Therefore, it is highly suggested to implement and evaluate different predicting algorithms to validate the proposed optimization model.
Uncertainty of the load demand may affect the operation cost of the prosumer. However, this study had not considered the load prediction for the day-ahead operation of the study, hence, it could be considered alongside the weather prediction in the future works. Moreover, a comprehensive model for depreciation cost of ESSs could be considered in the future works in which the gap between the highest and lowest SOC is minimized in every day.