Many efforts have been made to increase the utilization of renewable energy resources (RESs) in Iran. This paper aimed to evaluate the techno‐economic performance of an introduced hybrid microgrid (HMG) in eight climate zones of Iran. Therefore, ten cities are selected from the eight climate conditions of Iran. An electricity pricing strategy is also implemented according to the electricity tariffs defined by the Ministry of Energy (MOE) of Iran. The proposed electricity pricing strategy is applied to the HOMER software for investigating the optimal system configuration, RES electricity generation, and the economics of each understudy city. Optimization results indicate that Urmia (in moderate and rainy climate zone) has the least net present cost (NPC) (−5839$) and levelized cost of energy (COE) (−0.0122 $/kWh), whereas Golestan (in semimoderate and rainy climate zone) has the highest NPC (4520 $) and COE (0.012 $/kWh). It is shown that the combination of photovoltaic (PV)/wind turbine (WT)/converter in the grid‐connected operation mode is the most economical configuration. Moreover, the cities with higher potentials of wind speed and solar irradiance have lower NPC and COE. It is concluded that the utilization of the battery energy storage (BES) is technically and economically infeasible for all eight climate zones, even if the stored electricity is sold to the grid. Two sensitivity analyses are conducted to the electricity feed‐in‐tariff (FiT) and solar module price, respectively. The first sensitivity analysis indicates that by increasing FiT, more contribution of RESs is seen, which leads to lower COE and NPC. Furthermore, the two cities of Urmia and Yazd have the highest NPC and COE reductions. The second sensitivity analysis studies the module price impacts on the NPC and COE of each understudy city. It is revealed that the PV module price has a considerable effect on NPC and COE. However, this effect is more significant in some cities such as Bam, where a linear relationship is seen between the module price and economic results (NPC and COE).
In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.
Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.
Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise shortterm LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.
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