As the optimal size of the battery energy storage system (BESS) affects microgrid operation economically and technically, this paper focuses on a novel BESS sizing model. This model is based on the battery degradation process (BDP) and it can consider temperature impact on the BESS performance. The proposed model aims to accurately minimize microgrid costs. To this end, the major factors affecting the BDP such as temperature, depth of discharge, incomplete/complete cycles, state of charge, and time passage are linearly incorporated into the proposed model to estimate the amount of the BESS capacity loss. To calculate the BESS cycle aging because of the incomplete/complete cycles, a novel linear framework is introduced. Moreover, the relation between the temperature and the allowable BESS capacity is also modeled and integrated into the BESS sizing model to increase the accuracy of the BESS modeling and achieved results. Various case studies are carried out to show the applicability and effectiveness of the presented model. The conducted simulations demonstrate that the BDP and the temperature impact dramatically affect the BESS performance and the microgrid operation cost. The results also indicate that the proposed model can improve the accuracy of the obtained results by 4.72%.

Lithium-ion batteries have drawn considerable attention due to their different applications in smart grids. Nevertheless, various factors, including the charging/discharging process, can cause battery capacity degradation and reduce its lifetime. Considering the high investment cost of the battery, employing an appropriate approach to integrate the battery degradation cost into the scheduling problem is vital to optimizing the battery performance. This paper proposes a novel degradation cost model for optimal battery scheduling. A linear model based on the semi-empirical approach is introduced to model the battery capacity degradation process. Moreover, a novel linear algorithm based on the rain-flow algorithm is presented to count complete and incomplete cycles. Then, a degradation cost model is presented based on the amount of battery capacity fade and engineering economics principles. The battery scheduling problem is formulated as a mixed-integer linear programming (MILP) model. In the next stage, a novel approach based on model predictive control (MPC) is utilized to decline the degradation cost and improve battery energy management. The simulation results show that integrating the battery degradation cost into the battery scheduling problem significantly influences the charge/discharge strategy and achieves more benefits for battery owners. In this regard, the proposed scheme can decrease the battery degradation cost and the amount of capacity fade by 31.62% and 37.23%, respectively. Also, considering the battery degradation process in the optimization problem leads to a more optimal and accurate outcome.

This paper utilizes the fast response capability of the photovoltaic power plants (PVPPs) in providing active and reactive power to increase transient stability margin and oscillation damping in the power system along with improving energy conversion efficiency. The new half cyclic operation (HCO) method is based on the Bang-Bang control of active and reactive power in PVPPs, which does not need any power curtailment. While oscillation is detected, the control system starts to sequentially half-cyclic switch the control system between stability mode and maximum power point tracking (MPPT) mode. Switching between MPPT and stability modes helps with energy conversion efficiency increment, rotor oscillation damping, and transient stability improvement. This strategy automatically leads the operation point to MPPT as oscillation gets mitigated. IEEE 9-bus and IEEE 39-bus test systems are used for representing new control method performances. The obtained results prove the effectiveness of the proposed strategy.

This paper presents a novel battery degradation cost (BDC) model for lithium-ion batteries (LIBs) based on accurately estimating the battery lifetime. For this purpose, a linear cycle counting algorithm is devised to estimate the battery cycle aging. In this algorithm, the local maximum and minimum values of the profile of the battery state of charge are identified by the proposed linear formulations. Then, the battery cycle aging due to the complete and incomplete cycles is determined. In this step, the battery cycle aging during an incomplete cycle is calculated by converting it to two complete cycles. After that, the calendar aging process of the LIB is linearly formulated based on the semi-empirical model to estimate the BDC accurately. After linearizing the LIB degradation process, a mechanism for computing the BDC during the scheduling horizon is designed by modeling the BDC as a series of equal payments over the LIB lifetime. In order to incorporate the BDC in the battery energy management problem, an iterative algorithm is presented for efficiently calculating the BDC associated with the adopted charging/discharging strategy. The numerical simulation results indicate that integrating the battery degradation process into the battery scheduling problem can reduce the amount of the battery capacity fading by 32.81%, as well as increase the profit of battery owners by 1.21%. Moreover, the conducted analyses highlight the importance of considering the LIB calendar aging process in determining the optimal LIB capacity.

This study proposes a novel predictive energy management strategy to integrate the battery energy storage (BES) degradation cost into the BES scheduling problem and address the uncertainty in the energy management problem. As the first step, the factors affecting the BES calendar aging and cycle aging are linearly modelled. Furthermore, a linear algorithm is provided to calculate the BES cycle aging due to the BES complete and incomplete cycles. Subsequently, a novel approach to estimating the BES degradation cost function according to the BES specifications and degradation process is presented. Finally, taking into account the BES degradation cost model, the proposed predictive energy management strategy framework is implemented on an integrated photovoltaic and BES system to evaluate the applicability and efficiency of the proposed scheme in integrating the BES degradation cost in the energy management problem. The numerical simulation results indicate that integrating the BES degradation cost into the energy management problem significantly affects the BES charge/discharge strategy. Moreover, comparing the proposed predictive energy management strategy with a simple one, it is verified that the provided approach could decrease the BES capacity fade and degradation cost by 5.06% and 4.67%, respectively, and increase the photovoltaic farm profit by 1.10%.

Load forecasting is an essential issue in future smart grids where inaccurate forecasting causes energy waste, power shortages, or cross-blackouts. Therefore, increasing forecasting accuracy is crucial due to the expansion of the type of loads and the amount of consumption and parameters that affect the load changes. Machine learning is a powerful tool for achieving artificial intelligence, and it is used for load forecasting as one of its applications. In this paper, short-term load forecasting is performed using an ensemble supervised learning based on random forest method named Deep Forest Regression. This method is also derived from deep learning and deep neural network theory. This forecast has been done using the data of residential consumption of an Iranian city for five months, including from half of May to half of September. The data is gathered every 30 minutes and stored in the system. By comparing the proposed method with some common methods, it can be seen that the proposed method has higher accuracy than those.

Although proliferation of photovoltaic systems in power systems have caused some problems such as decreasing overall inertia, intermittency and etc., they have shown to have the potential to help improve power systems operation. For instance, photovoltaic systems have been proposed to provide frequency response. Previous research studies focusing on this subject have assumed photovoltaic systems with being subjected to shading conditions which is wrong. To fill this gap, this paper intends to investigate the effect of partial shading on the photovoltaic systems and propose a novel approach to enable photovoltaic systems to provide frequency response even under partial shading conditions. The simulation results prove the accuracy and efficiency of the proposed approach, being capable of enabling the photovoltaic systems to provide frequency response under normal (not shading) and partial shading conditions.

Photovoltaic (PV) generation share in global energy has been increasing in recent years. This growth has raised concern for the power grid operators because of the intermittency in the PV power generation and prediction. Battery energy storage (BES) is introduced as a possible solution to this challenge. BES needs to be operated optimally since their investment cost is not low enough. In this paper, an innovative control strategy based on model predictive control (MPC) is proposed to manage an integrated PV plant and BES in order to get maximum revenue. Using the hourly prediction of PV output power and electricity market price in the optimization problem, the PV plant can adjust its generation so as to handle the power fluctuation with regard to the committed power and reduce economic penalties. Moreover, the BES degradation cost is modeled to precisely evaluate the PV plant income and obtain more realistic answers. Numerical results can prove and validate the effectiveness of the proposed approach.

Nowadays, the cost of installing wind turbines is declining. Companies endeavor to install the high influential rate wind turbine. This desire to use wind turbines indicates that in the future a sufficient part of the electricity will be supplied by wind turbines. The basis of electricity generation in many countries is based on synchronous generators. Wind turbines, which are expected to have a significant share of production in the future, have different dynamics than today's conventional synchronous generators. If a fault occurs in such networks, which consist of wind turbines and conventional synchronous generators, it is significant how the transient stability of the network will be. This paper focuses on the transient stability of the grid when loads are not just static. Previously, the impact of dynamic loads has been ignored. Since dynamic load density is massive, these loads are aggregated to show high accuracy of the transient stability. In this paper, the growing trend of wind turbines on transient network stability is analyzed. This article demonstrates how the presence of wind turbines can assist the grid to be robust during fault. Turbines are variable-speed permanent magnet synchronous generators. Simulations are performed in Simulink / MATLAB environment.

Fluctuations in power generation in wind farms reduce their profits and utility grid reliability. Energy storage systems (ESS) are used to decrease the energy imbalance between actual wind power and scheduled wind power in wind farms. Therefore, ESS optimal control and planning will play an important role in reducing energy imbalance and increasing wind farm profits. In this paper, a novel control method based on model predictive control (MPC) is presented to fulfill the committed energy production of wind farms and increase the ESS operation benefits. Considering the short-term forecast of real-time market price and wind power in the optimization problem leads to more efficient scheduling for the ESS charge/discharge rate and the amount of energy purchased from the real-time market. It not only reduces energy imbalance but also increases wind farm profit. Numerical results are presented to show the effectiveness and validity of the proposed control method.

Nowadays, microgrids (MGs) have received significant attention. In a cost-effective MG, battery energy storage (BES) plays an important role. One of the most important challenges in the MGs is the optimal sizing of the BES that can lead to the MG better performance, more flexible, effective, and efficient than traditional power systems. This paper proposes a novel set of formulations to determine the optimal BES size, technology, depth of discharge (DOD), and replacement year considering its technical characteristics, service life, and capacity degradation to minimize the MG scheduling total cost and improve the precision and economic feasibility of the BES sizing method. Moreover, the modeling of BES considering capacity degradation is presented. The proposed model of the planning problem is formulated using mixed-integer linear programming (MILP) to ensure the convergence of the optimization problem. The effectiveness of the proposed approach is confirmed by numerical simulation based on historical data of a real MG.

The generalized Heffron–Phillips model (GHPM) for a microgrid containing a photovoltaic (PV)-diesel machine (DM)-induction motor (IM)-governor (GV) (PDIG) has been developed at the low voltage level. A GHPM is calculated by linearization method about a loading condition. An effective Maximum Power Point Tracking (MPPT) approach for PV network has been done using sliding mode control (SMC) to maximize output power. Additionally, to improve stability of microgrid for more penetration of renewable energy resources with nonlinear load, a complementary stabilizer has been presented. Imperialist competitive algorithm (ICA) is utilized to design of gains for the complementary stabilizer with the multiobjective function. The stability analysis of the PDIG system has been completed with eigenvalues analysis and nonlinear simulations. Robustness and validity of the proposed controllers on damping of electromechanical modes examine through time domain simulation under input mechanical torque disturbances.