Microgrid Power Research Articles

The least cost design of 100 % solar power microgrids in Africa: Sensitivity to meteorological and economic drivers and possibility for simple pre-sizing rules

Autonomous micro-grids based on solar photovoltaic (PV) are one of the most promising solutions to provide electricity access in many regions worldwide. Different storage/PV capacities can produce the same level of quality service, but an optimal design is typically identified to minimize the levelized cost of electricity. This cost optimization however relies on technical and economic hypothesis that come with large uncertainties and/or spatial disparities.This article explores the sensitivity of the optimal sizing to variations and uncertainties of such parameters. Using data from Heliosat and ERA5, we simulate the solar PV production and identify the least cost configurations for 200 locations in Africa.Our results show that the optimal configuration is highly dependent on the characteristics of the resource, and especially on its co-variability structure with the electric demand on different timescales. It is conversely rather insensitive to cost hypotheses, which allow us to propose simple pre-sizing rules based on the only characteristics of the solar resource and electricity demand.The optimal storage capacity can be estimated from the 75th percentile of the daily nocturnal demand and the optimal PV capacity from the mean demand and the standard deviation of the daily power difference between solar production and demand.

Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game

This paper proposes an energy trading framework for a combined heat and power (CHP) microgrid (MG) integrated with a Photovoltaic (PV) generation system. The parties involved in the CHP-MG energy market are a single MG operator (MGO) and multiple users with electric and thermal demand response (DR). The energy trading process between the MGO and users is formulated as a Nash bargaining game, where the users can bargain with the MGO regarding trading volumes and the prices of electricity and heat. The relationship between the Nash bargaining solution (NBS) and social welfare maximum is studied. A distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) is adopted to achieve NBS. Case studies demonstrate the effectiveness of the proposed trading framework, and show the promotion of economic benefits.

Joint coordination of optimal power management and energy storage system sizing for a full-scale marine current turbine considering microgrid integration constraint

The issues of variability and uncertainty of tidal speed result in severe challenges for standalone microgrid (as island grids for examples) operators. Indeed, to maintain the system power balance, existing classical generators which are associated with tidal power units must be able to frequently change their operating point, either up or down, to respond to the fast fluctuations of tidal power and consumption and sometimes exceed their maximal ramp capability. This in turn accelerates aging process and results in serious damage. The switching to a safer and more reliable system seems to be a promising and long-term solution. This paper proposes to associate a supercapacitor-based Energy Storage System (SC-ESS) to a tidal turbine to smooth the fast power production fluctuations. The association of this SC-ESS with the tidal turbine unit aims to meet requirements on the variation of injected tidal power in an islanded microgrid. An original smart power management strategy is proposed for the use of the ESS. Taking into account this strategy, an optimal design methodology based on simulations on the more severe encountered day cases is proposed. This methodology allows to determine a minimal sizing of the ESS to fulfil the grid requirements. The paper also proposes a complete modeling of the whole system behavior. The Energetic Macroscopic Representation (EMR) tool is used to realize the system model where the inversion-based control principle is then used to deduce the control schemes. This global methodology is applied to a test tidal turbine which is based on a real 1 MW prototype setup to the islanded grid of Ushant in France. Using real measured data, it is shown that a relatively small and low cost SCs pack is sufficient to accommodate the high tidal power fluctuations. A comparison is performed between the existing configuration where a resistor bank is used to shave the excessive power variation and the considered case where SCs pack is used. For one tidal cycle, it is shown that a reduction in energy loss of 18.2% is achieved, which presents a non-negligible gain for Ushant's Island microgrid.

The Architecture Optimization and Energy Management Technology of Aircraft Power Systems: A Review and Future Trends

With the development of More/All-Electric Aircraft, especially the progress of hybrid electrical propulsion or electrical propulsion aircraft, the problem of optimizing the energy system design and operation of the aircraft must be solved regarding the increasing electrical power demand-limited thermal sink capability. The paper overviews the state of the art in architecture optimization and an energy management system for the aircraft power system. The basic design method for power system architecture optimization in aircraft is reviewed from the multi-energy form in this paper. Renewable energy, such as the photo-voltaic battery and the fuel cell, is integrated into the electrical power system onboard which can also make the problem of optimal energy distribution in the aircraft complex because of the uncertainty and power response speed. The basic idea and research progress for the optimization, evaluation technology, and dynamic management control methods of the aircraft power system are analyzed and presented in this paper. The trend in optimization methods of engineering design for the energy system architecture in aircraft was summarized and derived from the multiple objective optimizations within the constraint conditions, such as weight, reliability, safety, efficiency, and characteristics of renewable energy. The cost function, based on the energy efficiency and power quality, was commented on and discussed according to different power flow relationships in the aircraft. The dynamic control strategies of different microgrid architectures in aircraft are compared with other methods in the review paper. Some integrated energy management optimization strategies or methods for electrical propulsion aircraft and more electric aircraft were reviewed. The mathematical consideration and expression of the energy optimization technologies of aircraft were analyzed and compared with some features and solution methods. The thermal and electric energy coupling relationship research field is discussed with the power quality and stability of the aircraft power system with some reference papers. Finally, the future energy interaction optimization problem between the airport microgrid and electric propulsion aircraft power system was also discussed and predicted in this review paper. Based on the state of the art technology development for EMS and architecture optimization, this paper intends to present the industry’s common sense and future trends on aircraft power system electrification and proposes an EMS+TMS+PHM to follow in the electrified aircraft propulsion system architecture selection

Coordinated control between a grid forming inverter and grid following inverters suppling power in a standalone microgrid

Battery energy storage systems, fuel cells, and photovoltaic generators are being adopted in distribution networks to achieve CO2 emission reduction. These power sources require inverters to connect to AC networks. However, sub- networks, such as a microgrids containing inverter power sources, may be disconnected from the utility network and operated in a standalone mode during extended blackouts. This study focuses on a standalone microgrid supplied by inverter power sources without a synchronous generator and proposes a new microgrid inverter control. In this inverter control, a single grid forming inverter is operated as a master power source to determine frequency and voltage, and other inverters, called grid following inverters, are operated as subordinate power sources with active and reactive power control. The necessary functions in the energy management system are examined, and the coordinated operation of all inverters in the microgrid is demonstrated via simulation.

A hierarchical control scheme with bi-level communication networks for the interconnected DC microgrids cluster

This paper proposes a hierarchical control scheme based on distributed alternating direction method of multipliers (ADMM) for the interconnected DC microgrids cluster. This scheme is fully distributed and consists of three coordinated control levels through bi-level distributed communication networks. The constraints of the economic dispatch problem are decoupled for tertiary control and decomposed into global and local optimization problems using the ADMM framework. To realize parallel computing of ADMM, we designed a fast second-order distributed average consensus algorithm to estimate the global average exchanged value and the average output power of a single microgrid, thus removing the coupling within the microgrids cluster and individual microgrid. Considering the voltage deviation caused by the traditional droop control, voltage control without droop based on the distributed algorithm is proposed to realize voltage restoration and power distribution among dispatchable generators for individual microgrid, combined with traditional primary and secondary control. Numerical simulations of an off-grid microgrids cluster are designed to validate the effectiveness of the proposed control method. Results show that the proposed hierarchical control scheme can ensure the independence and privacy of information, which is beneficial to solve technical and economic challenges brought by centralized optimization. In addition, the proposed algorithm can be applied to more realistic scenarios, such as conditions for time-varying loads and cost functions.

Multi-Objective Optimal Source–Load Interaction Scheduling of Combined Heat and Power Microgrid Considering Stable Supply and Demand

With the development of smart grids, it has become possible to take demand-side resource utilization into account to improve the comprehensive benefits of combined heat and power microgrids (CHP-MGs). In order to improve the benign interaction between the source and the load of the system, the source side decouples the thermoelectric linkage through energy storage devices and improves the system multi-energy supply capacity by introducing various energy flow forms of energy devices. On the demand side, considering the elasticity of electric heating load and the diversity of heating mode, an integrated demand response (IDR) model is established, and a flexible IDR price compensation mechanism is introduced. On this basis, aiming at the optimal stability of supply and demand and the minimum operating cost of the system, a multi-objective optimal operation model of combined heat and power source–load interaction is constructed, taking into account the user satisfaction with energy consumption and the internal equipment load constraints of the system. Finally, an improved multi-objective optimization algorithm is used to solve the model. The analysis of the algorithm shows that the source–load interaction multi-objective optimal scheduling of the cogeneration microgrid considering the stability of supply and demand can effectively improve the stability of supply and demand and the economy of the system.

Distributed secondary controller to ensure proportional sharing of reactive power in AC microgrid

In an ac microgrid, reactive power sharing accuracy is affected due to unequal values of interconnecting cable impedances. To resolve this issue, secondary controllers are used to compensate the effect of cable impedances. Various types of secondary controllers are suggested in the literature which includes linear proportional plus integral (PI) controllers to minimize the difference in reactive power sharing created by conventional E−Q droop law. The reference value of the PI controller is the average value of reactive powers supplied by the sources. When the PI controller is used to minimize the difference between the actual and reference value of reactive power, the difference between the algebraic sum of reactive powers supplied by the sources and the reactive powers demanded by the loads has a nonzero value. Therefore, the performance of these controllers becomes poor in the case of ac microgrid having sources of unequal ratings. To resolve this issue, a proportional reactive power-sharing (PRPS) controller-based distributed secondary controller is proposed in this paper. The PRPS controller modifies the droop gain of the E−Q droop control loop of each source in such a way that each source supplies reactive power equal to its proportional value of reactive power. The proportional value of reactive power supplied by each source is the power when the equivalent output impedance of all sources as seen by the loads are identical. The key advantage offered by the proposed controller is the accurate reactive power-sharing in the case of ac microgrid having sources of unequal ratings. The proposed controller ensures zero value of the difference between the algebraic sum of reactive powers supplied by the sources and the reactive powers demanded by the loads. This ensures accurate sharing of reactive power among the sources. The validation of the proposed controller is carried out for islanded mode of operation of ac microgrid. Further, the proposed controller requires of low bandwidth communication for its implementation. The effect of the proposed controller on the stability of the system is demonstrated using reduced order small-signal model. The effect of communication delay on the performance of the system is analyzed with the help of roots locus plots. To validate the efficacy of the proposed controller, detailed simulation studies are carried out in Matlab/Simulink.

Power-Sharing Analysis of Hybrid Microgrid Using Iterative Learning Controller (ILC) considering Source and Load Variation

In this study, a novel iterative learning controller is proposed to maintain power balance in hybrid microgrid considering source and load variation. A hybrid microgrid comprising of solar, battery, utility grid, and AC and DC loads are developed with ac and dc bus in MATLAB Simulink environment. The primary or local level control is implemented for solar and battery for maintaining stable dc bus voltage. The secondary or system level control is implemented by controlling the interlinking converter placed between ac and dc bus to supply stable voltage along with the smooth grid synchronization and ensuring the proper real power sharing between dc/ac buses. The simulation studies reveal that the proposed iterative controller has efficient and effective control over the voltage and power in hybrid microgrid under various modes of operation such as autonomous and grid connected modes.

Development of a method for analysis and forecast of the quality of electric power microgrid in the Far North

The article is devoted to the construction of an algorithm for predicting and evaluating the quality of MicroGrid electric power in the Far North. A fragment of the daily schedule of the electrical load of a small settlement in the Far North is given. The system features of electrical energy conversion in isolated power supply systems are shown. A structural model of the MicroGrid power supply system of the Far North has been built. A computational experiment was carried out to assess the quality of electrical energy MicroGrid in the Far North. The potential area for the output of electric energy quality indicators beyond the permissible limits according to GOST 32144 2013 is determined.

Power and Energy Management of a DC Microgrid for a Renewable Curtailment Case Due to the Integration of a Small-Scale Wind Turbine

Economic dispatch optimization and power management are the main concerns for a microgrid (MG). They are always studied and are considered to achieve an efficient operation of the MG by simplifying the control process and decreasing losses. The integration of a small-scale wind turbine (SSWD) into a direct current (DC) MG has an impact on its power and energy management. Excess power produced by renewable energy sources (RESs) is one of the problems that face the reliability of the MG and should be resolved. For this reason, a supervisory system is suggested to manage the excess of power. During the supervision process, some criteria, such as the physical limits and tariffs of the components are taken into account. Then, the suggested power management strategy aims to achieve an instantaneous power balance considering a rule-based power and depends on the above-mentioned criteria. To better meet the power balance, it is necessary to explore the constraints related to the control and supervision of the studied DC MG. Performance measures include the overall system energy cost and renewable curtailment (renewable energy that cannot be utilized and should be limited). Thus, the power limitation strategy consists of using two types of “shedding coefficients”, α and γ, to calculate the power that should be limited from each RES in the case of energy surplus. Simulation tests are carried out using two power management strategies: optimization and without optimization (i.e., storage priority). The results reveal that the coefficient γ reduces the overall energy cost and whatever the applied coefficient, optimization still provides good performances and significantly reduces the global energy cost.

Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk

A combined heating and power (CHP) microgrid has high flexibility and economy, but the output of renewable energy is uncertain. Meanwhile, excessive flexible load adjustment in the demand response process will increase user dissatisfaction. In order to solve the above problems, this paper quantifies uncertainty with the conditional value-at-risk (CVaR) of relative disturbance. Additionally, a multi-objective optimal scheduling model that takes into account both the operating economy and the demand-side power consumption satisfaction is established. In order to solve the multi-objective mixed-integer nonlinear programming problem well, we propose an improved sparrow search algorithm (ISSA), which solves the problem that the sparrow search algorithm (SSA) is prone to low accuracy, insufficient in population diversity and easy to be trapped in local optimum. Combined with the non-dominated solution ranking method, ISSA has the ability of multi-objective optimization. Finally, simulation on a typical CHP microgrid is performed. The optimization results under different confidence levels and risk preference coefficients are compared and analyzed. When the risk preference coefficient is 0.1, 2 and 5, the minimum rotating reserve capacity is 75.17 kW, 82.83 kW, and 105.70 kW in the electric part and 40.08 kW, 59.89 kW, and 61.94 kW in the thermal part. The effectiveness of the proposed CVaR of relative disturbance is verified.

A Hybrid Power Sharing Control to Enhance the Small Signal Stability in DC Microgrids

Low-frequency power oscillations pose serious problems to the operation of Distributed Generations (DGs) equipped with droop control. This paper presents a new hybrid power-sharing control for damping low-frequency power and current oscillations in DC microgrids. The proposed control consists of a decentralized droop control together with a centralized average current sharing control. A small-signal equivalent model is analytically derived for the proposed hybrid control and is incorporated into the DC microgrid model. The parameters of the PI controllers involved in the average current sharing control units are tuned individually to achieve a satisfactory performance in a stand-alone operation of DGs with a local load. The contributions of control signals contained in the hybrid control signal to the damping characteristic are analyzed. The dynamic response of the DC microgrid under a variety of disturbance scenarios is investigated. The power-sharing accuracy of the proposed control is also examined under the investigated scenarios. The method performance is also investigated for the oscillations caused by constant-power loads. Sensitivity analyses are conducted to evaluate the impact of the loading level and line parameters on the damping ratio associated with the dominant low-frequency modes. The effectiveness of the control method is examined in case of losing a number of communication links. The impact of the delay in the communication links on the method performance is also examined. The simulations are conducted on a multi-source DC microgrid, and the results are verified in the OPAL-RT real-time simulator.

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jointly</i> and each one is assigned to an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimized</i> violation rate. To highlight, the JCC problem with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimized</i> violation rates has been recognized as NP-hard and challenging to solve. This paper proposes a novel evolutionary algorithm that successfully solves this problem and reduces the solution conservativeness (i.e., operation cost) by around 50% compared with the baseline <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bonferroni Approximation</i> . We construct three data-driven ambiguity sets to model uncertain solar forecast error distributions. The solution is thus robust for any distribution in sets with the shared moments and shape assumptions. The proposed method is validated by robustness tests based on these sets and firmly secures the solution robustness.

Tri-Level Integrated Optimization Design Method of a CCHP Microgrid with Composite Energy Storage

Combined cooling, heating, and power (CCHP) microgrids are important means of solving the energy crisis and environmental problems. Multidimensional composite energy storage systems (CESSs) are vital to promoting the absorption of distributed renewable energy using CCHP microgrids and improving the level of energy cascade utilization. In this context, this paper proposes a multi-energy coupling structure that includes a multidimensional CESS with a compressed air energy storage (CAES) connected to a CCHP microgrid. Dividing design and operation causes some problems, such as low operating efficiency and difficult energy matching of CESSs. To solve the existing problems, an integrated design method is proposed that considers the capacity configuration of the equipment and the optimal operation of the system on a multi-timescale. The optimization result of the capacity configuration level is used as the constraint of the operational control level, and the equipment output plan of the operational control level is used as the optimized operation strategy and parameters of the system. The C-NSGA-II algorithm is adopted at the capacity configuration level and day-ahead scheduling level. Rolling optimization is solved using the PSO algorithm. The final result that satisfied the output design was obtained after several iterations. The average daily cost and CO2 emission reduction rate (CO2ERR) of capacity configuration levels are $2241 and 45.02%. The best CO2ERRs of day-ahead scheduling optimization levels are 39.9% and 45.9% in summer and winter, where the operating cost saving rate (OCSR) are 30.5% and 38.3% separately. Examples show that the integrated design method presented in this paper has significant advantages in enhancing energy-grade matching and improving the economy and environmental protection of the system.

Voltage Optimization Control Strategy for Islanded Microgrid Source-Grid-Load Active-Reactive Power Coordination Based on Collaborative Di-MPC

To cope with the volatility and randomness of wind power, photovoltaic (PV) power, and load demands in the islanded microgrid, and also to ensure the safety and economic operation of the islanded microgrid system. A collaborative Distributed model predictive control (Di-MPC) based voltage optimization control strategy is proposed, which considers the strong coupling characteristic of active and reactive power due to the impedance ratio of islanded microgrid, and the requirements of real-time and robustness in the optimization as well. By coordinating the controllable devices in the source-grid-load side of the islanded microgrid, the proposed strategy aims to make full use of the voltage regulation capability of each controllable device. Firstly, by considering the different operating characteristics of the controllable devices, a multi-time scale distributed voltage optimal control model is established. It divides the optimal control process into long-time scale and short-time scale and optimizes for respective objective functions and control variables in different time scales. Secondly, a collaborative Di-MPC-based voltage optimal control strategy is proposed. With the proposed collaboration mechanism, the power output increments of the distributed generators (DGs) are solved in the short-time scale, and the errors in the long-time scale control are also fixed. Finally, the simulation results show that compared with a traditional optimal control method and a centralized model predictive control (CMPC) method, the proposed voltage optimization control strategy can effectively reduce the voltage deviation and fluctuation at each node while ensuring the economic operation of the islanded microgrid system.

Distributionally Robust Optimal Dispatching of CHP Microgrid considering Concentrating Solar Power and Uncertainty

In order to solve the problem of diversified low-carbon energy supply with renewable energy as the main body, concentrating solar power (CSP) stations are introduced to act as cogeneration units. Taking full advantage of the flexible coupling and multienergy complementarity of electric, heat, and gas, an economic dispatch method for combined heat and power microgrid systems (CHP microgrid) with interconnected electric, heat, and gas is proposed. First, build the CSP-CHP microgrid structure and model the main equipment. Then, aiming at the minimum operating cost of the system, a regular scheduling model of the CSP-CHP microgrid system is established. On this basis, in order to deal with the uncertainty of renewable energy output, a distributionally robust optimization (DRO) model is introduced. In the DRO model, the Kullback–Leibler (KL) divergence is used to construct an ambiguity set about the predicted error of renewable energy output, and finally, the CSP-CHP microgrid DRO economic dispatch model is established. Finally, the system is simulated and analyzed in a typical CSP-CHP microgrid system, and the feasibility and effectiveness of the proposed method are verified by analysis. In addition, the necessity of introducing CSP and the advantages of the DRO model is further explained by comparison.

A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid.

Design and Optimization of Combined Cooling, Heating, and Power Microgrid with Energy Storage Station Service

This study aims to symmetrically improve the economy and environmental protection of combined cooling, heating and power microgrid. Hence, the characteristics of configuration ways of energy storage devices in traditional combined cooling, heating and power systems are analyzed, and a scheme for the operator to establish an energy storage station is designed. An improved aquila optimizer for the optimal configuration of the system is proposed to symmetrically enhance the economic and environmental protection performance. The feasibility of the proposed scheme is verified through experiments in three different places. The results show that the economic cost and exhaust emission of the system with energy storage station are reduced to varying degrees compared with the system with energy storage equipment alone and the system without energy storage equipment based on symmetry concept. Especially in Place 1, the scheme with energy storage station in the system can reduce the electric energy purchased from power grid by 43.29% and 61.09%, respectively, compared with other schemes. This study is conducive to promoting the development of clean energy, alleviating the energy crisis, reducing the power supply pressure of power grid, and improving the profits of operators by symmetrically considering the economic and environmental performance of the system.

Optimal scheduling of storage device, renewable resources and hydrogen storage in combined heat and power microgrids in the presence plug-in hybrid electric vehicles and their charging demand

Microgrids have faced an increasing penetration rate of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP), and storage systems. These elements need to be optimally scheduled so that the optimal operation of the microgrid is obtained. This study employs a novel random structure to optimally manage energy in microgrids which contain proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), RERs, PHEVs, as well as storage devices. The aim is to take into account the uncertainty of PHEVs and RERs models, in which Monte Carlo Simulation (MCS) is incorporated. The hydrogen storage strategy for PEMFC-CHP units is also used in this study, this strategy is considered by a mixed integer nonlinear programming (MINLP) problem. Moreover, smart charging plans are utilized to charger PHEVs. The objective function aims to maximize the market profit. This paper uses the modified adaptive differential evolution (MADE) technique for analyzing the optimal operation of the microgrid, where the intermittent behavior of uncertainty parameters is investigated. Differential evolution (DE) adopts an iteration-based strategy to enhance a candidate solution using a quality criterion and optimize the problem. Moreover, the algorithm is modified in order to enhance its search capability to be able to search and find local and global points. A conventional test system is implemented for verifying the efficiency of the suggested strategy and various planning durations are considered. A comparison is also made between this method and its counterparts for various situations and conditions.