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The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification. [ABSTRACT FROM AUTHOR]

This paper presents a flexible-reliable operation strategy for clean microgrids (MGs) consisting of power sources, energy storage systems (ESSs), and responsive loads. The proposed strategy attempts to provide the minimum expected operating cost for the MG, power sources, predicted pollutant emission, and expected energy not-supplied (EENS) because of an N − 1 event considering different applications. The objective function of the problem is subject to several constraints including AC optimal power flow equations, flexibility and reliability boundaries, and model of power sources and active loads. The Pareto optimization based on the weighted sum method is then incorporated to obtain a single-objective model. Probabilistic programming is also used to model the uncertainties of load, renewable energy, electrical energy price, and MG equipment accessibility. The ant-lion and crow search algorithms are merged to solve the problem and find a reliable optimal solution. A standard MG is employed to test the proposed strategy and indicate its capability in enhancing technical and economic indicators of the MG while ensuring that the MG is clean. [ABSTRACT FROM AUTHOR]

Linking AC and DC microgrids via a bidirectional AC-DC interfacing converter has emerged the hybrid AC-DC microgrid. The DC bus is connected to the grid by a series converter to mitigate the grid voltage's power quality problems. To enhance the reliability and to increase transferring active and nonactive powers between DC and AC main buses, the AC-DC bidirectional interfacing converters are connected in parallel. In this paper, the optimal number (n) of parallel-connected bidirectional interfacing converters is obtained to minimize the annual cost of investment and the reliability cost. A two-step deciding algorithm is proposed to find n. First, active and nonactive powers between DC and AC buses are obtained with power quality and power flow considerations. Based on two types of powers, two reliability indices are calculated, including the expected energy shortage (EES) based on active power and the expected Volt-Amps shortage (EVAS) based on nonactive power. The sum of two reliability index costs is considered as the reliability cost. Next, a decision-making strategy is executed to determine the optimal n by making a trade-off between minimization of two objectives: the annual cost of investment and the reliability cost. The simulations of a grid-tied hybrid AC-DC microgrid are done with the experimental measurement data of DC and AC loads and distributed generations. Simulation results verify the performance of the represented approach. [ABSTRACT FROM AUTHOR]

Multiple DC subgrids in close proximity can be connected to form a DC microgrid. The interconnected DC subgrids enhance reliability and efficient utilization of resources. In this paper, a novel coordinated power management strategy is proposed for interconnected subgrids of a DC microgrid. Each DC subgrid has PV and battery units. The proposed strategy is implemented on interlinking bidirectional converters (IBCs). The strategy considers the state-of-charge (SoC) and charging/discharging current of the batteries for SoC balancing-based power sharing through IBCs. A fuzzy logic controller is implemented to decide the amount of power flow through the IBCs from SoC and the charging/discharging current of battery units. Moreover, with the coordinated strategy, a high SoC battery takes more loads compared to a lower SoC battery. The proposed strategy supports fast charging and fast discharging of batteries depending on the SoC level. In addition, the coordinated strategy can seamlessly transfer the control of IBC from the power regulating mode to the voltage regulating mode without any additional control scheme during an outage of the battery unit. The feasibility of the proposed strategy is validated through simulation in MATLAB/Simulink and OPAL-RT real-time digital simulator. [ABSTRACT FROM AUTHOR]

Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. The only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. The designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to advance the recently progressing research on solid‐state transformers (SST) and examines the impact of uncertainty on load demand and distributed generation (DG) units while integrating SST into an active distribution network (ADN) in the presence of battery backed solar photovoltaic (BBSPV) and wind generation. The uncertainty related to solar irradiance, wind speeds, and load demands is modeled through kernel density estimation (KDE), generalized extreme value (GEV) distribution, and Gaussian distribution summed with the maximum likelihood estimation (MLE) approach respectively. The K‐medoid clustering procedure is utilized to group the load demand into multiple load levels, which helps in the intelligent scheduling of charging and discharging of battery energy storage systems (BESS). The distribution network planning process manages two conflicting criterion functions of real power loss (RPL) reduction and sum of square deviations of the expected voltage values, which are optimized utilizing the non‐dominated sorting genetic algorithm (NSGA‐II). The probabilistic power flow uses the direct load flow procedure utilizing bus injection to branch current (BIBC) matrix along with the forward sweep method to investigate each run of Monte Carlo simulation (MCS). Multiple case studies have been conducted on the IEEE 33 bus radial distribution network (RDN). The outcomes demonstrate the potential benefits of RPL reduction and voltage profile improvement (VPI) with increasing penetration levels of SST. [ABSTRACT FROM AUTHOR]

If sensitive loads are connected to the point of common coupling (PCC), compensation should be carried out in order to reduce PCC voltage harmonics. In this case, compensation is performed by the interface converters of distributed generation (DG) units. In this paper, a novel method is proposed for voltage harmonics compensation in islanded microgrids. The proposed method is capable of adjusting compensation percentage in proportion to the existing level of distortion at PCC. Also, this controller provides a proper sharing of compensation workload, considering the nominal capacity of DGs, which will improve the sharing of nonlinear load. Also, to decrease the asymmetry among phase impedances of microgrid, a novel structure is proposed to generate virtual impedance. At fundamental frequency, the proposed structure for the virtual impedance improves the control of the fundamental component of the power, and at harmonic frequencies, it acts to adaptively improve nonlinear load sharing among DG units. In the structures of the proposed harmonics compensator and the proposed virtual impedance, a self-tuning filter is used for separating the fundamental component from the harmonic components. This self-tuning filter decreases the number of the phase-locked loops. The simulation results in MATLAB/SIMULINK environment show the efficiency of the proposed approach in improving load sharing and decreasing voltage harmonics. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]