Search

This paper presents a two-layer framework for improving the resilience of a 118-bus active distribution network consisting of four microgrids, which includes hybrid storage systems, electric buses (EBs), and the direct load control (DLC) program. In the proposed model, the uncertainties of RESs generation, demand, and EBs' mobility are considered, and the robust optimization approach is used to tackle them. In the first layer, the planning of each microgrid is done separately and the energy purchase/sale request is sent to the control center. Then in the second layer, the control center performs the planning of the main network according to the requested program of the microgrids. Note that in this layer, the control center is able to rearrange the distribution feeder and send EBs to vital points of the network. Finally, the validity of the proposed model is evaluated through the implementation on seven case studies and the results show that the presence of hydrogen and electrical storage devices reduces forced load shedding (FLS) by 45.03% and 12.19%, respectively, during emergency situations. In addition, the results indicate that robust planning and the use of EBs for network recovery increase the resilience index by 3.35% and 3.98%, respectively. • Developing a decentralized robust framework for microgrid resilience enhancement. • Providing a two-layer model to guarantee the privacy of microgrids. • Increasing system resilience by electrical and hydrogen storage systems. • Improving system resilience by deploying EBs to vital nodes of the network. • Increasing resilience index through DFR and DLC. [ABSTRACT FROM AUTHOR]

With the emergence of smart grids, accurate very short-term load forecasting (VSTLF) has become a crucial tool for competitive energy markets. The number of behind-the-meter photovoltaic solar panels, which usually are not monitored are increasing. This could reduce the load visibility and also affects the VSTLF accuracy. While most of the research works focus on demand forecasting of large areas, the performance of VSTLF methods for the future scenario of high solar power penetration in the microgrid environment is unclear. This paper investigates the impact of high solar power integration on the forecasting accuracy of machine learning VSTLF models for microgrids. The performance of Neural Network, Support Vector Regression are evaluated for high penetration scenario and no solar penetration. The accuracy of these models is examined for three different forecasting horizons through various comparative experiments for two real-world microgrid datasets. According to simulation results, using the most relevant variables is highly recommended. Furthermore, the results demonstrate that machine-learning methods can tackle the nonlinear characteristics of net load forecasting as well as total load forecasting. These simulations show that high penetration of solar power generation could not significantly affect the accuracy of machine learning VSTLF if most relevant variables are selected and applied as model inputs. [ABSTRACT FROM AUTHOR]

Power management in microgrids is a major challenge due to its low total inertia and capacity. The lower the microgrid generation capacity is, the higher the share of each generation unit in total power will be, and the higher the frequency deviation in less time will be when an outage occurs. So, preventive actions can be more reasonable and affordable than corrective actions for microgrid power and frequency control. In this regard, a new primary frequency response-constrained unit commitment model is presented here to prevent excessive frequency deviations by more commitment of higher inertia power plants and more contribution of renewable energy resources or energy storage systems' fast inertia response. To have a mixed-integer linear programming model, the primary frequency response constraints are linearized. The model is solved by the combination of two commercial solvers named MOSEK and YALMIP in the MATLAB 2018 environment. The proposed model is examined on a real isolated microgrid (an island). The results show that by activating the primary frequency support of distributed energy resources, the power can be managed with lower costs because there will be less need to start up fast (and expensive) gas turbine generation units. In addition, although comparing the model with others shows the more expensive management procedure, better frequency stability is obtained in contingencies. [ABSTRACT FROM AUTHOR]

• A multiple chance-constrained model for microgrid scheduling is proposed. • The model considers unwanted islanding and outage of DGs and batteries. • The model considers uncertainty of renewable energies and demand. • A novel probabilistic formulation is proposed to determine the required reserve. • A novel index is proposed to evaluate the sufficiency of the reserve. Maintaining power balance under both normal and emergency conditions is one of the most important challenges in optimal scheduling of microgrids (MGs). In this paper, a multiple chance-constrained scheduling model is developed for optimal scheduling of microgrid considering the most important factors that affect the power balance of the MG such as the uncertainty of demand and renewable resources, sudden outage of distributed generators (DGs), and unwanted islanding. Due to the growing penetration and size of batteries, the outage of batteries is also considered. Simultaneous consideration of all above-mentioned factors complicates the scheduling problem. Hence, to reduce the complexity of the model, the chance constraints are reformulated innovatively by adding controllable variables to the chance constraints. In addition, to determine the required reserve in different conditions of MG operation (i.e. normal operation, outage of each DG, outage of each battery, and unwanted islanding), individual probability distribution functions (PDFs) are formulated. Based on these PDFs, a probabilistic index entitled probability of reserve sufficiency (PRS) is introduced. The model maintains a certain amount of PRS in different conditions of MG operation. Finally, the model is examined on a test microgrid for different case studies and the results are analyzed to evaluate the model. [ABSTRACT FROM AUTHOR]

This paper presents a new design of the hybrid AC/DC microgrid for optimization of the performance of the smart grid. This design by using the capacity of the DC microgrid and introducing a new unified power quality conditioner, named UPQC-DC, and providing appropriate control schemes for two back to back interface converter between the AC and DC microgrids, presents important characteristics such as power quality improvement, power flow control, reactive power compensation, and elimination of power swings. The proposed method guarantees the realization of all aforementioned goals in both grid connected and isolated modes for the first time. Several computer simulations are performed using Matlab/Simulink software for verifying the proposed scheme. [ABSTRACT FROM AUTHOR]

Considering the advantages of DC microgrids, the extension of the conventional AC distribution grid can be implemented using a DC microgrid. This justifies the realization of a hybrid AC/DC microgrid. In the present study, a new global solution is presented to improve the power quality and to fully compensate the reactive power of an AC microgrid using DC bus capacity while introducing a new design for a hybrid AC/DC microgrid. In the new design, back- to-back connections of two series and parallel converters, as well as the presentation of new controllers and simultaneous utilization of an earthing switch, are proposed. The proposed method guarantees the quality of the delivered voltage to consumers and the drawn current from the network according to the IEEE-519 and IEEE-1159 standards under different power quality problems (e.g., interruptions, sags, harmonics, and any variation in voltage/current signals from pure sinusoidal). Through the proposed design of the new hybrid AC/DC microgrid, as a new feature, the operation of the network in islanded mode can be achieved in accordance with power quality standards even in the worst load quality conditions. It should be noted that in common hybrid microgrids in islanded mode, the delivered voltage quality is proportional to the quality of the consumer's load current. Another possibility of the proposed design is the instantaneous VAR compensation of nonlinear and induction loads of consumers to keep the power factor of the distribution transformer close to unit value. Simulation results indicate that there are acceptable levels of compensation for different types of power quality problems. Total harmonic distortions and total demand distortions are below 3% in both the grid-connected and isolated modes of the hybrid AC/DC microgrid. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If \textPoI{\rm{ANN}} is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it (\textPoI{\rm{FUZZY}}) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to \textPoI{\rm{FUZZY}}. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Implementation of a flexible-reliable operation strategy by MG operator (MGO) on a green microgrid participated in the DA energy market. • Simultaneous modeling of operation, reliability, flexibility, economic, and environmental indices in MG power management scheme. • Integration of MG operation and data forecasting problems to achieve reliable network scheduling results in the DA energy market. • Using the hybrid ABC+SCA algorithm to reach the optimal solution with unique response conditions. This paper presents the flexible-reliable operation (FRO) of microgrids (MGs) constrained to supplying clean energy. The scheme minimizes the total expected costs of the MG and sources operation, MG reliability, and MG flexibility and is subject to power flow equations, operation, environmental, and reliability constraints, and formulation of sources and energy storage-based active loads. The problem is mixed-integer nonlinear programming (MINLP). To achieve an optimal reliable solution with unique response conditions, a combination of an artificial bee colony (ABC) and sine-cosine algorithm (SCA) is utilized in this study. In the given scheme, stochastic programming is employed to model the uncertainties of load, energy price, renewable power, and availability of MG equipment. Furthermore, mean and standard deviation values of the above-mentioned first three uncertainty parameters are obtained using the data forecasting method based on the hybrid ABC+SCA method, adaptive network-based fuzzy inference system (ANFIS), and wavelet transform (WT). Finally, by implementing the proposed strategy on a standard MG, the capabilities of the proposed scheme in improving economic, environmental, operation, reliability, and flexibility conditions of the network are validated. [ABSTRACT FROM AUTHOR]

In recent years, wind power has played a significant role in energy generation of micro-grids (MGs). However, randomness nature of wind speed leading to uncertainty in wind power forecast, imposes some problems such as overestimating wind power on optimized scheduling of MG. In this paper, we propose an adaptive probabilistic concept of confidence interval (APCCI) to address these problems. The main purpose of the proposed APCCI is to modify the risk we endure to schedule wind power with other distributed energy resources (DERs) in order to degrade the unnecessary rigors and upgrade the other ones. The forecasting method which is used in this paper is artificial neural network (ANN). In order to increase the accuracy of forecasting, wavelet decomposition (WD) is applied to the wind power time series then the results are sent to ANN. After that, dependable levels for the predicted wind power based on APCCI are obtained. An energy storage system (ESS) is utilized not only to decrease the impact of forecasting errors on the MG but also to increase the flexibility of the planning. A comprehensive formulation with operational constraints is employed to model the optimization problem. An economic dispatch based non-dominated sorting genetic algorithm II (EDNSGA-II) is proposed and applied to solve the multi-objective optimization problem. The optimization algorithm produces some alternatives which consist of different combination of objectives (cost and emission). Techniques for order preference by similarity to an ideal solution (TOPSIS) method is utilized to make a compromised decision between the alternatives. Eventually, the proposed algorithm is applied to a typical MG which consists of micro turbine (MT), fuel cell (FC), photo voltaic (PV), wind turbine (WT) and energy storage system (ESS). Evaluation of the results show that the proposed APCCI works well and can adapt the level of confidence interval in various situations. Moreover, the results confirm the superiority of WNN over ANN. The results also show that the proposed EDNSGA-II is more efficient in comparison with the well-known NSGA-II. [ABSTRACT FROM AUTHOR]

• A phasor-based fault location algorithm (FLA) is proposed in a three-terminal transmission line (TL) connected to an industrial microgrid. • The FLA does not require the parameters of the TL. • The FLA does not require the data of faulty phase detection. • The FLA does not require to detect the faulty section in three-terminal TLs. • The FLA is robust to the variations of different parameters of the industrial microgrid and uncertainties of renewables-based DGs. The connection of large industrial microgrids to three-terminal transmission lines greatly complicates the protection scheme of the lines due to the uncertainty in generating renewable energy sources, the probability of unintentional islanding, the abrupt switching of sensitive and critical loads, the influence of supply and demand management programs, and bidirectional active and reactive power flow. This paper presents a fault location algorithm for three-terminal transmission lines connected to an industrial microgrid that does not need the detection of faulty section, fault classification, and transmission line parameters. The proposed method uses voltage and current phasors of transmission lines measured by phasor measurement units (PMUs). One of the salient features of the proposed algorithm is that it does not need to determine the faulty section before the fault location operation. Furthermore, since power transmission lines are always exposed to changing climatic conditions and aging, their electrical characteristics vary over time in the long run compared to their rated value. Hence, this leads to the malfunction of protection schemes. This is solved in this study as the proposed fault location algorithm is independent of line impedances. On the other hand, the dependence of conventional fault location algorithms on fault classification data reduces the reliability of fault location schemes, making these algorithms dependent on fault classification data. However, the present study overcomes this problem as the algorithm is independent of the data of the faulty phase(s) detection. The proposed scheme is fully implemented in the MATLAB software environment, and the performance of the algorithm is analyzed for various fault types under different conditions. The validity of the algorithm is shown by various tests and sensitivity analyses. [ABSTRACT FROM AUTHOR]