Multiple Distributed Generation Units Research Articles

An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.

A review of recent control techniques of drooped inverter‐based AC microgrids

AbstractAs the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system.

A Kalman Filter-Based Distributed Cyber-Attack Mitigation Strategy for Distributed Generator Units in Meshed DC Microgrids

To ensure fast dynamics and the stability of multiple distributed generator units (DGUs) in DC microgrids, communication links among the controllers of DGUs are generally adopted. However, those communication channels are vulnerable to cyber-attacks. To alleviate this hassle, a Kalman Filter (KF)-based distributed cyber-attack mitigation strategy, which is highly involved in both primary and secondary control, is proposed in this paper. The KF, as a robust state estimator, is utilized to accurately estimate the authentic terminal voltages and currents of the DGUs. Based on the discrepancies between the estimated and measured parameters of the systems under cyber-attacks, the proposed control can adaptively compensate the attack signals via an adaptive proportional integral (API) controller and a fractional API (FAPI) controller in cyber-attack-mitigation layers. The main advantage of using the proposed control scheme compared to conventional schemes is the fast dynamic response. The simulation results verify this merit by comparing the adopted KF and comparing it with conventional artificial neural networks (ANN), while the experimental results validate that effectiveness of the proposed control and showcase the superiority of the FAPI control in terms of its perfect compensation for different types of cyber-attacks.

Correction: Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Correction: Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

SummaryThe large integration of distributed generation (DG) and electric vehicles (EVs) facilitates daily life and reduces carbon emissions. However, it brings problems such as increased uncertainty and power electronic permeability to the distribution network. In this context, it is important to solve the line loss optimization problem as closely as possible to the real situation. Therefore, this paper solves the line loss optimization problem based on a probabilistic model of three‐phase unbalanced modern power electrical distribution network. Firstly, the paper proposes a frequency domain model of the distribution network and the optimization problem model. Then, to deal with the uncertainty of the system, the point estimation method (PEM) and Monte Carlo Simulation (MCS) are used. Finally, this paper proposes two optimization schemes considering the power quality when single and multiple DGs are integrated. The simulation results demonstrate that the PEM using the Gram–Charlier series expansion of the fourth‐order statistical moments to estimate the probability density function (PDF) is significantly less time consuming than the traditional MCS method while maintaining accuracy. The comparative analysis reveals that under the optimization scheme with single DG integration, the expected value of system line loss and expected line loss rate are reduced from 283.95 kW to 218.96 kW (22.89% reduction) and from 13.44% to 10.68%, respectively. Under the dual DG integrated optimization scheme, the expected value of line losses and expected line loss rate are reduced from 283.95 kW to 208.88 kW (26.4% reduction) and from 13.44% to 10.25%, respectively. However, after taking the uncertainty into account, the operational reliability of the system has been enhanced under different scenarios. In addition, it is found that the optimization approach incorporating multiple DG units can effectively mitigate the system line loss under diverse operational scenarios while enhancing the system's DG integration capability.

A new optimal allocation of DGs in distribution networks by using coot bird optimization method

Energy is one of the most important topics in the world today and is considered as one of the most effective factors for the development of countries. Due to the limitation of non-renewable energy sources and undesirable effects of consuming these resources on the environment, the strategy of countries has changed towards the use of renewable energy. Renewable energy sources do not decrease over time and operate independently of price fluctuations and are more available, thus being able to play a greater role in modern power systems. Therefore, the optimal location and use of these resources will have an impact on modifying the parameters of the power grid. In this paper an analytical approach for optimal placement and sizing of distributed generation (DG) in power distribution networks to minimize the power loss, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. In the first step, determines the DG capacity causing maximum benefit at different buses, and then selects the best location for DG placement which corresponds to highest benefit in the buses. This method is applicable for sizing and siting of single as well as multiple DG units. The coot bird optimization method (CBOM) is proposed for solving optimal placement, size, and power factor (PF) of DG in distribution network. The suggested method is tested on the IEEE 33-bus, 69-bus, Distribution Networks. The proposed CBOM method has good performance to find optimal placement, size, and PF of DG and it can be applied for various distribution system.

Intelligent deep reinforcement learning based SMCF boundaries—An effective assessment of integrated network operating state

In this paper, applied energy system with multiple distributed generation (DG) units like solar PV, wind firm and biomass energy has been assessed and its operating state is estimated. Deep reinforcement learning (dRFL) based sensitive modulation control factor (SMCF) is proposed for assessment of entire applied energy system operating state during the utility grid connected or grid isolated mode/islanding mode. Grid isolated/island state identification is essentially required in integrated power grid when operating with DG to avoid the hazardous to human begins, as well as power electronic components. Time interval of distorted signal due to disturbance in integrated power grid has been taken for SMCF control. Variations in voltage (VRV), variations in frequency (VRF) beyond their limits i.e. lower limit (LL) and higher limit (HL) and these are considered to obtain SMCF in-phase component and quadrature phase component of circuit. Instant of SM control, tsm for a UV or OV are calculated and it is updated by the change in VRV. Obviously VRF also updates the instant of SM control and hence SMCF control factor is obtained. In-phase control variable of SMCF circuit i.e. αs_d with boundary limits i.e. αs_dLL and αs_dHL are used for the every 10 ms interval period/range to identify data of event i.e. either in island event (ISE) or non island event (NOIE). Now the dRFL is used for αsd training with a deterministic policy gradient (DMPG) evaluation. For a HL targets, two segment limits i.e. τ1, τ2 are taken for error minimization in dRFL training. According to first target; error Eα1 is validated the test condition of τ1<τ2 in a proposed dRFL training. Also error Eα2 is validated the test condition of τ1≥τ2 for second target in a proposed dRFL training. The completion of dRFL training gives the αHL and hence minimization of EαHL as per objective will be obtained. Error minimization of reinforcement learning (RFL) has been obtained by DMPG for every target and action of energy system operating state. Effective dRFL based SMCF control algorithm introduced to validate every ISE, NOIE including lower mismatch scenarios and avoid the false tripping signal.

Optimal location and sizing of distributed generation for distribution systems: An improved analytical technique

ABSTRACT Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network.

Optimal Allocation and Assessment of Distributed Generation Units with Multi-Objective Planning in Unbalanced Distribution Networks

—Real-world distribution networks are generally unbalanced due to unbalanced loads and asymmetric line parameters. Proper allocation of distributed generation (DG) units in unbalanced radial distribution networks to extract their maximum potential benefits is still quite difficult. Converter-coupled DG units have individual phase real and reactive power regulating ability and provide voltage unbalance compensation. This article proposes a new technique for the suitable allocation and capacity of converter-based multiple DG units considering the unbalanced operation of distribution networks. The objective function contains power loss minimization, multiphase voltage stability augmentation, and reduction of voltage unbalances among different phases in the system. A complex multi-objective optimization problem has been synthesized with the help of a Fast and Flexible Radial Power Flow (FFRPF) and solved by employing a Weighted aggregation (WA) based particle swarm optimization (PSO) approach. The performance of the proposed technique is validated on 19-bus, 25-bus, and 34-bus unbalanced radial distribution networks that consider various power factor modes of the converter-coupled DG units. The analysis of the simulation results indicates a significant enhancement in the power delivery efficiency in all the unbalanced test systems

Network reconfiguration in the presence of optimally integrated multiple distributed generation units in a radial distribution network

This article presents a hybrid of two metaheuristic techniques, namely the plant growth simulation (PGS) algorithm and particle swarm optimization (PSO), for power loss minimization in a radial distribution network (RDN). The PSO algorithm determines the optimal allocation of multiple distributed generators (DGs), followed by the PGS algorithm for optimal network reconfiguration (NR). Type I and IV DGs were considered. The load flow solutions were obtained using a branch matrix formulation. IEEE 33 and 69 buses were used for performance analysis and the results were compared with previously published results to validate the method in providing solutions even in complex system configurations. The superior results demonstrate that the penetration of multiple DGs along with the NR permits the network to operate successfully under different loading conditions, without violating the constraints.

A Robust Cooperative Distributed Secondary Control Strategy for DC Microgrids With Fewer Communication Requirements

This article proposes a robust cooperative distributed secondary control strategy for dc microgrids, with the main focus on reducing communication burdens. To this end, we adopt a sparsity-promoting consensus-based distributed secondary control framework for converter-interfaced dc microgrids consisting of multiple distributed generation (DG) units. The proposed control strategy relies on less (sparse) information exchange over a communication network and does not require exchanging voltage signals or their estimated values amongst neighboring DG units. Rigorous Lyapunov-based stability certificates for dc microgrids are derived. The stability conditions can be verified without the knowledge of distribution lines connecting different DG units. The effectiveness of the proposed secondary control approach is verified by experimental results and a comparative simulation case study on a multiple-DG dc microgrid.

Fault current limitation control of multiple distributed renewable generations under unbalanced conditions

For multiple distributed generation units (DG unit) parallel system, excessive fault current has adverse effects on the safe and stable operation of the utility grid. As a result, a fault current limitation control of multiple DG units is developed in this paper to limit the fault current of related fault branch and ride through the associated fault conditions. The injection fault current amplitude and phase angle of each DG unit for grid-side converter are controlled through the point of common coupling (PCC) voltage support and fault current limitation to limit the total current amplitude at the corresponding fault location. The validity of the proposed control strategy has been verified by simulation and hardware-in-loop (HIL) experiment results.

Distributed robust secondary control of islanded microgrid with stochastic time-varying delays and external disturbances

In the islanded microgrid (MG) with multiple distributed generation units (DGs), the stochastic time-varying communication delays and external disturbances have a great impact on its operation. In this paper, we propose a distributed secondary robust control to realize the robust stability of voltage and frequency. First, a small-signal model is built for a single DG unit, and then the global closed-loop state-space model for MG is derived. Afterward, a new Lyapunov–Krasovskii functional is introduced to analyze the robust stability of the MG. According to the proposed stability theorem, a controller design method optimizing the robust performance of the MG system is presented. Lastly, we verify the correctness of the robust stability theorem and the validity of the controller design method by simulation.

Optimal allocation of multiple distributed generation units in power distribution networks for voltage profile improvement and power losses minimization

Optimal allocation of multiple distributed generation units in power distribution networks for voltage profile improvement and power losses minimization

An efficient DLN2-CRSO approach based dynamic stability enhancement in micro-grid system

An efficient regulation of the frequency and voltage of operation of the microgrid in island mode is essential; otherwise, the voltage and frequency deviations in the microgrid will increase the energy imbalance by going beyond the tolerance range. In this manuscript, an efficient hybrid approach to improve the dynamic stability of the micro grid (MG) is proposed. The proposed hybrid approach is the combined performance of Deep learning neural network (DLNN) and chaotic rat swarm optimizer (CRSO), hence it is named as DLN2-CRSO. To improve the searching ability and speed of the convergence, RSO is improved by the chaotic version of RSO. The proposed DLN2-CRSO approach is utilized the low-signal stability analysis under the entire operating points. The major objective of the proposed approach is the stability enhancement. To improve the dynamic stability through the integration of multiple distributed generation units in the micro grid, deep planning neural network is used. The control parameters of the DPNNs are identified by the chaotic rat swarm optimizer (CRSO) approach. The proposed approach reduces the reactive power mismatches among the converters and guarantees the stable operation of MG. The performance of proposed approach is authenticated with simulation outcomes, which is performed with MATLAB/Simulink platform. The performance of the proposed technique is evaluated with statistical measures like mean, median and standard deviation with existing methods. Also the efficiency of the proposed technique reaches 95.6346%.

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks.

The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

Optimal Planning of Electric Vehicle Charging Station along with Multiple Distributed Generator Units

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

AbstractIn this paper, the issue of state‐of‐charge balance and load voltage regulation of DC microgrids used in solar‐powered vehicles is investigated. The system consists of multiple distributed generation units, each of which is equipped with photovoltaic cells and energy storage systems. Based on the information shared among the neighbors, a single‐layered distributed control strategy is proposed to solve two aspects of the problem simultaneously. Rigorous analysis of the closed‐loop system ensures stability and the effectiveness of the control strategy. The power constraints of each distributed generation unit are taken into consideration so that safety and applicability are guaranteed. To avoid the cycling of the batteries, the state‐of‐charge balance is achieved by regulating the output power feeding a common load, promoting efficiency and battery life. The proposed controller operates without any knowledge about load, line impedance, and global information of communication network, and consequently possesses the capability of plug‐and‐play. Finally, the effectiveness and applicability are illustrated by an example from the Sky‐Sailor project.

Decentralized Control Based on Hybrid Water Cycle and Moth-Flame Optimization of Fractional-Order Fuzzy PID in a Multiple DGs Faulty Autonomous Microgrid

This study presents the employment of a novel hybrid Metaheuristics optimization technique to improve and stabilize voltage secondary layer of control being applied in Fractional-Order Fuzzy Proportional-Integral-Derivative controllers in the case of a faulted autonomous 3-phase AC Microgrid. For the frequency control part, a Proportional-Integral-Derivative controller is used. The autonomous Microgrid under study comprises multiple distributed generation units connected with impedance lines along with local loads. The Fractional-Order Fuzzy Proportional-Integral-Derivative controller is considered to have superior performance than conventional ones, despite the increase in the number of tunable parameters. Therefore, a hybrid Water Cycle and Moth-Flame optimization algorithm is considered to reach the optimum solution for the applied decentralized controllers. The performance of the controller is compared through varying optimization techniques and changing the controller type. Under the discussed control approach, the AC Islanded Microgrid operation steadiness is augmented.

A novel transient search optimization for optimal allocation of multiple distributed generator in the radial electrical distribution network

Abstract Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.