Two-Loop Distributed Optimal Secondary Voltage Control of an Islanded Microgrid with Multiple Distributed Generators

The hierarchical control strategy enables seamless integration of power electronics-based distributed generators (DGs) into microgrids (MGs). This work presents a distributed coordinated secondary voltage and frequency control strategy of islanded MGs, utilizing distributed model predictive control (DMPC) approach. The Proposed approach is entirely distributed in nature, with plug and play features and requires minimal communication network with information exchange between immediate neighbors. This technique facilitates efficient information exchange and obviates the need for centralized control. Droop control is the primary controller for all the DGs, which leads to the deviations in DG voltage and operating frequency from standard nominal values. Secondary control enables the restoration of DG voltage and frequency to their nominal values. Input–output feedback linearization (IOFL) is used to transform nonlinear dynamics of inverter-interfaced DGs into first-order linear dynamics between input and output. Compared to some published works, the single-integrator dynamics eliminates the need to compute the derivative of the output voltage. Furthermore, DMPC has been used for all DG units as secondary controller, to achieve voltage and frequency regulation. This approach takes advantage of distributed architecture with optimal control actions and enhances reliability and flexibility of MG operations. Moreover, the proposed approach ensures that voltage and frequency of overall system are restored to its reference value while maintaining accuracy in real power sharing. Effectiveness of proposed control scheme is validated with simulations conducted on islanded MG test system using MATLAB/Simulink toolbox.

Diverse consensus-based distributed secondary control mechanisms of isolated AC micro-grids (MG) have been proposed for achieving the autonomous steady-state power sharing and frequency synchronizations. However, their transient responses are not specified. To tackle out this issue, a fully distributed optimal control strategy is studied in this paper in which their transient responses are characterized in the objective function. A two cascaded loops control strategy for each distributed generator (DG) is proposed to achieve this control objective. In the local inner loop, auxiliary inputs are designed for adjusting local frequency and voltage. In the outer control loop, the distributed optimization problem is proposed under both agent dynamics and consensus constraints. By defining the augmented Lagrangian, a fully distributed optimization algorithm can be developed by integrating the method of alternating direction method of multipliers (ADMM) and Lyapunov function approach. To demonstrate the applicability of the proposed method, real-time simulation studies of IEEE 34-node MG are performed. Comparison studies between the proposed approach and the distributed PI-based consensus control also indicate the effectiveness of the proposed method.

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can't travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC).

Summary A distribution network with distributed generation (DG) is usually called an active distribution network. The voltage quality in an active distribution network tends to degrade because of the bidirectional power flow in the network and the random output characteristic of DG. Existing voltage control methods cannot effectively address this issue. In this article, an improved differential evolution (DE) algorithm–based optimal series compensation voltage control method for an active distribution network is presented. It is assumed that the series compensating voltage is produced flexibly by modern power electronics equipment. The quantitative relationship connecting the node voltage deviation, network power loss and compensating voltage is analysed. The optimal control model, including the power loss and load node voltage, is constructed. Then an improved DE algorithm is proposed to solve the optimal model, which combines the standard DE approach with the simulated annealing and adaptive mutation operator method. The optimal control model can be solved effectively by the improved algorithm. A simulation model is developed based on the IEEE 33 standard distribution network with DG. Simulation results confirm the validity of the optimal control model and the advantages of the improved DE algorithm.

In this article, an optimal steady-state voltage control framework is developed based on a novel linear voltage-power dependence deducted from Gaussian process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and, thus, suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control, which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions, which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of effective voltage control source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

The high penetration of distributed generation (DG) is changing the energy flows in the grid, as well as the energy losses and voltage profile of the system. To solve these problems, the impact of the connection of Renewable Energy Generators (REGs) on the grid is being studied. This work is intended to present the voltage variation of each node of the distribution grid before and after the insertion of a wind turbine or solar panel and the combination of both. The proposed method is self-adaptive regulation by combining settings in P/Q or P/V mode with a setpoint decision making block (setpoint adaptation block) based on fuzzy logic in order to maintain a stable and optimal voltage profile.The simulation results obtained using Matlab®/Simulink® clearly show the efficiency of the controllers used and their optimal control capacity.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

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.

Impact of Distributed Generation on the Electric Protection System

Significant distributed generation (DG) will be connected to distribution systems. As a result, the integration of this generation may change the assumptions under which the systems were designed and results in the operation situations that do not occur in a conventional system without directly connected distributed generation. Careful engineering planning for DG penetration is therefore needed. Understanding the issues related to the DG integration is required to maintain the reliability and proper operation performance of the system. Proper knowledge and engineering can result in effective DG integration in distribution systems. This paper presents the results of a project recently performed by the power company, Vattenfall, in Sweden. The project investigates potential advantages related to DG integration, and performed a series of reliability simulations to analyze how the DG integration affects the system reliability. The results of the project indicate that DG can be utilized to improve the system reliability and reduce not supplied energy. The project shows that various factors can influence the benefits of DG integration. It is of great value to take into account these influencing factors and incorporate DG into the planning process to obtain the most beneficial solution of DG penetration. The details of the reliability simulations are addressed and discussed in the paper.

This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

In today’s world, where the grid structure becomes smarter day by day, microgrid, one of the fundamental methods for the integration of renewable energy, are taking firm steps to be building blocks of the modern electrical grid. Along with its advantages, the microgrid brings extra challenges which attracts the attention of many researchers in these days. This paper focuses on addressing two of the main challenges: voltage stability and reactive power sharing in islanded mode. Contents of this paper include: first, elaboration of general hierarchical control structures of inverter-based distributed generators (DGs), second, review of primary droop control methods and analysis of their drawbacks, third, review and comparison of current secondary reactive power control methods to find the gap of more practical secondary control method. In the paper, a centralized secondary controller based on intermittence level, effective-impedance to load and severity of voltage deviation is proposed to overcome the disadvantage of current secondary reactive power control methods. The proposed method takes practical aspects into consideration and prioritizes DGs in reactive power balancing and voltage stability. Performance of proposed method is illustrated by a simulation study.

Chapter 2 - Distributed voltage restoration and power allocation control in islanded DC microgrids