Effective Communication-Based Reactive Power Sharing Scheme for Meshed Microgrid in an Islanded Mode

Microgrids (MGs) are the most sought out and feasible solution for the present energy crisis. MG is a group of Distributed Generators (DGs) interacting with each other to provide energy to a defined local area. The inclusion of DGs into the conventional power system at various voltage levels has altered the topology of the power system and their control techniques. Hence, the MGs can no longer be considered as a traditional radial network but rather a meshed network. The control and operation of such practical MGs become a challenge, especially when operated in the islanded mode. This research paper considers a realistic meshed MG operating in an islanded mode for study. In an islanded MG, the issues of real and reactive power sharing among DGs are addressed so that the power contribution of each DG is proportional to its rating, thus preventing overload and ensuring reliable operation. A communication-based virtual impedance estimation is proposed in addition to the droop controller for proportionate real and reactive power sharing among DGs in a meshed MG. With the increased complexity of meshed MG, the proposed communication-based control scheme offers an efficient reactive power sharing between DGs without the feeder and network impedance requirements. A MATLAB simulation study proves the effectiveness of the proposed control strategy for a meshed MG with equal DG ratings and unequal DG ratings under changing load conditions.

Pinning Decision in Interconnected Systems with Communication Disruptions under Multi-Agent Distributed Control Topology

In this study, we propose a decision-making strategy for pinning-based distributed multi-agent (PDMA) automatic generation control (AGC) in islanded microgrids against stochastic communication disruptions. The target microgrid is construed as a cyber-physical system, wherein the physical microgrid is modeled as an inverter-interfaced autonomous grid with detailed system dynamic formulation, and the communication network topology is regarded as a cyber-system independent of its physical connection. The primal goal of the proposed method is to decide the minimum number of generators to be pinned and their identities amongst all distributed generators (DGs). The pinning-decisions are made based on complex network theories using the genetic algorithm (GA), for the purpose of synchronizing and regulating the frequencies and voltages of all generator bus-bars in a PDMA control structure, i.e., without resorting to a central AGC agent. Thereafter, the mapping of cyber-system topology and the pinning decision is constructed using deep-learning (DL) technique, so that the pinning-decision can be made nearly instantly upon detecting a new cyber-system topology after stochastic communication disruptions. The proposed decision-making approach is verified using a 10-generator, 38-bus microgrid through time-domain simulation for transient stability analysis. Simulations show that the proposed pinning decision making method can achieve robust frequency control with minimum number of active communication channels.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

Optimal allocation of Multiple Distributed Generators and Shunt Capacitors in a Distribution System using Political Optimization Algorithm

To meet the increasing real & reactive power demand of a distribution system (DS), it is essential to allocate the Distributed Generators (DGs) and Shunt capacitors (SCs) optimally. In this article, multiple DGs and SCs are allocated simultaneously in the DS aiming minimal power loss (PL), improved voltage stability index (VSI) and voltage profile of the system. A combined approach considering loss sensitivity factor (LSF) and political optimization algorithm (POA) is proposed to solve the allocation and sizing of DGs and SCs. The analysis is performed on an IEEE 33 bus system considering 9 different scenarios and results are compared with other Meta heuristic techniques. The analysis is extended for a 24 hour case study to prove the efficacy of the proposed combined approach. From all the performed simulations it can be observed that the combined approach helps in minimizing power loss and improving voltage profile and VSI for dynamic load variations effectively.

A Decentralized Passive Islanding Detection Method Based on the Variations of Estimated Droop Characteristics

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes a straightforward, distributed island detection technique only relying on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A “stiffness change” effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.

Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size.