Microgrid Power Flow Research Articles

Stochastic security-constrained optimal power flow for a microgrid considering tie-line switching

With the rapid development of microgrid, its tie-line switching from grid-connected to islanded mode is a topic worth discussing for considering both main grid resilience and microgrid security. In this paper, a stochastic security-constrained optimal power flow (OPF) method is proposed to deal with these conditions under high uncertainties. Firstly, a linear load flow model and a backward forward sweep algorithm are applied to present microgrid power flow with reduced computing burdens. Secondly, with consideration of tie-line switching from grid-connected to islanded operation mode, a security-constrained OPF problem for a microgrid is proposed to minimize operating cost and by optimizing microturbine setpoints and load shedding coefficient. To promise stable islanded operation after disconnection from the main grid, a Benders decomposition method is developed to decouple the OPF problem into a grid-connected master problem and an islanded sub-problem and then solve them iteratively with Benders cuts to guarantee microgrid security after tie-line switching. Last, a stochastic optimization method with probabilistic modelling is adopted to address the uncertainty issue caused by renewable energy sources and loads. The proposed stochastic security-constrained OPF method has been verified with high computing efficiency and robust security via comprehensive numerical simulations.

Summary of Research on Power Flow Optimization Algorithms for Microgrid

With the development of microgrid, more and more scholars have participated in the optimization research of microgrid power flow. This article mainly summarizes the algorithms for power flow optimization of microgrid, including the Newton-Raphson method, PQ decomposition method, nonlinear programming power flow algorithm, interior point method, forward and backward substitution method, and artificial intelligence algorithms such as particle swarm algorithm, genetic algorithm, simulated annealing and various hybrid algorithms.

Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market

Owing to the increasing utilization of renewable resources such as wind turbines (WT), photovoltaic (PV) into a microgrid (MG), optimal planning has become important to satisfy the energy demand due to inherent uncertainties. This paper proposes a new model of planning based on renewable energy uncertainty and demand response and electric vehicles (EVs) in order to minimize the electricity market’s total cost. Considering uncertainty challenges, energy storage system (ESS) and demand response programs based on time-of-use (TOU) are employed as a solution for managing the power flow in MG to warranty the essential load supporting and voltage stability and satisfy electrical and heat demands. Moreover, in this paper, the influence of price-based demand response (DR) for industrial, commercial, and residential loads is taken into account. Finally, the proposed problem is modeled as an optimization problem while the related decision variables are adjusted by a modified version of virus colony search (VCS) algorithm based on chaos theory in order to increase the exploitation and exploration terms. The proposed approach is tested on an MG system with several scenarios through analyzing the effect of DR programs based on the total cost reduction. As shown in the simulation results, DR highly reduced total cost (20–26% related to the case without DR), in which voltage dip (maximum 1.4%) and power deviation (maximum 1.2%) were enhanced.

Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation

Increased investiture towards Distribution Generation (DG) in recent past had prompted for transmutation of existing networks to form microgrids that can operate autonomously or in conjunction with main-grid. In particular, the augmenting zeal for renewable DGs is thriving at recent times; but non-dispatchability and uncertain nature pose specific challenges, which is the primary concern in this work. This paper employs Point Estimate Method (PEM) for handling uncertainties to solve the probabilistic-optimal power flow problem (POPF) with multiple objectives formulated in an islanded microgrid with droop coordinated DGs including uncertainties involved in load, wind and solar PV with suitable probability distributions. The devised POPF problem is proposed to solve for optimal droop parameters by multi-objective ant-lion optimization algorithm; tested and verified on modified 33-bus system. Furthermore, in actuality, spatial correlations among renewables play vital role in devising operational schedule for energy management strategies. This paper deals with PEM compounded with Nataf Transformation for POPF to handle spatial correlations in renewable generations. The dominance of wind correlation over solar PV correlations in POPF problem is highlighted. The robustness of proposed approach is verified with benchmark Monte Carlo Simulation (MCS) to affirm about its accuracy for suitable replacement of proposed approach to MCS.

Power Management Strategy Based on Virtual Inertia for DC Microgrids

This article presents a power management strategy (PMS) to control the power flow in a dc microgrid operating in the grid-connected mode. The microgrid model is composed of the ac utility grid interfaced with a voltage source inverter operating as a grid-forming converter (VSC), an energy storage system (ESS) formed by a battery bank and a bidirectional dc-dc converter operating as a grid-supporting unit, a distributed generation acting as a grid-feeding unit, and the customer loads with strict voltage regulation. The power management technique applies a virtual inertia concept together with a state of charge-based management function to regulate the charging and discharging process of the battery bank according to the dc microgrid power flow. Thus, high-rate peaks of power are avoided, which improves the ESS life cycle. With the proposed PMS, an autonomous power flow is achieved inside the dc microgrid, while the ac utility grid focuses exclusively upon forming the dc bus and processing the surplus or shortage of power. In addition, the proposed strategy simplifies the communication link between the grid inverter and the ESS, since the VSC power is the sole information exchanged. The experimental results show that the proposed PMS is reliable, leading to high ESS performance and power flow control within the dc microgrid, without degrading the dc bus voltage.

Decentralized Cooperative Optimal Power Flow of Multiple Interconnected Microgrids via Negotiation

The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number of interconnected microgrids. Thus, the optimal power flow (OPF) problem should be studied by properly taking account of the coupling between the microgrids. In this paper, the models of standalone microgrid and coupled microgrids are formulated first. Then, a decentralized approach is shown in detail to cooperatively solve the coupled OPF problem. Specifically, each microgrid solves the local OPF problem, leading to the optimal solution of the coupled OPF problem via negotiation between microgrids. The privacy of each microgrid is also preserved since no sensitive information is shared between microgrids. Finally, simulation results show that the optimality gaps between the proposed approach and an existing solver are small for both the AC OPF and the DC OPF. Furthermore, the total cost of the microgrids is reduced by the cooperative OPF.

Fault Ride-Through Capability with Mutual Inductance in Low-Voltage Single-Phase Microgrid

Improving the microgrid fault ride-through capability and enhancing the transient performance of microgrid operation due to voltage sags is of great importance. This paper investigates the ride-through capability and feasibility of integrating a mutual inductance between the main grid and the microgrid under short circuit condition. The fault ride-through capability of the microgrid DGs is examined by placing the mutual inductance at PCC to suppress the fault current and to maintain the microgrid grid connected. This inductance requires no control to operate in which the sudden current increase is the key point of its operation. The microgrid DGs can operate successfully in grid-connected mode without reactive power support during the voltage sag duration. The effectiveness of the inserted mutual inductance has been validated considering two case scenarios of the microgrid power flow condition. The proposed methodology shows promising results, in which it confirms the applicability of the mutual inductance in riding through the fault current and maintaining the microgrid DG operation in the grid-connected mode. SimPowerSystems and PLECS toolboxes of Matlab/Simulink software are utilized to implement the proposed work.

Stability and reliability of low voltage hybrid AC-DC microgrids power flow model in islanding operation

The problem of decreasing and increasing power flow in hybrid AC-DC microgrids in island operations is the concern of this research. This condition arises if one sub-microgrid cannot supply load power requirements, either due to a decrease in power at the source or an increase in load on the sub-microgrid network. As a result, in this research used the bidirectional converter as an interlinking converter, the converter can change the power flow from the ac sub microgrid to the dc sub microgrid and vice versa. With this bidirectional converter, load power requirements can be met even though there is a power change in one of the sub microgrid. The simulation in this research support by Simulink/Matlab software. The model is built in a low voltage system, and the power flow analysis is in steady-state condition with two different cases. The simulation results show the stability and reliability of the power flow in both sub microgrid with stable frequency and voltage when power and load change occur.

Predictive Hierarchical Control of Power Flow in Large-Scale PV Microgrids With Energy Storage

Large-scale PV microgrids constitute a growing portion of distributed renewable generation in many grids worldwide and are expected to continue their growth in the foreseeable future. Managing the operation of such microgrids and in particular their interaction with the main electricity grid is a challenging task as it involves controlling large intermittent PV resources. The addition of auxiliary DERs and energy storage systems to such microgrids is a promising solution that provides some flexibility to the operation of these fluctuating PV resources. In this work, a hierarchical predictive controller is designed to perform daily scheduling and real-time control of a PV microgrid with energy storage and auxiliary diesel generation. The controller aims to increase utilization of available PV resources, reduce the daily variability of power flow to the main grid, while at the same time controlling power fluctuations at the point of connection. The controller is designed to utilize updating load and PV predictions on a receding time horizon and uses the flexibility of energy storage and diesel resources to compensate for fluctuations and prediction errors. A study of multiple operational scenarios demonstrates successful performance of the proposed controller.

Predictive function control in tertiary level for power flow management of DC microgrid clusters

Tertiary control is the highest level in hierarchical control of microgrids to perform power exchange between the microgrid and external networks. In this Letter, a tertiary control strategy based on predictive function control (PFC) is proposed to manage power flow in DC microgrid (DCMG) clusters. Unlike linear PI control suitable for single-input and single-output systems, the PFC strategy is a nonlinear control method and capable of multiple input and multiple output control tasks with multiple objectives optimisation on-line. By solving the cost function in the finite receding horizon, voltage set points are computed in real-time to create bus voltage difference for power transfer between interconnected DCMGs. The PFC-based tertiary control for the DCMG cluster is verified through simulations.

A Calculation Method for Three-Phase Power Flow in Micro-Grid Based on Smooth Function

The existing three-phase imbalanced power flow calculation models for isolated micro-grid do not consider the non-smooth constraints such as voltage control limits and dead zone characteristics, which makes the power flow calculation results cannot meet the actual model constraints. In this paper, a more robust projected Levenberg-Marquardt (PLM) method is used to solve the problem after the non-smooth constraints are converted into smooth functions, which can reduce the model dependence on the initial values and solve the convergence problem caused by non-smooth constraints. The numerical experiments of micro-grid show that considering the non-smooth constraints can effectively improve the accuracy of power flow calculation. In addition, the PLM method has better convergence dealing with non-smooth constraints even poor initial value settings.

Multi‐slack power flow for islanded microgrids with radial and meshed topologies

This study proposes a new power flow formulation for islanded microgrids. The proposed power flow is based on the effect of the superposition principle and the solution of a small non-linear subproblem to determine the frequency of the microgrid and the voltage magnitude in the angular reference node in each iteration. These variables are determined by considering the following equations in the non-linear subproblem: active and reactive power balance equations and specified phase at the angular reference node. The application of the superposition principle allowed to obtain two versions of the proposed technique: one for radial networks - based on the current summation method - and another for meshed networks - based on the Gauss-Zbus method. Therefore, the iterative framework prosed in this study expands in a simple and integrated way the two most commonly used power flow methods in conventional distribution networks for islanded microgrids. The tests in microgrids with 33, 310 and 1438 nodes showed that the proposed approach has the same accuracy as Newton-Raphson algorithm, but with significantly lower computational cost in large scale microgrids. In addition, the proposed method for island microgrid showed good accuracy and convergence for the most common load models applied in power flow studies of islanded microgrids.

Enhanced Microgrid Power Flow Incorporating Hierarchical Control

An enhanced microgrid power flow (EMPF) is devised to incorporate hierarchical control effects. The new contributions are threefold: 1) an advanced-hierarchical-control-based Newton approach is established to accurately assess power sharing and voltage regulation effects; 2) a modified Jacobian matrix is derived to incorporate droop control and various secondary control modes; and 3) the secondary adjustment is calculated on top of the droop-control-based power flow results to ensure a robust Newton solution. Case studies validate that EMPF is efficacious and efficient and can serve as a powerful tool for microgrid operation and monitoring, especially for those highly meshed microgrids in urban areas.

Determination of Power Flows in Microgrids with Renewable Energy Sources by Using Special Computer Programs

The article discusses the issues of determining power flows in micro networks with renewable energy sources by using the Electrical Transient Analyzer Program (ETAP) software. In the model, the sources of electrical energy are: photovoltaic, microhydroelectric power stations, wind and other types of stations, as well as the main electric network. Using ETAP software, various experiments were conducted for the modes of the selected microgrid scheme and their results were obtained.

Power Balance Modes and Dynamic Grid Power Flow in Solar PV and Battery Storage Experimental DC-Link Microgrid

In this paper, an energy management system, based on different power balance modes and dynamic grid power flow, is proposed to operate a DC-link microgrid based on a solar photovoltaic generator and battery storage, with the option to request variable power from the grid to meet the load demand. The energy management provides the required references, for each mode, based on the solar source availability, the battery status, the power losses, and the grid billing rate. A fuzzy logic system is developed to provide a dynamic grid power flow based on the grid price. Eight power balance modes are defined based on the power generation, storage, and grid affordability to meet the load demand. The objectives are to minimize the energy cost and increase the lifespan of the storage device. The microgrid is controlled to maintain a constant DC-link voltage and regulate the battery current depending on the mode of operation. The proposed energy management system, based on the power balance modes, is experimentally validated on a laboratory-scale DC-link microgrid for different conditions. The experimental results have shown the satisfactory performance of the microgrid and smooth transitions between the different power balance modes.

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control.

A decoupled extended power flow analysis based on Newton-Raphson method for islanded microgrids

Recently, microgrid (MG) power flow (PF) studies have gained a lot of attention due to the emergence of autonomous MGs which feature distributed generations (DGs). There are some inherent limitations in the islanded operation mode of MGs which cannot be addressed by conventional PF methods. In this regard, recent studies have proposed some updates to the conventional approaches by inclusion of network frequency as a variable in their modeling and omission of the slack bus from the grid. These considerations specifically in the Newton-Raphson (NR) method change the Jacobean matrix (JM) formulations. This paper concentrates on improving the previous NR methods for MGs which include droop controlled DGs. For this purpose, the partial derivatives of both calculated and scheduled powers are considered in the Taylor series expansion of bus power injections. Thus, the convergence and accuracy of the PF method are enhanced by adding these derivatives in modeling of generations, loads and losses. Moreover, these extended PF equations are decoupled by reformulating the JM based on the consideration that the lines in MGs are mostly resistive, which results in simplified JM calculations and improved convergence speed. The effectiveness of the proposed decoupled extended NR (DENR) method for MG PF analysis is illustrated in several case studies including 6-bus and 38-bus networks. Moreover, two convergence enhancement methods are also incorporated into the proposed approaches and their merits are investigated.

An Energy Management Platform for the Optimal Control of Active and Reactive Powers in Sustainable Microgrids

This paper presents an energy management platform based on a receding-horizon scheme for the optimal control of active and reactive power flows in microgrids, including small-size photovoltaic, combined heat and power, wind generation, mini-hydro, and energy storage. The objective function to be minimized can be set both on the daily operational costs (economic indicator) and on the global CO2 emissions of the system (environmental indicator), whereas decision variables are the production schedules of the generators and the power flows across the grid. The tool includes the technical constraints characterizing low voltage/medium voltage (LV/MV) microgrids and gives the user the possibility to select different models for the electrical network (nonlinear power flow equations, linear approximation, and single bus-bar) and different optimization ranges. The energy management system has been validated through an experimental campaign on the smart polygeneration microgrid of the University of Genoa, which provides electricity and thermal energy to the Savona Campus, an “open-air” demo-site of an environmentally sustainable urban district with a population of about 2200 people. The results of the tests on the field highlight the robustness of the developed platform and the capability of the receding-horizon algorithm at the core of it of successfully treating data uncertainties.

Coordinated power flow control of DC microgrid consisting of distributed generations and energy storage system

In this paper, to operate DC microgrid in a flexible and reliable way, a coordinated power flow control strategy of DC microgrid and control methodology of power electronic converters are presented in both the grid-tied mode or islanded mode. For this purpose, the operation algorithm and power flow of DC microgrid are selected based on the distributed generation power, load demand, and energy storage system (ESS) status. The effectiveness of the system power balance and power flow control method are verified during operation mode transition through the simulation studies based on the PSIM software.

A Master/Slave Approach to Power Flow and Overvoltage Control in Low-Voltage Microgrids

This paper proposes a technique to control distributed energy resources in low-voltage microgrids aiming at (i) allowing power flow control at the point of connection with the upstream grid, (ii) keeping voltage profiles within the operational limits. The first feature is crucial in smart low-voltage power systems. In fact, it enables both demand-responses, which is extremely valuable from the point of view of distribution system operators and for energy trading, and the autonomous operation of the microgrid. The latter can be achieved by regulating to zero the power exchanged with the main grid. The second feature allows to limit voltage increases due to active power injection by distributed energy resources and, thus, to limit stresses on the electrical infrastructure and the served loads, which is a concrete issue as renewables become widely deployed in the low-voltage scenario. The proposed approach is firstly described in detail, then a systematic analysis of its local and global properties is reported. All the obtained results are verified considering the IEEE 37 test feeder in realistic operating conditions.