Microgrid Reconfiguration Research Articles

Optimal Reconfiguration of Bipolar DC Networks Using Differential Evolution

The search for more efficient power grids has led to the concept of microgrids, based on the integration of new-generation technologies and energy storage systems. These devices inherently operate in DC, making DC microgrids a potential solution for improving power system operation. In particular, bipolar DC microgrids offer more flexibility due to their two voltage levels. However, more complex tools, such as optimal power flow (OPF) analysis, are required to analyze these systems. In line with these requirements, this paper proposes an OPF for bipolar DC microgrid reconfiguration aimed at minimizing power losses, considering dispersed generation (DG) and asymmetrical loads. This is a mixed-integer nonlinear optimization problem in which integer variables are associated with the switch statuses, and continuous variables are associated with the nodal voltages in each pole. The problem is formulated based on current injections and is solved by a hybridization of the differential evolution algorithm (to handle the integer variables) and the interior point method-based OPF (to minimize power losses). The results show a reduction in power losses of approximately 48.22% (33-bus microgrid without DG), 2.87% (33-bus microgrid with DG), 50.90% (69-bus microgrid without DG), and 50.50% (69-bus microgrid with DG) compared to the base case.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Dynamic Reconfiguration for Resilient State Estimation Against Cyber Attacks

The increasing complexity and connectivity of critical infrastructures makes it increasingly likely that they will be subject to malicious attacks that compromise operation. Recent studies have shown that these systems are vulnerable to a wide range of cyber-attacks, including False Data Injection (FDI) attacks that bypasses conventional detection techniques. Conventional security monitoring, protection, and control tools are based primarily on passive defense strategies. In this paper, we propose an approach for active defense that improves system security and the detection rate of FDI attacks. The key insight for this approach is that emerging micro-grids can utilize distributed energy resources to dynamically reconfigure the system (e.g. power flow paths), across multiple acceptable configurations. Instead of using information from only a single static configuration to detect FDI attacks, our proposed approach uses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic reconfiguration</i> to compare measured and estimated states under multiple configurations to accurately detect FDI attacks. We describe an implementation and supporting infrastructure for a secure reconfigurable microgrid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic reconfiguration</i> also makes it more difficult for attackers to gain knowledge on the system's parameters, which increases the difficulty for attackers to construct hidden attack vectors. We evaluate our approach in the specific scenario of emerging micro-grids. We develop a novel technique for state estimation using multiple configurations and demonstrate that this approach significantly improves FDI detection accuracy.

Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology

On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement.

Development of PLC-Based Hardware Test-Bench Prototype for Solar-Wind-Battery-Based Microgrid System’s Control Algorithm Validation

Programmable Logic Controllers (PLCs) are gaining traction in microgrid operation due to their real-time deployment, reliable and robust operation, and fast response, even in harsh environments. Recognizing the critical need for robust and reconfigurable microgrid control and operation, this research proposes a hardware test-bench prototype using the Reconfigurable Allen-Bradley—Micro820/2080-LC20-20QBB PLC to demonstrate control algorithms aiming for efficient control, energy management, and resilient microgrid automation. The design and construction of a prototype controller test bench, along with the PLC configuration, wiring diagrams, and control logic, constitute the significant contributions of this research. The proposed controller test bench’s performance is evaluated through similar condition simulations in the presence of disturbances induced by the reduced contributions of renewable energy sources, such as solar and wind. The results illustrate the effectiveness of the developed control algorithm implemented in the proposed controller, ensuring the stable operation of the microgrid. Consequently, this work advances the simulation-based validation of microgrid control algorithms and provides insights into the practical application of a PLC-based hardware test bench under various environmental conditions.

Solar PV and Wind Energy Based Reconfigurable Microgrid for Optimal Load Dispatch

Solar PV and Wind Energy Based Reconfigurable Microgrid for Optimal Load Dispatch

Enhanced IDA-PBC Applied to a Three-Phase PWM Rectifier for Stable Interfacing Between AC and DC Microgrids Embedded in More Electrical Aircraft

To cope with future goal of efficiency, next generation of more electrical aircraft is likely to embed reconfigurable microgrid for the primary electrical distribution. The stability analysis of such time-varying structure is challenging. Conventional linear and nonlinear approaches failed to produce proper answers from a practical point of view. Under conditions, passivity property of each equipment is sufficient to ensure stability of the overall microgrid. But with improper passivity-based control (PBC) design, the passivity property may not be propagated. The main contribution of this article is to propose a modified IDA-PBC procedure that will ensure the passivity properties of the system regarding its electrical inputs and outputs involved in the interconnection. Thus, for electrical microgrids resulting from the interconnection of controllable equipment, input/output filters, and electrical lines, if all the equipment embed the proposed control, the whole microgrid have passive properties and the global stability of the system is ensured. Moreover, this article will also present how to achieve the proposed control with high transient and robust performances thanks to nonlinear parameters’ estimator preserving the passivity proper. Experimental validation of the proposed control in representative electrical conditions is provided.

Overarching Sustainable Energy Management of PV Integrated EV Parking Lots in Reconfigurable Microgrids Using Generative Adversarial Networks

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users’ behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users’ behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 3.37% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses

The number of microgrids within a smart distribution grid can be raised in the future. Microgrid-based distribution network reconfiguration is analyzed in this research by taking demand response programs and power-sharing into account to optimize costs and reduce power losses. The suggested method determined the ideal distribution network configuration to fulfil the best scheduling goals. The ideal way of interconnecting switches between microgrids and the main grid was also identified. For each hour of operation, the ideal topology of microgrid-based distribution networks was determined using optimal power flow. The results were produced with and without the use of a demand response program and power-sharing in each microgrid. Different load profiles, such as residential, industrial, commercial, and academic, were taken into account and modified using appropriate demand response programs and power-sharing using the Artificial Bee Colony algorithm. Various scenarios were explored independently to suit the diverse aims considered by the distribution network operator for improved observation. The ABC optimization in this research attempted to reduce the system’s total operation costs and power losses through efficient networked microgrid reconfiguration. The results of optimal microgrid topology revealed the effects of power-sharing and demand response (TOU) programs. The results obtained in the proposed idea shows that costs were reduced by 8.3% and power losses were reduced by 4%. The IEEE 33-bus test system was used to demonstrate the effectiveness of the proposed approach.

Optimal scheduling of reconfigurable microgrids incorporating the PEVs and uncertainty effects

Here, an intelligent optimization method for optimal switching in reconfigurable microgrids is proposed to reduce the cost of microgrids with the presence of distributed generation resources and electrical vehicles. The suggested model deploys the point estimation to manage the uncertainties in the microgrid. The uncertainties are due to load demand, market price, electric vehicle charging and discharging, wind turbine production capacity and photovoltaic cells. The formulation structure is in the form of a nonlinear and single-objective problem that minimizes the total microgrid asking price by observing the constraints in the problem. In this research, an improved algorithm is used to solve the optimization problem to advance the performance of the process. The capability and efficiency of the proposed method have been investigated on the IEEE 33-Bus standard test network and the simulation results have shown that optimal switching can be useful in more fitting power distribution and reduction of microgrid cost with the presence of electric vehicle and various scattered products.

Day-ahead complex power scheduling in a reconfigurable hybrid-energy islanded microgrid with responsive demand considering uncertainty and different load models

This paper proposes a new operation management scheme (OMS) for next-day complex power scheduling in an islanded reconfigurable microgrid. Energy supply in the microgrid comes from wind turbines and photovoltaic generators as renewable dispersed generators (RDG). Meanwhile, there are two microturbine units and a fuel cell unit as dispatchable distributed generators (DDG). Also, a battery bank energy storage system (BESS) is integrated. The microgrid has a number of controllable tie-switches to change the microgrid configuration and supplies residential, commercial, and industrial loads with different electrical models. Some of loads participate in a demand control program. The OMS is mathematically formulated as a multi-period mixed-integer stochastic nonlinear optimization. It has to decide the hourly power share of each DDG, the states of the control switches, and specifications of demand response. The OMS aims to minimize the total operation cost, unmet load, and curtailed renewable power maintaining all technical constraints. The uncertainty of renewable resources and load is analyzed in the OMS by Monte-Carlo simulation while scenario-reduction approach is used to reduce the computational burden. Besides, a new version of bat algorithm (IBA) is developed as a robust optimization solver. Simulation results and comparative evaluation of the OMS are presented using a 45-bus case study system to prove the merits of the method.

Supplementary Feedforward Control of DGs in a Reconfigurable Microgrid for Load Restoration

Network reconfiguration (NR) has received attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and determine the desired dynamics of the feedforward controllers when integrated with feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for the improvement of the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays.

Multimode Robust Control for Reconfigurable Microgrid With Dynamic and EV Loads

This article deals with the control and implementation of a multiobjective reconfigurable microgrid with electric vehicles (EVs), dynamic loads [induction motor (IM)], and home loads. This microgrid is reconfigurable in an islanded and the grid-connected modes with a robust synchronization control. For EVs, the microgrid provides the charging facility, and for home and dynamic loads, the microgrid balances and regulates the point of common coupling (PCC) voltage and frequency in an islanded mode. Usually, in an islanded mode of microgrid, the PCC voltage and frequency are regulated under the sudden start of an IM load, EV charging, and unbalanced nonlinear loads condition. An improved-diffusion proportionate affine projection sign-based control algorithm is used for voltage source converter to regulate the voltage and frequency of the islanded mode of microgrid and also improving power quality. Moreover, the robust grid synchronization control is also introduced in the microgrid control strategy to connect it to the grid/diesel generator set with a seamless mode of transition. The performance of the microgrid is demonstrated through simulated results obtained in the MATLAB/Simulink, and test results are taken from the experimental prototype, which is realized in the laboratory.

Congestion Management of Microgrids With Renewable Energy Resources and Energy Storage Systems

With the increasing share of renewable energy resources in the microgrid, the microgrid faces more and more challenges in its reliable operation. One major challenge is the potential congestion caused by the uncoordinated operation of flexible demands such as heat pumps and the high penetration of renewable energy resources such as photovoltaics. Therefore, it is important to conduct microgrid energy management to ensure its reliable operation. The energy storage system (ESS) scheduling as an efficient means to alleviate congestion has been widely used. However, in the existing literature, the ESSs are usually scheduled by the microgrid system operator (MSO) in a direct control manner, which is impractical in the case where customers own ESSs and are willing to schedule ESSs by themselves. To resolve this issue, this study proposes a network reconfiguration integrated dynamic tariff–subsidy (DTS) congestion management method to utilize ESSs and network reconfiguration to alleviate congestion in microgrids caused by renewable energy resources and flexible demands. In the proposed method, the MSO controls sectionalization switches while customers or aggregators schedule ESSs in response to DTS to alleviate congestion. The DTS calculation model is formulated as a mixed-integer linear programming model, considering heat pumps (HPs), ESSs, and reconfigurable microgrid topology. The numerical results demonstrate that the proposed method can effectively use ESSs and network topology to alleviate congestion and the MSO does not need to take over the scheduling of the ESS.

A Novel Islanding Detection Technique Based on Event Index Value for Reconfigurable Microgrid

This article presents an islanding detection technique based on event index value, which is derived from the superimposed components of sequence impedance with the consideration of all the operating configurations of the microgrid. Voltage and current signals are acquired at the terminal of distributed generators to compute the impedance. The proposed technique successfully discriminates the islanding, and non-islanding events (NIE) under different operating conditions, including zero power mismatch, and hence eliminates the non-detection zone and unwanted tripping due to the various types of NIEs. The detection was performed within half cycle for all the conditions. The proposed technique has been illustrated on a 7-bus reconfigurable microgrid test system. A large number of non-islanding and islanding events have been simulated for a range of power mismatches. The proposed technique has also been verified on the modified IEEE 34-bus system. The obtained results have been compared with the existing techniques reported in the literature, which proves the supremacy of the proposed technique. The effects of sampling frequency and noise have also been studied.

Economic-environmental analysis of combined heat and power-based reconfigurable microgrid integrated with multiple energy storage and demand response program

Microgrids (MGs) are solutions to integrate high shares of variable renewable energy which can contribute to more economical and environmental benefits, as well as improving the energy supply efficiency. One significant potential of MGs is an expanded opportunity to use the waste heating energy from the conversion of the primary fuel (such as natural gas) to generate electricity. The use of waste heat in combined heat and power (CHP)-based MG is more efficient to meet local load and decrease the emission pollution. Hence, this paper elaborates on optimal multi-objective scheduling of CHP-based MG coupled with compressed air energy storage (CAES), renewable energy, thermal energy storage (TES), and demand response programs through shiftable loads, which considers a reconfiguration capability. The embedded CAES, in addition to the charging/discharging scheme, can operate in a simple cycling mode and serve as a generation resource to supply local load in an emergency condition. The daily reconfiguration of MG will introduce a new generation of MG named reconfigurable microgrid (RMG) that offers more flexibility and enhances system reliability. The RMG is coupled with TES to facilitate the integration of the CHP unit that enables the operator to participate in the thermal market, in addition to the power market. The main intents of the proposed multi-objective problem are to minimize the operation cost along with a reduction in carbon emission. The epsilon-constraint technique is used to solve the multi-objective problem while fuzzy decision making is implemented to select an optimal solution among all the Pareto solutions. The electricity prices and wind power generation variation are captured as random variables in the model and the scenario-based stochastic approach is used to handle them. Simulation results prove that the simultaneous integration of multiple technologies in CHP-based RMG decreases the operation cost and emission up to 3 % and 10.28 %, respectively.

Optimized Power Trading of Reconfigurable Microgrids in Distribution Energy Market

Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

Operation and Control of DC Soft Open Points for Improving the Reliability and Voltage Profile of Reconfigurable DC Microgrids Powered by Renewable Energy Sources

Operation and Control of DC Soft Open Points for Improving the Reliability and Voltage Profile of Reconfigurable DC Microgrids Powered by Renewable Energy Sources

Islanding detection for reconfigurable microgrid with RES

Islanding detection for reconfigurable microgrid with RES