Interconnected Microgrids Research Articles

Energy Optimization Dispatch for Multiple Interconnected Microgrids with a Low-Carbon Economy Objective

Background: The multi-microgrid interconnection system is an effective method to solve the problem of distributed energy consumption, enhance the stability and reliability of the power grid, connect multiple microgrids with the main power grid, and achieve energy sharing and exchange, thereby improving energy utilization and reducing transaction costs. Objective: Develop advanced multi-microgrid energy optimization scheduling strategy, which can better integrate and utilize renewable energy, reduce carbon emissions, obtain more economic energy scheduling scheme, and reduce operation and maintenance costs. Methods: It achieves a three-layer scheduling strategy by coordinating microgrids with the main grid, microgrids with other microgrids, and distributed power sources within microgrids. It first satisfies the maximum consumption of renewable energy in multi-microgrid systems, and then satisfies the economic scheduling between multi-microgrids and the main grid, thus achieving the goal of low-carbon economic operation. When scheduling, priority should be given to coordinating the energy flow among microgrids. When the distributed power sources within a microgrid cannot satisfy the electricity demand, or when the electricity demand is too low, resulting in the energy storage system (ESS) being fully charged and leading to an excess of renewable energy, the interconnection scheduling between microgrids and the main grid should be initiated to achieve optimal energy sharing scheduling. Results: To validate the effectiveness of the proposed model, a case study involving three interconnected microgrids was conducted. A comparison is made between the proposed optimized dispatch model and the scenario where microgrids are not interconnected. The results indicate a total cost reduction of 128.1 CNY and an environmental governance cost reduction of 13 CNY. Conclusion: The results demonstrate that the proposed economic dispatch model for interconnected microgrids reduces both operational and environmental governance costs, enhances the utilization of renewable energy, decreases pollutant emissions, and aligns with the operational goals of a low-carbon economy. The feasibility and effectiveness of this method for energy scheduling in multi-microgrid interconnected systems are verified.

Interconnected Microgrids Load‐Frequency Control Using Stage‐by‐Stage Optimized TIDA+1 Error Signal Regulator

ABSTRACTLoad‐frequency control (LFC) is essential for maintaining system stability and ensuring high power quality in microgrids (MGs), particularly those heavily reliant on renewable energy sources (RES) and operating independently of the main grid. This paper introduces a novel control strategy aimed at improving LFC performance in interconnected MGs by correcting the error signal. The proposed controller, denoted as TIDA+1, combines tilt, integrator, derivative, and acceleration operators in a parallel configuration to refine the incoming error signal. The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time‐varying acceleration coefficients (NTVAC). The controller's effectiveness is validated through four distinct scenarios, including sudden load variations, system modeling uncertainties, fluctuations in RES outputs, and the impact of nonlinearities. Additionally, a lab‐scale evaluation of the controller has been conducted to further assess its practical applicability. Comparative results demonstrate that the TIDA+1 controller outperforms traditional controllers such as PID and FOPID, especially under complex operational conditions. The study highlights the TIDA+1 controller as a robust and viable solution for LFC in MGs, with potential for future scalability and application in larger systems.

Discrimination of high impedance fault in microgrid power network using semi-supervised machine learning algorithm

This work proposes a semi-supervised classification approach for discriminating high-impedance (HI) faults and other transients in a photovoltaic (PV) interconnected microgrid (MG) network. The suggested classifier combines unsupervised K-means clustering with the supervised multi-layer perceptron neural network algorithm. The K-means clustering technique is utilized in the first phase to detect and remove irrelevant instances from multiple events in the data set. To obtain the final predictions of targeted labels, clustered cases from the first phase are utilized to learn the multi-layer perceptron neural network classifier in the next phase. The suggested method outperforms stand-alone classifiers (K-means clustering and multi-layer perceptron) by providing enhanced accuracy and success rate of discriminating HI fault under standard test conditions and weather intermittency of PV. Furthermore, the results of the performance study clearly show that the suggested model is more resilient and offers superior performance than the stand-alone classifiers under the standard test condition and uncertainty of PV in MG networks.

Towards the integration of interconnected microgrids to deregulated electricity markets

In response to rising energy consumption and its adverse environmental impact, there is an urgent need to integrate innovative technologies in intelligent energy management systems (EMS). This work develops sophisticated systems to optimize energy management enabling Microgrids (MGs) to participate optimally in Peer-to-Peer (P2P) electricity markets. MGs under examination comprise residential buildings, including thermal and electrical loads, renewable energy sources (RES), and plug-in electric vehicles (PEVs). It presents a comprehensive framework for the economic operation of interconnected MGs, combining two strategies: 1) MG energy management and 2) MG participation to P2P electricity market. The energy management problem is solved at MG level to minimize its operation cost over a time horizon. P2P electricity market participation is achieved through a non-cooperative game where each MG can have its distinct objectives, which is a vital characteristic for liberalized markets. Within this framework, each MG adapts its operations to market dynamics, aligning with economic goals and operational efficiency. Notably, during P2P electricity market participation, MGs share information only about the offered energy and price, maintaining the highest possible confidentiality.

Advanced transient switching and coordinated power control strategies for flexible interconnection of multiple microgrids

Multiple microgrid (MG) distribution systems are facing challenges owing to variations in the operational statuses of the individual MGs, which experience voltage and current fluctuations during transient interconnections. The impedances of the interconnecting lines further exacerbate the unevenness of power distribution among the MGs, hence threatening the operational stability of the system. To achieve flexible and seamless interconnections between multiple MGs, we fully analyzed the interconnected structures and operation modes of the MGs; then, we designed a transient switching control method based on investigation of the transient interconnection processes to ensure smooth transition of the MGs. Additionally, to balance the power distribution among the interconnected MGs, a voltage–current-based coordinated power control strategy was synthesized using advanced synchronized fixed-frequency technology. Simulation case studies were conducted, and the results indicate that the proposed coordinated power control scheme effectively facilitated instantaneous interconnections between the isolated regions, thereby avoiding voltage disturbances and current surges. Furthermore, it efficiently equalized and distributed the output power from the distributed energy sources, thereby enhancing the operational flexibilities and stabilities of the MGs and distribution system.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

Distributed Autonomous Economic Control Strategy for the AC/DC Interconnected Microgrid Considering the Regulation Boundary of the Consensus Variable

ABSTRACTThe autonomous economic control for the islanded AC/DC interconnected microgrid has various modes due to the restriction of the feasible regions of inner and inter‐microgrid resources. To properly handle the cooperative relationship between resources with different regulation characteristics and realize the frequency, voltage stability, and economic operation of the AC/DC interconnected microgrid, a distributed control strategy considering the regulation boundary of the consensus variable is proposed. Firstly, the influence of the variable feasible regions on the consensus control objective is analyzed, and the fundamental principle of the consensus control algorithm considering the variable regulation boundary is expounded. Secondly, the trilevel control strategy consisting of the local equipment control and inner and inter‐microgrid cooperative control is constructed. The local equipment control adopts droop control, and the cooperative control adopts consensus control to realize the elimination of the frequency and voltage deviation and the economic dispatch of active power within and between microgrids. Finally, the AC/DC interconnected microgrid model is built based on PSCAD/EMTDC, and the simulation results verify the effectiveness of the proposed control strategy.

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

Load-Frequency Stability Analysis in Interconnected Micro-Grid powered by Renewable Energy Sources using Enhanced PID Controller Based on The Whale Optimisation Algorithm

Load-Frequency Stability Analysis in Interconnected Micro-Grid powered by Renewable Energy Sources using Enhanced PID Controller Based on The Whale Optimisation Algorithm

Optimal design and three-level stochastic energy management for an interconnected microgrid with hydrogen production and storage for fuel cell electric vehicle refueling stations

A new trend in the transportation sector globally can be observed in a shift away from gasoline-powered vehicles to Hydrogen-based fuel-cell electric vehicles (FCEVs), aimed at reducing harmful emissions. However, there are challenges in producing hydrogen for vehicle stations related to microgrid design and energy management under uncertain conditions. This research sought to identify the optimum design of an electric microgrid to provide the required energy for electric loads, together with a hydrogen refueling station. The microgrid under study consists of various renewable energy resources (RERs), such as photovoltaic (PV) devices, wind power systems, and hydrogen storage systems. The energy management strategy (EMS) aims to reduce the total costs (investment, operation, replacement, procurement energy costs) considering four uncertain parameters associated with PV panels, FCEVs, wind turbines, and power demand. A three-level EMS is proposed based on testing various solutions: without RERs or a hydrogen energy storage system (Level 1); with RERs and a hydrogen energy storage system (Level 2), with RERs and hydrogen energy storage that includes demand side response (DSR) (Level 3). The results indicate annual cost savings of 1.946 E+06 $ for Level 2 and 2.001 E+06 $ for Level 3, compared to Level 1.

Microgrid Interconnection Decision Making Method Based on Economic Index Considering Supply Reliability

Microgrid Interconnection Decision Making Method Based on Economic Index Considering Supply Reliability

Distributed cooperative control for global power sharing management of interconnected microgrids in flexible multi-terminal DC connection scheme

Flexible multi-terminal direct current (MTDC) connection scheme is an ideal way to integrate multiple adjacent microgrids and form interconnected microgrids (IMGs). However, under the traditional control mode, MTDC will lead to frequency/voltage decoupling among all the microgrids. Moreover, improper control scheme design of bidirectional interlinking converters (BICs) in MTDC will make global active/reactive power sharing management impossible, which is considered as the significant task in IMGs. Therefore, a novel distributed cooperative control strategy is designed in this article. In the proposed control strategy, frequency/voltage regulations and local active/reactive power sharing management are achieved through distributed secondary control within each MG. Accurate global active/reactive power sharing management is realized by coordinating the power exchange instructions of each BIC in a distributed manner. The control dynamics of these two levels are designed to be decoupled. The stability of the proposed control strategy is demonstrated by the Lyapunov method, and its effectiveness is verified by simulating a test IMG system in MATLAB/Simulink.

Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.

Component, design, and prospects of self-consistent energy systems for transport infrastructures

ABSTRACT This paper introduces a Transportation Self-Consistent Energy System (TSCES) to meet the increasing electricity requirements of transport infrastructure in various traffic environments, including highway, railway, and waterway. The TSCES consists of four primary components: power source, load, energy storage, and interconnected microgrid. The power source component can be categorized into natural energy endowment and energy-electricity conversion components. Load components can be classified, quantified, and forecasted based on diverse transportation facilities, traffic volume, and importance levels. Energy storage components necessitate thorough consideration of the technical attributes of various energy storage methods, along with the functional requirements of the traffic environment, power sources, and loads. Interconnected microgrid components establish connections among all components, followed by configuration optimization and choosing appropriate network topological structures based on techno-economic conditions, ensuring that all components adhere to constraints imposed by transportation space, regulations, and policy indicators. Additionally, the design procedure and architectural patterns of TSCES are presented, which indicates that the TSCES has excellent adaptability and research application value in the field of transportation.

Frequency Stabilization Based on a TFOID-Accelerated Fractional Controller for Intelligent Electrical Vehicles Integration in Low-Inertia Microgrid Systems

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller’s parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers.

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

Peer-to-Peer (P2P) energy trading between Interconnected Micro-Grids (IMGs) presents a promising approach for enhancing the economic advantages for prosumers while mitigating supply–demand imbalances within individual Micro-Grids (MGs). This paper proposes a trustful double auction mechanism for P2P energy trading among prosumers in IMGs, structured into two distinct stages. Initially, concurrent auctions are conducted within each MG to facilitate intra-grid trading, followed by subsequent auctions facilitating inter-grid trading across IMGs. We figure out the allocation and pricing rules that satisfy the required properties in mechanism design even when the auction consists of two stages. Given that energy transactions across IMGs entail non-negligible power losses, equitable allocation of these losses among prosumers is paramount. To address this, we integrate a fair cost distribution methodology into our auction mechanism, implemented by an iterative algorithm. Rigorous analysis substantiates our proposed auction mechanism’s incentive compatibility, individual rationality, and budget balance, thereby fostering truthful and voluntary prosumer participation while averting potential market deficits. Numerical analysis underscores the efficacy of our approach, showcasing a significant improvement in P2P transactions and supply–demand balance enhancement compared to trading solely within individual MGs.

A Stochastic Sequential Service Restoration Model for Distribution Systems Considering Microgrid Interconnection

A Stochastic Sequential Service Restoration Model for Distribution Systems Considering Microgrid Interconnection

Real-Time Simulation and Validation of Interconnected Microgrid Load Frequency Control With Uncertainties Using PDN-I$^{\lambda }$ D$^{\mu }$ Controller Based on Improved Smell Agent Optimization

Real-Time Simulation and Validation of Interconnected Microgrid Load Frequency Control With Uncertainties Using PDN-I$^{\lambda }$ D$^{\mu }$ Controller Based on Improved Smell Agent Optimization

A novel scheme of load frequency control for a multi-microgrids power system utilizing electric vehicles and supercapacitors

Incorporation of renewable energy sources (RESs) into power systems has recently experienced a rapid surge, primarily driven by environmental concerns and the desire for more sustainable power generation. However, the replacement of traditional generating units with RESs lowers the system inertia significantly and causes intermittent generation. These issues make frequency stability a greater concern, particularly in microgrids (MGs), and produce mechanical stress on the conventional generation units. Moreover, in interconnected MGs, the complexity is further magnified owing to interaction between microgrids through the tie power which may increase the disturbance propagation among microgrids. This paper suggests a novel control scheme based on a centralized fractional order proportional integral controller cascaded with a virtual inertia emulator (C.FOPI+VI). The virtual inertia emulation is provided using a supercapacitor (SC) along with electric vehicles (EVs) controlled through a virtual inertia controller. However, the EV is suffering from uncertainty, hence the supercapacitor can mitigate this uncertainty. The control system is applied on three interconnected microgrids, each has a thermal plant, a supercapacitor, and an electric vehicle aggregator. The performance of the system incorporating the proposed control technique is compared with five different inertia emulation topologies known as virtual inertia (VI), virtual synchronous generator (VSG), distributed fractional order proportional integral controller cascaded with virtual inertia (D.FOPI+VI), distributed fractional order proportional integral controller cascaded with virtual synchronous generator (D.FOPI+VSG), and without synthetic inertia. To optimize the parameters of different controllers, the Transit Search (TS) optimization algorithm is employed considering the frequency deviation, the restoration time, and the tie power. The proposed C.FOPI+VI has proven its superiority in providing fast frequency restoration, less stress on the conventional generation units, and minimizing the spreading of the disturbance within the system. Furthermore, the proposed controller enhances the Gain Margin (G.M) to 21.1 dB compared to operation without inertia support (0.322 dB), VI (3.38 dB), VSG (5.23 dB), DFOPI+VI (8.25), and DFOPI+VSG (8.32).