Microgrid Clusters Research Articles

Optimal allocation of energy storage in distribution network considering microgrid cluster and system economic operation

A two-stage optimization model for the location of distribution grid energy storage (ES) configuration and its capacity determination is proposed for different operating conditions of the distribution grid, consider the impact of microgrid cluster operation strategies and sub-microgrid cooperation on the distribution network (DN). In the first stage, location, number of ES devices, the charging/discharging strategy were solved for using GAMS software, the minimum operating cost of the distribution system is used as the target for the daily operating timescale. In the second stage, the minimum annual operating cost is taken as the target for a one-year time scale, consisting of investment, ES equipment’s operation and maintenance in the DN, and is solved using a particle swarm optimisation algorithm with nested ES lifetime calculations in MATLAB software. The calculations show that the optimal allocation method of ES equipment in DN can effectively reduce the operating costs of DN as well as the investment and maintenance costs of ES under different operating conditions, taking into account the economic operation of microgrid clusters and distribution systems. As a result, the economy of the DN can be substantially improved.

Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue

The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account.

A Sequential Sampling-based Particle Swarm Optimization to Control Droop Coefficients of Distributed Generation Units in Microgrid Clusters

A Sequential Sampling-based Particle Swarm Optimization to Control Droop Coefficients of Distributed Generation Units in Microgrid Clusters

Voltage-sag mitigation by stability-constrained partitioning scheme

Microgrids have the ability to function in islanded or grid-connected modes of operation. The increased penetration of inverter-based Distributed Energy Resources (DERs) in the system promotes the concept of partitioning the system into self-governing and self-adequate microgrids. Most of the partitioning techniques determine virtual boundaries and did not consider the survivability of the constructed microgrids. In this paper, a stability-constrained partitioning scheme is proposed based on small-signal stability to ensure microgrids' survivability when physically partitioned. Moreover, a sensitivity analysis of active power droop gain is utilized to define a novel index for the microgrid’s marginal stability. The application targeted in this paper is the mitigation of voltage-sag events caused by low impedance faults. The system will be partitioned into clusters of survivable microgrids during faults to isolate the faulted zone that caused the voltage-sag event. By isolating the voltage-sag origin from the rest of the system, voltage-sag mitigation is accomplished. Also, a microgrid re-connection method is proposed. This method allows multiple droop-controlled DERs to adjust the frequency, phase, and magnitude of their output voltages to facilitate seamless re-connection of microgrids. Simulation results are given that show a seamless re-connection of two different microgrids when the proposed method is utilized. The effectiveness of the proposed mitigation algorithm is validated using a modified IEEE 33-bus distribution system simulated on MATLAB/SIMULINK platform.

Preface

We are pleased to provide you with the proceedings of 2022 4th International Conference on Resources and Environment Sciences (ICRES 2022).The conference was expected to be held during June 10-12, 2022 in Bangkok, Thailand, while the situation of COVID-19 pandemic is unpredictable and unstable. Most of conference participants could not travel to attend the conference venue to do oral presentations. Taking all conditions into consideration, conference committee decided to change physical conference into virtual conference. It was held online by ZOOM application successfully during the same date.The conference was highlighted by four outstanding Keynote Speakers and two invited speakers. Keynote speakers include Prof. Kaimin Shih, The University of Hong Kong, China with his topic “Metal Stabilization and Resource Recovery Examples in Urban Environment”; Prof. Nur Islami, University of Riau, Indonesia who presented a talk on “An Valuable Approach to Study Groundwater Contamination in a Shallow Aquifer System”; Prof. Danny Sutanto, University of Wollongong, Australia who shared a speech on “Solid-State Transformer for Smart Power Grid Applications”; Assoc. Prof. Phebe Ding, Universiti Putra Malaysia, Malaysia who presented a talk about “Role of Postharvest Technology in Producing Quality Fresh Horticultural Produces”. Additionally, two excellent invited speakers, Assoc. Prof. Chunrong Jia from University of Memphis, Tennessee, USA with speech title “Apportioning variability of polycyclic aromatic hydrocarbons (PAHs) in the ambient air in the Memphis Tri-State Area, USA”, and Assoc. Prof. Farhad Shahnia from Murdoch University, Australia with speech title “Recent and Future Research on Microgrid Clusters”.Each normal oral presenter had about 12 Minutes of Presentation and 3 Minutes of Question and Answer. Conference was organized in 5 sessions with various topics: Environmental Management, Waste Utilization and Sustainable Development, Wastewater Treatment, Water Analysis and Hydraulic Engineering, Renewable Energy Technology, Chemical Engineering and Fluid Mechanics, Resources and Environmental Science & Sustainable Development, Energy and Chemical Engineering.All accepted papers presented at the ICRES 2022 were included in this volume, which contained three chapters with topics: (1) Environmental Pollution and Control (2) Waste Management and Utilization (3) Clean Energy and Technology. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on high quality and high relevancy to the conference scope.We would like to express our sincere gratitude to organizing committee and the volunteers who have dedicated their time and efforts in planning, promoting, and helping the conference. We hope that the readers would gain some valuable knowledge from this effort.List of Committees, Statement of Peer Review are available in this Pdf.

Full-deployed energy management system tested in a microgrid cluster

Under a hierarchical structure perspective, the integration of tools like the internet of things, cloud computing, and machine learning into a microgrid real-time energy management system involves superior capabilities like an autonomous and scalable design, massive storage capabilities, real-time information analysis and processing, and security issues control, to mention a few. This paper evaluates most of them using a cloud-based real-time energy management system integrated into a real-life hardware-in-the-loop testbed. The proposed cloud-based system is tested by solving an economic power dispatch problem including the equalization of the multiple battery-based energy storage systems interacting within a multi-microgrid environment. The test assessment combined reviewing and running microgrid models, incorporating these models into a real-life power-hardware-in-the-loop unit, linking the testbed to a cloud server, and merging the energy management system with on-demand computing tools, primarily machine learning and the internet of things. As established by the experimental evidence, this paper cites the benefits of combining machine learning techniques and internet of things tools with a scalable and autonomous real-life cloud-based energy management system architecture to improve the framework's functionality, enhance the energy forecasting for generation and usage, and cut down the price paid to the service provider.

Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization

Environmental and climate change concerns are pushing the rapid development of new energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality introduced by energy routers (ERs), offers an appealing alternative for DER systems. However, previous centralized control schemes for EI systems that follow a top-down architecture are unreliable for future power systems. This study proposes a distributed control scheme for bottom-up EI architecture. Second, model-based distributed control methods are not sufficiently flexible to deal with the complex uncertainties associated with multi-energy demands and DERs. A novel model-free/data-driven multiagent deep reinforcement learning (MADRL) method is proposed to learn the optimal operation strategy for the bottom-layer microgrid (MG) cluster. Unlike existing single-agent deep reinforcement learning methods that rely on homogeneous MG settings, the proposed MADRL adopts a form of decentralized execution, in which agents operate independently to meet local customized energy demands while preserving privacy. Third, an attention mechanism is added to the centralized critic, which can effectively accelerate the learning speed. Considering the bottom-layer power exchange request and the predicted electricity price, model predictive control of the upper layer determines the optimal power dispatching between the ERs and main grid. Simulations with other alternatives demonstrate the effectiveness of the proposed control scheme.

MPC-based three-phase unbalanced power coordination control method for microgrid clusters

The microgrid cluster group-level bus voltage is high, the capacity is large, and the interconnection bus is connected to various microgrid subsystems. It is challenging for the microsource inverters in the subsystems to directly participate in the imbalance regulation of the group-level bus voltage, so it is currently difficult to solve the three-phase imbalance of the group-level bus voltage. In this paper, a particular structure of a series microgrid is applied to the cluster system, and a three-phase unbalanced control strategy based on model predictive control for the cluster system is proposed. First, the grid-connected current prediction control model of the series microgrid inverter using an LCL filter is established, a medium-voltage high-capacity three-level neutral point clamped (NPC) power electronic converter is introduced into the system and used as the central inverter of the DC microgrid, and the model prediction control method is applied to this central inverter. Second, considering possible load asymmetry operating conditions of the group bus, the p-q-o method is used to calculate the load imbalance current component that must be compensated, which is used as a part of the output current of the series microgrid or DC microgrid, based on which the three-phase unbalanced power coordinated control method between two heterogeneous microgrids is studied. The simulation results show that the series microgrid and DC microgrid exhibit good three-phase unbalance suppression under load unbalanced conditions, and the coordinated control between them can effectively improve the three-phase unbalance suppression capability of the system, which confirms that the proposed control strategy can achieve high-quality voltage output of the microgrid cluster system in multiple scenarios.

New Reliability Indices for Microgrids and Provisional Microgrids in Smart Distribution Systems

The construction of provisional microgrids paired with microgrids was recently introduced taking into consideration, the less critical zones of the distribution system. Similar to the microgrids, the provisional microgrids have distributed generators and a master controller. But these provisional microgrids cannot switch to islanded mode as a single entity as in the case of microgrids. Instead, they move to islanded mode along with the coupled microgrid, thus meeting the economic and reliability requirements of the less sensitive zones of the distribution system. This work first proposes few new device-based reliability indices for the sustained faults that cater to the new requirements arising due to the presence of distributed generators all over the system, embedded in clusters of microgrids and provisional microgrids. Secondly, considering the occurrences of temporary faults in similar systems, another new load-based reliability index is proposed. Later, for the chosen example distribution system with 69-buses, the existing reliability indices, and the proposed new indices are calculated. At last, the influence of these indices on generation expansion planning problems like placement and sizing of the distributed generators, construction of clusters of microgrids and provisional microgrids, and economic decisions on scheduling of maintenance is discussed in detail.

Accurate Peer-to-Peer Hierarchical Control Method for Hybrid DC Microgrid Clusters

Hybrid DC microgrid clusters contain various types of converters such as BOOST, BUCK, and bidirectional DC/DC converters, making the control strategy complex and difficult to achieve plug-and-play. The common master–slave hierarchical control strategy makes it difficult to achieve accurate and stable system control. This paper proposes an accurate peer-to-peer hierarchical control method for the hybrid DC microgrid cluster, and the working principle of this hierarchical control method is analyzed in detail. The microgrid cluster consists of three sub-microgrids, where sub-microgrid A consists of three BUCK converters, sub-microgrid B consists of three BOOST converters, and sub-microgrid C consists of two bidirectional DC/DC converters. According to all possible operations of various sub-microgrids in the microgrid cluster, the top-, mid-, and bottom-level controls are designed to solve the coordination control problem among different types of sub-microgrids. In this paper, a hybrid microgrid cluster simulation model is built in the PLECS simulation environment, and an experimental hardware platform is designed. The simulation and experiment results verified the accuracy of the proposed control strategy and its fast plug-and-play regulation ability for the system.

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

Research on power to hydrogen optimization and profit distribution of microgrid cluster considering shared hydrogen storage

With the development of renewable energy, the number of microgrid systems is gradually increasing, which puts forward higher requirements for the power grid. How to make full use of the internal resources of microgrid, guarantee power supply and relieve power grid's burden is an urgent problem to be solved. In this paper, a planning and optimization strategy of a microgrid cluster consisting of shared hydrogen storage is proposed. This study is mainly divided into three stages. In the first stage, capacity planning and daily operation optimization of energy storage schemes in different scenarios are carried out through a two-layer optimization model. In the second stage, the comprehensive benefits of different scenarios are calculated by comparing whether the microgrids are in a cluster and whether the energy storage is shared. The third stage is to reasonably distribute the benefits of the members of the microgrid cluster. The results show that the complementarity between microgrids and the configuration of hydrogen energy storage can eliminate the phenomenon of power failure, reduce the dependence of microgrids on the superior grid, improve the peak regulation capacity, promote the consumption of renewable energy, and obtain economic benefits.

Control Techniques for Interconnections of Cluster of Neighboring Islanded Hybrid Smart Microgrids

This paper presents an architecture for interconnections of cluster of neighboring islanded mix smart microgrids (both DC and AC), for sharing the power among various areas. The cluster of microgrids consists of three microgrids i.e. two DC microgrids operating at different voltages and one AC microgrid. All microgrids have ring type architectures, which consist of various renewable distributed generations, battery energy storage systems, and various AC and DC loads. Two DC microgrids are interconnected through the bidirectional DC–DC converter, and the DC microgrid and AC microgrid are interconnected through the bidirectional single-phase voltage source converter (VSC). In this work, the control techniques of the bidirectional VSCs, used for interconnections of three microgrids, have also been developed, for controlling the operation of the interconnected microgrids system. The developed control techniques use the feed-back and feed-forward control signals. The developed control techniques are aimed to manage the bidirectional power flow between the interconnected microgrids, and to maintain constant desired rated voltages of all microgrids, under islanded mode, for different operating conditions. To verify the robustness of the proposed interconnected microgrids system and developed control techniques, the simulations are carried out under various operating scenarios, in the MATLAB environment.

Optimization of Multi-microgrid System Operation Cost with Different Energy Management Tools in the Presence of Energy Storage System

— Given the increasing capacity of distributed generation and renewable resources, how to use this capacity and proper energy management will be a major challenge for low-capacity networks such as microgrids. With the help of peripherals tools, an arrangement can be made to achieve a more desirable result in the energy management system (EMS). Storage resources and at a higher level, the storage module is used as one of these tools in the subsystems of the EMS. In this article, in addition to taking advantage of the connection of several microgrids to each other and reducing interactions with the main network, by using the storage module and other related tools, the cost of providing energy to a cluster of microgrids is reduced to the lowest possible value. By implementing a mathematical model based on mixed-integer linear programming, the best program for generation resources obtains in such a way that the cost of energy supply reduces to the minimum value. The study results have been analyzed in two case studies; a modified laboratory microgrid and the IEEE 33-bus distribution network. Although different solvers in Gams will provide almost similar answers, the optimal answer was found by the Cplex solver.

Distributed model predictive control strategy based on block‐wised alternating direction multiplier method for microgird clusters

Microgrid clusters (MGC) can improve the consumption of renewable energy and the system reliability. The control of microgrid cluster with large-scale microgrids is the focus of microgrid cluster research. In order to better realize the coordination control and energy scheduling among microgrids, this paper proposes a distributed optimal strategy for complex MGC based on model predictive control (MPC) and block-wised alternating direction multiplier method (BADMM). Firstly, an optimization model is established based on a honeycomb active distribution network (HADN); then the network is decomposed into several regions according to BADMM. The global optimization goal of the cluster is achieved by iterating the optimization results of each region in a time domain. Finally, the case study results verify the effectiveness and convergence of the proposed distributed optimization strategy, and provide a reference for energy management of MGC.

Stability Evaluation of Grid-Connected Microgrid Clusters in Asymmetrical Grids

Stability of grid-connected microgrids could be to a high extent guaranteed by designing a robust control system for inverters. However, this is not necessarily the case when microgrids are connected to an asymmetrical grid, where the grid impedance could have a different value in each phase. In this condition, the asymmetry of the grid impedance causes couplings between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> - or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -frames in the average model, and therefore, the control system could not be simplified as a single-input single-output system anymore. In this case, the system turns into a multi-input multi-output system, and therefore, stability evaluation of such a system consisting of several microgrids that form microgrid clusters with multiparallel inverters could be a challenging task, which needs heavy mathematical derivations. In this article, a straightforward method is used for modeling the grid-connected microgrid clusters, in which the asymmetry of the grid impedance has been considered. Based on this approach, all the inverters in a microgrid are lumped in a frequency-domain-based model using the inverters’ Norton equivalent models. Then, based on the derived model, the generalized Nyquist criterion is used to evaluate the stability of the whole system. The proposed method considerably reduces the mathematical burden, while accurately predicts the system stability. The experimental results in a small-scale test bench in the laboratory validate the accuracy of the proposed method.

A Noncooperative Game-Based Approach for Microgrid Planning Considering Existing Interconnected and Clustered Microgrids on an Island

This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies.

Collaborative Optimization to Enable Economical and Grid Friendly Energy Interactions for Residential Microgrid Clusters

Collaborative Optimization to Enable Economical and Grid Friendly Energy Interactions for Residential Microgrid Clusters

Preface to EREE 2022

The 2022 International Conference on Environment, Resources and Energy Engineering (EREE2022) was scheduled to be held in Bangkok, Thailand, however, due to unexpected global surge in COVID-19 variant in the last three months, for safety and also travel restriction reasons, the conference was held virtually via “Zoom”.Delegates from around the world including Thailand, Bulgaria, Ecuador, Indonesia, Cambodia, Chile, Philippines and Sri Lanka took the opportunity to share their research results and discuss potential scientific and engineering development from their work that contributed to the success of the conference.All papers in these proceedings have passed the vigorous review process involving reviewers of the International Technical Committee. Authors benefited from valuable comments and improved their submissions to the satisfaction of reviewers. The virtual presentation serves as another opportunity for the conference delegates to address critiques in the real time online meetings with the expert audience.There were four keynote speakers and two invited speakers who gave talks covering different areas of the conference. The keynote speakers are (i) Prof Kaimin Shih (The University of Hong Kong, China) who gave a talk on on “Metal Stabilization and Resource Recovery Examples in Urban Environment”, (ii) Prof. Nur Islami (University of Riau, Indonesia) who gave a talk on “A Valuable Approach to Study Groundwater Contamination in a Shallow Aquifer System”, (iii) Prof Danny Sutanto (University of Wollongong, Australia) who gave a talk on “Solid State Transformer for Smart Power Grid Applications”, and (iv) Prof Phebe Ding (Universiti Putra Malaysia, Malaysia) who gave a talk on ”Role of Postharvest Technology in Producing Quality Fresh Horticultural Products.”. The invited speakers are (i) Assoc. Prof Chunrong Jia (University of Memphis, Tennessee, USA) who gave a talk on “Apportioning variability of polycyclic aromatic hydrocarbon (PAHs) in the ambient air in the Memphis Tri-State Area, USA”, and (ii) Assoc. Prof Farhad Shania (Murdoch University, Australia) who gave a talk on “Recent and Future Research in Microgrid Cluster”.The proceedings record papers presented during the conference, all of them have been divided into 3 sessions in the proceedings: Session 1-Resource & Environment Management and Sustainable Development, Session 2-Energy Chemistry and Chemical Engineering, and Session 3 Renewable Energy Technology and Energy Consumption Analysis.The variety of research topics presented in the conference and novelty exhibited in the papers published in the proceedings once again demonstrated the value of EREE2022.On behalf of the conference committee, I would like to thank the Technical Program Committee members, the Conference Program Coordinator, the keynote speakers and all participants, whose papers are presented in the conference proceedings, all contributing to the success of the conference.List of Conference Committees are available in this Pdf.

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges.