Microgrid Energy Research Articles

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach

This work presents a distributed robust operation framework for the microgrids within the power distribution network. It addresses the uncertainties in photovoltaic generation, as well as active, and reactive loads in the network, by modeling them using polyhedral sets. The energy management problems are formulated as two-stage optimization problems in microgrids and power distribution networks for which Benders decomposition is used to solve the problems. The power flow constraints are relaxed using second-order cone relaxation and moment relaxation techniques in the power distribution network and microgrid energy management problems, respectively. The interaction between the microgrids and the power distribution network is represented using the price signal and the exchanged active and reactive power at the microgrids’ points of common coupling. The effectiveness of the proposed framework is shown using two case studies on IEEE 37-bus and IEEE 123-bus systems. The effect of the energy storage system on the operational cost of the system is assessed. It is shown that total operation cost is decreased by 9.69% when the energy storage system is integrated into the power distribution network. Additionally, the solution derived from the robust problem is compared with that of the deterministic problem.

Optimal selection and analysis of microgrid energy system using Markov process

This study investigated the reliability of a microgrid (MG) energy system using the Markov process reliability method. Twenty possible MG configurations based on photovoltaic (PV), wind turbine generator (WTG), biomass generator (BMG), battery bank (BB), diesel generator (DG) and an electric vehicle (EVs) have been considered. This study also selects an optimal configuration based on reliability indices (failure rate, repair time, unavailability, expected energy not supplied (EENS) and loss of load probability (LOLP)), annually load demand, availability of the system and its components. The results show that the hybrid PV/WTG/BMG/BB/EVs/DG microgrid system has the lowest system failure rate (0.01012 /year), repair time (1.1858 hrs), unavailability (0.0120 hrs/year), EENS (4.5 kWh/year) and LOLP (1.36986E-06 /year). This MG configuration has an availability of 99%. The PV array (97.55%) and the changeover switch (99.5%) are the parts of the MG system that are the most reliable. The least stable elements of an MG system are battery (63.64%), diesel generator (80%) and biomass generator (83.89%). PV (62%) and WTG (28% of total) are the main contributors to electricity generation, with the battery, electric vehicles, and diesel generator acting as backup sources. Under different weather and load conditions, the hybrid PV/WTG/BMG/EVs/BB/DG microgrid was found to be a reliable and efficient system for remote areas. This research carries a significant impact for policymakers, government, energy planners, and researchers working on stand-alone microgrid systems as it provides valuable insights into optimizing system configuration for reliability and efficiency.

A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture

Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy optimization method for microgrid energy management in the new power system scenarios. This article constructs a microgrid cloud edge collaboration architecture, which collects interactive network status data through terminal devices and network edge sides. A microgrid energy management model is constructed based on Bi-LSTM attention in the network cloud. And the model is sunk to provide real-time and efficient comprehensive load and power generation prediction output optimal scheduling decisions at the edge of the network, achieving collaborative control of microgrid light load storage. The simulation based on the actual available microgrid data shows that the proposed Bi-LSTM attention energy management model can achieve rapid analysis and optimize decision-making within 7.3 seconds for complex microgrid operation scenarios.

A new control method of hybrid energy storage system for DC microgrid application

AbstractEnergy storage system play a crucial role in safeguarding the reliability and steady voltage supply within microgrids. While batteries are the prevalent choice for energy storage in such applications, their limitation in handling high‐frequency discharging and charging necessitates the incorporation of high‐energy density and high‐power density storage devices to enhance power quality, maintain power balance, and reduce fluctuations. In this study, we introduce a hybrid energy storage system (HESS) solution, combining a battery and a supercapacitor, to address intermittent power supply challenges. The effective management of this HESS is pivotal for constant DC voltage and sustaining microgrid stability. To achieve this, we propose an innovative control strategy called the Adaptive Filter‐Based Method (AFBM) for DC microgrid operation, which prioritizes stable and smooth performance while addressing safety and degradation concerns related to the storage devices. Our control strategy determines the distribution of charging and discharging currents for each storage device within the DC microgrid based on their state of charge. We conducted simulations in MATLAB/Simulink, comparing the proposed AFBM method with the conventional filter‐based approach using identical configurations, parameters, and operational modes. The results demonstrate the superior performance of our proposed system, showcasing improved voltage stability, more equitable load distribution, and efficient charging and discharging of storage devices compared to the traditional method. This research contributes to the advancement of microgrid energy storage solutions, enhancing their reliability and performance.

State-of-the-art review on energy management systems, challenges and top trends of renewable energy based microgrids

Due to the increasing demand for electrical energy worldwide and environmental concerns, modern power systems are looking for a fundamental change. These changes include reducing dependence on the primary electricity grid and using renewable energy sources on a large scale. The emergence of microgrids in electrical energy systems will improve the level of these systems due to technical, economic, and environmental benefits. In this research work, the authors have conducted extensive studies on control methods, types of power sources, and the size of microgrids and analyzed them in tabular form. In addition, the review of communication technologies and standards in microgrids, as well as the review of microgrid energy management systems to optimize the efficiency of microgrids, is one of the main goals of the authors in this article. Also, in this article, the top 10 trends of microgrids in 2023 have been examined to increase the flexibility of network infrastructure, which helps readers to improve their strategic decisions by providing an overview of emerging technologies in the energy industry.

Urban Energy Transition Policy Implementation: Navigating Complex Political Realities and Overcoming Barriers to Effective Strategy Deployment

As the transition from traditional power grids to smart grids gains momentum, the integration of a cyber-layer into the system becomes imperative specially be considering policy and government intervention. However, this advancement also exposes the system to potential cyber threats, raising concerns about its security and resilience. This study focuses on bolstering cyber security resilience and detecting cyber-attacks (CAs) within urban microgrids. Specifically, the research investigates the impact of false data injection cyber-attacks, a significant threat to microgrids, on the steady-state performance of isolated microgrids. To combat such threats, a novel approach utilizing generative adversarial networks (GANs) is introduced to identify attacks on smart metering systems within microgrids. However, the GANs alone cannot make informed decisions, necessitating a decision-making framework. To address this requirement, a novel method based on prediction intervals is proposed to establish decision boundaries, determining the legitimacy of received data. To evaluate the proposed methodology's performance and accuracy, real-world data from a wind farm is employed. Results demonstrate the method's effectiveness and high accuracy in detecting false data injection attacks. Additionally, the study simulates a false data injection attack on a test microgrid to assess its resilience against CAs, revealing that a successful attack can significantly disrupt normal system operations. This research contributes to enhancing urban microgrid energy security by considering policy-informed decision-making, navigating complexity, and surmounting deployment barriers considering policy and government intervention.

Evolutionary Game Dynamics between Distributed Energy Resources and Microgrid Operator: Balancing Act for Power Factor Improvement

This article investigates the intricate dynamics between Distributed Energy Resources (DERs) and the Microgrid Operator (MGO) within a microgrid interconnected with the main grid. Employing an evolutionary game framework, the study scrutinizes the strategic evolution of DERs’ decision-making processes in their interactions with the MGO. Modeled as an evolutionary game, these interactions encapsulate the strategies adopted by DERs, resulting in stable equilibrium strategies over time. Motivated by direct benefits linked to increased active power production, DERs strive to sell all available power, while the MGO focuses on optimizing the microgrid’s overall performance. The study assesses the microgrid’s performance in terms of its power factor, emphasizing the strategic balance DERs must achieve in their active power generation to avoid penalization. This penalization results in decreased individual utility for DERs due to the overall power factor decrease resulting from their prioritization of active power generation. Additionally, the diminished overall power factor implies a decrease in MGO utility. The individual utility of each DER is further influenced by the strategies adopted by other DERs, impacting the penalization factor. Leveraging a modified IEEE 13-node distribution microgrid consisting of three DERs, the study presents case studies encompassing both cooperative and non-cooperative evolutionary game scenarios. These case studies illuminate the intricacies of interactions and the resulting equilibrium outcomes.

Enhanced Multiobjective Optimization Algorithm for Intelligent Grid Management of Renewable Energy Sources

Optimal scheduling of microgrids (MGs) is a crucial component of smart grid optimization, playing a vital role in minimizing energy consumption and environmental degradation. However, existing methods tend to consider only a single optimization and do not consider the multiobjective optimization problem of MGs in a comprehensive and integrated way. This study proposes a comprehensive multiobjective optimal scheduling methodology for renewable energy MGs, incorporating demand-side management (DSM) considerations. Initially, a DSM multiobjective optimization model is formulated, focusing on the load shifting of controllable devices within the MG to refine the electricity consumption structure. This model contemplates the renewable energy consumption of the MG, customer electricity purchase costs, and load smoothness. Subsequently, a multiobjective optimization model for grid-connected MGs, encompassing wind and photovoltaic power generation, is constructed with the dual objectives of economic and environmental optimization for the MG. Ultimately, a multimodal multiobjective optimization algorithm, amalgamating a local convergence index and an environment selection strategy, is proposed to solve the model. The experimental results show that compared with other methods, the proposed method in this paper can reduce the integrated cost by 32.6% and 38.9% in summer and 19.4% and 40.2% in winter. This stands out as a unique contribution in the field of MG optimization, as it integrates DSM considerations into a multiobjective optimization model. This methodology achieves a balance between minimizing energy consumption and environmental degradation while also enhancing economic efficiency.

A decentralized dispatch model for multiple micro energy grids system considering renewable energy uncertainties and energy interactions

Micro energy grids (MEGs) play a vital role in realizing carbon neutrality and efficient utilization of renewable energy resources. This research focuses on optimizing the synergy of MEG interconnections. Given the diverse development paths of different operating entities within the system, information barriers emerge among MEGs, creating great difficulties for the collaborative system management. In response, this paper proposes a decentralized coordinated dispatch model targeting multiple stakeholders within the system. This model accounts for energy interactions between MEGs and the inherent uncertainty associated with renewable energy sources. Specifically, stochastic optimization approach was applied to characterize the uncertainty of renewable energy output by generating stochastic scenarios. Furthermore, it incorporates the analytical target cascading (ATC) method to decouple objective functions and constraints, creating autonomous scheduling sub-models for individual MEGs. This decentralized approach ensures independent modeling and coordinated problem-solving. Simulations verify that (1) the ATC-based inter-MEG energy interaction strategy effectively achieves decentralized coordinated scheduling of multiple MEGs and (2) the decentralized coordinated scheduling solution closely approximates the global optimum while considering the interest of various system entities.

Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid

To aggregate rural biomass energy, distributed power supply, flexibility load, and other resources, a novel structure of the rural Biomass-derived Fuel -based new energy microgrid (BDF-NEM) is proposed. It includes the Biomass Waste Conversion System (BWS), Distributed Renewable Energy (DRE), and Flexible Load Cluster (FLC). The two-stage scheduling optimization framework is designed for the BDF-NEM, minimizing operating costs in the Day-ahead phase and system deviation costs in the Intra-day phase. Uncertainty analysis of wind power plant (WPP) and photovoltaic generators (PV) is conducted using Latin hypercube sampling and the Kantorovich distance approach. To guarantee the equitable allocation of benefits, a benefit allocation mechanism grounded in the enhanced Sharp value approach takes into account operational risks. The analysis of a rural area in northern China demonstrates: (1) After BDF - NEM joined the tiered carbon trading mechanism, although it increased carbon trading costs by 31.27 %, wind power and photovoltaic output increased by 36.08 %, improving new energy consumption; (2) The two-stage scheduling optimization model with daily plan and daily correction can effectively alleviate the deviation caused by wind and solar uncertainty. Although the adjustment cost is 3966.41 yuan, it can avoid the penalty of load loss; (3) The improved benefit allocation based on the Shapley value method reflects the marginal contribution of each system module at the risk level, and when the system modules cooperate with each other, the return is 18.17 % higher than when operating alone, effectively stimulating the enthusiasm to participate in transactions. In summary, the BDF NEM proposed in this article can improve the utilization rate of wind and solar energy, promote the low-carbon development of rural power plants, and achieve local resource allocation in rural areas.

Green Energy Storage Solutions: A Research

One of the key elements of decarbonizing global energy networks and integrating renewable energy sources is green energy storage technology. Energy Storage Systems (ESS), which store surplus produced electricity and make it available on demand, are essential for reducing fluctuations. Electromechanical, electromagnetic, thermodynamic, chemical and hybrid approaches have all been used in the development of energy storage technologies. A comprehensive list of current papers in the literature section is compiled to illustrate the range of advancements in this field. This paper reviews green energy storage systems, focusing on their primary uses. Power utilities will benefit from this thorough analysis of energy storage systems; the researchers choose the finest and newest energy storage technology based on its practicality and affordability. These days, several nations use energy storage systems to plan for future energy needs. Variations in solar radiation cause a solar photovoltaic generator to overproduce electricity. The implementation of a hybrid energy storage system would help to increase the reliability of solar-powered power generation. The microgrid is a crucial component of the smart grid network for solar installations. This study looks at the microgrid's energy storage system for photovoltaic systems. The topologies and storage system configurations of the microgrid are analyzed together with power electronic interference, control systems, and optimization of the energy storage system and renewable sources. a general technique for sizing the HESS of PV systems using design space as well as pinch analysis. HESS scales that link generator ratings to storage capacity are developed in the proper sizes by using pinch analysis to load and resources data.

A real-time energy dispatch strategy based on the energy cooperative optimization complementarity model of multi-microgrids

With the wide application of high proportion of distributed clean energy in regional microgrids, the issue of maximizing the utilization of renewable energy among multi-microgrids has become a research hotspot. Based on the proposed multi-microgrids' energy collaborative optimization and complementation model, a multi-microgrids' energy real-time optimization management and dispatch strategy is proposed that fully considers the real-time complementarity of renewable energy between multi-microgrids and achieves the best coordinated dispatch of energy. Two typical scenarios were set up in the IEEE 33-bus network model for verification, and the synergistic effects of different schemes were compared and set up. The data obtained demonstrate that the dispatch and management strategy proposed in this paper can achieve the maximum integration of renewable energy and the lowest operating cost among multi-microgrids, and it also validates the real-time, feasibility, and effectiveness of the proposed strategy.

Low-carbon and economic optimal dispatching method of micro-energy grid considering pumped storage and comprehensive demand response

Low-carbon and economic optimal dispatching method of micro-energy grid considering pumped storage and comprehensive demand response

Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm.

This study aims to address optimization and operational challenges in multi-energy coupled microgrids to enhance system stability and reliability. After analyzing the requirements of such systems within comprehensive energy systems, an improved fireworks algorithm (IFWA) is proposed. This algorithm combines an adaptive resource allocation strategy with a community genetic strategy, automatically adjusting explosion range and spark quantity based on individual optimization status to meet actual needs. Additionally, a multi-objective optimization model considering active power network losses and static voltage is constructed, utilizing the shuffled frog-leaping algorithm (SFLA) to solve constrained multi-objective optimization problems. Through simulation experiments on a typical northern comprehensive energy system, conducted with a scheduling period of T = 24, the feasibility and superiority of IFWA-SFLA are validated. Results indicate that IFWA-SFLA performs well in optimizing microgrid stability, managing electrical energy flow effectively within the microgrid, and reducing voltage fluctuations. Furthermore, the circuit structure and control strategy of microgrid energy storage bidirectional inverters based on IFWA are discussed, along with relevant simulation results.

Review of Computational Intelligence Approaches for Microgrid Energy Management

Review of Computational Intelligence Approaches for Microgrid Energy Management

Community Microgrid Energy Co-Scheduling Based on Deep Reinforcement Learning and Contribution Mechanisms

Community Microgrid Energy Co-Scheduling Based on Deep Reinforcement Learning and Contribution Mechanisms

Application of IoT and Non-Intrusive Load Monitoring Techniques in Microgrid Energy Management and Monitoring Systems

Application of IoT and Non-Intrusive Load Monitoring Techniques in Microgrid Energy Management and Monitoring Systems

Empowering Microgrid Energy Management with Artificial Intelligence and Model Predictive Control

This paper presents an innovative approach to microgrid energy management by integrating Model Predictive Control (MPC) with Artificial Intelligence (AI), focusing on the application of Long Short-Term Memory (LSTM) networks for load forecasting. We show that AI-enhanced MPC can significantly improve the efficiency and reliability of microgrid energy management. The fundamental results of the LSTM models highlight the effectiveness of our methodology in improving predictive accuracy and operational performance.

Utilizing Artificial Neural Networks for Intelligent Battery Energy Storage Control in Microgrid Energy Management

Utilizing Artificial Neural Networks for Intelligent Battery Energy Storage Control in Microgrid Energy Management