Microgrid Energy Research Articles

Integrated Energy Microgrid Economic Dispatch Optimization Model Based on Information-Gap Decision Theory

To address the problems of “difficult to consume” renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and “electric-heat coupling” characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management.

Potential of technological innovation to reduce the carbon footprint of urban facility agriculture: A food–energy–water–waste nexus perspective

As an emerging form of agriculture, urban facility agriculture is an important supplement to traditional agriculture and one of the ways to alleviate the urban food crisis, but it may generate a high carbon footprint. A comprehensive assessment of urban facility agriculture is a necessity for promoting its low-carbon development. In this study, the carbon footprint of urban facility agriculture under four different technological innovation models was simulated by life cycle assessment and a system dynamics model for a carbon footprint accounting without considering economic risk. Case 1, as the basic case, is Household farm facility agriculture. Case 2 is the introduction of vertical hydroponic technology based on Case 1, Case 3 is the introduction of distributed hybrid renewable energy micro-grid technology based on Case 2, and Case 4 is the introduction of automatic composting technology based on Case 3. These four cases demonstrate the gradual optimization of the food–energy–water–waste nexus in urban facility agriculture. This study further uses the system dynamics model for carbon reduction potential considering economic risk to simulate the diffusion (promotion) scale and carbon reduction potential of different technological innovations. Research results show that with the superposition of technologies, the carbon footprint per unit land area is gradually reduced, and the carbon footprint of Case 4 is the lowest at 4.78e+06 kg CO2eq. However, the gradual superposition of technologies will further limit the diffusion scale of technological innovation, thereby reducing the carbon reduction potential of technological innovation. In Chongming District, Shanghai, under theoretical circumstances, the carbon reduction potential of Case 4 is the highest at 1.6e+09 kg CO2eq, but the actual carbon reduction potential is only 1.8e+07 kg CO2eq due to excessive economic risks. By contrast, the actual carbon reduction potential of Case 2 is the highest with 9.6e+08 kg CO2eq. To fully achieve the carbon reduction potential of technology innovation, it is necessary to promote the scale diffusion of Urban facility agricultural technology innovation by raising the sales price of agricultural products and the grid connection price of renewable electricity.

Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system’s operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency.

Optimal energy management of CHP‐based microgrid examining different types of district heating networks

AbstractHeat recovery by combined heat and power (CHP) systems to provide heat load reduces the cost of microgrid (MG) energy management. However, heat transfer from CHP systems to consumers is limited due to the heat loss of pipelines. The amount of heat loss varies depending on pipeline material and environmental characteristics. Here, a day‐ahead energy management of CHP‐based microgrid, including various types of CHP systems to supply electrical and thermal loads, is performed. In this regard, the accurate model of heat transfer losses (HTL) through pipelines in the energy management problem is considered. Moreover, the optimal scheduling of different types of MGs in terms of HTL specifications is compared with each other. Evaluating the simulation results, it is inferred that accurate modeling of HTL in the energy management problem has a significant impact on the optimal scheduling and operation cost of MG. To solve the optimization problem, a new enhanced imperialist competitive algorithm (E‐ICA) is examined and validated. The E‐ICA method improves the efficiency of the classical ICA method to prevent getting stuck in local optimums by enhancing exploration and exploitation. The results of the energy management problem show E‐ICA superiority compared to other evolutionary algorithms.

Hybrid DC-AC microgrid energy management system using grey wolf optimized neural network

Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures.

Micro-grid with Vehicle-to-Grid Technology and DC Quick Charging Architecture

Abstract: Batteries from electric vehicles (EVs) can be used as potential energy storage systems in micro-grids. By storing energy when there is an excess (grid-to-vehicle) and supplying energy back to the grid (vehicle-to-grid), they can aid in micro-grid energy management. It is necessary to establish appropriate infrastructure and control mechanisms in order to implement this idea. This study presents an architecture for level-3 EV quick charging in a Vehicle to Grid-Grid to Vehicle system on a microgrid. A DC rapid charging station is modelled as part of a test micro-grid system for connecting EVs. Vehicle to Grid-Grid to Vehicle power transfer is demonstrated through simulation research. According to test results, EV batteries actively regulate power in the micro-grid using Grid to Vehicle and Vehicle to Grid operating modes. The controller provides good dynamic performance in terms of dc bus voltage stability, and the charging station design ensures minimal harmonic distortion of gridinjected current.

An optimized fuzzy logic-based energy management strategy for renewable energy microgrid with hydrogen storage system

An optimized fuzzy logic-based energy management strategy for renewable energy microgrid with hydrogen storage system

JAYA Algorithm-Based Energy Management for a Grid-Connected Micro-Grid with PV-Wind-Microturbine-Storage Energy System

In this study, the Jaya optimization algorithm is used to address the micro-grid energy management optimization problem using a hybrid PV-wind-microturbine-storage energy system. The main goals of this study are to reduce environmental pollution, increase microturbine operating efficiency, and minimize the cost of power generated. The overall objective of the proposed optimization method employed in the PV-WECS system is to run the PV-WECS systems at full capacity while running the microturbine when the PV-WECS systems are unable to produce all of the required power. The amount of emissions and costs of generated energy are reduced when BESS is used in the microgrid system. Furthermore, it is observed from the results that there is about 61.39% cost saving in the micro-grid operational costs and 38% carbon emissions reductions using the proposed optimization algorithm compared to the other metaheuristic algorithms used in this study. To demonstrate the appropriateness and supremacy of the proposed algorithm over the various optimization techniques for energy management of the proposed micro-grid systems, simulation results from the proposed algorithm are compared with those from other population-based metaheuristic algorithms, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Genetic Algorithms (GA). It is clear that the proposed algorithm outperforms and produces better results than the existing metaheuristic optimization techniques. More importantly, it illustrates the viability and efficacy of the proposed JAYA optimization approach in addressing the issue of energy management for large-scale power systems.

Survey of Sustainable Energy Sources for Microgrid Energy Management: A Review

Renewable energy sources are nowadays a viable choice to satisfy the rising energy consumption and promote the advancement of sustainable development. These systems are integrated into microgrids using a variety of technological solutions to ensure customer communication and distributed generation facilities in an optimal way. Energy management in microgrids refers to the information and control system that provides the necessary functionality to guarantee that the generating and distribution systems produce energy at the lowest expenses. This study analyzes the various optimization objectives, constraints, problem-solving techniques, and simulation tools used for connected and freestanding microgrids. It reviews the literature on energy control in microgrids powered by sustainable energy. Energy storage technology is also viewed as an intriguing alternative to managing the intermittent nature of renewable energy because of its advanced techniques, increased energy efficiency, and capacity to perform tasks such as frequency response. The final phase suggests future suggestions, particularly for the model-based prediction of energy storage systems.

IoT-Based Technologies for Wind Energy Microgrids Management and Control

As the world continues to shift towards clean and renewable energy sources, wind energy has emerged as a promising alternative to traditional fossil fuels [...]

Wind SRG-Based Bipolar DC Microgrid with Grid-Connected and Plug-In Energy Supporting Functions

Although a switched reluctance generator (SRG) is not the mainstream wind generator, it possesses the application potential and is worth developing for its many structural merits and high developed power ability. This paper presents a wind SRG-based bipolar DC microgrid having grid-connected and plug-in energy supporting functions. First, a surface-mounted permanent magnet synchronous motor (SPMSM)-driven wind turbine emulator (WTE) is established. Next, the wind SRG with an asymmetric bridge converter is developed. Good generating characteristics are obtained through proper designs of power circuit, commutation mechanism, external excitation source, voltage and current controllers. Third, a DC/DC boost interface converter and a bipolar voltage balancer are constructed to establish the 500 V microgrid bipolar DC-bus. To preserve the microgrid power supplying quality, a battery energy storage system (BESS) with bidirectional DC/DC interface converter is equipped. A dump load leg is added across the bus to limit the DC-bus voltage under energy surplus condition. In load side, a three-phase bidirectional load inverter is developed, which can be operated as a single-phase three-wire (1P3W) inverter or a three-phase three-wire (3P3W) inverter. Good sinusoidal voltage waveform and regulation characteristics are obtained using the proportional-resonant (PR) control. The microgrid to load and microgrid to grid operations are conductible. Finally, to further improve the powering reliability of microgrid, a three-phase T-type Vienna switch-mode rectifier (SMR) based plug-in energy supporting scheme is developed. When the microgrid energy shortage occurs, the possible harvested energy can be used to supply the microgrid.

Multi-layer energy management of smart integrated-energy microgrid systems considering generation and demand-side flexibility

This paper proposes a stochastic framework for the operation scheduling of integrated renewable-based energy microgrid systems. The proposed model presents comprehensive scheduling that simultaneously considers total generation costs, generation flexibility, and demand-side flexibility. This operation management approach is modeled as the tri-layer framework. At the first layer, the microgrid system attempts to minimize daily operation costs considering the probabilistic behavior of renewable generation, signal prices, and loads. The desalination unit and water tank storage have been incorporated into the proposed structure to supply potable water for the system. The second layer reschedules the obtained management of the first layer to increase the thermal flexibility and electrical flexibility of local generation resources. To this end, the integrated energy system tries to maximize the spinning reserve of the local energy resources in the second layer. The last layer is responsible to increase demand-side flexibility. In this layer, a hybrid max–min and min–max approach is developed to uniform the load profile by demand-side management programs. The proposed framework is applied to the general structure of energy systems and the day-ahead results demonstrate that the electrical generating flexibility index and thermal generating flexibility index are improved by 22.98% and 34.64% in the proposed model.

Consensus-Based Distributed Optimal Dispatch of Integrated Energy Microgrid

In recent years, the energy form of microgrids is constantly enriching, while the decentralization requirements of microgrids are constantly developing. Considering the economic benefits of an integrated energy microgrid (IEM), this paper focuses on the distributed optimal dispatch of IEM based on a consensus algorithm. The microgrid structure and multi-agent system are combined organically to get the decentralized architecture of IEM. This paper takes the incremental cost rate of each unit in IEM as a consensus variable. Based on the consensus theory, iterative optimization is carried out to achieve the optimal economic operation and power supply-demand balance of IEM. The distributed optimal dispatch is realized, and the convergence of the algorithm is proved. The experiment is carried out with LabVIEW and MATLAB and verifies the effectiveness of the algorithm. The results show that the distributed optimal dispatch algorithm can effectively reduce the power generation cost of the integrated energy system.

Deakin microgrid digital twin and analysis of AI models for power generation prediction

To achieve carbon neutral by 2025, Deakin University launched a AUD 23 million Renewable Energy Microgrid in 2020 with a 7-megawatt solar farm, the largest at an Australian University. A web-based digital twin (DT) is developed to provide operators with intelligence and insights through several AI-driven capabilities. Accurate and computationally efficient power generation prediction is one of the critical elements in this DT. To this end, we researched the literature and identified the commonly used Machine Learning-based prediction models and compared them computationally using power generation and weather sensor data obtained from the solar farm. From the computational experiments, we find that, overall, Artificial Neural Network (ANN) has achieved the highest R2-score (0.944) and the lowest RMSE (14.848). To obtain further insights, we compared the methods using our two novel metrics, the x-percentile Closeness scores and the x-percentile Absolute error scores. The new metrics provide us with a spectrum to measure the consistency and robustness of the prediction methods instead of just a single value. Further, power generation can fluctuate substantially, and a prediction model should be accurate regardless of the magnitude of the output, hence measuring the relative error has its merits. By our two new metrics, using the data from our Deakin Microgrid, Random Forrest (RF) outperformed the other methods tested, with the smallest absolute relative error across the whole spectrum (from 0.011 to 0.457). RF is also the fastest in model training time at 4.894 s and XGBoost came second at 5.115 s–a big contrast to ANN at 144.102 s. All prediction times are under 1 s. RF is therefore used as a power prediction algorithm in our Deakin Microgrid Digital Twin.

Adaptive global energy optimal management strategy based on the Pontryagin’s minimum principle: An isolated microgrid example

Optimal control is a common control strategy to solve complex non-linear systems. Pontryagin’s minimum principle, as a typical optimal control theory, is widely used in energy management systems. Due to the randomness of new energy generation and the diversity of load power consumption, microgrid energy management system is a complex non-linear stochastic system. The efficient operation of microgrid energy management strategy largely depends on the real-time performance of the control algorithm. Pontryagin’s minimum principle is an offline control strategy, whose co-state variable λ depends on expert experience and needs to be manually adjusted according to different working conditions, so it is difficult to achieve real-time optimal control. First, the energy management system model of island-type microgrid is established in this paper. In order to improve the economy of power generation and prevent battery over-charge or over-discharge, the state of charge (SOC) change of battery pack is comprehensively considered, and the fuel cost and pollutant treatment cost are taken as the objective function. Second, an adaptive Pontryagin’s minimum principle energy management strategy is proposed. By introducing the penalty mechanism and adjusting the parameters adaptively, the problem that the open-loop control of Pontryagin’s minimum principle needs to adjust the co-state variables in real time is solved. Comparative experiments show that compared with Pontryagin’s minimum principle and particle swarm optimization, adaptive Pontryagin’s minimum principle can not only limit battery SOC within the ideal range but also effectively reduce the daily operation cost of microgrid. Experimental results verify the effectiveness of the proposed algorithm.

Optimization of Microgrid Energy Cost with Electrical Vehicles and Demand Response

Challenges with current power systems include increasing energy costs and Greenhouse gasses. Participation in distributed generators (DGs) and demand response (DR) strategies inside the distribution network may solve these issues. This paper's primary objective is to suggest an energy management system (EMS) strategy for campus microgrid reduce all the issues mentioned above. To practice this strategy a microgrid is considered. Conventionally the microgrid uses a utility grid to fulfill load demands. But in the proposed model the integration of a micro turbine, photovoltaic (PV) system, battery energy storage system (BESS), and Electric vehicles is proposed. Mix integer linear programming MILP technique is used in MATLAB to solve the non-linear optimization problem. The energy management system EMS and demand response policies are used to optimally schedule the available resources and reduce energy costs. The integration of EVs is also observed as source and load. The simulation results are discussed and different case studies are analyzed. Simulation results show that 46% cost saving is observed while using EVs in the system as source. The real-time energy interruption problem is also observed in this paper.

Intelligent Microgrid Energy Management Method Based on Deep Q Network (DQN)

With the high proportion of renewable energy in microgrid, it brings more and more challenges to the energy management of microgrid. In the meantime, the rise of artificial intelligence has brought new solutions for microgrid energy management. This paper mainly studies the method of integrating deep learning with microgrid. When a variety of flexible resources are connected to microgrid, different energy sources can be coordinated and controlled by determining preferential resources, various demand control signals and real-time electricity prices. In this paper, a deep Q network (DQN) based on deep reinforcement learning algorithm is implemented and combined with a new microgrid model. The numerical results show the effectiveness, adaptability and stability of the deep reinforcement learning method in the optimization control problem of microgrid, and also show the possibility of combining artificial intelligence with power system.

An Overview on Potential Problems and Solution of Hybrid Microgrids

The world's energy demand has increased significantly in the past two decades, and there is a growing concern over energy security and adequacy. The use of renewable energy sources and microgrids can play a significant role in ensuring a sustainable energy future. Effective energy resource planning is critical to achieving global commitments towards sustainable development. It involves managing and optimizing the supply and storage of energy resources, including renewable energy sources. Microgrids can contribute to this effort by improving the efficiency of energy systems and reducing energy losses. Hybrid microgrids, which combine multiple energy resources and storage systems, offer several benefits for energy management and optimization. However, integrating renewable energy sources into the existing energy system can pose several challenges, such as voltage and frequency regulation, low-inertia issues, and system stability. Researchers have been working on developing advanced control strategies and optimization techniques, including metaheuristic optimization techniques, to address these challenges and improve the performance of hybrid microgrids. Overall, energy resource management and the use of microgrids and renewable energy sources are essential for achieving a sustainable energy future and mitigating the impacts of climate change.

Design and Evaluation of a Micro-Grid Energy Management Scheme Focusing on the Integration of Electric Vehicles

Design and Evaluation of a Micro-Grid Energy Management Scheme Focusing on the Integration of Electric Vehicles

Techno-economic optimal planning of an industrial microgrid considering integrated energy resources

Microgrids have benefited energy systems by getting rid of the abundant use of traditional energy resources, replacing them with distributed generations (DGs). The use of DGs in microgrids not only provides clean and green energy but also reduces the gigantic losses in transmission and distribution lines. Widespread adoption of renewable energy in microgrids saves customers from high fuel prices and provides a sustainable replacement for future fossil fuel depletion. Microgrids have proven to be the latest and most advanced power systems due to their new capabilities to operate in both grid-connected and isolated modes. Microgrids use multiple energy sources simultaneously, which can offset the intermittent behavior of renewable energy sources. In this study, we planned and optimized an industrial microgrid with an annual increase in load, which contains dispatchable generation, non-dispatched generation, and energy storage. In addition, to test the different behavior of a microgrid operating independently or in grid-tied mode, four typical days were selected to show the load demand and solar and wind energy forecast data for the entire year. The power output curves for each typical day were analyzed for every year in both stand-alone and grid-connected modes. Furthermore, to validate the feasibility of the microgrid, the investment and operations costs were also calculated in the paper.