Microgrid Energy Research Articles

A Blockchain Based Energy Management System for Microgrids

Abstract: The use of renewable energy sources is increasing, but their volatility makes it difficult to maintain a balance between energy generation and consumption. To address this issue, we propose a peer-to-peer microgrid energy market where consumers and prosumers can trade self-produced energy and keep the profits. This encourages investment in renewable energy plants and local balancing of supply and demand. To make this possible, we propose a blockchain-based microgrid energy market without central intermediaries. The permissioned blockchain framework ensures that only eligible participants can join, and each entity maintains its own transaction information while being able to retrieve information from other entities with permission. This model is distributed and promotes plug-and-play. We have developed a WebApp SET, which uses a blockchain wallet to sign in users and allow them to share their energy needs anonymously. The simulation results show that this method is feasible and suitable for privacy protection and P2P energy management in a decentralized energy system.

Energy Performance and Control Strategy for Dynamic Façade with Perovskite PV Panels—Technical Analysis and Case Study

Effective implementation of renewable energy sources (RES) is one of the main challenges in regard to the organization of local energy microgrids with buildings. One of the solutions is the use of dynamic building façades with photovoltaic (PV) panels, in particular the innovative perovskite solar cells (PSCs). This paper describes a case study performed on a pilot installation of perovskite PV panels located in Poland, Central-Eastern Europe. Results of preliminary measurements on this installation are provided in terms of verifying its energy efficiency and the possibility of selecting settings for the façade dynamics control system. Our experiments have considered the sun-tracking mechanism and its energy consumption as well as the impact of weather conditions at different times of the year. The energy efficiency results for the PV system, with average levels below 10%, are rather low. Therefore, even small energy savings in the operation of the PV system itself are significant. Changes in control scenarios for sun-tracking have been proposed and have obtained a reduction from 5% to 1% of energy consumption in autumn and from almost 3.2% to 0.6% in spring, in relation to overall energy produced by the PV system. The need for further experimental research from the perspective of the development and extension of the analyzed installation is pointed out as well.

An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

A microgrid is an autonomous electrical system that consists of renewable energy and efficiently achieves power balance in a network. The complexity in the distribution network arises due to the intermittent nature of renewable generation units and varying power. One of the important objectives of a microgrid is to perform energy management based on situational awareness and solve an optimization problem. This paper proposes an enhanced multi-objective multi-verse optimizer algorithm (MOMVO) for stochastic generation power optimization in a renewable energy-based islanded microgrid framework. The proposed algorithm is utilized for optimum power scheduling among various available generation sources to minimize the microgrid’s generation costs and power losses. The performance of MOMVO is assessed on a 6-unit and 10-unit test system. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms for multi-objective optimization.

Optimal schedule of 100% renewable energy microgrid considering demand response of EVs

With the breakthrough of key technologies of renewable energy, microgrid is gradually developing towards 100% renewable energy. However, aiming at the characteristics of renewable energy power such as randomness and intermittency, the microgrid needs to cope with the “double” fluctuations from the power source side and the load side, which will pay more costs for absorbing renewable energy. To implement “source-grid-load-storage” integration, this research establishes a hierarchical scheduling model for microgrid, which considers the demand response (DR) characteristics of electric vehicle (EV) users and is 100% powered by renewable energy. The model includes two layers, EV layer and renewable energy microgrid (REMG) layer. The first layer is EV layer. This layer formulates an appropriate charging price according to the user’s price response characteristics, and initially regulates the fluctuation of the original load while taking into account the customer satisfaction. The second layer is REMG layer. Based on the preliminarily optimized load, this layer adjusts the renewable energy rejection, the interactive power of main grid, and the output of pumped storage and battery storage under grid connection mode and island mode. The REMG layer can reduce system operating costs, while suppressing interactive power fluctuations and increasing the utilization rate of renewable energy. Simulation shows that the proposed optimal scheduling model can achieve win-win between EV and users.

Learning assisted column generation for model predictive control based energy management in microgrids

Model predictive control is an effective approach for microgrid energy management. However, the main downside of such method is its expensive online computational cost, which is not amenable to most practical microgrid implementations. To address this issue, we propose a deep neural network assisted column generation approach that can accelerate the solution procedure of model predictive control. In each iteration, our approach leverages different deep neural networks to predict the optimal solutions of all the subproblems in column generation, which can accelerate the computation of all the subproblems and the entire process of column generation. The pre-existing knowledge of the microgrid is also exploited to guarantee the feasibility of the neural network outputs using multi-parametric programming theory. The numerical results show that our approach leads to a reduction in computational time of approximately 50% in a medium-sized microgrid compared with the full mixed integer solution based on traditional branch and bound method, while the optimality loss is only 0.02% in terms of operating costs.

ADAPTIVE CONTROL ELECTRICITY CONSUMPTION IN LOCAL MICROENERGY SYSTEM WITH POLYGENERATION BASED ON CLUSTERING OF A CONDITIONAL DYNAMIC TARIFF

The paper is devoted the increasing the efficiency to use of traditional and renewable sources in microenergy systems. The development of combined power supply systems of local objects with the stochastic nature generation of renewable sources has led to a number of fundamental problems.
 It is associated with the lack of a decision-making system regarding the ratio of power sources of electricity and the coordination of their generation levels with the electricity consumption schedule at the stage of designing such microgrids. The development of microenergy systems with polygeneration is conditioned by the possibility of technical and information support for the formation of the energy balance of local objects to minimize the costs of their electricity supply.
 By applying elements of inductive system-analytical technologies, an approach to solving the problem of modeling dynamic scenarios of energy management in local microgrids with polygeneration based on determining the cost of electricity and adaptive coordination of the electricity consumption schedule in real time is proposed.
 On the basis of statistical data, modeling was carried out and daily graphs of changes in the current cost of electricity with half-hour discreteness of the calculation were obtained, and the clustering procedure of the conditional dynamic tariff was showed.
 The results can be applied in the evaluation of electricity pricing in microgrid energy management systems or the development of control systems for Smart grids with renewable sources.

A Transdisciplinary Blueprint for Energy Markets Modelled after Mycorrhizal Networks

The resource distribution strategies of trees and plants in the forest are applied here as inspiration for the development of a blueprint for transactive, hybrid solar and storage microgrids. We used the Biomimicry Institute’s Biomimicry Spiral and their toolbox as a design methodology to inform the structural and functional characteristics of this peer-to-peer microgrid energy market and propose its utility in addressing some of the challenges associated with grid integration of distributed energy resources (DERs). We reviewed literature from the ecological domain on mycorrhizal networks and biological market theory to extract key insights into the possible structure and function of a transactive energy market modeled after the mutualism between trees and mycorrhizae. Our process revealed insights into how overlapping, virtual energy markets might grow, contract, adapt, and evolve through a dynamic network-based protocol to compete and survive in rapidly changing environments. We conclude with a discussion of the promise and limitations involved in translating the derived conceptual blueprints into a cyber-physical system and its potential for deployment in the real world as a novel energy market infrastructure.

Frequency Regulation Strategy of Two-Area Microgrid System with Electric Vehicle Support Using Novel Fuzzy-Based Dual-Stage Controller and Modified Dragonfly Algorithm

Energy in microgrids (MGs) can now be generated from a variety of renewable sources, but their effective and sustainable use is dependent on electrical energy storage (EES) systems. Consequently, the expansion of MGs is greatly reliant on EES systems. The high infiltration of electric vehicles (EVs) causes some problems for the smooth functioning of the electric power system. However, EVs are also able to offer ancillary services, such as energy storage, to power systems. The research presented in this paper aims to develop a novel frequency regulation (FR) approach for biogas diesel engines (wind), the organic Rankine cycle (ORC), and solar-based two-area islanded microgrids with EVs in both areas. This article discusses the introduction of a fuzzy logic controller (FLC) for FR with scaled factors configured as proportional integral (PI) and proportional derivative with filter (PDF), i.e., a FLC-SF-PI-PDF controller. A recently created modified dragonfly algorithm is used to determine the best values for the controller parameters. To justify the effectiveness of the proposed controller with the presence of EVs, the execution of the proposed controller is associated with and without the presence of EVs. This research also looks at the different uncertain conditions, non-linearities, and eigenvalue stability analysis to validate the supremacy of the proposed approach.

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.