Industrial Microgrid Research Articles

A Coordinated Emergency Frequency Control Strategy Based on Output Regulation Approach for an Isolated Industrial Microgrid

Constructing isolated industrial microgrids with wind power is beneficial for improving the economic benefits of high-energy-consuming production, such as the electrolytic aluminum industry. Due to the specialized structure of industrial microgrids and the unique characteristics of the electrolytic aluminum load (EAL), the common emergency frequency control methods do not apply to the specific operational requirements of isolated industrial microgrids. Since EALs have huge regulating capacities and fast responses, this paper proposes a coordinated emergency frequency control scheme to deal with power disturbances in isolated industrial microgrids. The coordinated frequency control model of an industrial microgrid considering demand-side participation is derived. With the help of output regulation theory, a practical, feasible coordinated frequency controller is designed by introducing frequency deviation and power disturbance as feedback control signals. The proposed control scheme achieves reserve power distribution between the generation and demand sides. The microgrid frequency can be maintained within a permitted range in the presence of large power imbalances. The simulation results conducted in an actual isolated industrial microgrid validate the effectiveness and dynamic performance of the proposed control scheme.

Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

Assessment of Technoeconomic Opportunities in Automation for Nuclear Microreactors

Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.

Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.

Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea

With the rise of environmental policies and advanced technologies, power systems are transitioning from centralized to decentralized systems, incorporating more distributed energy resources (DERs). This shift has increased interest in the operational functions of microgrids (MGs). The “Renewable Energy 100%” (RE100) campaign is pushing companies to adopt renewable energy. In South Korea, industrial complex microgrids (ICMGs) aim to achieve RE100 through corporate power purchase agreements (PPAs) with renewable energy providers. ICMGs need to operate in both grid-connected and islanded modes, facing challenges in power transactions due to different operating agents. This study proposes a decentralized optimal power flow (OPF) method using the separable augmented Lagrangian relaxation (SALR) algorithm to solve these power transaction problems without disclosing internal information. The proposed method decomposes the centralized OPF problem into subproblems for each ICMG and solves them in a distributed manner, sharing only transaction prices and amounts. Numerical results from the case study validate the effectiveness of the proposed method.

Implementing a Behind-the-Meter Commercial and Industrial Microgrid: Key lessons in grounding, protection, and control

Implementing a Behind-the-Meter Commercial and Industrial Microgrid: Key lessons in grounding, protection, and control

Isolated industrial micro-grid demand response with assembly process based on distributionally robust chance constraint

In this paper, demand response for isolated industrial micro-grid is investigated, in which the typical manufacturing assembly process, battery, conventional generator and PV generation are contained. The typical manufacturing assembly process is modelled by fully considering the coupling relationships between tasks and buffers. On the other hand, the uncertainty for PV generation is taken into account, where the data-driven distributionally robust chance constraints are designed and the error between day-ahead forecast and real-time operation is dealt with by the reserve capacity of conventional generator. In addition, the distributionally robust chance constraints and the expectation operator are reformulated as linear constraints, which can be solved by commercial solver directly. Finally, a case study using lithium-ion battery factory is presented to prove the superiority and effectiveness of the proposed model.

Design of daily load profiles in environmentally friendly commercial and industrial microgrids

The purpose of the study is to solve the problem of design of daily load profiles for optimal control of an environmentally friendly commercial and industrial microgrid (CIM) (power is generated only by renewable energy sources) connected to the power system by a power transmission line. This goal is achieved by adjusting the planned daily load profiles of consumers located on the territory of the CIM. The adjustment means shifting power consumption to another time of the day in question (power consumption is delayed). The problem of the delayed power allocation is represented as an optimization multiple knapsack problem that adapts to the problem-solving process. The proposed algorithm was tested on a 6-node system according to the scenario that involved adjustment of the load profile in the CIM to ensure that the power flow from the power system remains within specified limits. Compliance with the limits guarantees uninterrupted power supply from the power system, which is a fundamental requirement when developing load profiles. Experiments were carried out to evaluate the delivery of uninterrupted power supply to CIM consumers depending on the initial data. The findings indicate that the disruptions in power supply to CIM consumers are completely eliminated if the load can be divided to shift it to other hours of the day and when the load of 0.151 MW is disconnected in operating state 7. The total load should be divided into at least three parts. Disconnection of 0.151 MW is performed to prevent disconnection of the commercial and industrial microgrid from the power system, which would result in a power deficit of 4.652 MW.

A ground fault location algorithm in double‐circuit transmission lines with T‐off connection to an industrial microgrid by using current and voltage phasors information of a single terminal

AbstractThe paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm.

A profitability optimization approach of virtual power plants comprised of residential and industrial microgrids for demand-side ancillary services

Nowadays, the high proportion of renewable energy integration continuously boosts the grid’s demand for regulation capacity. But the reasonable utilization remains challenged between the regulation capacity of demand-side resources and the complementary characteristics between market players. This paper primarily investigates the issues of complementary synergy among different types of resources and different microgrids. This is currently a focal point in enhancing the regulation capacity of the power grid. This study considers a variety of resources to form microgrids with distinct characteristics, including residential and industrial loads as typical demand-side resources, which have distinct power and energy features in the demand-side regulation market. Furthermore, this study considers virtual power plants (VPPs) consisting of multiple microgrids (MMGs) in the energy market and regulation market as a two-stage problem with day-ahead scheduling and real-time energy consumption control. Based on this, we develop optimal scheduling models and an adaptive MG participation mechanism to maximize the economic benefit of the VPP and help ensure the frequency stability of the power grid. Innovatively, this work designs an adaptive MG participation mechanism, when the operational cost of the adaptive microgrid is high and the regulation capacity of other microgrids is sufficient to provide frequency support for the grid, the adaptive microgrid will be set as backup. Case studies are conducted to verify the proposed profitability optimization method for VPP.

Joint production and energy supply planning of an industrial microgrid

Joint production and energy supply planning of an industrial microgrid

Design of daily load profiles in commercial and industrial microgrids based on renewable energy sources

The commercial and industrial microgrid (CIM) based on renewable energy sources is a solution to the environmental problems. This paper examines CIM based on renewable energy sources connected to the power system by one power transmission line. The power flow from the power system and the limits of its deviations are determined in advance. Compliance with the limits is a basic condition when design of daily load profiles in CIM, because this guarantees uninterrupted power supply from the power system at normal prices. The research is aimed at solving the problem of generating daily load profiles for CIM. The problem is solved using the resources of active consumers registered in the demand management program based on the “load shifting” technique and considering the resources of energy storage systems. An algorithm for generating daily load profiles is developed, which allows the use of various methods for calculating the optimal allocation of shifted load. The proposed algorithm was tested on a 6-node circuit. The resulting daily load profiles satisfy the specified conditions.

Hybrid algorithm for industrial micro-grid demand response with assembly process based on distributionally robust chance constraint

Hybrid algorithm for industrial micro-grid demand response with assembly process based on distributionally robust chance constraint

Interconnecting industrial multi-microgrids using bidirectional hybrid energy links

Sharing and exchange energy among nearby industrial microgrids are crucial, especially with high energy requirements for their production targets and costly energy storage systems that may be oversized for their operations. Facilitating energy exchange can provide an economic advantage for industrial production by utilizing cheaper energy sources and reducing production costs. This manuscript presents an efficient approach for transferring large energy packets with minimal energy losses using high-voltage direct current (HVDC) energy transmission. The manuscript methodology focuses on implementing an industrial multi-microgrid using a modular multilevel converter. This converter utilizes two power link channels: a three-phase AC and an HVDC link, creating a hybrid energy transmission between microgrids. When a substantial amount of energy to transfer, the HVDC method enhances overall efficiency by reducing copper losses and mitigating issues associated with the AC link, such as harmonics and skin effects. The modular multilevel converter topology offers high flexibility and the use of fewer converters. Additionally, the HVDC link eliminates distance restrictions for energy transfer between industrial microgrids. A case study illustrates the functionality of this topology, demonstrating optimized power transfer and decreased energy losses. This methodology allows industrial microgrids to enhance energy efficiency and productivity while minimizing operational costs.

Smart Management of Energy Storage in Microgrid: Adapting the Control Algorithm to Specific Industrial Facility Conditions

The article introduces a method for optimizing energy storage system scheduling in industrial microgrids. It employs a PSO-based heuristic algorithm using daily generation and load forecasts. The objective is economic optimization, minimizing energy costs, and maximizing profits. Market energy prices and distributor tariffs are the base of the objective function. An algorithm maintains the plan by controlling storage power based on real-time microgrid measurements, aligning with the intended power exchange curve. Due to PSO’s ability to perform multidimensional optimization, it is possible to find the global optimum of the objective function. To validate the practical applicability of this approach, it is exemplified through its implementation within a real-world industrial microgrid setting. The presented results indicate the method’s effectiveness but also show its weaknesses. For the two considered cases, a decrease in operating costs of 6.7% and 10.8% was achieved, respectively. On the other hand, the best results are obtained for shorter forecasts, which is why the algorithm, despite long planning periods, revises the ESS operation plan whenever there are significant deviations between the forecast and the actual power.

Optimizing Energy Utilization in the Weaving Industry: Advanced Electrokinetic Solutions with Modified Piezo Matrix and Super Lift Luo Converter

This project aims to revolutionize energy utilization in the textile industry by optimizing Modified Piezo Matrix-based electro-kinetic energy generation from weaving power looms. It focuses on harnessing kinetic energy from two sources—the open-end head frame and the Weaving X-Y Shuttle box—both generating sequential kinetic energy during weaving. The experimental setup employs a specialized configuration: a 4(4x2) matrix piezo for the open-end head frame and a 2(2x10) matrix piezo for the Weaving X-Y Shuttle box. This arrangement efficiently captures and converts kinetic energy, resulting in remarkable energy outputs of 9.51 Hp and 2.60 Hp for the Open end Head frame and Weaving X-Y shuttle box, respectively. To further enhance energy utilization and integration, a second-level DC-DC power conversion approach is employed, utilizing the Super Lift Luo converter(90%η). This strategy ensures efficient energy transfer and seamless integration with the Industrial DC microgrid. The project’s objectives encompass minimizing reliance on fossil fuels, promoting sustainability, and highlighting the potential of electrokinetic solutions for industrial energy optimization. By tapping into previously overlooked kinetic energy sources and maximizing their conversion, this project presents a pioneering effort toward sustainable practices in the textile sector, contributing to environmentally-conscious production methods.

Energy management of multi-microgrids with renewables and electric vehicles considering price-elasticity based demand response: A bi-level hybrid optimization approach

This article develops an energy management scheme for multi-microgrid systems involving the scheduling strategy of distributed resources, renewables and plug-in electric vehicles penetration to enhance the economic-environmental benefits of the system. The proposed framework also integrates the price elasticity-based augmented demand response program into the energy management (EM) problem to investigate the influence of dynamic energy pricing and incentives provided to consumers. The local power trading between adjacent microgrids and external trading with the primary grid through each microgrid's generation resources scheduling facilitates the microgrids’ cost reduction while maintaining supply-demand balance in the system. But, the ubiquitous intermittent nature of renewables, load demands, and vehicle charging imply complexities in the system. Hence, this work formulates the EM problem incorporating the uncertainties of renewables and load demands by the worst-case realization and employs a bi-level hybrid grey wolf-whale optimization; the first level is optimized from the microgrid's standpoint to obtain the dynamic pricing, and the load demands modification, whereas the operational cost of the multi-microgrid system and the utility profit are optimized in the second level. The proposed approach is studied on a test system consisting of residential, commercial and industrial microgrids; substantial simulation results are presented to validate the effectiveness.

Uncovering the energy-carbon-water footprint of waste rubber recycling: Integrated environmental and economic perspectives

The commitment to waste management has gained increasing momentum as global waste generation continues to skyrocket and threaten the environment. However, detailed assessments and clear insights remain absent to address the global waste utilization conundrum. This study evaluated the impact-oriented energy, carbon, and water (ECW) footprints of three typical scenarios for a waste recycling activity (i.e., waste rubber recycling) from environmental and economic dimensions, and explored key factors, nexus characteristics, and optimization measures. Results indicated that the rubber powder as an asphalt modifier scenario had a 93% greater environmental impact and 87% higher economic cost compared with the pyrolysis and reclaimed rubber production scenarios. Key processes, such as direct processes, electricity generation, and transportation, were identified as the major contributors to the ECW footprints, with the internal costs of raw materials, equipment, and taxes coupled with the external costs of human health dominating the economic impact. The nexus analysis results highlighted the urgent need to optimize the energy system for waste rubber recycling. Greening the production process revealed the benefits, with natural additives mitigating 85% of the environmental burden and 97% of the external costs compared with conventional additives. Industrial green microgrids, clean energy generation, proximity waste management, and electrified transportation were explored to foster sustainable optimization of waste rubber recycling systems. Moreover, a joint tax-subsidy mechanism for rubber production-recycling systems can stimulate recycling-oriented product design and increase the motivation to recycle waste rubber.

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.