Microgrid Energy Management System Research Articles

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs

Recent advancements in sensor technologies have significantly improved the monitoring and control of various energy parameters, enabling more precise and adaptive management strategies for smart microgrids. This work presents a novel model of an energy management system (EMS) for grid-connected polygeneration microgrids that allows optimizing the management of electrical storage systems, electric vehicles, and other deferrable loads such as heat pumps. The main novelty of this model is that it incorporates both climate comfort variables and the consideration of the degradation of the energy storage capacity in the control strategy, as well as a penalty for the dumping of surpluses. The model has been applied to a smart, sustainable building as a case study. The results show that the proposed model is highly adaptable to diverse weather conditions, minimizing renewable energy losses while satisfying the energy demand and providing comfort to the building’s users. The study shows (i) that EVs’ dynamic charging schedules play a crucial role, (ii) that it is possible to minimize a battery’s degradation by optimizing its cycling, averaging one cycle per day, and (iii) the critical impact of seasonal weather patterns on microgrid energy management and the strategic role of EVs and storage systems in maintaining energy balance and efficiency.

A distributed energy management system of autonomous DC microgrid in office buildings based on DC bus signaling

With the development of renewable energy, the character of building energy system gradually transitions from electricity consumer to an integrated role that considers both consume and supply side. Abundant flexible resources within the buildings remain to be exploited and utilized. This paper proposes a distributed energy management system of DC microgrid in office buildings based on DC bus signaling. The system enables to control decentralized terminals in office buildings including photovoltaic (PV), battery, un-shiftable load (UL), thermostatically controlled load (TCL), and electric vehicle (EV). The DC bus voltage is regulated in real-time according to the difference between actual grid power and the grid requirement power, which is arranged as a constant power curve in this paper to grid relief. The viability and effectiveness of the proposed system are validated by the simulation results. The DC bus voltage is varied dynamically and the power of utility grid is almost the same as the demand power. In addition, the impact of terminal capacity and arranged control sequence with the proposed strategy are analyzed and evaluated. For a typical operating condition, the annual adjustment contribution of PV, battery, EV, and TCL among all the terminals take 9.3 %, 70.3 %, 19.0 %, and 1.4 %, respectively.

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

A Modified Redundancy-Based Energy Management System for Microgrids: An SoC Enhancement Approach

A Modified Redundancy-Based Energy Management System for Microgrids: An SoC Enhancement Approach

Multivariate Deep Learning Long Short-Term Memory-Based Forecasting for Microgrid Energy Management Systems

In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks’ performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost.

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

AbstractThe transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

An enhanced energy management system for coordinated energy storage and exchange in grid-connected photovoltaic-based community microgrids

This paper introduces an enhanced coordinated community energy management system (CEMS) for a community microgrid. It is designed to optimize residential energy consumption and facilitate energy sharing among smart homes. The proposed solution integrates constrained mixed-integer programming (CMIP) approach along with advanced optimization technique and cooperative game theory-based pricing mechanism to address critical challenges in existing energy management strategies, including load scheduling and efficient peer-to-peer energy trading. The CEMS approach is adopted for grid-connected community power systems that incorporate local energy sources such as photovoltaic and battery storage systems. This solution is implemented under a time-of-use dynamic pricing model for energy transfer from the main grid to the community peers, a modified mid-market pricing mechanism for the P2P energy exchange, and feed-in tariff for energy transfer from the community peers to the main grid. Besides, seagull optimization algorithm is applied to relax the CMIP problem to get the optimal rational solution of the scheduling problem which is subjected to a rounding process to obtain the best feasible solution. The obtained results indicate a substantial reduction in electricity costs, achieving daily savings of 72.21 % and reducing grid dependency by 41.95 %. Moreover, 82.2 % of the community's energy demand was met through locally generated resources with 18.39 % enhancement of the overall CMG self-consumption ratio comparing to the reference operating scenario. Ultimately, the study demonstrates the effectiveness of the management system in improving load profiles, maximizing the use of local renewable energy sources, and reducing electricity expenses, thereby providing a sustainable and economically viable model for future smart grids.

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.

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

An improved microgrid energy management system based on hybrid energy storage system using ANN NARMA-L2 controller

This research describes an intelligent Energy Management System (EMS) for a microgrid application that employs a Nonlinear Autoregressive Moving Average Level 2 (NARMA-L2) artificial neural network (ANN). The hybrid energy resources (PV/WIND), a hybrid energy storage system (HESS) with batteries and supercapacitors (SC), and loads are all integrated into the microgrid. Maximum Power Point Tracking (MPPT) in a photovoltaic (PV) system is based on the Nonlinear Autoregressive with exogenous inputs (NARX) approach. The flowchart, rules, memberships, and scenarios that allocate energy among various components based on battery State of Charge (SoC), PV/wind power, and load factors are described in the article. To validate the EMS's effectiveness, simulation tests under various conditions are presented. Supercapacitors (SCs) reduce DC bus voltage swings, lowering battery stress and extending battery life, it has been demonstrated using various load variations, as well as various sun irradiation and wind speed values under various conditions. The simulation results in the MATLAB/Simulink environment, validate the effectiveness of the proposed approach and produce effective DC BUS stabilizing and maintaining frequency stability of system that shows the importance and efficiency of the control strategy used.

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.

Energy management system in networked microgrids: an overview

Energy management system in networked microgrids: an overview

ENERGY MANAGEMENT SYSTEM OF MICROGRID WITH RENEWABLE ENERGY SOURCES

The most recent development in the electrical system is the microgrid. Many scattered, networked energy storage, loads, and generation units make up a typical microgrid system. An energy system made up entirely of renewable resources is called a microgrid (MG), an energy storage device, and loads that can operate in grid-connected or islanded mode. The demand for the load and the power flow within MG should be managed by scheduling renewable resources. This paper presents a hybrid microgrid energy management system (MEMS) that combines grid-connected solar (PV), wind power from microturbines, and battery energy storage systems (BESS). Due to their increased energy efficiency, microgrid units are becoming more and more well-liked every day. Energy management for microgrids is required since renewable energy sources provide an imbalance in the Power System due to their stochastic behavior. Controlling the DC/DC bidirectional converter (DBC), which links the Ni-H battery to the DC bus of the grid-connected microgrid, is the primary goal of this study. The start-up of a wind/photovoltaic power generation system to the load is covered in this study. MATLAB/SIMULINK was used to model a microgrid system that is connected to the grid. Key Words: Microgrid, Photovoltaic (PV)

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

Renewable-based microgrid energy management: A comprehensive exploration of techniques, strategies, and long-term viability

Renewable-based microgrid energy management systems have emerged as a promising solution to address energy security, sustainability and resilience concerns. This paper offers a comprehensive exploration of the techniques, strategies and long-term viability of such systems. The purpose of the study delves into the implementation of smart management and control systems, which enable real-time data analysis, grid optimization and effective load management, thus maximizing the utilization of renewable energy and enhancing overall system efficiency. It demonstrates the significance of decentralized energy management and its role in fostering energy independence and sustainability within local communities and small-scale industrial setups. The paper further investigates the resilience and redundancy aspects of microgrids, highlighting the importance of autonomous operation during grid failures and emergencies. Thus, the incorporation of energy-efficient practices, appliances and demand-side management strategies to promote energy conservation and reduce overall energy consumption of solar power, wind power, hydro power, biopower and geothermal. The important outreach and training are creating environmentally friendly energy consumption and improving the long-term sustainability of microgrid energy control systems that rely on energy generated from renewable sources. The study seeks to enhance ecological energy handling techniques and the utilization of clean sources of energy by offering knowledge regarding the promise of the microgrid to become a versatile and environmentally friendly power solution via this examination.

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Efficient microgrid energy management with neural-fuzzy optimization

This research introduces a pioneering Energy Management System (EMS) for microgrids, integrating fuzzy neural networks and a modified particle swarm optimization (MPSO) algorithm. The key contribution lies in minimizing production costs while optimizing the use of renewable sources like photovoltaic (PV), wind turbines (WT), and energy storage. The novel approach considers time-dependent constraints, ensuring adaptability and superior system performance. Additionally, the study introduces an innovative demand response (DR) analysis using a neural-fuzzy network, enhancing customer response and energy cost dynamics. The MPSO algorithm addresses economic load distribution challenges, demonstrating superior performance in comparative analysis. This integrated approach offers a groundbreaking solution for sustainable and efficient energy planning in microgrids. The analysis demonstrates that the proposed method achieves higher energy savings (83%) compared to baseline levels of 72%, showcasing its superior efficiency. Comparative analysis with genetic and particle swarm optimization algorithms reveals consistently lower average expenses and increased cost-effectiveness with the proposed approach.