Microgrid Energy Management System Research Articles

Review of Energy Storage and Energy Management System Control Strategies in Microgrids

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.

Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids

With the rapid proliferation of non-dispatchable energy sources, the need for demand-side management (DSM) strategies has become crucial to ensure affordability and reliability for end-users. The application of various diversified DSM strategies in the microgrid energy management system (EMS) is gaining popularity. This paper aims to solve the energy management problem of the microgrid in conjunction with both customer-oriented and utility-oriented DSM strategies for the first time in the literature. In light of this, a stochastic EMS framework is developed to implement and analyze the flexible load shaping DSM strategy, price-based, and incentive-based demand response programs (DRPs) in the presence of non-dispatchable energy resources. Further, the flexible price-oriented load model is adopted for price-driven and incentive-driven DRPs to depict the realistic assessment of consumers’ sensitivity to market prices. The scenario construction approach is employed to address the stochastic nature of renewable power generation, market prices, and load demand. With the complexities as mentioned above, the problem needs to be solved with a powerful optimizer sufficiently to enhance energy efficiency and optimize energy utilization. Hence, the recently reported novel metaheuristic algorithm (Black Widow Optimization) is applied to solve the proposed MG energy management problem in the MATLAB environment. The IEEE-34 node distribution feeder-based MG network is modified to study the proposed algorithm's performance, and a detailed analysis of various techno-economic indices is presented. The obtained simulation results are compared with existing popular algorithms to prove the efficacy of the proposed algorithm in terms of convergence, computational time, an optimum solution and the real-time market bid prices were considered in analysis for day-ahead scheduling of microgrid network.

A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy sources (RES) are being deployed in the power system to meet the energy demand. The microgrid concept (AC, DC) is introduced, in which distributed energy resources (DERs), the energy storage system (ESS) and loads are interconnected. DC microgrids are appreciated due to their high efficiency and reliability performance. Despite its significant growth, the DC microgrid is still relatively novel in terms of grid architecture and control systems. In this context, an energy management system (EMS) is essential for the optimal use of DERs in secure, reliable, and intelligent ways. Therefore, this paper strives to shed light on DC microgrid architecture, control structure, and EMS. With an extensive literature survey on EMSs’ role, different methods and strategies related to microgrid energy management are covered in this article. More attention is centered on the EMS for DC microgrids in terms of size and cost optimization. A very concise analysis of multiple optimization methods and techniques has been presented exclusively for residential applications.

An optimal stochastic energy management system for resilient microgrids

This paper presents a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase of alternating current microgrids. The proposed model considers the following random variables: nodal demands, nodal renewable generation and voltage reference at the point of common coupling. Furthermore, the proposed model is aimed at providing resilient energy management system solutions via contingency constraints. The proposed mixed-integer nonlinear programming model is transformed into a mixed-integer linear programming model through a set of linearizations that can be solved via off-the-shelf convex programming solvers. The analyzed microgrid comprises photovoltaic generation, energy storage systems, electric vehicle chargers, direct load control, and non-renewable generation, which operates when the microgrid is in islanded mode. The stochastic nature of the problem is considered through a scenario-based approach. The solution to the model determines the day-ahead operation of the microgrid resources that minimizes the average operational cost. An unexpected islanded operation at any given time is considered via contingency constraints. Tests are performed using data of the real microgrid at the Laboratory of Intelligent Electrical Networks (LabREI), at University of Campinas. Results show that the proposed model produces resilient day-ahead energy management system solutions while minimizing the average operational costs and maximizing the use of local renewable energy sources.

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

Modeling of Small Productive Processes for the Operation of a Microgrid

Small productive processes (SPPs) are promising drivers that promote the economic use of energy in microgrids (MGs). Both the complex nature of the SPPs and voltage variations make the operation of MGs challenging, since the quality of an energy management system’s (EMS) decisions depend on its characterization. The aim of this work is to propose a methodology for SPPs modeling, and to consider the influence of voltage on load consumption, which has general validity, and can be efficiently integrated into different MG EMS approaches. For this purpose, a novel extended multi-zone ZIP approach for the characterization of SPP loads and sensitivity to voltage changes is proposed. The associated framework herein presented was assessed using actual data collected from SPPs installed near the city of Arica, in northern Chile. The results showed that the proposed methodology was capable of representing the complex load behavior of the SPPs, properly considering the voltage influence. These results were compared with those obtained through common approaches found in the literature. The effectiveness of the proposed approach in representing SPP loads and their sensitivity to voltage changes was verified. The proposed scheme can be efficiently integrated into a wide range of EMS for MGs that include SPPs.

Customer‐orientedenergy demand management of grid connected microgrids

The consumers' active participation in production and energy consumption in response to market changes is an integral characteristic of the smart grid paradigm. In other words, their role in energy trading, either at the individual capacity or as aggregators, will shape the future of fulfilling energy needs. Despite the foreseen technological advancements, the design and implementation of demand response programs (DRPs) with accurate customer responsiveness have remained essential for network operators. Moreover, with non-dispatchable energy sources' involvement, DRPs play a vital role in shaping the load profile and minimizing the operating costs. It calls for a need to develop a suitable energy management system (EMS) to promote consumers' active participation. With this sense, the incentive-based DRPs are incorporated into the microgrid EMS to investigate their impact on day-ahead scheduling costs and managing consumers load profile. A stochastic-based EMS framework is proposed to this end, and five distinct types of DRPs are implemented on a practical microgrid network, including commercial, industrial, and residential loads. The flexible price elasticity concept is adopted to model the realistic behavior of price responsive loads under different DRPs. Finally, the recently reported novel optimizer is employed to solve the proposed EMS problem and investigate the flexible price model's impact on optimizing the grid-connected microgrid's daily operating costs. The obtained simulation results are compared with the existing state-of-the-art metaheuristic optimizers to test the attributes related to solution efficiency and convergence rate.

Random Modeling of Optimal Economic, Security and Environmental Operation of Micro-grid by Managing Responsive Loads and Charging and Discharging Electric Vehicles

The micro-grid operator must provide the energy required by its customers at the lowest cost and consider issues such as greenhouse gas emissions and security. The operator is faced with a multi-objective optimization problem in which customer demand must be provided at the lowest cost and safely. This research provides a new energy management system for islanded micro-grids. The small size of the islanded micro-grids, the high level of intermittentoperationand the low inertia of distributed generation ofinverter energy production resources make the frequency and voltage security two vital factors in the energy management system, which must be managed alongside economic-environmental policies. In this study, two practical tools are provided to help with the optimal operation and increase the profitability of the micro-grid operator. The first tool is the optimal and managed use of the V2G mode of electric vehicles. In the proposed approach, not only the penetration of electric vehicles in the network is managed but also this equipment is used to solve some of the network's challenges. The second tool is responsive loads and demand response programs in order to achieve the goals of the micro-grid operator. Covering the uncertainty of renewable energy sourcesby responsive loads, and how to model a demand response program in a micro-grid, arefollowed in this study.The strategy pursued several goals, including reducing energy and load costs, reducing the cost of charging EVs, and improving network parameters and security, such as voltage and frequency.The results confirm the effectiveness of the proposed approach

An Intelligent Multi Agent based Approach for Autonomous Energy Management in a Microgrid

The integration of highly intermittent and unpredictable distributed energy resources increases the complexities in the energy management system (EMS). Therefore, the conventional control used in microgrid EMS is no more effective and requires alternative control strategies that can cope up with the new dynamics in the system. Adopted from computer science, a distributed artificial intelligence technique called multi agent system (MAS) can serve this purpose well due to its inherent autonomous nature, scalability, flexibility, and adaptability. Consequently, in this paper, a new multi agent based architecture for microgrid energy management system has been proposed. The proposed model considers different forecasting agents and a real time correction agent for microgrid energy management. The performance of proposed MAS based microgrid EMS modeled in a new platform called Stateflow has been evaluated by developing different scenarios considering resource fluctuations and load variations. The simulation results demonstrate the effectiveness of the proposed architecture in outperforming the conventional microgrid energy management system and the effective usage of each component of the microgrid.

Integrated energy management system for Microgrids of building prosumers

In this paper, an easy to apply and computationally efficient energy management system (EMS) for microgrids of building prosumers is proposed. It is based on a hierarchical multi-agent system (MAS), aiming to minimize the operation cost of a microgrid comprising a group of buildings, renewable energy sources (RES), energy storage systems (ESS) and plug-in electric vehicles (PEVs). The optimization problem is suitably set up to ensure low computation requirements and it is solved with particle swarm optimization (PSO). Optimal operation scheduling for grid-connected and autonomous operation of microgrids of building prosumers, considering their thermal and electrical systems, integrated RES, conventional and dynamic ESS is proposed. The models used are not computationally demanding, allowing the application to complex systems and they are not being limited to one building scale which is the case for the majority of relative research. Another innovative feature of the proposed algorithm, among others, is that it ensures autonomous operation at certain time periods without the use of any emergency generator sets. Detailed simulation results of different operation scenarios, showed that cost savings in range of 11% can be achieved while a large number of operation and technical constraints are satisfactorily met in grid-connected and autonomous operation.

Energy Management System for Hybrid Microgrids

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.

Energy management of a renewable-based isolated micro-grid by optimal utilization of dump loads and plug-in electric vehicles

This paper proposes a new day-ahead energy management system (EMS) for an off-grid fishing island micro-grid (MG). The MG considered in this work is well equipped with smart grid infrastructure and embedded with plug-in electric vehicles (PEVs). Moreover, it is a green and fossil fuel free MG without any conventional power plant. The MG provides its load demand by solely renewable generations, including wind farms (WF) and photovoltaic (PV) power plants. Therefore, keeping balance between generation and demand is a difficult task in day-ahead operation planning of this MG. In order to overcome this obstacle, the MG uses a mega-scale battery energy storage system (BESS). However, the capacity of the BESS is limited and it is not economically feasible to increase the BESS capacity. Therefore, the MG considers PEVs G2V/V2G operation modes planning and dump loads optimal utilization as supplementary balance options. The main dump loads in this MG are industrial fish refrigerators (IFRs) and reverse osmosis desalination system (RODS). These dump loads in addition to PEVs’ G2V/V2G modes of operation are scheduled in the EMS proposed in this paper. Numerical studies show that the proposed EMS reduces total daily wasted energy and energy not served by 96% and 30% respectively.

Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction

This study deals with the development of an optimal power scheduling controller for energy management of distributed energy resources in the microgrid system. The developed optimized controller is implemented using lightning search algorithm to overcome the uncertainties of microgrid energy management and to provide an optimum power delivery to loads with minimum cost. The primary objectives of the proposed optimized controller are to: (i) develop an optimized controller for microgrids energy management, (ii) minimize the total operating cost of the distributed energy resources units, (iii) reduce the environmental emission, and (iv) solve the complicated constraint optimization problems. The proposed optimization algorithm is implemented in the modified IEEE 14-bus test system to optimize the microgrid power management schedule. The optimized controller is executed based on the real load varying conditions recorded in Perlis, Malaysia. It is observed that the optimized controller successfully reduced the amount of power consumption from 971.65 MW to 364.3 MW which in turn saving cost of RM 265432.06. The proposed scheduling optimized controller performance is compared with the recent reported work of backtracking search algorithm optimization for validation. Result shows that the lightning search algorithm based MG controller produced a cost-effective system with 62.5% of cost saving and 61.98% of carbon dioxide emission reduction which is much higher compared to with MG and backtracking search algorithm based MG optimization, respectively. The effectiveness of the proposed approach outperformed other techniques in terms of minimum total operating cost of distributed energy resources and solving complicated constraints in optimization problems.

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

Coordinated Control of Independent Microgrid Based on SOC Deviation Rate

System failure due to high failure rate of independent microgrid energy management system (EMS) in high altitude and cold area, considering the difference in charge state (SOC) between multiple energy storage devices in the system, a power coordinated control strategy based on the SOC deviation rate of energy storage devices is proposed, adjust the output power of the energy storage converter according to the difference in SOC of each energy storage device, reduce the difference in SOC of each energy storage device, after the system EMS fails, the operation status of the energy storage equipment is consistent to ensure the normal operation of the system. The RT-LAB simulation platform is used to establish a simulation model and design a digital control system. The control system can quickly carry out power coordination control according to the SOC state of energy storage devices, so it makes the devices output power differently according to the SOC level, reduces the SOC difference of energy storage devices, makes the devices SOC reach the same, and verifies the feasibility and effectiveness of the coordination control strategy.

Cyber-attack localisation and tolerant control for microgrid energy management system based on set-membership estimation

This paper addresses the cyber-security issue of microgrid energy management system, where cyber-attacks, appearing in communication networks, corrupt the transmitted data and falsify the state estimates. This can potentially threaten the physical system and lead to severe physical consequences. Therefore, it is of great significance to detect, locate and tolerate cyber-attacks. To this end, set-membership estimation is employed to detect the occurrence and locate the position of DoS attacks. The model predictive control technique is utilised to schedule the energy management by using the forecasts of photovoltaic generation and load demand. It is shown that the cyber-attack localisation and the desired tolerant control performance against attacks can be both achieved. Simulation results are provided to demonstrate the effectiveness of the proposed strategy.

Deep learning neural networks for short-term photovoltaic power forecasting

Accurate short-term forecasting of photovoltaic (PV) power is indispensable for controlling and designing smart energy management systems for microgrids. In this paper, different kinds of deep learning neural networks (DLNN) for short-term output PV power forecasting have been developed and compared: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), One-Dimension Convolutional Neural Network (CNN1D), as well as other hybrid configurations such as CNN1D-LSTM and CNN1D-GRU. A database of the PV power produced by the microgrid installed at the University of Trieste (Italy) is used to train and comparatively test the neural networks. The performance has been evaluated over four different time horizons (1 min, 5 min, 30 min and 60 min), for one-Step and multi-step ahead. The results show that the investigated DLNNs provide very good accuracy, particularly in the case of 1 min time horizon with one-step ahead (correlation coefficient is close to 1), while for the case of multi-step ahead (up to 8 steps ahead) the results are found to be acceptable (correlation coefficient ranges between 96.9% and 98%).

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids

In this work, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.