Microgrid Energy Management System Research Articles

MPC and robustness optimisation‐based EMS for microgrids with high penetration of intermittent renewable energy

This study develops a three-stage energy management system (EMS) for renewable energy microgrid operation. The core of this framework is based on a unit commitment problem integrated with model predictive control (MPC) to address the problem of uncertainty in renewable sources. Meanwhile, it is shown that an MPC approach may be insufficient to fully address the hurdles for optimal and safe operation of wind power-integrated energy systems due to the severity of wind speed fluctuations within even short time intervals. Spinning reserve resources can have a positive impact to ensure a reliable operation, yet their availability is highly dependent on the existence and capacity of dispatchable energy sources, such as diesel generators, in energy systems. Consequently, a supplementary Constrained Information Gap Decision Theory approach is utilised in this study to optimise the system's robustness against severe uncertainty of wind generations. In order to evaluate the presented framework, a descriptive index is first introduced, and then the model is applied to an isolated microgrid. The results indicate that by deploying these three stages, the renewable energy support index increases, ensuring an optimal, reliable, and safe operation.

Microgrid Energy Management Systems Design by Computational Intelligence Techniques

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5%. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset.

Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.

Cooperative energy management system for networked microgrids

Abstract This paper proposes a hybrid Energy Management Systems (EMS) architecture based on canonical coalition games for cooperative power exchange management of networked microgrids, interconnected with the main grid through a macro station. A central EMS and local EMSs perform three main processes: scheduling process, monitoring and rescheduling process, and benefits distribution process. A value function verifying the superadditivity property, which guarantees the grand coalition is the best coalition structure, and a computationally efficient non-linear scheduling model, which allows obtaining the power exchange strategy that minimizes transmission and transformation power losses, are defined. The scheduling process uses the scheduling model to generate a cooperative schedule for a rolling horizon. In order to enhance the performance of networked microgrids, the monitoring process uses efficiency thresholds for monitoring the schedule execution to identify schedule disruptions. For preserving the private information of microgrids, each local EMS only informs the central EMS of their state profiles, which specifies its power surplus or deficit for each time period of the scheduling horizon. The benefit-distribution process uses a Core-based model for distributing saved power loss obtained as a result of the power exchanged during the schedule execution. By comparing the proposed approach with a coalition formation game-based algorithm recently reported in the literature, it is concluded that the problem of power exchange management of networked microgrids should be modeled as a canonical coalition game and not as a coalition formation game. Results obtained from a prototype testing of the proposed EMS are presented.

Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform

Accurate prediction of load has become one of the most crucial issue in the energy management system of the microgrid. Therefore, a precise load forecasting tool is necessary for efficient power management in the microgrid, which can lead to economic benefits for consumers and power industries. This paper proposes a hybrid approach for short-term forecasting of load demand in a typical microgrid, which is a combination of the best-basis stationary wavelet packet transform and Harris hawks optimization-based feed-forward neural network. The Harris hawks optimization is applied to the feed-forward neural network as an alternative training algorithm for optimizing the weight and basis of neurons. The proposed model is applied to predict load demand in the Queensland electric market and is compared with existing competitive models. Numerical results are obtained using MATLAB. These results demonstrate that the proposed approach reduces the average mean absolute percentage error by 33.30%, 49.54% and 60.76% as compared to the particle swarm optimization (PSO) based artificial neural network, PSO based least-square-support vector machine and back-propagation based neural network, respectively.

An intelligent load priority list–based integrated energy management system in microgrids

In contrast to most contemporary literature on demand-side management (DSM) in microgrids (MG), which often neglects the granularity of the load importance degree prior to the shedding time or at best arbitrarily fixes a certain load priority list (LPL), this paper introduces spatially and temporally varying LPL-based DSM that determines the load category and corresponding amount that should be curtailed. The high penetration of the intrinsically stochastic renewable resources in MGs elevates some reliability problems and supply–demand balance instabilities in MGs, raising the necessity for applying robust and prompt demand response (DR) actions to avoid purchasing expensive energy from the grid, especially during peak hours. The proposed neuro-fuzzy LPL-based DSM is developed and implemented to offer smarter and less severe DR actions that will ensure a minimised operational cost, without shedding critical or essential loads. By applying the methodology on an MG testbed with real and local multi-category demand and hybrid renewable generation data, the results showed the effective optimization of the MG self-generation adequacy through DSM, especially during grid’s high-tariff periods.

A Computationally Efficient Consensus-Based Multiagent Distributed EMS for DC Microgrids

In this article, we present a fully distributed optimization approach for real-time energy management of dc microgrids. In the proposed approach, the energy management system (EMS) is formulated with a consensus algorithm based on incremental cost, which operates in a distributed manner for both generation and demand-side management. The proposed algorithm is privacy-preserving, scalable, plug-and-play capable, and robust against time-varying communication topologies. Furthermore, it converges very fast in terms of computation time and iterations. The scalability of the proposed algorithm was evaluated using IEEE bus systems in various sizes, number of agents, and communication topologies. A cooperative multilayered EMS optimally coordinated by multiple agents was also proposed in this article for integration. The effectiveness of the proposed system was validated through computer simulations and hardware experiments.

Hydrogen vs. Battery in the Long-term Operation. A Comparative Between Energy Management Strategies for Hybrid Renewable Microgrids

The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand. In this sense, the most implemented ESS in local energy districts are small–medium-scale electrochemical batteries. However, hydrogen systems are viable for storing larger energy quantities thanks to its intrinsic high mass-energy density. To match generation, demand and storage, energy management systems (EMSs) become crucial. This paper compares two strategies for an energy management system based on hydrogen-priority vs. battery-priority for the operation of a hybrid renewable microgrid. The overall performance of the two mentioned strategies is compared in the long-term operation via a set of evaluation parameters defined by the unmet load, storage efficiency, operating hours and cumulative energy. The results show that the hydrogen-priority strategy allows the microgrid to be led towards island operation because it saves a higher amount of energy, while the battery-priority strategy reduces the energy efficiency in the storage round trip. The main contribution of this work lies in the demonstration that conventional EMS for microgrids’ operation based on battery-priority strategy should turn into hydrogen-priority to keep the reliability and independence of the microgrid in the long-term operation.

A Stochastic Voltage Stability Constrained EMS for Isolated Microgrids in the Presence of PEVs Using a Coordinated UC-OPF Framework

Microgrids are increasingly affected by volatile wind energy due to the high penetration of this kind of renewable energy sources. This study investigates the impact of wind energy uncertainty on voltage stability and energy management system (EMS) of isolated microgrids in the presence of plug-in electric vehicles (PEVs). A stochastic voltage stability constrained microgrid energy management system (SVSC-MEMS) architecture based on a coordinated unit commitment-optimal power flow (UC-OPF) methodology, which simultaneously considers the UC and OPF constraints, is proposed. The proposed model guarantees the system security from the voltage stability standpoint, considering a specific loading margin. Besides, the role of PEVs in the microgrid EMS is examined. In order to validate the performance of the introduced SVSC-MEMS, a CIGRE benchmark test system is used. The numerical results, obtained from the implementation of the proposed model in the general algebraic modeling system (GAMS) environment, demonstrate the importance of voltage stability and wind power uncertainty on the microgrid EMS, and furthermore, the key role of PEVs in the emerging microgrids.

A new stochastic gain adaptive energy management system for smart microgrids considering frequency responsive loads

Islanded microgrids as flexible, adaptive and sustainable smart cells of distribution power systems should be operated in accordance to both techno-economic purposes. Motivated by this need, the microgrid operators are in charge to elevate the active accommodation of both demand-side and supply-side distributed energy resources. To that end, in this paper, a new flexible frequency dependent energy management system is proposed through which distributed generators have time varying droop controllers with a gain-adaptive strategy. Besides to cope economically with uncertainty arise frequency excursions, a new, comfort-aware and versatile frequency dependent demand response program is mathematically formulated and conducted to the energy management system. It is aimed to co-optimize the microgrid energy resources such a way the day-ahead operational costs are managed subject to a secure frequency control portfolio. The presented model is solved using a two-stage stochastic programming and by a tractable efficient mixed integer linear programming approach. The simulation results are derived in 24-h scheduling time horizon and implemented on a typical test microgrid. The effectiveness of the proposed hourly gain assignment and frequency responsive load management program has been verified thoroughly by analyzing the results.

A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics

A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decision-making process.

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers.

Reducing the computational burden of a microgrid energy management system

As renewable technology advances and decreases in cost, microgrids are becoming an appealing means of distributed generation both for isolated communities and integrated with existing electrical grid systems. Due to their small size, however, microgrids may have financial limitations which preclude them from using commercial software to optimize control of their assets. Open-source optimization solvers are a viable alternative, but increase computation time. This work expands on a rolling horizon optimization framework for economic dispatch within an existing residential microgrid located in Hoover, Alabama. The microgrid has an open-source solver requirement and a need for quick solution time on a rolling horizon as opposed to a day-ahead commitment. We present a method of reducing integer variables by relaxation which completes two goals: reduction in computation time for real-time operations, and reduction in daily operational cost for the microgrid. Seasonal data for load and photovoltaic (PV) power was also collected from the microgrid to facilitate simulation testing. Computation time was successfully reduced using multiple variations of the relaxation method, while obtaining solution quality with operational cost similar to or better than the original model.

Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty

This paper suggests a new energy management system for a grid-connected microgrid with various renewable energy resources including a photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT) and battery energy storage system (BESS). For the PV system operating in the microgrid, an innovative mathematical modelling is presented. In this model, the effect of various irradiances in different days and seasons on day-ahead scheduling of the microgrid is evaluated. Moreover, the uncertainties in the output power of the PV system and WT, load demand forecasting error and grid bid changes for the optimal energy management of microgrid are modelled via a scenario-based technique. To cope with the optimal energy management of the grid-connected microgrid with a high degree of uncertainties, a modified bat algorithm (MBA) is employed. The proposed algorithm leads to a faster computation of the best location and more accurate result in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The simulation results demonstrate that the use of practical PV model in a real environment improve the accuracy of the energy management system and decreases the total operational cost of the grid-connected microgrid.

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

An Energy Management System for Isolated Microgrids With Thermal Energy Resources

A novel Energy Management System (EMS) model for an isolated microgrid, integrating thermal energy resources, such as Combined Heat and Power (CHP) units, boilers, Heat Pumps (HPs), and Thermal Storage System (TSS), while considering thermal load models, is proposed in this paper. The developed EMS is tested and validated with a real testbed microgrid located in Bari, Italy, which supplies both electricity and heat to a building at the Politecnico di Bari. The proposed EMS aims to minimize the fuel cost and includes thermal comfort requirements and building models, along with suitable models for CHP units and hot water-based TSS, based on an optimization problem formulated as a Mixed Integer Linear Programming (MILP) problem, which is readily handled with commercial solvers, making the EMS fit for online applications. The proposed EMS is compared with an electrical-only EMS, i.e., a practical EMS that does not include thermal systems, with the simulations carried out for different winter days demonstrating the economic benefits of accounting for thermal system models in a microgrid EMS, resulting in significant savings in the daily fuel cost.

A Comparison of Energy Management System for a DC Microgrid

This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.

Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures

This paper presents both an extensive literature review and a qualitative and quantitative study conducted on nearly 200 publications from the last six years (based on international experience and a top-down analysis framework with five classification levels) to establish the main trends in the field of centralized energy management systems (EMS) for microgrids. No systematic trend analyses have been observed in this field in previous literature reviews. EMS attributes for several features such as objective functions, resolution techniques, operating models, integration of uncertainties, optimization horizons, and modeling detail levels are considered for main trend identification. The main contribution of this study is the identification of four specific existing research trends: (i) dealing with uncertainties (comprises 33% of the references), (ii) multi-objective strategy (29%), (iii) traditional paradigm (21%), and (iv) P-Q challenge (17%). Each trend is described and analyzed based on the main drive of these separate research fields. The key challenges and the way to cope with them are described based on the rationality of each trend, the results of previous reviews, and the previous experience of the authors. Overall, finding these main trends, together with a complete paper database and their features, serve as a useful outcome for a better understanding of the current research-specific challenges, opportunities, potential barriers, and open questions regarding the creation of future centralized EMS developments. The traditional numerical analysis is insufficient to identify research trends. Therefore, the need of further analyses based on the clustering approach is emphasized.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.