Performance Of Energy Management System Research Articles

Energy Management System in Smart Micro-Grid

This paper focuses on discussing an energy management system (EMS) for a smart microgrid integrating multiple renewable sources. The task of the EMS is to efficiently balance power generation and consumption by controlling various energy sources, including photovoltaic systems, energy storage units, engine generator set and the utility grid. An EMS optimizes power flow between the microgrid components and keeps the micro-grid stable, by using different control strategies. In this microgrid, the PV system serves as the primary energy source, while the other sources of electrical energy act as backups. The energy management system prioritizes supplying load demand from either the PV system or the other sources, with load shedding during peak hours if the supplied energy is insufficient. The approaches based on StateMachine and StateFlow are discussed in this paper for enhancing the energy management system's performance.

Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854.

Development of an Energy Management System for a Renewable Energy Community and Performance Analysis via Global Sensitivity Analysis

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty— not being adequately addressed in the EMS literature— that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.

Hierarchical energy management system with multiple operation modes for hybrid DC microgrid

This paper presents a developed energy management system (EMS) with a hierarchical three-level distributed control approach proposed for a photovoltaic/wind turbine/diesel generator with energy storage in an islanded hybrid DC microgrid (MG). In the hierarchical three-level control structure, the power to be transferred by the distributed energy sources (DER) is provided by controlling the converters in the primary control layer. The secondary control layer is used to achieve DC bus voltage restoration and increase current-sharing accuracy simultaneously. In the tertiary control layer, it is aimed to minimize the production cost by performing optimum load-sharing. For the operation of this control structure, a strategy is proposed to optimally manage the energy demand of the hybrid system autonomously under variable load conditions. The EMS with three different operating modes (Mode-I, Mode-II, Mode-III) is proposed to share power between DC MG sources optimally. In Mode-I, renewable energy sources (RES) are enabled to operate with MPPT operation. Energy consumption minimization strategy (ECMS)-based linear programming (LP), genetic algorithm (GA) and pattern search (PS) algorithms are used to minimize the fuel consumption and carbon emissions of the diesel generator (DG) in Mode-II operating Mode. In Mode-III operating mode, DG is not needed, and the power flow between DERs is optimized by using the fuzzy logic controller (FLC) and state machine control strategy (SMCS). The amount of fuel consumed, the cost of fuel consumption and ESS's SOC level are used as the EMS's performance criteria for the MG. The minimum fuel consumption of the DG is calculated as 2.328 L for the operation of LP-based ECMS, and the fuel cost, in this case, is found to be $3.26. Considering the charging of the ESS, the highest SOC level is achieved with 64.87% for the PS-based ECMS-SMCS combination. By considering fuel minimization and SOC level together, it can be seen that the best result is achieved with the LP-based ECMS-SMCS combination.

Optimized droop controller based energy management for stand-alone micro-grid using hybrid monarch butterfly and sine-cosine algorithm

Microgrids with hybrid renewable energy systems are an advance and useful technology for the development of environment-friendly sustainability. A microgrid mainly consists of small units like PV, wind, diesel generator and battery storage and an energy management system. The main objective of this research is to enhance the performance of energy management system by archiving economical optimal scheduling of the generation units and battery storage and maintain the stability of the microgrid. Thus, it is very much important for the energy management system to make sure that the power is shared among various sources to meet the required load demand and at the same time the operational cost of the microgrid is kept as small as possible. This problem can be developed by using an optimization algorithm. Therefore, to resolve the problem an optimization algorithm named as sine–cosine based monarch butterfly optimization (SCMBO) is proposed in this work to obtain the optimal energy management solutions for day-ahead scheduling in a stand-alone DC microgrid. The proposed algorithm efficacy is validated by comparing its results with other conventional optimization algorithms like MBO and PSO. Further, a control mechanism known as coordinated droop control is proposed for the DC microgrid.

Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids

Accurate forecasting is a crucial task for energy management systems (EMSs) used in microgrids. Despite forecasting models destined to EMSs having been largely investigated, the analysis of criteria for the practical execution of this task, in the framework of an energy management algorithm, has not been properly investigated yet. On such a basis, this paper aims at exploring the effect of daily forecasting frequency on the performance of rolling-horizon EMSs devised to reduce demand uncertainty in microgrids by adhering to a reference planned profile. Specifically, the performance of a sample EMS, where the forecasting task is committed to a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), has been studied under different daily forecasting frequencies, revealing a representative trend relating the forecasting execution frequency in the EMS and the reduction of uncertainty in the electrical demand. On the basis of such a trend, it is possible to establish how often is convenient to repeat the forecasting task for obtaining increasing performance of the EMS. The obtained results have been generalized by extending the analysis to different test scenarios, whose results have been found coherent with the identified trend.

An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building

This paper proposes an improved non-schedulable load forecasting (NLF) technique that can be utilized to enhance the performance of the energy management system (EMS) in a nearly zero energy building (nZEB). The suggested NLF is based on a long short-term memory (LSTM) framework in conjunction with a semi-supervised clustering (SSC) technique, considering the most important features which may affect the energy consumption of the non-schedulable appliances (NAs), i.e. number and identity of residents, energy consumption, weather, temperature, humidity, outdoor irradiance, correlation with other loads, day of the week and holidays. The SSC algorithm is utilized to fill the uncompleted information for the residents’ presence in the house and its output constitutes one of the inputs of the LSTM based technique which provides as output a set of forecasting sequences of the NAs’ energy consumption. Unlike the published techniques, the proposed NLF method is not only based on the modeling of the residents’ preferences and habits, but it considers them as variables which affect the nZEB’s microgrid and EMS performance. Therefore, it predicts the residents’ behavior considering its interdependence with the nZEB’s microgrid, which can considerably contribute to the enhancement of the EMS effectiveness and performance. For the implementation of the proposed NLF, no additional hardware is required, but only amendments in the EMS to consider the NLF’s outcomes. To validate the effectiveness of the proposed NLF, selective Hardware-in-the-Loop results from a real nZEB are presented.

Intelligent energy flow management of a nanogrid fast charging station equipped with second life batteries

In this paper we investigate a public Fast Charge (FC) station nanogrid equipped with a Photovoltaic (PV) system and an Energy Storage System (ESS) using second-life Electric Vehicle (EV) batteries. Since the nanogrid is intended for installation in urban areas, it is designed with a very limited connection with the grid to assure peak shaving and encourage PV autoconsumption. To demostrate the effectiveness of this approach to FC stations, an Energy Management System (EMS) is developed to manage the energy demand uncertainty of EVs and the power gap between the grid connection and the FC service. In particular, we propose a machine learning procedure for the automatic synthesis of a suitable (fuzzy) rule-based EMS. Indeed, we posit that a prediction based EMS would result not effective because of the stochastic and intermittent behavior of the FC load, and that a crisp rule-based system, defined by expert knowledge, would be too limited to capture uncertain behavior. The concept is demonstrated in a simulated environment inspired by the “Smart Columbus” project, implementing a mixed deterministic-stochastic process to simulate EV energy demand. In particular, different EV fleets and PV sizes are considered for EMS training, offering insights into the optimal size of PV system and nanogrid system effectiveness. The proposed approach is evaluated by comparing the EMS performance with related optimal benchmark solutions, evaluated by considering known a priori the overall PV and FC demand. The results show that the EMS performance is approximately within 10% of the benchmark optimal value.

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

The design and implementation of appropriate advanced control strategies is a key factor for the effective integration of micro-grids into the electrical network. In view of this, the study proposes an Adaptive Model-Based Horizon Control technique in the bid to addressing issues related to the Energy Management System in micro-grid operations. The main objective of the energy management system is to balance energy generation and demand through energy storage, so as to optimize the operation of the micro-grid with high penetrations of renewable energy sources. This paper further investigates the impacts of considering the prediction of disturbances on the performance of the Energy Management System based on the adaptive model predictive control algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. The adaptive model predictive control algorithm solves the energy optimization problem in a renewable energy-based micro-grid with various types of energy storage systems that exchange energy with the host grid. More so, this optimization problem is resolved at each sampling period in order to determine the minimum running costs while satisfying demand and taking into account technical and physical constraints. The simulation results under different conditions have demonstrated how the use of an adaptive model predictive control based energy management system can enhance micro-grid operation, provided there is effective forecasting, and consequently minimized the running operating costs of micro-grid. More so, it is evident in the cost function, J, obtained from the three scenarios conducted, that the perfect knowledge of the disturbance prediction is essential for effective micro-grid operations.

Stochastic scheduling of an electric vessel-based energy management system in pelagic clustering islands

The pelagic clustering islands (PCIs) can be developed into the resource rich islands (RRIs) and load center island (LCI) using the electric vessel (EV) to achieve interisland energy flow. However, energy balance between the supply and demand cannot be directly achieved, resulting in crucial need for effective day-ahead energy management for PCIs. To address this issue, this paper proposes a novel scenario-based energy management system (EMS) to optimize the operation of PCIs. To ensure the EMS performance, the comprehensive impact of environmental factors including wind, solar radiation and ocean currents on energy supply, transmission and demand is analysed in detail. The dynamic energy transmission channel and power flow balance relationship are modelled by evaluating the location and charging/discharging power of the EV. Considering the forecasting inaccuracies in environmental factors using refined stratified sampling (RSS) and scenario reduction methods, a stochastic optimization model is proposed to maximize the operational economy and reliability of PCIs, followed by a 2-stage solution consisting of preprocessing and group-search optimization with multiple producers (GSOMP). Simulation studies on Taiping Island, Hongma Island, Bolan Reef and Anda Reef in the South China Sea show that the proposed EMS is feasible and economical to describe the energy supply of PCIs.

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.

ANFIS Microgrid Energy Management System Synthesis by Hyperplane Clustering Supported by Neurofuzzy Min–Max Classifier

A novel energy management system (EMS) synthesis procedure based on adaptive neurofuzzy inference systems (ANFISs) by hyperplane clustering is investigated in this paper. In particular, since it is known that clustering input–output samples in hyperplane space does not consider clusters’ separability in the input space, a Min–Max classifier is applied to properly cut and update those hyperplanes defined on scattered clusters in order to refine the ANFIS membership functions. Furthermore, three different clustering techniques have been compared for the ANFIS rule synthesis as well, both with and without considering the classifier support. The procedure under analysis has been applied for designing a microgrid EMS equipped with a photovoltaic generator and an energy storage system (ESS). The EMS is in charge of intelligently defining how to redistribute the prosumer energy balance between the ESS and the connected grid in order to maximize the profit generated by the energy exchange with the grid, assuming a time of use energy price policy. Results on real-world data show very interesting performances, close to optimal values evaluated with a mixed integer linear programming problem formulation by approximately 12%. Moreover, the contribution of the Min–Max classifier improves the EMS performance by approximately 50% with respect to the same algorithm without refining fuzzy rules by the classification step.

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling.

The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts.

Driving-Style-Oriented Adaptive Equivalent Consumption Minimization Strategies for HEVs

The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers’ driving style. This paper proposes a driving-style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, first, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on energy management strategies are discussed by analyzing the performance of the equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS.

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism.

Energy management system based on techno-economic optimization for microgrids

This paper presents a new Energy Management System (EMS) for a microgrid based on four energy sources: a wind turbine (WT), photovoltaic (PV) solar panels, a battery, and a hydrogen system, which is composed of a fuel cell (FC) and an electrolyzer. This novel control strategy optimizes the total cost of the hybrid system (generation and reposition costs) through lifetime estimations calculated hourly for each energy storage device (the battery and hydrogen system). This control strategy links the expected lifespans of the energy sources to their generation costs, i.e., when the lifespan is low, the generation cost increases and, consequently, this energy source will start to be used less. The performance of the novel EMS, including these estimations, was tested for an isolated load located in Alora (Spain) and compared with two simpler EMSs: EMS-1, which considered fixed generation costs and lifetimes, and EMS-2, which prioritized the use of the battery. Simulation results show the appropriate behavior for the novel EMS to optimize the generation costs and the number of required elements throughout the expected lifetime of the hybrid system (25 years).

ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries

This paper describes and evaluates an adaptive neuro-fuzzy inference system (ANFIS)-based energy management system (EMS) of a grid-connected hybrid system. It presents a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and an energy storage system (ESS) based on hydrogen (fuel cell -FC-, hydrogen tank and electrolyzer) and battery. All of the energy sources use dc/dc power converters in order to connect them to a central DC bus. An ANFIS-based supervisory control system determines the power that must be generated by/stored in the hydrogen and battery, taking into account the power demanded by the grid, the available power, the hydrogen tank level and the state-of-charge (SOC) of the battery. Furthermore, an ANFIS-based control is applied to the three-phase inverter, which connects the hybrid system to grid. Otherwise, this new EMS is compared with a classical EMS composed of state-based supervisory control system based on states and inverter control system based on PI controllers. Dynamic simulations demonstrate the right performance of the ANFIS-based EMS for the hybrid system under study and the better performance with respect to the classical EMS.