Energy Management Methodology Research Articles

Methodology for Energy Management in a Smart Microgrid Based on the Efficiency of Dispatchable Renewable Generation Sources and Distributed Storage Systems

This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energías Renovables—Centro de Investi-gaciones Energéticas, Medioambientales y Tecnológicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 kWh per year, which is equal to savings of more than 8500 €. It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill.

AI-Powered Energy Management System for Industry

The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability

A deep reinforced markov action learning based hybridized energy management strategy for electric vehicle application

Due to the severe lack of fossil fuels and existing environmental pollution, developing Electric Vehicles (EV) powered by clean, new energy is essential to finding a way to address this problem. The hybrid EV powered by fuel cell is the most practical and suitable option in present days. Several researchers have concluded that a key factor in determining an EV's performance is the hybrid energy management system's output performance. Moreover, proper energy management and performance optimization are the most essential solutions to ensure the normal operation of the hybridized system. In the existing studies, several energy management methodologies are developed using a hybridized energy sources, yet it facing some challenges in terms of high cost, increased system designing complexity, stability issues, and unreliable power supply. Therefore, the proposed work intends to implement a novel Energy Management System (EMS) for EVs with the use of advanced learning algorithms. In the proposed topology, the combination of Proton Exchange Membrane Fuel Cell (PEMFC), battery, and Super-Capacitor (SC) have been used as the major energy sources. Then, a high gain DC-DC converter is utilized to boost the regulate output voltage with reduced stress and loss factors. For improving the performance of converter, a recently developed Gorilla Troop Optimization Algorithm (GTOA) is implemented, which provides the best solution to choose the controlling parameters for generating the switching pulses. Moreover, the Deep Reinforced Markov Action Learning (DRLA) is employed to assure an efficient energy management in EV systems. For validation, the simulation and comparative analyses are carried out in this study with the use of numerous parameters. Index termsElectric Vehicle (EV), Proton Exchange Membrane Fuel Cell (PEMFC), Battery, Super-Capacitor (SC), Renewable Energy Sources (RES), High Gain DC-DC Converter, Gorilla Troop Optimization Algorithm (GTOA), and Deep Reinforced Markov Action Learning (DRLA).

Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies

For power plant networks in developing countries like Iraq, balancing electricity demand and generation continues to be a major challenge. Energy management (EM) in either demand-side (DS) or generation-side (GS) strategies, which is frequently utilized in Iraq due to a lack of adequate power generation, has a small impact on the power balancing mechanism. Most previous studies in similar countries discussed only the application of DS strategies. The purpose of this paper is to contrast and review various energy management methodologies being used in developing nations facing power outages, to be able to recommend suitable ones according to the country’s situation. To assess potential EM-based solutions to improve the total energy efficiency of the Iraqi electrical community, a thorough and methodical analysis was carried out. The main objective of this review paper is to discuss the causes of power outages and the energy management strategies addressed here as methods to mitigate or avoid power outages. Unlike existing reviews that concentrated on demand-side energy management, this study specifically focuses on power outage causes in developing countries like Iraq rather than all management strategies. It also introduces the consequences of power outages including analysis of distribution power losses, financial loss from power blackouts, and power blackouts in firms in a typical month. Therefore, it presents readers with state-of-the-art strategies and recommends a generation-based EM strategy to mitigate such issues.

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

Efficient energy management framework for enhancing the techno-economic-environmental performance of grid-connected microgrids under uncertain conditions

Due to the limited availability of fossil fuels and environmental concerns over greenhouse gas emissions, micro-grid systems (MGSs) have become crucial for integrating a high penetration of renewables. Nonetheless, the complexity of the operation of MGS increases in the actual operating process due to the irregular nature of renewables, electrical loads (load demands), and uncertain market prices. The core aim of this paper is to suggest a bi-objective optimization model for managing energy efficiently in a grid-connected MG while considering the unsteadiness of electrical load demands, market prices, and renewable output powers. Also, the suggested model tries to improve the MG’s scheduling while considering the wind turbine, photovoltaic, loads, and market pricing uncertainty. MG operators can develop strategies that balance cost savings with risk management and ensure long-term sustainability by considering the uncertainty of the different generation/demand resources and markets. The suggested optimization model includes two distinct objective functions – minimizing the 24-hour horizon’s total projected cost of the grid-connected MG and reducing MG emissions during the considered period. Further, a simultaneous reduction of the overall operating cost and the pollutant emission MG over 24 h is presented. The general algebraic modeling system (GAMS) has been used to solve the presented multi-objective problem. The findings clearly demonstrate the efficacy of the suggested energy management (EM) methodology for improving the techno-economic-environmental performance of grid-connected MGs under uncertain situations.

Robust energy management in isolated microgrids with hydrogen storage and demand response

Hydrogen is called to be one of the main energy vectors in future energy systems, especially for energy storage, where this carrier presents some interesting features. Its application in microgrids might help to improve economic, environmental and reliability indicators, providing greater storage capacity than other technologies, like batteries. Following this idea, this paper develops a robust energy management methodology for isolated microgrids considering hydrogen storage and demand response initiatives. The new proposal is raised as a nested max-min optimization framework. To reduce the original problem to a tractable single-level one, a master-slave scheme is developed by which binary variables can be fixed and thus the inner problem can be reduced to its equivalent Karush-Kuhn-Tucker conditions. Then, the overall optimization paradigm is solved in an iterative fashion used the Constraint-and-Column Generation Algorithm. The resulting tool is applied to a benchmark isolated microgrid, thus validating it for industry applications. Moreover, different relevant results are analysed in-depth. Thus, the role of hydrogen storage and demand response initiatives is discussed, concluding that flexible demand has a more notable impact on monetary savings than hydrogen storage, reducing the total cost by 6 % with respect to the base case. Some intrinsic issues are also identified. For example, it is observed that flexible consumers are more frequently called when the hydrogen chain is enabled, which may provoke undesirable effects like response fatigue.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

On the optimal demand-side management in microgrids through polygonal composition

This article presents a novel methodology for energy management in microgrids focused on the demand side. It is inspired by the Tangram puzzle. The energy demand and production profiles are represented by polygons and managed through computational geometry. Therefore, an optimization problem is defined to place n energy demand profiles (pieces) to cover the total energy production profile (target shape). The optimization problem is addressed with a genetic algorithm. It tries to calculate the optimal positions of the polygons of the demands covering the maximum energy production. Since the referred production comes from renewable energy sources in the microgrid, this method allows reducing both the consumption of fossil fuels and energy bills.

Grid Interactive Solar PV and Battery Operated Air Conditioning System: Energy Management and Power Quality Improvement

The drop in solar panel cost over past decade has accelerated the usage of solar photovoltaic (SPV) in various applications. In tropical countries, air conditioning unit is extensively used for cooling comfort. In this paper, PV generation is utilized with a battery energy storage (BES) for an air conditioner to reduce the impact of energy consumption from utility grid. Recently, air conditioning units are adopted with variable speed drive (VFD) that creates peaky nature of the input grid current due to the AC-DC conversion. The diode bridge rectifier (DBR) used for such conversion, does not allow bidirectional current flow. In this paper, considering such facts and taking the benefit of the VFD technology, an energy management methodology is proposed using PV array and BES to reduce the power consumption of air conditioning unit as well as it feeds excess PV generation to the grid with improved power quality. The DC power produced by the PV panel and BES is fed to DC bus of the VFD directly with the help of dual active bridge (DAB) converter, that eliminates DC-AC conversion stage. A voltage source converter (VSC) is interfaced as a front-end converter of VFD, to feed PV generation to the grid when air conditioner is not operating. This system is useful from multistory buildings to small consumers where the air conditioners are extensively used and PV array is installed. The concept of the system is validated by implementing in a prototype in the laboratory scale. The system is investigated with different possible scenarios. Under all test conditions, the THD of the grid current is found within the limits of the IEC 6100-3-2 standard.

USE OF ANALYTICAL MODEL FOR SYNTHESIS OF ALGORITHMS FOR CONTROL OF TRANSPORT CONVEYOR PARAMETERS

This study presents a methodology for synthesizing optimal control algorithms for the flow parameters of a conveyor-type transport system with a variable transport delay. A multi-section transport conveyor is a complex dynamic system with a variable transport delay. The transport conveyor is an important element of the production system, used to synchronize technological operations and move material. The Analytical PiKh-model of the conveyor section was used as a model for designing a control system for flow parameters. The characteristic dimensionless parameters of the conveyor section are introduced and the similarity criteria for the conveyor sections are determined. The model of a conveyor section in a dimensionless form is used to develop a methodology for synthesizing algorithms for optimal control of the flow parameters of a transport conveyor section. The dependencies between the value of the input and output material flow of the section are determined, taking into account the initial distribution of the material along the conveyor section, variable transport delay, restrictions on the specific density of the material, and restrictions on the speed of the belt. The dependencies between the value of the input and output material flow for the case of a constant transport delay are analyzed. A technique for synthesizing algorithms for optimal belt speed control based on the PiKh-model of a conveyor section is presented. As a simplification, a two-stage belt speed control is considered. Particular attention is paid to the methodology for synthesizing optimal control algorithms based on the energy management methodology (TOU-Tariffs). The criteria of control quality are introduced and problems of optimal control of flow parameters of the transport system are formulated. Taking into account differential connections and restrictions on phase variables and admissible controls, which are typical for the conveyor section, the Pontryagin function and the adjoint system of equations are written. As examples demonstrating the design of optimal control, algorithms for optimal control of the flow parameters of the transport system are synthesized and analysis of optimal controls is performed.

Embedded solution for data acquisition and management strategy dedicated to a hybrid renewable energy source for remote electricity supply

Electrifying people using renewable energy is a promising alternative that is gaining increasing importance for remote areas needs, but this solution is quite sensitive to the intermittent nature of environmental data, which puts more strain on the designer to overcome these problems to surely electrify consumers. In this work, the authors tackle a key topic of energetic and environmental data acquisition and energy management for electrifying a typical remote zone using a hybrid photovoltaic-wind system associated to an electrochemical storage system. An innovative alternative for data acquisition and intelligent on-board management of this system based on a Raspberry PI programmable target is presented. The first part of this article outlines the photovoltaic-wind turbine-batteries generation system architecture. The data acquisition and energy management methodology is described in the second part of this article. The results are finally discussed from the point of view of the suitability of the proposed embedded solution.

An Aggregated Energy Management Methodology for Air Conditioning System With DAB Converter

An air conditioner load has a major role in power consumption from utility grid in tropical countries. Most of the air conditioning systems are migrated to variable speed drive technology that optimizes the power utilization. Renewable energy sources like photovoltaics (PV) and energy storage system like batteries produce dc power. In this article, considering the advantages of variable speed drive technology, the power is directly injected to the dc side of the variable speed drive from PV array and the battery. The compressor at the outdoor unit consumes major portion of power from the grid and therefore the additional power is integrated to this unit to support the grid. The voltage level of the battery is boosted to match with the dc bus voltage level of the grid using a bidirectional isolated dc–dc converter. A low-power dc–ac conversion stage is present to run the indoor unit and to supply the auxiliary power. In this methodology, air conditioner is operated uninterruptedly and high-power dc to ac (50 Hz) conversion stages are eliminated, thus it reduces the blukiness of the system, cost, and conversion losses. A seamless bidirectional power is exchanged between the battery and utility grid without interrupting the main operation of the air conditioner. The bidirectional power flow is controlled at the dc side based on the time-of-use tarriff levied by the utility and the frequency of the grid. A small-scale labratory prototype of the system is set up for the validation of the concept. The effect of injecting power is investigated with different scenarios. With dc side power support, the consumer is benefitted with reduced power consumption during high tariff periods. Moreover, the harmonic components are reduced and the power quality is significantly improved. This scheme is utilized in building energy management system where air conditioner is extensively used.

건물에너지 절감량 평가를 위한 외기온 정보 활용에 관한 연구

Improving building energy efficiency is a major goal of energy policies. In recent years, various energy efficiency projects have been underway to reduce greenhouse gases. The general method of evaluating energy savings focuses on performance analysis of energy saving technologies. However, fluctuations in energy consumption of buildings can be caused by issues other than changes in specific energy saving facilities. Therefore, it is cost-effective to determine energy savings through changes in the overall building energy consumption before and after implementation of ECM (Energy Conservation Measures). Through this, we statistically verified a method of considering outdoor temperatures when calculating the energy savings using the energy efficiency measurement method IPMVP (International Performance Measurement and Verification Protocol). Based on this analysis, unlike methods that assume numerous data will be highly correlated, we found that our model considering the data resolutions of the average daily temperature and daily power consumption was the most suitable and efficient among the models with various data resolutions. This shows that statistically appropriate results can be obtained even if there is only small amounts of outdoor temperature and daily energy consumption data needed for energy savings assessment. It is expected that energy management methodologies for practitioners will improve if analysis methods considering energy consumption patterns of buildings are further developed.

Energy cost assessment of a dairy industry wastewater treatment plant

Water and energy are closely interlinked during their production and consumption processes. The limited and temporary distribution of energy and water resources poses a significant environmental challenge. Industrial wastewater treatment plants are essential elements of water production and also significant energy consumers. This study proposes a methodology for energy management of a wastewater treatment plant. Specifically, it examines the impact of optimum operating conditions on energy costs for a dairy wastewater treatment plant using a dissolved air flotation process. Monte Carlo simulation was used to optimize the parameters and to determine the reuse potential of dairy effluent. Firstly, the optimum operating conditions were determined. The results revealed a maximum fat, oil, and grease removal efficiency of 97% and a chemical oxygen demand removal efficiency of 70%. The optimum conditions were pH of 8, a saturation pressure of 5 bars, and a recirculation ratio of 33%. The optimum concentrations of coagulant and flocculant that contain polyaluminum chloride and cationic polymer were 20 mg/L and 25 mg/L, respectively. The results of the simulation study gave a recirculation ratio of 26.31%, a polyaluminum chloride concentration of 42.5 mg/L, a cationic polymer concentration of 36.31 mg/L, and a saturation pressure of 4.61 bars. Finally, energy cost assessment was performed using a newly developed model which showed that the energy cost indicator of the existing process was lower than optimum operating conditions. The reuse potential of dairy effluent as cooling water was found to be 52%.

Energy Management Methodology for Sustainable Water Development and Servicing, Considering the POET Based Concept

This paper aims at analysing the energy management activities for commercial bulk water development and servicing in South Africa. The energy efficiency, in terms of performance, operation, equipment and technology (POET), is reviewed. The sustainability of a general energy management program is discussed within this POET framework. As an application of this discussion to the commercial bulk water development scenario, the POET efficiency of an energy system is a measure of energy efficiency, which is determined by external but deterministic system indicators, such as production period, energy sources, environmental impact and technical indicators, amongst others. The case study concludes that the POET, based framework does not only cover all major energy management activities, but further identifies energy efficiency improvement opportunities.

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

AbstractThis dissertation shows energy scheduling in grid‐connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN–BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid‐connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.

An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.

An Optimal Energy Management of Grid-Connected Residential Photovoltaic-Wind-Battery System under Step-rate and Time-of-Use Tariffs

This paper explores optimization for energy management for a given residential application. The study proposes an approach that can minimize the utilization of the grid for electricity usage in the load and maximize the sale from renewable energy (photovoltaic (PV) and wind turbine (WT)). Besides, the battery storage is permitted to sell the excess power during high price period. A time-of-use (ToU) and step-rate tariffs are incorporated in the model to control the efficiency of the utility grid and also to consider the fluctuation of the electricity price. Therefore, two energy management methodologies are developed to adjust the tariffs for great benefits. Â Various residential feed-in tariffs (FIT) are implemented in order to reach the potential of cost-saving and earn more cost-benefit. A typical Moroccan house, containing a grid-connected PV-WT-Battery system, is used for study validation. On the one hand, simulation results show that the developed energy management strategies are effective in controlling the power sources optimally. On the other hand, applying the method under a greater FIT, a lesser requirement of battery capacity will be performed as well as a maximum cost saving. Then, the model as a Non-Linear Problem (NLP) is compared with another existing optimization approach to ascertain the effectiveness and validity of the algorithm.

Development of Optimized Methodology for Improvement Domestic Energy Management

Electrical energy management (EEM) is an object that has proceeds appointed importance in the 21th- century in order to its assistance to economic development and ecological ascertainment. “EEM” may be perfected on the supply side “(SS)” or demand side “(DS)”. On the supply side, “EEM” is cultivated when: There is an outgrowth desire “(demand requirement is higher than supply)”. “EEM” assists to suspend the design a resent generation station. On the “DS”, “EEM” is used to minimize the cost of electrical energy consumption and the interrelated forfeitures. The technique utilized for “EEM” is demand side load management that plan at ending valley filling, peak clipping and strategic preservation of electrical systems [1]. Seeming new inventions like “distributed generation (DG)”, “distributed storage (DS)” and “DSLM” will modify the method we use and generate energy. A smart grid (SG) is an electrical network that manages electricity demand in an unstoppable sustainable, reliable and economic manner. A smart grid uses smart net meters to overcome the sickliness of traditional electrical grid. “(DSM)” is a vital advantage of “(SG)” to progress power efficiency, minimize the peak average load and minimize the cost. From basic purposes of DSM is shifting load from peak hours to off-peak hours and reducing consumption during peak hours. Generally, a deregulated grid system is considered where the retailer purchases electricity from the electricity market to cover the end users’ energy need. In this research, Demand Side Management (DSM) techniques (load shifting and Peak clipping) are used to maximize the profit for Retailer Company by reducing total power demand pending peak demand periods and achieve an optimal daily load schedule using linear programming method and Genetic Algorithm. This method is performed on the 69-bus radial network. Also, a short term Artificial Neural Network technique is used to get forecasted wind speed, solar radiation and forecasted users load for date 15-Aug-2019. The neural network here uses an actual hourly load data, actual hourly wind speed and solar radiation data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for Retailer Company. Then comparison between profit using linear programing and genetic algorithm are made. The optimized DSM succeeded to maximize the profits of the company.