Effective Energy Management System Research Articles

Energy Consumption Forecasting through Data Mining

Forecasting is vital for effective energy management systems to project future energy requirements and establish demand-supply equilibrium. In this study, we propose to evaluate the effectiveness of a new weather-free forecasting model. The model is created using advanced data mining techniques on an extensive database that contains relevant historical power production data. The motivation behind developing a weather-free model stems from the challenges associated with obtaining reliable weather data in specific scenarios and the desire to reduce computational complexity. By eliminating the reliance on weather data, our model offers a promising alternative approach to energy forecasting, potentially enhancing accuracy and efficiency. In this work, our objectives are twofold. Firstly, we aim to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Anomalies in energy consumption patterns can significantly impact forecasting accuracy, and thus, understanding their detection and management is crucial. Secondly, we seek to determine the optimal performance metric among three defined metrics explicitly tailored for this application. By addressing these objectives, we aim to contribute to advancing energy forecasting methodologies, providing valuable insights for practical implementation in energy management systems. Our findings have the potential to inform decision-making processes and optimize resource allocation in energy distribution networks, ultimately leading to more sustainable and resilient energy infrastructures.

An Enhanced Rule-Based Approach for Optimizing Energy Costs and Battery Health in Microgrid Systems

Renewable energy resource systems have increased significantly as a result of the transition to microgrids. These systems require an effective energy management system (EMS) in order to optimize the flow of energy from the different sources. This paper proposes an enhanced rule-based EMS (EnRB-EMS) for a grid connected photovoltaic (PV)-battery system, in order to optimize the operation of the system to meet the escalating energy demand, minimize the cost of electricity, and increase the battery life by improving the power quality. The proposed strategy is based on a set of real-time information, such as the load demand, the state of charge (SoC) of the battery, and the cost of electricity. The performance of the proposed strategy is evaluated under different scenarios, demonstrating the effectiveness of the proposed strategy in achieving optimal energy management, reducing the electricity cost, and increasing the battery life.

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.

PEM fuel cell and supercapacitor hybrid power system for four in-wheel switched reluctance motors drive EV using geographic information system

In recent years, Electric Vehicles (EVs) powered by a Proton Exchange Membrane Fuel Cell (PEMFC) which has high energy density and efficiency have become an attractive solution for the automotive industry. However, since the power density of the PEMFC is not high enough for different road conditions such as accelerating and going uphill, a supercapacitor (SC) is preferred as the auxiliary energy source for high power density in EVs. Thus, in this paper, a hybrid power system consisting of the Fuel Cell (FC) and SC for a Four-Wheel Drive Electric Vehicle (4WDEV) is proposed. Since energy requirement of the EV depends on the travel route, an effective Energy Management System can be designed by using a Geographic Information System (GIS). According to the road trajectory, the power requirement of the EV is estimated and thus, energy consumption is reduced. The proposed system is validated by using the Matlab Simscape Toolbox.

An Analysis of the Requirement for Energy Management Systems in India for Electric Vehicles

Conventional fuels used in combustion engines are the main sources of carbon dioxide emissions, which affect the environment. If energy is available from renewable sources compared to conventional sources, electric vehicles (EVs) offer efficient and cost-effective solutions to the above issue. However, EVs employ batteries for energy storage, which presents a number of issues. For example, overheating produced by chemical reactions during the charging and discharging process in high temperatures can result in the battery's fatal destruction. Hence, an effective energy management system (EMS) is in need of the technology required for the accomplishment of EVs in the long term. Monitoring and optimizing electricity use is the aim of energy management, which aims to cut costs and emissions without interfering with operations. When lifetime CO2 emissions are taken into consideration, EVs will be far more environmentally friendly than regular fuel vehicles because of the incorporation of sustainable power. Distributed solar energy will help reduce the distribution and transmission losses, which will further lower the lifetime CO2 emissions and operating costs of EVs and hasten their commercial viability. This paper presents a review of energy management challenges and their necessity. EV energy management is very important as it helps to minimize EV charging costs.

Implementation of Nonlinear Controller to Improve DC Microgrid Stability: A Comparative Analysis of Sliding Mode Control Variants

Electricity generation from sustainable renewable energy sources is constantly accelerating due to a rapid increase in demand from consumers. This requires an effective energy management and control system to fulfil the power demand without compromising the system’s performance. For this application, a nonlinear barrier sliding mode controller (BSMC) for a microgrid formed with PV, a fuel cell and an energy storage system comprising a battery and supercapacitor working in grid-connected mode is implemented. The advantages of the BSMC are twofold: The sliding surface oscillates in the close vicinity of zero by adapting an optimal gain value to ensure the smooth tracking of power to its references without overestimating the gains. Secondly, it exhibits a noticeable robustness to variations and disturbance, which is the bottleneck of the problem in a grid-connected mode. The stability of the presented controllers was analyzed with the Lyapunov stability criterion. Moreover, a comparison of the BSMC with sliding mode and supertwisting sliding mode controllers was carried out in MATLAB/Simulink (2020b) with real PV experimental data. The results and the numerical analysis verify the effectiveness of the BSMC in regulating the DC bus voltage in the presence of an external disturbance under varying conventional load and environmental conditions.

Power Sources for Unmanned Aerial Vehicles: A State-of-the Art

Over the past few years, there has been an increasing fascination with electric unmanned aerial vehicles (UAVs) because of their capacity to undertake demanding and perilous missions while also delivering advantages in terms of flexibility, safety, and expenses. These UAVs are revolutionizing various public services, encompassing real-time surveillance, search and rescue operations, wildlife assessments, delivery services, wireless connectivity, and precise farming. To enhance their efficiency and duration, UAVs typically employ a hybrid power system. This system integrates diverse energy sources, such as fuel cells, batteries, solar cells, and supercapacitors. The selection of an appropriate hybrid power arrangement and the implementation of an effective energy management system are crucial for the successful functioning of advanced UAVs. This article specifically concentrates on UAV platforms powered by batteries, incorporating innovative technologies, like in-flight recharging via laser beams and tethering. It provides an all-encompassing and evaluative examination of the current cutting-edge power supply configurations, with the objective of identifying deficiencies, presenting perspectives, and offering recommendations for future consideration in this domain.

An effective energy management system for intensified grid-connected microgrids

The utility's utilization of communication technology and renewable energy sources has paved the path for self-sustaining microgrids (MGs). However, the intermittency of solar and wind energies raises concerns about meeting demand effectively. To ensure optimal performance of distributed MGs, an efficient energy management system (EMS) is crucial to tackle this uncertainty. Historically, MGs have primarily achieved operational cost reduction through optimal functioning. Integrating demand response (DR) into the EMS could further enhance operational efficiency and peak reduction. This research work addresses this challenge by incorporating DR programs into grid-connected MGs' energy management. Stochastic programming is employed to account for the unpredictable solar and wind behaviours. Flexible price elasticity is used to calculate price elasticity coefficients, portraying customer responses effectively. The implemented research work compares the Dragon Fly Algorithm with other heuristic approaches, resulting in a 12.42 % reduction in overall operating costs and the efficacy of the proposed algorithm is shown.. Using the Analytic Hierarchy Process (AHP), the User Satisfaction Index is assessed, revealing that the CPP demand response initiative tops the satisfaction scale with a score of 0.92881.. Moreover, this research offers an exhaustive evaluation of techno-economic markers for each scenario, systematically ranked using the proposed AHP methodology..

Deep Reinforcement Learning DDPG Algorithm with AM based Transferable EMS for FCHEVs

Hydrogen fuel cell is used to run fuel cell hybrid electrical vehicles (FCHEVs). These FCHEVs are more efficient than vehicles based on conventional internal combustion engines due to no tailpipe emissions. FCHEVs emit water vapor and warm air. FCHEVs are demanding fast dynamic responses during acceleration and braking. To balance dynamic responsiveness, develop hybrid electric cars with fuel cell (FC) and auxiliary energy storage source batteries. This research paper discusses the development of an energy management strategy (EMS) for power-split FC-based hybrid electric cars using an algorithm called deep deterministic policy gradient (DDPG) which is based on deep reinforcement learning (DRL). DRL-based energy management techniques lack constraint capacity, learning speed, and convergence stability. To address these limitations proposes an action masking (AM) technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated. In addition, the transfer learning (TL) approach of the DDPG-based strategy was investigated in order to circumvent the need for repetitive neural network training throughout the various driving cycles. The findings demonstrated that the suggested DDPG-based approach in conjunction with the AM method and TL method overcomes the limitations of current DRL-based approaches, providing an effective energy management system for power-split FCHEVs with reduced agent training time.

Implementation of energy-saving projects within the framework of the energy mana-gement strategy at budget facilities

The article is devoted to the issues of implementing energy-saving projects within the framework of the energy management strategy at the objects of the budget sphere. Based on the research, it was proposed to arrange the adaptation tools for managing energy-saving projects. The purpose of the study was to study the implementation of energy-saving projects within the framework of the energy management strategy at the objects of the budget sphere. Effective energy management system implementation considers not only material and financial resources but also human resources and previous experience in implementing energy-saving measures. The specified features of the energy management principles must be followed when implementing energy-saving projects at budget facilities. Attention is focused on the expediency of analyzing the stages of general energy quality management in detail, which must be considered for energy saving. Some obstacles are given that prevent energy management from creating the proper conditions for increasing energy saving at budget-sector facilities. It has been proven that developing a general strategy for implementing the energy management system at public sector facilities is essential for creating an energy-saving program. The approach to the formation of energy characteristics at the objects of the budget sphere allows the top management to present historical consumption trends, energy costs, the level of competence in energy management, a list of possible projects with an indication of the ratio of costs and benefits; study of experience and results achieved in other institutions. For this, it is necessary to strengthen the commitment of management, which is essential for the success of the energy management system. This commitment includes defining the organizational structure, setting, and approving goals, obtaining the necessary resources, and systematically spreading and supporting the energy conservation program. Modeling the organizational structure of the energy management system is based on interconnected components: the creation of an energy-saving block or zone, the creation of an energy-saving commission, and the hiring of an advisory group. Therefore, within the framework of the conducted research, it is proposed, with the use of adaptive management tools of energy-saving projects, at the beginning of the process, it is necessary to determine the priorities of different local communities and the location features of budget objects in order to ensure a fair transition in the distribution of resources between budget objects about the implementation of energy saving projects within energy management strategies. Keywords: energy diagnostics, energy-saving projects, strategy, energy management, budget objects.

The main aspects of the energy management system implementation by higher education institutions of Ukraine

The purpose is to investigate the main components of the construction of the energy management system for compliance with the requirements of DSTU ISO 50001:2020 in Ukrainian health care institutions.
 Materials and methods: informational, retrospective, based on the study of scientific literature, internet materials – resources and own conclusions.
 Research results. The dynamics of the implementation of energy management systems by Ukrainian organizations were studied. The main components of the implementation of the energy management system are analyzed: a risk-oriented approach, the concept of PDCA, senior management develops energy policy and goals and forms a series of energy efficiency measures, the energy planning process should be based on objective and operational incoming data, the constant active participation of personnel in issues energy saving and energy saving, systematic energy audits.
 Conclusions. The developed effective energy management system in Ukrainian health care institutions will make it possible to optimize energy costs, build a reliable energy saving system, and constantly improve energy efficiency as a whole.
 Key words: energy management system, energy consumption, energy saving, energy resources, energy saving, energy efficiency.

Five Level H-Bridge Configuration Based Microgrid with Sugeno Fuzzy Controller for New Energy Generation from Renewable Systems

Hybrid microgrids run by renewable energy sources are gaining popularity around the world. Solar (PV) and permanent magnet synchronous generator (PMSG) based wind energy systems (WES) are well-known and easy to install renewable energy options. Unfortunately, wind speeds and solar irradiance levels fluctuate unpredictably. Energy generation from both WES and PV panels must therefore fluctuate. Simultaneously, the load is fluctuating irregularly. Hence, storage devices must be incorporated into hybrid systems in order to keep the generation and consumption of electricity in equilibrium. In addition, for a fuel cell and electrolyzer that run on hydrogen, a tiny battery is added into the system to keep costs down. In order to enhance power quality and reliability, all the components in a microgrid need to be connected to an effective energy management system. For optimal use, renewable energy sources are combined with maximum power point trackers. When there are sudden shifts in both the energy supply and demand on a standalone microgrid, the energy balance and frequency response are crucial. In this study, a Takagi Sugeno based innovative fuzzy controller is implemented for a system to manage energy in order to achieve a precious and rapid reaction. The suggested system's Hardware-In-the-Loop is built using OPAL-RT modules in order to demonstrate detailed findings.

Development and Application of an Energy Management System for Electric Vehicles Integrated with Multi-input DC-DC Bidirectional Buck-Boost Converter

The rise in environmental pollution, demand for fossil fuels, and higher fuel economy vehicles has raised concerns about the creation of new and efficient transportation vehicles in recent days. These days, most developments in electric vehicles concentrate on making the vehicles more pleasant to ride in. Nonetheless, the emphasis now should be on energy and its most efficient use. To do this, you must give your attention to the origin of the automobile. The answer to this problem may be found in hybrid energy storage systems (HESS). This work is concerned with the design and implementation of an effective energy management system in electric vehicles (EVs) equipped with an active HESS consisting of a battery and a super capacitor via the incorporation of load sharing into this hybridization under a variety of load demand scenarios. To address the demands of high fuel efficiency vehicles, automotive firms are focusing on the development of diesel-engine operated vehicles, electric vehicles, fuel-cell vehicles, plug-in electric vehicles, and hybrid electric vehicles. A Multi-input Bidirectional Buck-Boost (MIB3) DC-DC converter is proposed in this dissertation to provide a greater conversion ratio to the input DC voltage. The multi-input converter recommended has fewer components and a simpler control method, making it more trustworthy and cost-effective. This converter also has bidirectional power flow functionality, making it suitable for charging the battery during regenerative braking in an electric or hybrid vehicle. Three different energy sources are used in the suggested topology: a photovoltaic (PV) panel, a battery, and an ultra-capacitor

A novel Three-Stage demand side management framework for stochastic energy scheduling of renewable microgrids

By integrating renewable energy resources and communication technology into the structure, microgrids (MG) have become self-sustaining with less relying of fossil-fuel based units. In this regard, it is significant to have a highly effective energy management system (EMS) to handle the high volatility of clean energy resources, the uncertainty of time-varying loads, and the fluctuating nature of the market price. Due to the technical limitations, the MG's optimal performance is currently restricted to maximizing operational prices. A study must be conducted to appraise the effects of demand-side management (DSM) on the overall operational costs and peak reductions when integrated with EMS. This study investigates the effect of a utility-driven mutable load shaping method on non-dispatchable energy resources in the renewable microgrids considering the digital twin of solar and wind units. It proposes a three-stage stochastic EMS frame in order to solve the optimal day-ahead planning and to minimize the operating price of network-coupled MG. An initial stage involves creating four potential cases for solar and wind energy production profiles based on real-time meteorological information with the aim o time t, the active power interchange to or from utility for.f minimizing the uncertainty. Stage two involves configuring the MG system, determining the operating limitations, and incorporating DSM load participation information into the objective function. Stage 3 involves designing an optimal power dispatch configuration for DG units, increasing exports to utilities, and comparing the outcomes of each scenario including or excluding DSM involvement. According to the simulation results obtained for the suggested stochastic model implementing 20% DSM, the suggested approach could sucessfuly achieve a price decrease of almost 43.8%.

Lens-oppositional duck pack algorithm based smart home energy management system for demand response in smart grids

Smart grid (SG) is one of the emergent technologies providing effective solutions for increased power demand globally. Due to the advances of Internet of Things (IoT), smart devices are integrated with the smart home (SH) environment which actively participates in electricity market through demand response (DR) programs for energy management. DR refers to the modifications in the electricity utilization of the end user from the common utilization pattern in response to the variation in electricity prices. An effective energy management system (EMS) with price-based DR model is essential for IoT enabled SH environment. In this view, this article develops a Lens-Oppositional Duck Pack Algorithm based Smart Home Energy Management System (LODPA-SMEMS) for DR in SGs. The major focus of the LODPA-SMEMS model is to achieve optimal management of energy usage by scheduling process. The LODPA is derived by the incorporation of lens-oppositional based learning (LOBL) with conventional DPA. The LODPA-SMEMS model is mainly derived to reduce peak-to-average ratio (PAR) and electricity price with maximum user comfort (UC). The experimental outcome of the LODPA-SMEMS model outperforms the other methods under several aspects. The results reported that the LODPA-SMEMS model improves overall energy usage and consequently, the sustainability of IoT enabled SH gets enhanced. According to the findings, the LODPA-SMEMS model increases overall energy efficiency, hence increasing the sustainability of IoT-enabled SH. The experimental values demonstrated that the LODPA-SMEMS model has accomplished lower PAPR of 2.10 whereas GA, BPSO, GWDO, GBPSO, and WBFA models have attained higher PAPR of 3.70, 3.10, 3.80, 3.60, and 2.40 respectively. Also, EEC analysis of LODPA-SMEMS on DAPS, RTPS, TUPS: 3.53kWh, 1.81kWh, 1.85kWh. Similarly, LODPA-SMEMS model has achieved superior performance with least ET of 68 s.

A Review on Optimal Energy Management in Commercial Buildings

The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals.

Energy forecasting of the building-integrated photovoltaic façade using hybrid LSTM.

Effective building energy management systems need a reliable approach to estimating future energy needs using renewable energy sources. However, nonlinear and nonstationary trends in building energy use data make prediction more challenging for integrating the photovoltaic system. To estimate future energy forecast, this work presents a hybrid approach based on random forest (RF) and long short-term memory (LSTM) using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Initial steps in our suggested procedure include utilizing CEEMDAN to translate the raw energy usage data into multiple components. Then, the component with the most significant frequency is predicted using RF, and the other components are forecasted using hybrid LSTM. Finally, all of the individual parts' predictions are combined to form a whole. Real-world output energy usage data has been predicted to test the suggested strategy. Results from the experiments show that the suggested strategy outperforms the reference methods.

Impact of demand side management on the operational cost of microgrids using ABC algorithm

Self-sustaining microgrids (MG) are now possible due to the integration of renewable energy and communication technology in utility. It is essential to have an effective energy management system (EMS) because of the unpredictable response of these resources, the uncertainty of the load variations, and the market pricing. Only operational expenses have been considered while discussing MG’s optimum operation so far. It is necessary to examine the potential of adding demand-side management (DSM) to the energy management system challenges and its impact on overall operational costs and peak reduction. This article investigates the influence of the load shaping approach that is imposed by the utility on non-dispatchable energy sources. A stochastic EMS framework is developed to come up with an optimum solution for day-ahead scheduling and minimize operating costs for grid-connected MG. Using real-time weather data, four different solar and wind power production profiles are developed in the first step to address the issue of unpredictability. MG system design, operational restrictions, and allocating demand side management load participation data to the goal function are all addressed in this second step of the algorithm development. Artificial Bee Colony (ABC) is designed in the third stage to find the ideal setup of DG units for maximum electricity dispatch and comparing outcomes for all scenarios with and without DSM involvement. It has been shown that with a 20% DSM load participation, a proposed stochastic framework may save costs by 62%, according to the simulation results.

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification.

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

Energy Management System for Grid-Connected Nanogrid during COVID-19

An effective energy management system (EMS) was designed based on the Stateflow (SF) approach for a grid-connected nanogrid (NG) composed of a photovoltaic (PV) array with a battery bank and supercapacitor (SC) energy storage system (ESS). The PV energy system, battery bank and SC (ESS), dual active bridge DC/DC converters, DC/AC inverters, control algorithms, and controllers were developed to test the operation of the NG. The average and high-frequency power components are separated using frequency division of the ESS power utilizing a low-pass filter; the average power is absorbed by the battery bank, while the high-frequency power is absorbed by the SC. The aim of this paper is to design an EMS to manage the energy of a grid-connected NG system considering the availability of the PV array, ESS, and demand requirements. Different scenarios of operation were tested to check the EMS behaviour during the day with a random demand profile, including: (1) a PV array with the grid supplying the load without an EMS; (2) a PV array, batteries, and the grid supplying the load with an EMS; (3) a PV array, batteries, an SC, and the grid supplying the load with an EMS; (4) a PV array, batteries, an SC, and the grid supplying the load with an EMS, with load profile reduction by 20% due to COVID-19. As per the simulation results, the proposed EMS enables the flow of power in the NG system and demonstrates the impact on the ESS by minimising carbon emissions via a reduction in grid consumption. Furthermore, the SF method is regarded as a helpful alternative to popular design approaches employing conventional software tools.