Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.

Sustainable Development of Energy Systems and Climate Systems:Key Issues and Perspectives

Climate change and energy security issues are prominent challenges in current energy system management,which should be governed synergistically due to the feedback relationships between them.The“Energy Systems Management and Climate Change”Special Collection Issue in the journal of Energy Engineering provide insights into the field of energy systems management and climate change.From an extended perspective,this study discusses the key issues,research methods and models for energy system management and climate change research.Comprehensive and accurate prediction of energy supply and demand,the evaluation on the energy system resilience to climate change and the coupling methodology application of both nature and social science field maybe the frontier topics around achieving sustainable development goals of energy systems.

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.

Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit

Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions.Hence,to balance the interests of the environment and the building users,this paper proposes an optimal operation scheme for the photovoltaic,energy storage system,and flexible building power system(PEFB),considering the combined benefit of building.Based on the model of conventional photovoltaic(PV)and energy storage system(ESS),the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy,society,and environment as the optimization objective,taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints.The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’costs and the combined benefits of the building.The efficiency and effectiveness of the proposed methods are verified by simulated experiments.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

Effective Energy Management Scheme by IMPC

The primary purpose of the Energy Management Scheme(EMS)is to monitor the energy fluctuations present in the load profile.In this paper,the improved model predictive controller is adopted for the EMS in the power system.Emperor Penguin Optimization(EPO)algorithm optimized Artificial Neural Network(ANN)with Model Predictive Control(MPC)scheme for accurate prediction of load and power forecasting at the time of preoptimizing EMS is presented.For the power generation,Renewable Energy Sources(RES)such as photo voltaic(PV)and wind turbine(WT)are utilized along with that the fuel cell is also presented in case of failure by the RES.Such a setup is connected with the grid and applies to the household appliances.In improved model predictive control(IMPC),the set of constraints for the powerflow in the system is optimized by the ANN,which is trained by EPO.Such a tuning based prediction model is presented in the IMPC technique.The proposed work is implemented in the MATLAB/Simulink platform.The energy management capability of the proposed system is analyzed for different atmospheric conditions.The total system cost,life cycle cost and annualized cost for IMPC are 48%,45%and 15%,respectively.From the performance analysis,the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Modeling Energy Consumption in the Production Processes of Industrial Units Based on Load Response Programs in the Energy Market

The optimal operation of microgrids is of great significance for the sake of efficient and economical management of its energy resources.The microgrid energy management system should plan to operate the microgrid while simultaneously considering the electric and thermal load.The present study proposes energy management to minimize the costs of operating an industrial microgrid.In fact,planning for energy supply is among the critical issues that distribution companies deal with daily in the competitive environment.A distribution company usually meets customer(end customer)demands by purchasing energy from a wholesale market.Given the load curtailment,distribution companies have more choices and interactions in the market.Distribution companies face the two uncertainties of load changes and price fluctuations in their daily energy supply planning which could lead to the risk of loss resulting from the distribution company’s decision-making for daily energy supply planning.Thus,these companies face the challenge of maximizing profit in a risk-based environment.Therefore,the present study presents an optimal model of energy consumption in the production processes of aluminum and cement industrial units.The presented model was then used in planning the day-ahead energy of a microgrid containing these industrial units.Since the studied subject has many limitations,it would be difficult to solve it using mathematical methods.To resolve this issue then,the present study introduces a newly developed algorithm inspired by bee colonies.The proposed method seeks to significantly improve in the local and global search capabilities.In addition to confirming the validity of the proposed model,results indicate that the implementation of load-response programs and the cooperation of industrial units in the ancillary services and energy market can increase the profits of units and microgrids as well as correct the demand curve.According to the obtained results from the first and second test cases,the total profit of the aluminum unit was$188,103 and$237,805,respectively.Similarly,this profit for the cement industrial unit is$104,350 and$233,195.3,respectively.From the results,it can be observed that the final profit of the second unit has increased by 61%.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Reinforcement Learning-Based Electric Vehicles Energy Management Strategy with Battery Thermal Model

The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.

Powering a Low Power Wireless Sensor in a Harsh Industrial Environment: Energy Recovery with a Thermoelectric Generator and Storage on Supercapacitors

Wireless sensor networks are widely used for monitoring in remote areas. They mainly consist of wireless sensor nodes, which are usually powered by batteries with limited capacity, but are expected to last for long periods of time. To overcome these limitations and achieve perpetual autonomy, an energy harvesting technique using a thermoelectric generator (TEG) coupled with storage on supercapacitors is proposed. The originality of the work lies in the presentation of a maintenance-free, robust, and tested solution, well adapted to a harsh industrial context with a permanent temperature gradient. The harvesting part, which is attached to the hot spot in a few seconds using magnets, can withstand temperatures of 200°C. The storage unit, which contains the electronics and supercapacitors, operates at temperatures of up to 80°C. More specifically, this article describes the final design of a 3.3 V 60 mA battery-free power supply. An analysis of the thermal potential and the electrical power that can be recovered is presented, followed by the design of the main electronic stages: energy recovery using a BQ25504, storage on supercapacitors and finally shaping the output voltage with a boost (TPS610995) followed by an LDO (TPS71533).

Critical Success Factors Influencing the Implementation of Sustainable Energy System in Uganda: A Case of Inter-University Council of East Africa Energy Project at the Head Quarters in Kampala, Uganda

The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.

Assessment of the Implementation of Energy Conservation Opportunities Arising from Energy Audits;A Study of Four-Star and Five-Star Hotels in Nairobi Kenya

This study assesses the implementation of energy conservation opportunities in four-star and five-star hotels in Nairobi. The Covid-19 pandemic had a significant impact on the Hospitality Industry. Currently, there is a growing inclination to furnish guests with superior and sustainable services in an energy-efficient and eco-friendly way. Comprehensive research was conducted from energy audits gathered from the establishments and contracted auditing companies, on top of this, hotel staff were given digital questionnaires. To add to the data, the researcher surveyed the hotels with engineering managers. The Energy Audits found that all 10 hotels had adopted Energy Conservation Opportunities (ECOs). After further analysis, the mean adoption rate of Energy Conservation Opportunities (ECOs) during the past three years was 55.83%, which was below the aim of 100%. According to studies, hotel staff manages energy to cut costs. The researcher found that hotels use up a lot of energy. However, they have conservation potential, depending on government policies, costs, ease of implementation, and management commitment to sustainable practices. Essentially, Energy Conservation Opportunities (ECOs) reduce energy expenditures and boost reliable revenues, especially during high energy prices and uncertainty.

A digital twin model-based approach to cost optimization of residential community microgrids

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.