Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage

In recent years, increasing interest in developing small-scale fully integrated energy resources in distributed power networks and their production has led to the emergence of smart Microgrids (MG), in particular for distributed renewable energy resources integrated with wind turbine, photovoltaic and energy storage assets. In this paper, a sustainable day-ahead scheduling of the grid-connected home-type Microgrids (H-MG) with the integration of non-dispatchable/dispatchable distributed energy resources and responsive load demand is co-investigated, in particular to study the simultaneously existed uncontrollable and controllable production resources despite the existence of responsive and non-responding loads. An efficient energy management system (EMS) optimization algorithm based on mixed-integer linear programming (MILP) (termed as EMS-MILP) using the GAMS implementation for producing power optimization with minimum hourly power system operational cost and sustainable electricity generation of within a H-MG. The day-ahead scheduling feature of electric power and energy systems shared with renewable resources as a MILP problem characteristic for solving the hourly economic dispatch-constraint unit commitment is also modelled to demonstrate the ability of an EMS-MILP algorithm for a H-MG under realistic technical constraints connected to the upstream grid. Numerical simulations highlights the effectiveness of the proposed algorithmic optimization capabilities for sustainable operations of smart H-MGs connected to a variety of global loads and resources to postulate best power economization. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 21% in comparison with conventional EMS

Load Forecasting and Synthetic Data Generation for Smart Home Energy Management System

A number of recent trends, such as the increased power consumption in developed and developing countries, the dangers associated with greenhouse gases, the potential shortages of fossil fuels, and the increasing availability of solar and wind energy act as motivating factors for the development of more intelligent and efficient systems both on the power provider as well as the consumer side. One of the most important prerequisites for making efficient energy management decisions is the ability to predict energy production and consumption patterns. While long-term forecasting of average consumption had been extensively used to direct investments in the energy grid, short-term predictions of energy consumption became practical only recently. Most of the existing work in this domain operates at the level of individual households. However, the availability of historical power consumption data can be an issue due to concerns such as privacy, data size or data quality. Researchers have been provided with synthetic smart home energy management systems that mimic the statistical and functional properties of the actual smart grid in order to improve their access to public system models. Through developing time series to represent different operating conditions of these synthetic systems, the potential of artificial smart home energy management system applications will be further enhanced. The work described in this dissertation extends the ability to predict and control power consumption to the level of individual devices in the home. This work is made possible by several recent developments. Internet of things technologies that connect individual devices to the internet allows the remote tracking of energy consumption and the remote control and scheduling of the devices. At the same time, progress in artificial intelligence and machine learning techniques improve the accuracy of predictions. These components often form the basis of smart home energy management systems (HEMS). One of our insights that facilitates the prediction of the energy consumption of individual devices is that the history of consumption contains important information about future consumption. Thus, we propose to use a long short-term memory (LSTM) recurrent neural network for prediction. In a second contribution, we extend this model into a sequence-to-sequence model which uses several interconnected LSTM cells on both the input and the output sides. We show that these approaches produce better predictions compared to memoryless machine learning techniques. The prediction of energy consumption delivers maximum value when it is integrated with the active component of a HEMS. We design a reinforcement learning-based technique where a Q-learning model is trained offline based on the prediction results. This system is then validated only using real data from PV power generation and load consumption. Considering the scarcity of data among the smart grid users, in our third contribution, we propose the Variational Autoencoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model capable of learning various types of data distributions, such as electrical load consumption, PV power production and electric vehicles charging load consumption, and generating plausible sample data from the same distribution without first performing any pre-training analysis on the data. Our extensive experiments have shown the accuracy of our approach in synthesizing smart home datasets. There is a high degree of resemblance between the distribution of VAE-GAN synthetic data and the distribution of real data. The next step will be to incorporate Q-learning for offline optimization of HEMS using synthetic data and to test its performance with real test data

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Optimization of Household Energy Management Based on the Simplex Algorithm

Small scale intermittent renewable energy consisting of roof-mounted photovoltaic generators and micro wind turbines for the household residential have been widely integrated into the power grid. Meanwhile, more and more home appliances are utilized, including schedulable and non-schedulable home appliances. With the deployment of smart technologies, the control strategies of home energy management system are developed and this provides the possibility to minimize the energy bill and improve the energy efficiency by scheduling the controllable home appliances without sacrificing preference of the customer. In this thesis, an optimization strategy based on the Simplex algorithm has been proposed. The target is to optimize the energy consumption in households by scheduling the household appliances, considering the day-ahead electricity price from Nord Pool market and the roof-mounted PV production. Firstly, the schedules are generated from the optimization algorithm and then interpreted to control the appliances to achieve energy bill saving. In order to evaluate the optimization algorithm, the water boiler is used as the controllable load. Two case studies for 24-hour illustrate that the implementation controlled by HEMS using this algorithm can contribute greatly to the energy bill savings. According to the two implementations, around 43% of reduction for the energy bill can be achieved. Considering the PV production integrated into HEMS and support from the distribution network operators, the more benefits can be achieved. The switch panel in the AC Microgrid laboratory acts a crucial part in the implementations and it has seven 3-phase channels that can be utilized to connect with electrical appliances, home energy storage systems, and distributed generations such as micro wind turbines and roof-mounted PV panels. Each channel is equipped with two connectors to control the operation, the top of which is controlled by HEMS computer using wireless Z-Wave and the other one is controlled by dSpace which can be used to emulate the consumption pattern in the households. The algorithm and Z-wave interface are implemented in Matlab / Simulink environment and Z-wave makes it possible to control the boiler or other loads in real-time

Optimal Scheduling for Energy Storage Systems in Distribution Networks

[EN] Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper's results demonstrate numerically the good performance of the developed methodology.This research was funded by European Regional Development Fund (Comunidad Valenciana FEDER 2014-2020 PO, CCI number: 2014ES16RFOP013) and the ITE-IVACE collaboration agreement corresponding to the annuity 2019 (file: IMDEEA-2019-38).Escoto Simó, M.; Montagud, M.; González-Cobos, N.; Belinchón, A.; Trujillo, AV.; Romero-Chavarro, JC.; Diaz-Cabrera, JC.... (2020). Optimal Scheduling for Energy Storage Systems in Distribution Networks. Energies. 13(15):1-13. https://doi.org/10.3390/en13153921S1131315The Impact of the Covid-19 Crisis on Clean Energy Progresshttps://www.iea.org/articles/the-impact-of-the-covid-19-crisis-on-clean-energy-progressSustainable Development Goalshttps://www.un.org/sustainabledevelopment/Mesarić, P., & Krajcar, S. (2015). Home demand side management integrated with electric vehicles and renewable energy sources. 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S., Abdel-Galil, T. K., & Salama, M. M. A. (2015). Probabilistic ESS sizing and scheduling for improved integration of PHEVs and PV systems in residential distribution systems. Electric Power Systems Research, 125, 55-66. doi:10.1016/j.epsr.2015.03.029Li, Y., Yang, Z., Li, G., Zhao, D., & Tian, W. (2019). Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties. IEEE Transactions on Industrial Electronics, 66(2), 1565-1575. doi:10.1109/tie.2018.2840498Ciupăgeanu, D.-A., Lăzăroiu, G., & Barelli, L. (2019). Wind energy integration: Variability analysis and power system impact assessment. Energy, 185, 1183-1196. doi:10.1016/j.energy.2019.07.136Hemmati, R., Saboori, H., & Jirdehi, M. A. (2017). Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources. Energy, 133, 380-387. doi:10.1016/j.energy.2017.05.167Xie, S., Hu, Z., & Wang, J. (2020). Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks. Applied Energy, 261, 114412. doi:10.1016/j.apenergy.2019.114412Saboori, H., & Jadid, S. (2020). Optimal scheduling of mobile utility-scale battery energy storage systems in electric power distribution networks. Journal of Energy Storage, 31, 101615. doi:10.1016/j.est.2020.101615Kassai, M. (2017). Prediction of the HVAC Energy Demand and Consumption of a Single Family House with Different Calculation Methods. Energy Procedia, 112, 585-594. doi:10.1016/j.egypro.2017.03.1121Zheng, Y., Zhao, J., Song, Y., Luo, F., Meng, K., Qiu, J., & Hill, D. J. (2018). Optimal Operation of Battery Energy Storage System Considering Distribution System Uncertainty. IEEE Transactions on Sustainable Energy, 9(3), 1051-1060. doi:10.1109/tste.2017.2762364Jayasekara, N., Masoum, M. A. S., & Wolfs, P. J. (2016). Optimal Operation of Distributed Energy Storage Systems to Improve Distribution Network Load and Generation Hosting Capability. IEEE Transactions on Sustainable Energy, 7(1), 250-261. doi:10.1109/tste.2015.2487360Mehrjerdi, H., & Hemmati, R. (2019). Modeling and optimal scheduling of battery energy storage systems in electric power distribution networks. Journal of Cleaner Production, 234, 810-821. doi:10.1016/j.jclepro.2019.06.195Macedo, L. H., Franco, J. F., Rider, M. J., & Romero, R. (2015). Optimal Operation of Distribution Networks Considering Energy Storage Devices. IEEE Transactions on Smart Grid, 6(6), 2825-2836. doi:10.1109/tsg.2015.2419134Lunci Hua, Jia Wang, & Chi Zhou. (2014). Adaptive Electric Vehicle Charging Coordination on Distribution Network. IEEE Transactions on Smart Grid, 5(6), 2666-2675. doi:10.1109/tsg.2014.2336623Guo, X., Guo, X., & Su, J. (2013). Improved Support Vector Machine Short-term Power Load Forecast Model Based on Particle Swarm Optimization Parameters. Journal of Applied Sciences, 13(9), 1467-1472. doi:10.3923/jas.2013.1467.1472Bordin, C., Anuta, H. O., Crossland, A., Gutierrez, I. L., Dent, C. J., & Vigo, D. (2017). A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renewable Energy, 101, 417-430. doi:10.1016/j.renene.2016.08.066IEEE PES AMPS DSAS Test Feeder Working Grouphttps://site.ieee.org/pes-testfeeders/resources/Lotero, R. C., & Contreras, J. (2011). Distribution System Planning With Reliability. IEEE Transactions on Power Delivery, 26(4), 2552-2562. doi:10.1109/tpwrd.2011.2167990Munoz-Delgado, G., Contreras, J., & Arroyo, J. M. (2015). Joint Expansion Planning of Distributed Generation and Distribution Networks. IEEE Transactions on Power Systems, 30(5), 2579-2590. doi:10.1109/tpwrs.2014.236496

Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment

textThis work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals.Chemical Engineerin

Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reporte

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea