An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation

Hosseinnezhad, Vahid; Shafie‐khah, Miadreza; Siano, Pierluigi; Catalão, João P.S.

doi:10.1109/access.2020.2968038

Cited by 32 publications

(22 citation statements)

References 33 publications

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“…In addition, the distribution system operator (DSO) encourages prosumers to achieve benefits and reduction in maintenance and expansion cost of equipment. In the U.S. and some European countries, the development of this kind of market has led to achieving a great balance between supplies and demand as well [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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“…HEMS capabilities have been progressively extended to prioritize appliances [11], include demand response combined with storage [12], and electric vehicles (EV) [13]. Hosseinnezhad et al [14][15] uses artificial intelligence techniques to solve the HEMS scheduling problem. Hosseinnezhad et al [14] demonstrates benefits of using a self-healing strategy that will sectionalize an isolated area of the distribution system into island partitions to provide reliable power supply to the critical loads continuously.…”

Section: Literature Surveymentioning

confidence: 99%

“…It shows how an intelligent network reconfiguration strategy can add to system resilience. Hosseinnezhad et al [15] presents a HEMS with day-ahead management and real-time regulation. Their case studies show that the model can reliably locate the optimal operating scenario.…”

Section: Literature Surveymentioning

confidence: 99%

Battery Storage for Resilient Homes

Chatterji

Bazilian

2020

IEEE Access

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Small-scale battery energy storage systems (BESS), especially for behind-the-meter applications, are still relatively expensive, but we show that it can be a potent solution to render homes resilient to storm related power outages. We present a stochastic programming model formulation to optimize PV/BESS explicitly accounting for resilience benefits these investments entail, over and above their ability to reduce cost of supply. The stochastic optimization considers uncertainties around storm related grid supply failures as well as variability of solar PV. The model includes an embedded Monte Carlo simulation module that considers storm related outage risks using climate model reanalysis data. It is a least-cost planning framework that optimizes selection of BESS, solar PV, grid supply, and diesel generator, from a homeowner's perspective. We present two case studies with low and high storm risks that demonstrate how different risk exposures can impact on the selection of alternative options to build resilience. Duals, or shadow prices, of demand-supply constraints from the model for both normal days and for storm related contingencies, provide interesting insights into the marginal cost of supply that can inform innovative pricing schemes to promote customer level resilience measures. The case study results reveal significant merits of BESS, in combination with PV, to enhance resilience. We find that in low-risk areas like Bethesda, MD, incremental PV and BESS required for a more resilient system can add $79 (4%) to expected annual electricity costs for a typical household, and a considerably higher $208 (10.6%) in Miami, FL which is at a much greater risk. These options are, however, 27% (in Bethesda) and 20% (in Florida) less expensive than the conventional solution of installing a diesel generator. These results provide insights into the value of BESS as part of a resilient and clean energy solution for households.

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“…Thomas et al [13], for instance, developed a mixed integer linear programming model that considers the impact of PV uncertainty in scheduling of the HEMS. Hosseinnezhad et al [14][15] used artificial intelligence techniques in order to solve the HEMS 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 2001 1999 2016 2010 2012 2002 2006 2017 2007 2000 2014 1998 2015 2013 2005 2008 2011 2004 2009 2018 2003 Annual solar kWh 0 2 4 6 8 10 12 14…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Meter Data to Optimize Combined Roof-Top Solar and Battery Systems Using a Stochastic Mixed Integer Programming Model

Chatterji

Bazilian

2020

IEEE Access

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This paper presents the design and results of a model that uses household smart meter data, electric vehicle (EV) travel load and charging options, and multiple solar resource profiles, to make decisions on optimal combinations of photovoltaics (PV), battery energy storage systems (BESS) and EV charging strategies. The least-cost planning model is formulated as a stochastic mixed integer programming (MIP) problem that makes first stage decisions on PV/BESS investments, and recourse decisions on purchase/sell from/to the grid to minimize expected household electricity costs. The model undertakes a customer-centric optimization taking into consideration net metering policy, time-of-use grid pricing, and uncertainties around inter-annual variability of solar irradiance. The model adds to the existing literature in terms of stochastic representation of inter-annual variability of solar irradiance, together with BESS capacity optimization, and EV charging mode selection. Three case studies are presented: two for a residential house with and without EV load, and a third for a larger community facility. Results from the model for the first residential house case study are compared with commercially available software to show the impacts of an accurate load profile and different policy parameters. The stochastic feature of the model proves useful in understanding the impact of variability in solar resource profiles on PV sizing. Finally, simulations of alternative EV travel patterns and tariff policies that discourage charging during the evening peak show the efficacy of 'super off-peak' pricing being introduced in the state of Maryland.

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An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation

Cited by 32 publications

References 33 publications

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Battery Storage for Resilient Homes

Smart Meter Data to Optimize Combined Roof-Top Solar and Battery Systems Using a Stochastic Mixed Integer Programming Model

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