Nonlinear predictive energy management of residential buildings with photovoltaics &amp; batteries

Sun, Chao; Sun, Fuchun; Moura, Scott J.

doi:10.1016/j.jpowsour.2016.06.076

Cited by 102 publications

(61 citation statements)

References 23 publications

(39 reference statements)

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“…The proposed real-time universal charging scheduling implemented in the fully decentralized manner have potential application in the large power systems with too many customers and the micro-grids equipped with the renewable power generation similar to work in [17]. The model considered in this paper is completely general and can be implemented by different residential/commercial/industrial/organizational customers having one or a group of the PEVs with some minor manipulations.…”

Section: B Contributionsmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.Index Terms-Plug-in electric vehicles (PEVs), decentralized charging, Nash Folk strategy, regret matching.

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Section: B Contributionsmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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“…They use receding horizon control to minimize the power consumption from the grid via a set of implemented rules. A model predictive control scheme is employed in [10], combined with an artificial neural network for load forecasting and variable prices. To solve the nonlinear optimization problem, also here dynamic programming is utilized.…”

Section: Fig 1 Possible Directions Of the Energy Flow When Includinmentioning

confidence: 99%

Cost-optimal Control of Photovoltaic Systems with Battery Storage under Variable Electricity Tariffs

Kirchsteiger

Rechberger

Steinmaurer

2016

Elektrotech. Inftech.

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In this paper, domestic homes with a photovoltaic system, a battery storage and variable electricity tariffs are considered. First, a control oriented model of the system is developed. Second, three controllers are designed with the aim to minimize costs for the user, and at the same time help the energy provider maintaining grid stability, by proper battery load management. A base-level controller serves as performance reference for the advanced optimization based controllers (a nonlinear controller with full system knowledge and a linear controller) which utilize forecasts of energy production and consumption. Performance evaluation was done in simulation using real-world measurement data over a full year. Results show that significant cost reductions can be achieved with the nonlinear controller while the linear one suffers from imprecise system knowledge.

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“…The studies present in the literature can be mainly grouped into two macro-categories: (i) those which refer to the forecast of domestic consumption [14,15], and (ii) those concerning the forecast of commercial or industrial consumption [13,16,17]. The opportunity to distinguish between these two groups mainly concerns the needs that characterize the two energy profiles and the purposes for which these forecasts are generated.…”

Section: Introductionmentioning

confidence: 99%

A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †

Bruno¹,

Dellino²,

Scala³

et al. 2019

Energies

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The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings.

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Nonlinear predictive energy management of residential buildings with photovoltaics & batteries

Cited by 102 publications

References 23 publications

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Cost-optimal Control of Photovoltaic Systems with Battery Storage under Variable Electricity Tariffs

A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †

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