Nonlinear demand response programs for residential customers with nonlinear behavioral models

Rahmani-Andebili, Mehdi

doi:10.1016/j.enbuild.2016.03.013

Cited by 31 publications

(13 citation statements)

References 19 publications

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“…From Figure 5, the red dashed line is the peak limit which is defined for each distribution in DPD. The peak propensity (probability to exceed the peak limit) for each distribution in DPD is evaluated using (2). The reduction is achieved by approximating all distributions found in a partition with the distribution having the maximum peak propensity (3).…”

Section: Piecewise Peak Approximationmentioning

confidence: 99%

“…Building energy-management systems (BEMS) have been widely deployed for DSM using various techniques such as price and incentive-based DR programs [2][3][4]. In recent years, most DSM techniques resort to the use of battery energy storage systems (BESS) due to their benefits such as modularity which enables them to be implemented for different application and purposes, and fast and high power response as compared to traditional energy sources [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Demand Side Management Algorithm Using Stochastic Optimization

2018

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A demand side management technique is deployed along with battery energy-storage systems (BESS) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study, we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand.

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Section: Piecewise Peak Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Demand Side Management Algorithm Using Stochastic Optimization

2018

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“…A disadvantage of this method is the many parameters you need, since N is usually a high value (in the order of [40][41][42][43][44][45][46][47][48][49][50]. The prediction will be given by:…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…In addition, in [43], distributed energy resources scheduling problem of the set of smart homes (SHs) has been investigated considering their cooperation with their neighbors by applying a stochastic MPC. In [44], the implementation of demand response (DR) programs is investigated considering the nonlinear behavioral models of the residential customers.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

et al. 2018

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Abstract:The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

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“…In DRPs, the customer signs a contract with the Independent system Operator (ISO) or the local utility to reduce its demand when requested [7]. The customer benefits from participating in DRPs are particularly from incentives provided by the ISO or local utility and decreasing of electricity bill [7]. DRPs are currently under operation in many ISO's around the world [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Negawatt Demand Response Resource Modeling and Prioritizing in Power Markets

2017

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In recent years, integrated use of demand-and supply-side resources has been performed by electric utilities, because of its potential attractiveness, both at operation and economic levels. Demand Response Resources (DRRs) can be used as demand side options which are the consequence of implementing Demand Response Programs (DRPs). DRPs comprise the actions taken by end-use customers to reduce their electricity consumption in response to electricity market's high prices; and/or reliability problems on the electricity network. In this paper, a dynamic economic model of DRPs is derived based upon the concept of flexible elasticity of demand and the customer benefit function. Precise modeling of these virtual negawatt resources helps system operators to investigate the impact of responsive loads on power system studies. This paper also aims to prioritize multifarious DRPs by means of

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Nonlinear demand response programs for residential customers with nonlinear behavioral models

Cited by 31 publications

References 19 publications

Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

Dynamic Negawatt Demand Response Resource Modeling and Prioritizing in Power Markets

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