Multi-scale stochastic optimization for Home Energy Management

Jia, Lei; Yu, Zhe; Murphy-Hoye, Mary; Pratt, Annabelle; Piccioli, Ellen G.; Tong, Lang

doi:10.1109/camsap.2011.6135900

Cited by 35 publications

(16 citation statements)

References 7 publications

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“…In contrast with a centralized optimization-based approach [1,3,4,[6][7][8][9][10][11], the market-based is a decentralized approach where each participant bids according to its local information. This section presents and discusses the market-based optimization conditions which lead to system cost minimization.…”

Section: Microeconomic Theorymentioning

confidence: 99%

“…In the literature, most authors [7,9,10,13,14] consider perfect forecasts and specific evaluation scenarios over a short time period, neglecting the results dependency on seasonal and user profiles. Instead, this paper uses Monte Carlo simulations to assess the optimality of the different HEMS approaches and extract conclusion as general as possible.…”

Section: Case Studymentioning

confidence: 99%

“…HEMS objective function may be formulated differently depending on whether it leverages forecast uncertainty information i.e., stochastic optimization [1][2][3] or not i.e., deterministic optimization [4][5][6][7][8][9][10][11]. Among them, the most common HEMS optimization approaches formulate this problem as a linear [6,7], mixed integer linear (MILP) [4,5,8,9], quadratic [10,11] or dynamic programming [1] yielding typically 24-h schedule for the appliances.…”

Section: Introductionmentioning

confidence: 99%

“…Authors [4,7,9,10,13,14] assess the HEMS under specific seasonal conditions and over few days with perfect forecasts, leading to conclusions that lack generality. Instead, the results presented here consider different forecast errors and a broad set of scenarios with Monte Carlo simulations to extract more general conclusions than in the literature, i.e., independent of the user behavior and the weather conditions.…”

Section: Introductionmentioning

confidence: 99%

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A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment

Feron

Monti

2018

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Abstract:The increase of domestic electrical and thermal controllable devices and the emergence of dynamic electrical pricing leads to the opportunity to integrate and optimize electrical and thermal energy at a house level using a home energy management system (HEMS) in order to minimize the energy costs. In the literature, optimization-based algorithms yielding 24-h schedules are used in spite of their growing complexity with the number of controllable devices and their sensitivity to forecast errors which leads, in most of the cases, to suboptimal schedules. To overcome this weakness, this paper introduces a domestic thermal and electrical control based on a market approach. In contrast with the optimization-based HEMS, the proposed market-based approach targets a scalable and reactive optimal control. This paper first formulates the market-based optimization problem with generality and discusses its optimality conditions with regards to the microeconomic theory. Secondly, this paper compares its optimality to an optimization-based approach and a rule-based approach under forecast errors using Monte Carlo simulations. Finally, this paper quantifies and identifies the effectiveness boundaries of the different approaches.

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Section: Microeconomic Theorymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment

Feron

Monti

2018

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“…In [9], Callaway and Hiskens discussed the some key issues and applications of nondisruptive control strategies for aggregated electric loads. In terms of responding to different prices, the approaches in [10], [11], [12] used MPC (Model Predictive Control) method to control HVAC.…”

Section: B Related Workmentioning

confidence: 99%

Optimal pricing for residential demand response: A stochastic optimization approach

Jia

Tong

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-The problem of optimizing retail electricity price for residential demand response is considered. A two stage stochastic optimization is formulated in which the retailer optimizes the day ahead price in the first stage, and residential customers schedule their demands optimally in respond to the optimized retail price and in a distributed fashion. For the control of thermal dynamic loads, the optimal residential demand response policy is obtained based on a form of consumer surplus that captures the tradeoff between comfort level and cost. It is shown that the optimal control is an affine function of the retail price with a negative definitive factor matrix. The optimal retail pricing is obtained through a convex program that maximizes average profit or a form of conditional value at risk. Effects of incorporating renewable energy are also considered.

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Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

Schminke

2021

Intl J of Energy Research

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The decentralization of energy supply and the fluctuating feed-in from renewable energies lead to an increasing need to generate electricity and heat locally and efficiently. Therefore, different energy systems are used together at building level. Consequently, energy management systems (EMSs) in the building sector and households are becoming increasingly complex and are facing new challenges. To face these challenges, EMSs are required to enable efficient and combined operation of multiple energy systems and components. The increasing research into EMS is leading to a growing field of research with different characteristics, objectives, and algorithms. However, the resulting diversification of the research topic is accompanied by a lack of transparency. Therefore, this article is the first to provide a comprehensive quantitative evaluation of the state of research. For this purpose, 98 relevant publications are quantitatively evaluated. This article focuses on methods, model characteristics, optimization objectives, price structures, observation horizon, and components and devices used of EMS. The detailed evaluation of these topics creates a high level of transparency in EMS research to provide precise insights into current research priorities and potential weaknesses, to allow researchers to classify their own work and to identify potential research topics. Highlights• For the first time, a quantitative study on EMS research is presented • The evaluation of 98 relevant publications provides detailed insights • Insights about methods, characteristics, objectives, prices, horizons, and components • Detailed graphical evaluations for each sub-area of the study K E Y W O R D S energy price structures, energy management systems in buildings, optimization goals, overview

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Multi-scale stochastic optimization for Home Energy Management

Cited by 35 publications

References 7 publications

A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment

A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment

Optimal pricing for residential demand response: A stochastic optimization approach

Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

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