Demand response of district heating using model predictive control to prevent the draught risk of cold window in an office building

Wu, Yuxin; Mäki, Aleksi; Jokisalo, Juha; Kosonen, Risto; Kilpeläinen, Simo; Salo, Sonja; Hong, Liu; Li, Baizhan

doi:10.1016/j.jobe.2020.101855

Cited by 21 publications

(6 citation statements)

References 61 publications

(68 reference statements)

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“…The conclusions derived from this study might not be applicable for different populations such as the children and the elderly [29] without rectification. The accuracies of these proposed methods might also be reduced in non-uniform or asymmetric thermal environments [69]. Other individual factors, such as body mass, body shape, posture and so on [70], might also affect the suitability of the weighting formulas.…”

Section: Discussion Of the Difference In Other Conditionsmentioning

confidence: 99%

Evaluation and modification of the weighting formulas for mean skin temperature of human body in winter conditions

Liu

et al. 2020

Energy and Buildings

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Section: Discussion Of the Difference In Other Conditionsmentioning

confidence: 99%

Evaluation and modification of the weighting formulas for mean skin temperature of human body in winter conditions

Liu

et al. 2020

Energy and Buildings

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“…In other words, they manage and program their consumption. To motivate the end-users to manage their consumption then, incentives are offered to them in addition to reducing their electricity costs [6]. All these processes promote grid confidence during faults.…”

Section: Costmentioning

confidence: 99%

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Zhang¹,

Yang²,

Li³

2022

Energy Engineering

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Buildings are the main energy consumers across the world, especially in urban communities. Building smartization, or the smartification of housing, therefore, is a major step towards energy grid smartization too. By controlling the energy consumption of lighting, heating, and cooling systems, energy consumption can be optimized. All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources (RES), which mitigates environmental impacts and reduces peak demand for electrical energy. In this paper, a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes. In this way, large power stations that work with fossil fuels will no longer be developed. The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles (EVs) with bidirectional power exchangeability with the power grid, an energy storage system (ESS), and solar panels. Additionally, the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work. Different case studies, including the sales of electrical energy resulting from PV panels' generated power to the power grid, time-variable loads such as washing machines, and different demand response (DR) strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs, BESS, and solar panels. The proposed model was simulated in MATLAB. A hybrid particle swarm optimization (PSO) and gravitational search (GS) algorithm were utilized for optimization. Scenario generation and reduction were performed via LHS and backward methods, respectively. Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use (STOU) loads, EV, ESS, and PV system. Based on the results, the proposed model markedly reduced the electricity costs of the smart house.

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“…A couple of behavioral parameters such as action-regulation theory, high-performance cycle and planned behavior theory have been embedded in their methodology, and yielded results show that the proposed model can increase the energy-saving up to 4.5%. Cost-saving and emission reduction considering thermal comfort during DR programs have been investigated in Wu et al (2020) [92] where also dedicated experimental tests using a thermal manikin to assess the effect of draught from cold windows have been performed. The MPC algorithm exploits a dynamic DH price model and included constraints to restrict the temperature of cold windows.…”

Section: Demand-side Management (Dsm) In Dhc Systemmentioning

confidence: 99%

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

et al. 2021

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Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

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Demand response of district heating using model predictive control to prevent the draught risk of cold window in an office building

Cited by 21 publications

References 61 publications

Evaluation and modification of the weighting formulas for mean skin temperature of human body in winter conditions

Evaluation and modification of the weighting formulas for mean skin temperature of human body in winter conditions

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

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