Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Yoon, Ah-Yun; Kang, Hyun-Koo; Moon, Seung-II

doi:10.3390/en13040862

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…The location of aforementioned BHEs is shown in Figure 4. Similar studies for various BHE designs have already been described in literature [1,12,26]. • how the measurement results are averaged, as shown in this article.…”

Section: The Scope Of the Conducted Researchmentioning

confidence: 63%

“…Energies 2020, 13, 3737; doi:10.3390/en13143737 www.mdpi.com/journal/energies Energies 2020, 13, 3737 2 of 20 Among them are not only those safe for the environment, but also those with renewable resources. New solutions in the field of heating and air conditioning were developed [1]. Due to the rapid growth of geothermal energy development, individual users gaining independency in acquisition and distribution of geothermal energy became common.…”

Section: Introductionmentioning

confidence: 99%

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Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers

et al. 2020

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This work concerns borehole heat exchangers and their testing using apparatus for thermal response tests. In the theoretical part of the article, an equation was derived from the known equation of heat flow, on which the interpretation of the thermal response test was based. The practical part presents the results of several measurements taken in the AGH Laboratory of Geoenergetics. They were aimed at examining the potential heat exchange capacity between the heat carrier and rock mass. Measurement results in the form of graphs are shown in relation to the examined, briefly described wells. Result analysis made it possible to draw conclusions regarding the interpretation of the thermal response test. The method of averaging the measurement results was subjected to further study. The measuring apparatus recorded data at a frequency of one second, however such accuracy was too large to be analyzed efficiently. Therefore, an average of every 1 min, every 10 min, and every 60 min was proposed. The conclusions stemming from the differences in the values of effective thermal conductivity in the borehole heat exchanger, resulting from different data averaging, were described. In the case of three borehole heat exchangers, ground properties were identical. The effective thermal conductivity λeff was shown to depend on various borehole heat exchanger (BHE) designs, heat carrier flow geometry, and grout parameters. It is important to consider the position of the pipes relative to each other. As shown in the charts, the best (the highest) effective thermal conductivity λeff occurred in BHE-1 with a coaxial construction. At the same time, this value was closest to the theoretical value of thermal conductivity of rocks λ, determined on the basis of literature. The standard deviation and the coefficient of variation confirmed that the effective thermal conductivity λeff, calculated for different time intervals, showed little variation in value. The values of effective thermal conductivity λeff for each time interval for the same borehole exchanger were similar in value. The lowest values of effective thermal conductivity λeff most often appeared for analysis with averaging every 60 min, and the highest—for analysis with averaging every 1 min. For safety reasons, when designing (number of BHEs), safer values should be taken for analysis, i.e., lower, averaging every 60 min.

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Section: The Scope Of the Conducted Researchmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers

et al. 2020

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“…This formulation is named the simple multi-period energy tariff optimization problem (SMETOP) in [24], where its NP-hardness is proved in case of multiple followers. Various papers address the extensions of the SMETOP, including generic piecewise linear, quadratic, or other non-linear follower utility functions [3,4]; battery storage at the follower [2], multi-energy systems [25]; or the heating, ventilation, and air-conditioning (HVAC) of buildings using a dedicated thermal model [26]. The typical solution approach is reformulating the bilevel problem into an equivalent single-level problem using the KKT conditions, eliminating non-linear terms, and then solving the model as a mixed-integer linear program (MILP) [3,5,27,28].…”

Section: Literature Reviewmentioning

confidence: 99%

On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management

2021

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This paper investigates bilevel optimization models for demand response management, and highlights the often overlooked consequences of a common modeling assumption in the field. That is, the overwhelming majority of existing research deals with the so-called optimistic variant of the problem where, in case of multiple optimal consumption schedules for a consumer (follower), the consumer chooses an optimal schedule that is the most favorable for the electricity retailer (leader). However, this assumption is usually illegitimate in practice; as a result, consumers may easily deviate from their expected behavior during realization, and the retailer suffers significant losses. One way out is to solve the pessimistic variant instead, where the retailer prepares for the least favorable optimal responses from the consumers. The main contribution of the paper is an exact procedure for solving the pessimistic variant of the problem. First, key properties of optimal solutions are formally proven and efficiently solvable special cases are identified. Then, a detailed investigation of the optimistic and pessimistic variants of the problem is presented. It is demonstrated that the set of optimal consumption schedules typically contains various responses that are equal for the follower, but bring radically different profits for the leader. The main procedure for solving the pessimistic variant reduces the problem to solving the optimistic variant with slightly perturbed problem data. A numerical case study shows that the optimistic solution may perform poorly in practice, while the pessimistic solution gives very close to the highest profit that can be achieved theoretically. To the best of the authors’ knowledge, this paper is the first to propose an exact solution approach for the pessimistic variant of the problem.

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“…Demand response (DR) has been recognized as a powerful tool to better balance supply and demand by influencing the behavior of the demand side in electrical grid [1][2][3]. Specifically, due to the uncertainty of renewable energy, electricity market prices and the cumulation of prediction errors [4], the pressure to ensure stable electricity supply continues to increase.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode

Guo

Wang

2020

Energies

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Demand response (DR) can offer a wide range of advantages for electrical systems by facilitating the interaction and balance between supply and demand. However, DR always requires a central agent, giving rise to issues of security and trust. Besides this, differences in user response cost characteristics are not taken into consideration during incentive pricing, which would affect the equal participation of users in DR and increase the costs borne by the electricity retail company. In this paper, a blockchain-enabled DR scheme with an individualized incentive pricing mode is proposed. First, a blockchain-enabled DR framework is proposed to promote the secure implementation of DR. Next, a dual-incentive mechanism is designed to successfully implement the blockchain to DR, which consists of a profit-based and a contribution-based model. An individualized incentive pricing mode is adopted in the profit-based model to decrease the imbalance in response frequency of users and reduce the costs borne by the electricity retail company. Then, the Stackelberg game model is constructed and Differential Evolution (DE) is used to produce equilibrium optimal individualized incentive prices. Finally, case studies are conducted. The results demonstrate that the proposed scheme can reduce the cost borne by the electricity retail company and decrease the imbalance among users in response frequency.

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Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Cited by 14 publications

References 30 publications

Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers

Method of Averaging the Effective Thermal Conductivity Based on Thermal Response Tests of Borehole Heat Exchangers

On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management

Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode

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