Optimal Retail Pricing for Demand Response of HVAC Systems in Commercial Buildings Considering Distribution Network Voltages

Yoon, Ah-Yun; Kim, Young‐Jin; Moon, Seong-Yong

doi:10.1109/tsg.2018.2883701

Cited by 23 publications

(22 citation statements)

References 34 publications

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“…As the end-users like to minimize their electricity costs, according to retail prices, they participate in the DR program. Therefore, the DISCO, which is responsible for the effective network operation, determines an optimal retail price to induce DR of the end-users [16]. The proposed pricing strategy considering the ANNbased DR model is largely divided into three parts, as shown in Fig.…”

Section: Overall Frameworkmentioning

confidence: 99%

“…References [11]- [15] applied single-level models for the pricing strategy. By contrast, [16]- [18] implemented bi-level decision-making structures. References [16]- [18] are based on the leaderand-followers game theory, indicating the hierarchical relation between the DISCO and the end-user.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, [16]- [18] implemented bi-level decision-making structures. References [16]- [18] are based on the leaderand-followers game theory, indicating the hierarchical relation between the DISCO and the end-user. Mathematical optimization methods based on the Karush-Kuhn-Tucker (KKT) conditions were applied to transform the bi-level problems into single-level ones and find the optimal retail prices in [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…References [16]- [18] are based on the leaderand-followers game theory, indicating the hierarchical relation between the DISCO and the end-user. Mathematical optimization methods based on the Karush-Kuhn-Tucker (KKT) conditions were applied to transform the bi-level problems into single-level ones and find the optimal retail prices in [16]- [18]. The bi-level model reflects the optimal schedules of end-users more precisely than the simple methods mentioned earlier.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

et al. 2021

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Section: Overall Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

et al. 2021

Self Cite

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“…6a, it can be seen that reliance on DA market is high than the RT market. This is justifiable because the prices in DA is less than RT except at time intervals t = 9, 13,14,15,16,18. Hence, only during those periods, DRA procures the power from the RT market.…”

Section: Dra's and Dgo's Strategymentioning

confidence: 99%

Heuristic approach for transactive energy management in active distribution systems

Rajasekhar

Pindoriya

2020

IET Smart Grid

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The advent of distributed energy resources (DERs), which include small conventional and renewable generation units, energy storage, and flexible loads in distribution network needs distributed coordination for effective management. In this paper, a stochastic transactive management framework is proposed to minimize overall cost and avoid network constraints violation at the distribution network level. This framework includes day-ahead scheduling of electric vehicles and air conditioning loads under demand response aggregators (DRAs), and DERs under distributed generation owners (DGOs) into day-ahead wholesale market in a network managed by a distribution network operator (DNO). An agent called distribution independent system operator (DISO) is responsible for coordination among DRAs, DGOs, and DNO. A heuristic step-size update approach is proposed to calculate the Lagrange multiplier iteratively and improve the convergence speed. This framework is modeled as a quadratic constraint programming (QCP) problem and solved using the GAMS solver. Simulation results on a modified 33-bus system with considerable penetration of loads and DERs, shows that the suggested framework can efficiently reduce the iterations to converge and returns an optimal schedule. And demonstrate the effect of network congestion, demand, and generation uncertainties on the resulting objective values of the agents and magnitude of the Lagrange multiplier values.

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Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning

Yang,

Yu,

Wang

2024

Adv Control Appl

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In an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.

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Optimal Retail Pricing for Demand Response of HVAC Systems in Commercial Buildings Considering Distribution Network Voltages

Cited by 23 publications

References 34 publications

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

Heuristic approach for transactive energy management in active distribution systems

Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning

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