Retail electricity pricing via online-learning of data-driven demand response of HVAC systems

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

doi:10.1016/j.apenergy.2020.114771

Cited by 30 publications

(5 citation statements)

References 27 publications

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“…Each share is set to be positive, and the power purchase of the ESS-ER matches with the retail. 11 1, 0…”

Section: Electricity Allocation Optimization Modelmentioning

confidence: 99%

“…Athanasios et al [10] introduced an integrated model that perform the simulation of the dayahead electricity market, and estimates the income and price elasticities of electricity demand for estimating the retailers' profitability with demand responsive consumers. Yoon et al [11] proposes an online-learning-based strategy for a distribution system operator-based electricity retailer to determine optimal retail prices, considering the optimal operations of data-driven demand response using the explicit an artificial neural network (ANN) model.…”

Section: Introductionmentioning

confidence: 99%

“…Power retailers can be seen as participants of DR in the purchase side. Only Athanasios et al [10] and Yoon et al [11], regarding DR as goods between its providers and buyers, discussed its development mode. (2) Some research have considered the impact of the uncertainty of uncontrollable power supply such as electricity price volatility and wind or photovoltaic (PV) on power purchasing strategy, and put forward a variety of uncertainty decision-making models, but failed to consider that the retailers with energy storage system (ESS-ER) can also participate in DR, which can flexibly adjust the charging and discharging behaviors, so as to alleviate the above uncertainty risks.…”

Section: Introductionmentioning

confidence: 99%

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Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response

Lin

et al. 2020

Applied Energy

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A new two-stage demand response is designed for the electricity retailers with energy storage system (ESS-ER) in the deregulated power market. The ESS-ER could response to the output of different power sources by adjusting the charging-discharging behavior according to the bidding power price. The paper models the twostage demand response for electric power retailers and proposed a two-layer coordinated optimal model for the purchase and sale of the electric power retailers. In the upper layer model, the conditional value at risk method and robust stochastic theory are applied to describe the uncertainty influence of wind power and Photovoltaic (PV) power, and the minimum whole cost of power purchasing is taken as the objective. In the lower-layer, the power consumption behaviors of different customers are considered to get the maximum revenue of power selling by implementing differentiated demand response. Then, to solve the two-layer mathematical model, the lowerlayer model is converted into the Karush-Kuhn-Tucker (KKT) optimality conditions. The results show that: (1) The two-stage demand response could smooth the curves of power purchasing and terminal users' load, which could bring more flexible transaction space. (2) The proposed two-layer transaction model could balance the cost and risk of power purchasing, bringing more trading opportunities for wind power and PV, which can also reduce the energy consumption cost of the end-users. (3) By introducing the risk cost coefficient, confidence degree and robust coefficient, the decision-makers can adjust the power trading behaviors, and establish the optimal power trading scheme in line with their expected situation. (4) When higher energy storage capacity is set, the efficiency of demand response rises. When the capacity ratio of wind to energy storage is 4:1, the efficiency of demand response reaches the best. When larger energy storage capacity is set, the demand response turns to be more effective. However, when the capacity ratio of wind and PV to energy storage is 4:1, the effect of demand response reaches the best. Overall, the proposed model could provide an effective tool for power retailers in China's electric power market.

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“…Each share is set to be positive, and the power purchase of the ESS-ER matches with the retail. 11 1, 0…”

Section: Electricity Allocation Optimization Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response

Lin

et al. 2020

Applied Energy

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“…In the context of smart cities, buildings are playing a more and more crucial part in the distribution network operation due to its high electricity consumption. According to [1], buildings represent for more than 50% of the global electricity consumption, and among the electricity consumed by buildings, about 32% comes from air conditioner loads (ACLs), especially in the hot summer day. Besides, the ACLs can also participate in demand response (DR) projects due to their heat storage buffers [2], which means that buildings can reduce their electricity costs by the directly control of ACLs.…”

Section: Indicesmentioning

confidence: 99%

Optimal Operation of Smart Buildings with Stochastic Connection of Electric Vehicles

Huang

Wang

et al. 2020

2020 IEEE International Smart Cities Conference (ISC2)

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One of the key domains in smart cities is smart energy in which smart grid is a main focus. In recent years, with the development of smart grid, controllable air conditioning load participating in demand response projects and the application of renewable energy sources have drawn wide research interests. The integration of photovoltaic (PV) system and electric vehicles into the micro grid has also brought vitality to the stable operation of smart grids. In this paper, a novel control scheme is proposed to optimize the scheduling of building micro grid that integrate controllable air conditioner loads, PV panels and electric vehicles. The optimal operation problem is modeled and further converted into a mixed integer linear programming (MILP) problem whose objective function is minimizing the electricity cost of the building. The stochastic characteristics of electric vehicles are also considered in this paper to better model electric vehicle behaviors. Simulations are conducted on an office building micro grid and the simulation results verify the feasibility of proposed control strategy.

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“…The use of a cohort of flexible devices, which includes responsive buildings, can also be leveraged to minimize grid voltage and frequency deviations and simultaneously alleviate harmonic distortions, as shown by Hong et al [101]. The role of responsive buildings can also be of prime importance in defining new tariffs, energy-pricing structures, and contractual/transactive energy-usage schemes [102,103]. From a software perspective, intricate opensource platforms that use modern technologies such as cloud services have been devised to enable buildings to interact with the grid more effectively [104].…”

Section: Controls For Building-to-grid Interactionmentioning

confidence: 99%

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Bae

Cui

et al. 2020

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Retail electricity pricing via online-learning of data-driven demand response of HVAC systems

Cited by 30 publications

References 27 publications

Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response

Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response

Optimal Operation of Smart Buildings with Stochastic Connection of Electric Vehicles

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

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