Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

Liu, Lei; Xu, Da; Lam, Chi-Seng

doi:10.1016/j.enconman.2023.117208

Cited by 15 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bao et al , 129 in response to the shortcomings of AC charging stacks where the harmonic current of the on-board charger cannot be reduced, proposed the incorporation of the filtering function into the design of slow-charging AC charging piles to filter the harmonics in the current and inhibit the effects of harmonics on the electricity bill. Liu et al 130 developed a two-tier management mechanism based on heating, ventilation, and air conditioning, where the first tier considers the economization of electricity, heat, and gas, and the second tier manages the AC slow charging of EVs using real-time supply and demand requests.…”

Section: Electric Vehicle Charging Strategymentioning

confidence: 99%

Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review

Li,

Ma,

Zhao

et al. 2024

Sustainable Energy Fuels

View full text Add to dashboard Cite

show abstract

Section: Electric Vehicle Charging Strategymentioning

confidence: 99%

Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review

Li,

Ma,

Zhao

et al. 2024

Sustainable Energy Fuels

View full text Add to dashboard Cite

show abstract

“…While the daily travel time and daily return time of EVs are assumed to follow the standard normal distribution, the daily mileage and the expected SOC are assumed to follow the lognormal distribution [1,24]. The SOC at the time of the return of EV i is calculated:…”

Section: Computation-electricity Allocations In Individual Bvppsmentioning

confidence: 99%

“…The feed-in price is given as 0.01 $/kWh, and the related electricity price is obtained from [28]. The parameters of base energy consumptions, storages, and generations of three BVPPs are obtained from [1,2,22].…”

Section: System Descriptionmentioning

confidence: 99%

“…Approximately 33% of global greenhouse gas emissions and 40% of global energy consumption come from commercial buildings. To reduce energy consumption costs, facades and rooftops carry generated energy from photovoltaic thermal systems (PVT) and wind turbines (WT) as close to the consumer as possible [1]. A potential pattern that has never been seen before, the virtual power plant (VPP), is opened by combining battery energy storage (BES) and renewable energy sources (RESs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Peer-to-Peer Transactive Computation–Electricity Trading for Interconnected Virtual Power Plant Buildings

Gao,

Kang,

Chen

et al. 2023

Buildings

Self Cite

View full text Add to dashboard Cite

Advancements of the virtual power plant (VPP) concept have aggregated buildings as their power plants and/or service providers. This paper proposes a peer-to-peer transactive computation–electricity trading framework for multiple-building virtual power plants (BVPPs). In this framework, the interconnected BVPPs can proactively trade their available computation–electricity with each other. Multiple BVPP trading is an intractable optimization problem due to its strong computation–electricity decision-making couplings. Thus, the original problem is described as a game theoretic problem and resolved into the sequential subproblems of social computation–electricity allocation and payoff allocation. By considering the local decision-making of heterogeneous BVPPs, a fully distributed algorithm is further designed to optimize the trading problem by sharing only limited trading information. Finally, a three-BVPP system is used to verify the merits of system resource utilization and operational economy.

show abstract

Quantitative method and influencing factors analysis of demand response performance of air conditioning load with rebound effect

Wang,

Xin,

Liu

et al. 2024

Build. Simul.

View full text Add to dashboard Cite

Under the emerging trend of the new power systems, enhancing the energy flexibility of air conditioning loads to promote electricity demand response is crucial for regulating the real-time balance. As a typical temperature-controlled loads, air conditioning loads can generate rebound effect when participating in demand response, resulting in sudden load increases and posing risks to grid security. However, the existing research mainly focuses on the energy flexibility, which leads to an imperfect demand response mechanism and thus affects the optimal scheduling strategy. Therefore, the study proposes a comprehensive quantification method in view of rebound effect for the demand response performance of air conditioning loads, by using probability distribution, Latin hypercube sampling, Monte Carlo simulation, and scenario analysis methods. The demand response event was divided into response phase and recovery phase, and by considering energy flexibility during the response phase and rebound effect during the recovery phase, three dimensionless evaluation indexes for comprehensive demand response performance were constructed. Using this quantification method, the impact patterns of three types of random variables were compared, including meteorological, design variables, and control variables. Additionally, considering the differences in building types (office and hotel buildings) and building capacities (small, medium, and large), the effectiveness of air conditioning load participation in demand response measures in different building application scenarios was explored. The results show that the influence of the design variables on the response performance is less than that of the control variables, but significant, reaching 45% compared to the control variables. Moreover, the influence varies with building type, capacity and climate zone, and building demand response design has more potential in the following scenarios: the cold climate, the hot summer and cold winter climate, the medium building and the hotel building.

show abstract

Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

Cited by 15 publications

References 26 publications

Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review

Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review

Peer-to-Peer Transactive Computation–Electricity Trading for Interconnected Virtual Power Plant Buildings

Quantitative method and influencing factors analysis of demand response performance of air conditioning load with rebound effect

Contact Info

Product

Resources

About