Hongkun Bai’s research while affiliated with State Grid Energy Research Institute and other places

... The results show that the proposed strategy effectively influences EV charging via dynamic energy price signals and achieves optimal energy exchange, reducing overall system costs [28]. Jia et al. (2023) examined the impact of uncertain EV charging and discharging behavior on the stability of distribution networks, highlighting the sensitivity of EV loads to electricity pricing. For solving the problem, they proposed a virtual power plant optimization scheduling model that leverages incentive-based demand response strategies and dynamic load compensation under time-of-use pricing. ...

Reference:

Charging Scheduling of Electric Vehicles Considering Uncertain Arrival Times and Time-of-Use Price

... As smart meters are increasingly used in households, there is a growing body of data available for research in this area [12]. Smart homes are now emerging as an interesting solution in the trend to reduce energy consumption using Energy Management Systems (EMS). ...

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

... Reference [7] proposes a wind-hydrogen energy system model, consisting of a wind turbine power generation system and a hydrogen storage system, and optimizes the configured capacity of the hydrogen storage system with the objective function of minimizing the system investment cost, operation, and maintenance cost. Reference [8] proposes an integrated energy system integrating hydrogen and electricity, considering both the operating costs of the system and the environmental costs associated with the buyback for constructing an optimal scheduling model for the coupled electricity-hydrogen-carbon system. Reference [9], an integrated energy system the probability distribution or fuzzy affiliation function of uncertain parameters is not known [27,28]. ...

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Economic Dispatch of Integrated Energy Systems Considering Wind–Photovoltaic Uncertainty and Efficient Utilization of Electrolyzer Thermal Energy

... It is noted that most of the modeling of EVs relies on probability distribution functions; however, real-time travel data for accurately estimating the EV demand in the distribution network are generally lacking in [11-14, 16, 18-20]. Also, the algorithms used in these studies are either based on traditional methods [12,14,17], or do not involve recent metaheuristic techniques [11,19]. Furthermore, the distribution networks analyzed are not large-scale, which limits the ability to capture a comprehensive view of real-time EV charging demand in large residential networks and its potential impacts. ...

Reference:

Mitigation of Residential EV Charging Effects on Power Distribution Networks with Optimal Allocation of DGs Using Coati Optimization

... However, LSTMs' sequential computation leads to signi¯cant time overhead, especially when processing long sequences, as noted in studies such as Zhu et al. 30 To address these limitations, TCN has emerged as an e±cient alternative, employing causal and dilated convolutions to capture long-term dependencies while ensuring the causality of time series information. 31 Recent studies, such as the ITCN algorithm proposed by Han et al. 32 for high-precision power load allocation, have further improved TCN-based approaches by incorporating data augmentation techniques like missing data completion and class balancing. ...

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Temporal Fusion Network for Noisy Long-Time Series Industrial Data Processing

... However, scholars currently have little research on energy big data business models. Wengfeng Li (2020) and others emphasized that the full utilization of energy big data is the key to improving the country's energy utilization efficiency, and proposed the E-BDBC (Energy Big Data Business Canvas) operation model analysis tool, which provides an analysis of the provincial energy big data market situation, The profit model, ecological strategy and operation model selection are analyzed to provide reference for the operation plan of the provincial energy big data center [6]. Based on the business canvas theory, Na Li et al. (2020) discussed the energy big data business model suitable for the new situation from nine aspects such as market entities, customer relationships, value propositions, key businesses, propaganda and other aspects [7]. ...

Reference:

Exploring the promotion method and pathway of energy big data service business

... Integrated Demand Response (IDR), as an extension and expansion of traditional demand response (DR) in RIESs, can not only adjust energy consumption through price and incentive response mechanisms to effectively reduce load fluctuations but also reduce system operating costs, achieving multi-energy coupling and complementarity [25]. In [26,27], taking into account the coupling characteristics of electricity, heat, and gas loads, a RIES model incorporating IDR for electricity, heat, and gas is proposed. The case study results demonstrate that the application of DR can enhance the low-carbon economy of RIESs. ...

Reference:

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

... Depending on how the system is controlled, there are different ways of charging process and discharging process using electric vehicles such as indirect control, intelligent control, bidirectional control, and multi-stage hierarchical control method and it has been referred to in Table 1. [33][34][35][36][37][38] EV owners, local aggregators, and a central coordinator ...

Reference:

A Review on Charging Control and Discharging Control of Plug-in Electric Vehicles in the Distribution Grid

... Also, Power-to-Gas technologies innovations have additionally been examined as a potential system in integrated electricity and natural gas networks to handle the unpredictable nature of wind electricity power supply [16]. When Power-togas technology was present, the authors of [17] proposed a reliable method for identifying the best scheduling of coupled electricity and natural gas networks. With evaluations of both low and high-power demands in Great Britain, which has a large penetration of wind electricity power, the authors in [18] investigated the capacity of Power-to-gas in integrated networks to reduce the cost of delivering the gas and electricity loads. ...

Reference:

Stochastic Expansion Planning Model for a Coordinated Natural Gas and Electricity Networks Coupled With Gas-Fired Generators, Power-to-Gas Facilities, and Renewable Power

... Sun et al. [28], Zhao and Wei [29], and Wu et al. [30], respectively, introduced the applications of the SSBM-DEA model, the traditional DEA model, and the DEA-BCC model to the efficiency analysis of the domestic energy industry, and evaluated the energy efficiency of the industry and manufacturing industry in a holistic way. Liu et al. [31] optimized the traditional DEA model and used the optimized model to calculate the industrial energy efficiencies of 30 provinces. The traditional DEA model is the default in homogenous environmental conditions to measure input and output efficiencies, but in real production environments, the SFA Tobit regression model is considered, combined with the DEA model, respectively, in two new stages, and the three stages of the DEA method are used to evaluate energy enterprises' real technical levels and scale economies. ...

Reference:

Input–Output Efficiency of Chinese Power Generation Enterprises and Its Improvement Direction-Based on Three-Stage DEA Model