Hierarchical game for integrated energy system and electricity-hydrogen hybrid charging station under distributionally robust optimization

Vehicular network energy storage system with renewable analysis using deep learning architectures

In order to reduce building energy consumption and make wall insulation
 materials meet fire protection requirements, the authors put forward a kind
 of energy saving model of building exterior wall under green and energy
 saving nanomaterials. Mainly using PBECA energy saving calculation
 software. By setting up multi-component, multi-material method. The energy
 consumption of building aggregate in light energy saving device modified by
 nanomaterials is simulated and analyzed, and the energy consumption of
 building agent area is compared. The energy saving effect of light energy
 saving system modified by nanomaterial fly ash in prefabricated buildings
 was verified. According to the calculation of PBECA energy-saving
 calculation software, the annual energy consumption of the building
 assembled by nanomaterials modified fly ash lightweight energy-saving units
 is 5981 kWh, and the energy consumption per unit area is 35.01 kWh/m2. In
 the 1980's, the annual energy consumption of household agents was 86237 kWh,
 and the energy consumption per unit area was 50.42 kWh/m2. Through the
 analysis of the calculation results, it is concluded that the energy saving
 efficiency of replacing the light-saving wall with nanomaterials fly ash in
 precast area A can reach 65% of the energy saving standard.

/pdf/building-exterior-wall-thermal-energy-saving-model-based-on-1q32lssw.pdf

Building exterior wall thermal energy saving model based on green energy-saving nanomaterials

New energy and distributed power generation system of smart energy station need to cooperate with energy storage device to carry out peak cutting and valley filling, so as to meet the requirements of grid connection. Rational distribution of power suppression tasks of batteries and other energy storage components will reduce the generation cost of smart energy stations, protect energy storage devices and prolong their life. In this paper, variational modal decomposition algorithm is used to decompose the difference between the original power and the target power and distribute it to each energy storage unit. In addition to considering the decomposition mode number of VMD algorithm, the size of the secondary penalty factor and the power boundary frequency of the energy storage system, the grid-connected target power is also taken as the quantity to be optimized, which further reduces the requirements on the maximum instantaneous power and capacity of the energy storage system. Simulation results show that the proposed method is effective.

https://iopscience.iop.org/article/10.1088/1742-6596/2205/1/012004/pdf

Energy saving optimization scheduling of intelligent energy station based on beetle antennae searching-particle swarm optimization algorithm

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big-M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

Optimal Operation of Energy Hub: An Integrated Model Combined Distributionally Robust Optimization Method With Stackelberg Game

In order to improve the efficiency of the automatic demand response of the energy storage resource system, a user authentication and key agreement scheme for wireless sensor networks based on blockchain is proposed. The user does not need to register with the gateway node. As the light node of the blockchain network, the two can directly authenticate each other. The user obtains the public information of the sensor node from the blockchain through the client and verifies its legitimacy. It solves the problem that the traditional wireless sensor network user authentication scheme cannot be applied to the distributed Internet of things environment and the authentication process is low in security and efficiency. Mode 1 is the disordered charging mode. The charging facility provides continuous constant power charging service for the connected response subject until the user leaves. If the response subject is full before this, the charging will stop. Mode 2 is the BADR operation mode under charging optimization only, that is, the V2G function of PEV is not considered. In this case, 
 
 0
 ≤
 
 
 P
 
 
 l
 ,
 k
 
 
 ≤
 
 
 P
 
 
 l
 
 
 max
 c
 
 
 
 . Mode 3 is the BADR operating mode with V2G functionality in mind. The ELN load characteristics under the above three operating modes are discussed, respectively. Calculate and compare with economy on both sides of supply and demand. The experimental results show that the discharge compensation coefficient will not affect mode 2. In mode 3, as the compensation coefficient increases (from 0.4 to 1.2 at intervals of 0.2), the average total cost of the PEV cluster continues to decrease and the average total cost of the energy local network (ELN) system increases monotonically. Comparing the curves of the economic impact of the compensation coefficient on both sides of mode 2 and mode 3, it can be seen that the reasonable setting of the compensation coefficient is one of the key parameters for controlling the distribution of benefits. It proves that the proposed blockchain-based automated demand response (BADR) method can not only meet the satisfaction of energy demand but also coordinate the available energy storage resources in the system, so that the load level curve can track the output of new energy to the greatest extent and improve the balance between supply and demand of the system, which is suitable for a large-scale decentralized energy storage system.

/pdf/automatic-demand-response-method-for-the-energy-storage-2dp3atgg.pdf

Qian Yu

Papers

Energy saving optimization scheduling of intelligent energy station based on beetle antennae searching-particle swarm optimization algorithm

Optimal Operation of Energy Hub: An Integrated Model Combined Distributionally Robust Optimization Method With Stackelberg Game

Automatic Demand Response Method for the Energy Storage Resource System Based on the Blockchain Technology Combined with Sensors