2018 International Conference on Physics, Computing and Mathematical Modeling (PCMM 2018) ISBN: 978-1-60595-549-0
Research on Distributed Power Supply Consumption Method Combined
with Electric Heating System
Shen-zhao HAN
1,*, Si-wei LI
2, Bo YU
3, Liang YUE
2and Xiao-dan LIU
21Tianjin Electric Power Company Electric Power Academy of Sciences, Tianjin 300384, China
2Beijing Guodiantong Network Technology Co., Ltd., Beijing 100070, China
3Tianjin Energy Services Corporation of State Grid, Tianjin 300384, China
*Corresponding author
Keywords: Peak shift of the power grid, Demand side management of the power grid, Electric air conditioner.
Abstract. This paper realizes load regulation through the regulation method of electric boilers and electric air conditioners that implement the peak shift of the power grid. The load regulation method of intelligent electric boilers reduces the peak load of the power grid, effectively decreases the economic investment of power grid operation, and substantially lowers the infrastructure investment of the power grid. By attaching great importance to the influence of electric boiler load on power grid security, stability and economic operation, it realizes the effective management and active utilization of air conditioning load through economic, technical and administrative means of demand side management, which effectively alleviates the contradiction of power supply shortage and ensures the stable and safe operation of the power grid. Through the analysis of the types of electric heating users, the rational use of peaking resources and the correct use of control modes, this study aims to optimize the method of electricity consumption and enhance the efficiency of energy use.
Introduction
In recent years, with the intensification of the contradiction between power supply and demand and the change of power consumption structure, heating in winter has changed from traditional coal-fired cogeneration to gas heating and electric heating, so the load characteristics of power grid have changed greatly. According to statistics, influenced by the weather with low temperature and high humidity in winter, the proportion of electric boiler load to the load of the entire power grid is becoming increasingly large, and the peak load in winter has posed a severe challenge to the normal operation of power grid and the planning and construction of power grid. In the meanwhile, with the high-proportion access of distributed power supply on the user side and the intrusive enhancement of flexible load control means, the regulation and control methods on the demand side are characterized by flexibility and uncertainty and ascertainable power grid virtual peaking means become prominently important. Regulating the credible capacity and the objective combination has become the core technology to study this area, and meanwhile, the technology method is applicable to the power grid virtual peaking application of energy and the regional distributed photovoltaic power dissipation technology on the user side (including regional energy and microgrid).
User Type
Management Resources
[image:2.595.150.457.334.540.2]Peaking resources are mainly to achieve balanced power grid peak load output or virtual load output devices, and this paper uses the method of combining distributed photovoltaic, virtual energy storage and actual energy storage. The combination of the three results in that each two methods complement each other and the three are integrated a whole, which well balances the energy required for the regional power grid. The characteristic of the distributed photovoltaic in summer lies in that the output generally increases with the load and it has certain stability after reaching the peak. There are two main types of virtual energy storage, one is flexible load management, mainly adopting the method of heat storage electric boiler deloading, the actual deloading adopts four methods, and the specific approval amount is determined according to the load of actual running units. The characteristic of this kind of virtual energy storage is mainly that the load is reduced by means of gradient reduction and cannot be reduced once for all; the other is the rigid load management, mainly the non-production load such as illumination, which can realize direct control, and such virtual storage is characterized in that the control is in place, but the load capacity is relatively small. The actual energy storage mode has the command and strategy responding to the peak regulation and frequency modulation of the power grid, but the actual application capacity is less.
Figure 1. Diagram of centralized air conditioning system control.
Regulation Mode
In terms of participation mode, first of all, the paper determines the controllable load resources and types through users’ energy diagnosis, which can be divided into adjustable and interruptible control types in detail, summarizes the load data by installing and controlling the measurement terminal, and makes use of big data intelligent analysis methods to realize the multi-time scale power consumption analysis on the demand side.
Table 1. Power threshold table for electric equipment.
User name Equipment State Threshold
value
Type Running habit time JOY CITY 1 Illumination 1 Operation 20kW Interruptible 10:00~21:00
JOY CITY 2 Boiler host 1 Standby 300kW Adjustable 09:30~20:40
North Finance Building1
[image:2.595.67.532.699.773.2]In respect of regulation mode, the paper first summarizes the data based on the participation mode of participants, then makes a classification of industry and equipment based on the control types, and finally divides them into three categories, mainly global optimization response, response with the minimum number of users and emergency demand response.
Figure 2. Logic diagram of virtual peak regulation for large scale air conditioning load in buildings.
Global Optimization Response
In the actual operation process of global optimization variables, this paper first establishes users' "optimal selection algorithm" based on the classification of control modes to screen user participation. In addition, by establishing the global constraint target and controlling the mathematical model and building the credible capacity boundary calculation of the power grid, it finally determines the control target through equipment alignment.
1
2
3
4
a
a+1n
users
:
Figure 3. Diagram of global optimization response.
Constraint conditions:
P P
P P
P
a
i t a
i t
j t
1
m ax , 1
m ax ,
, (1)
[image:3.595.167.411.525.614.2]j t
P, means response capacity, and P means the actual response boundary. Mathematical expression:
a i a i s D n j
t P P L
P IL IL
0
,
(2)
In the above formula, represents interruptible load user participation factor, means
uninterruptible user participation factor, PIL means interruptible load, PDIL means uninterruptible
load, LS means adjustable coefficient. Optimized objective function:
） （ 。
a 1 i-DLC a 1 i-IL PGrid - Price - Price Price min minPrice t (3) ] ... 2 1 0 [ , 1 ) ( 1 )
( x n
C C C x k n N x n M N x
M 、、
The overall adjustable rate of the system
Temperature arrangement
n i n t n i n t n i n t n i n t j t n n P n n P n n P n n P P 0 83 , 0 82 , 0 81 , 0 80 , , ] ,.. 1 [ , ] ,.. 1 [ , ] ,.. 1 [ , ] ,.. 1 [ ,The particle swarm algorithm boasts the advantages as below: easy to realize, high solving speed, strong global optimization capability, few parameter and the like, and can quickly realize the consumption method for combining the electric boiler with the distributed power supply. By starting, it can establish a data matrix with n dimensions, which can represent a variety of users and be selected by means of the optimization method. Secondly, the simulation results can be obtained by using constraints and optimization objectives.
1150 ) 25 ( 1100 ) 26 ( 900 ) 26 ( 850 ) 28 ( 850 ) 29 ( 1000 ) 27 ( 900 ) 27 ( 800 ) 27 ( 1000 ) 28 ( 9 8 7 6 5 4 3 2 1 x x x x x x x x x Conclusion
The control method of electric boiler and electric air conditioner to realize the peak shift of power grid is the service-based foundation of the power grid and user friendly interaction, and it is also an important means and key link for power grid companies to control the load on the power consumption side of residents. The control method of intelligent electric boiler load reduces the peak load of the power grid, effectively decreases the economic input of the grid operation and greatly lowers the infrastructure investment of the power grid.
the power consumption mode, improve the efficiency of energy use, and exerting a far-reaching influence to constructing an economical society and realizing the optimal allocation and sustainable development of power resources.
References
[1]Q/GDW -518. Technical Guide for Smart Power Utilization Service System [S]. 2010.
[2]Tong Shulin, Wen Fushuan, Chen Liang. A Two-dimension Wavelet Threshold De-noising Method for Electric Load Data Pre-processing[J]; Automation of Electric Power Systems; Issue 01, 2012.
[3]Yin Shugang, Zhang Yu, Bai Keming. Intelligent Power System Based on Real-time Prices [J]. Power Grid Technology, 2009, 33(19): 11-16.
[4]Zhang Zhiqiang. Analysis of Air Conditioning Load Characteristics Based on Grid Side and Research on Control Measures [J].
[5]Zhang Zhi-qiang et al. Its control measures based on the grid side of the air conditioning load characteristics [J].
[6]Tong Shulin, Wen Fushuan. Calculation and Analysis of the Annual Maximum High-temperature Related Load in the Energy Saving and Emission Reduction Environment in Guangdong Province [J]. Journal of North China Electric Power University (natural science edition), Issue 05, 2010.
[7]Tong Shu-Lin. Wen Fu-shuan. Measurement and analysis of energy saving environment, Guangdong Province, the largest annual cooling load [J]. North China Electric Power University (Natural Science), 2010.05.
[8]Wen Quan, Li Jingru, Zhao Jing; Air-conditioning Load Calculation Method and Application [J]; Power Demand Side Management; Issue 04, 2005.
[9]Cheng Yu, Zhang Li-zi. The Co-integration Analysis of Power Tariff and Demand[J]. Proceedings of the CSEE, 2006, 26(7): 118-122.
[10]Angel A., Aquino L, Ray K. A control framework for the smart grid for voltage support using agent-based technologies[J]. IEEE Trans on Smart Grid, 2011, 2(1): 161-168.
[11]Pedrasa A, Spooner D, MacGill I F. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services[J]. IEEE Trans on Smart Grid, 2010, 2(1): 161-168.
[12]Negenborn R, Houwing M, Schutter D B, et al. Adaptive prediction model accuracy in the control of residential energy resources[C].Proceedings of 2008 IEEE International Conference on Control Application. Pisa, Italy: IEEE, 2008: 311-316.
