Research on Dynamic Industrial Differential Electricity Price for Energy Conservation and Emission Reduction

Available online at www.sciencedirect.com

ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia

Energy Procedia 142 Energy Procedia 00(2017) (2017)2348–2353 000–000 www.elsevier.com/locate/procedia

9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK

Research on Dynamic Industrial Differential Electricity Price for The 15th International Symposium on District Heating and Cooling Energy Conservation and Emission Reduction a a* Assessing the feasibility of using the heat demand-outdoor Jingmin Wang , Wenhai Yang , Bingkang Li b, Yuwei Wang b* temperature function for a long-term district heat demand forecast a School of Economy and Management, North China Electric Power University, Beishi District, Baoding, 071003, China b School of Economy and Management, North China Electric Power University, Changping District, Beijing 102206, China

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

Abstract

Starting from the perspective of demand response, a dynamic differential electricity price for industrial users is proposed in this paper, which aims to stimulate the dynamism of users, and make themselves adjust their consumption behaviors. As a result, a lot of energy can be saved and the air pollution problems can be relieved. The price of electricity is adjusted by the mechanism with Abstract the change of the air quality index, which means the adjustment factor is larger when the air pollution is more severe, and the adjusted price of electricity is higher. When it comes to determine the adjustment factor of the price of electricity, a multiDistrict heating networks in the as one of the most effective solutionsreduction for decreasing the objective optimization modelareis commonly established,addressed in which the userliterature satisfaction energy conservation and emission are taken greenhouse gas emissions from theand building sector. Hierarchy These systems require high investments are returned through the heat into comprehensive consideration, the Analytic Process is employed to modifywhich the objective functions based on sales. Due toofthe changed climate conditions and building renovation policies, heat in optimally. the future And could decrease, the properties different users. The model is solved by the Genetic Algorithm based on demand the Pareto finally the prolonging the investment return period. rationality and effectiveness of the proposed mechanism is proved by an example. main of this paper isby to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2017 Thescope Authors. Published Ltd. ©forecast. 2017 The Authors. Published by Ltd. The district of Alvalade, in Lisbon (Portugal), used as aConference case study.onThe district is consisted of 665 Peer-review under responsibility of Elsevier thelocated scientific committee of the 9thwas International Applied Energy. Peer-review under responsibility of the scientific of theThree 9th International Conference Applied high) Energy. buildings that vary in both construction periodcommittee and typology. weather scenarios (low,onmedium, and three district renovation wereDynamic developed (shallow, intermediate, deep). To estimate theHierarchy error, obtained Keywords: Airscenarios Quality Index, Differential Electricity Price, User Satisfaction, Analytic Process heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the There are dozens of days of fog and haze every year in some parts of China. The increasingly serious air pollution decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and problems not only caused by different levels of hazards to the safety of transportation, agricultural production and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the power of China, but also posed could a great the health of people. Manyfor surveys show that industrial coupledsupply scenarios). The values suggested be threat used totomodify the function parameters the scenarios considered, and emissions and coal combustion are the main sources of air pollution, and the thermal power coal accounts for nearly improve the accuracy of heat demand estimations.

half of the total coal consumption. In addition, industrial electricity consumption accounts for about 70% of China's © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Wenhai Yang. Tel.: 13603243370. Cooling. E-mail address: [email protected].

Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.165

2

Jingmin Wang et al. / Energy Procedia 142 (2017) 2348–2353 Author name / Energy Procedia 00 (2017) 000–000

2349

total electricity consumption. The main compulsory measures to deal with air pollution problems at present are as follows: limiting production of heavy polluting enterprises; limiting vehicle emissions and improving traffic management; optimizing city planning and reducing construction site dust; improving the air quality evaluation criteria and the early warning mechanism and so on. The compulsory measures taken by China in the process of controlling the air pollution, often can’t achieve the desired effect. Many users do not respond positively to national policy measures and produce their products without permission, which results in a waste of resources and emission of large quantities of pollutants. According to the literatures [1-3], the main pricing methods (mechanisms) in the electricity market currently are as follows: the maximum profit pricing, the marginal cost pricing, the accounting cost pricing, the two-part pricing, and so on. The main electricity tariff structures [4-6] are: the unitary price, the two-part price, the time-of-use price, the ladder price, the power factor adjusting price, differential price, and so on. Differential pricing is a pricing policy employed by power supply enterprises, which means appropriately modifying the basic price to sell electric power products according to different demand and specific properties of users. The current differential electricity price implemented in China is a pricing policy for energy-intensive industries, which aims at limiting the blind development of high energy consumption enterprises and reducing energy consumption as well as improving energy consumption per unit of output. From the perspective of demand side response, and considering the user satisfaction and demand of energy conservation, a dynamic differential electricity price for industrial users is proposed in this paper. The tariff starts or not, based on the air quality circumstances, which means the price will be implemented if the air quality is poor. The range of the adjustment corresponds with the air pollution levels. Moreover, the differential electricity price should be dynamically modified according to the property of users, such as energy consumption, emissions and social status. As a result, a lot of energy can be saved and the problem of air pollution can be relieved with the production and income of industrial users taken into account. 2. Demand response model based on the discrete attraction In order to quantitatively describe the users’ reaction to electricity price, the price elasticity matrix is introduced to represent the users’ price elasticity of demand for electricity[7] [8]. Relation of the price and electricity demand in periods 1-n can be expressed by price elasticity matrix E as follows.  11  12    E   21 22      n 1  n 2

  1n     2n        nn 

(1)

m

 ii  i 

ie i  p nn  i n 1

m

 e j p nn  j

j 1

Where n stands for number of periods;

 ii



 i(1  S ai )

 ij  i 

ie

j

m

pn  n

n

 j

1

m

 e j p nn  j 

  i S aj

j 1

stands for self-elasticity coefficient of price;  ij stands for cross-elasticity

coefficient of price To calculate  ii and  ij , the discrete attraction is introduced[9-12].. S ai stands for market share of commodity a in period i; exp() stands for power function,  i stands for the fixed influence coefficient of attraction of the price on the demand in period i;  n stands for the influence coefficient of attraction of the price on the demand in period n; pn stands for the price in period n, n=1,…,m；  i stands for the error term of price in period i. 3. Dynamic differential pricing model The quality of the air reflects the degree of air pollution, in order to quantitatively describe the situation of the air quality; it has already been proposed the concept of the Air Quality Index (AQI). According to the air quality index level, the air quality is divided into four levels: good（0-100）, moderately polluted（101-200）, heavily polluted

2350

Jingmin Wang et al. / Energy Procedia 142 (2017) 2348–2353 Author name / Energy Procedia 00 (2017) 000–000

3

（201-300） and seriously polluted（more than 300）. The Tariff adjustment coefficient k is also divided into four levels corresponding thereto.The dynamic differential pricing model established in this paper is based on the original price, and the electricity price is adjusted according to the changes in the air quality index, which means the adjusted price is k times to the original price. In determining the tariff adjustment coefficient k, user satisfaction, energy saving and user-specific attributes are taken into comprehensive consideration. 3.1. The Objective Functions (1) Maximize users’ overall satisfaction:

(2) F1  max S0  max  1Sm   2 Sc  In the equation: S 0 stands for users’ overall satisfaction [13]; S m stands for users’ satisfaction to the way of using electricity; S c stands for users’ satisfaction to electricity expenses;  1 stands for weight of users’ satisfaction to the way of using electricity;  2 stands for weight of users’ satisfaction to electricity expenses.  1   2  1 . a. The power consumption mode satisfaction The power consumption mode satisfaction is based on the difference between the adjusted load curve and the original load curve [14]. It is specifically expressed as: n

Sm  1 

 Q 'Q i 1

i

(3)

i

n

Q i 1

i

b. The electricity costs satisfaction The electricity costs satisfaction defined here is a measure of user satisfaction with the amount of change in electricity costs[15]. It is specifically expressed as: n

Sc  1 

 (Q ' p 'Q p ) i

i 1

i

i

i

(4)

n

Q p i 1

(2) Minimize users’ total electricity demand.

i

i

n

F2  min  Qi '

(5)

i 1

(3) Minimize users’ electricity demand when air quality index is relatively high (more than 300). As follows: N

F3  min  Q j '

(6)

j 1

In the equation, Q j ' stands for the electricity demand of users in high AQI (over 200); N stands for the number of days with AQI more than 200. 3.2. The Constraints We should set the following constraints into the price adjustment model when taking cost of electricity generation and users’ affordability into account.to make adjustment programs. (7) pmin  pi '  pmax In the equation: pmin and p max stand for minimum and maximum values of price stipulated by regulatory authorities in period i. 4. Solution to the model 4.1. The Modification of Model Based on Analytic Hierarchy Process (AHP) The objective function will be adjusted to:

4

Jingmin Wang et al. / Energy Procedia 142 (2017) 2348–2353 Author name / Energy Procedia 00 (2017) 000–000

2351

  F1  min 1[ 1   2 ] n  F 2  min  2  Q i i 1  N  F 3  min  3  Q j  j 1

(8)

Among them:1,2,3respectively on behalf of the three weights of the objective functions. 1+2+3=1, and corresponding to different industrial users, 1,2,3will be different. The weights of each objective function[16] will be determined by AHP. The establishment of a hierarchical structure model, shown in Figure 1. Adjust the weights of objective functions

Target level

A

Improving user Reducing the total satisfaction（1） load（）

Program level C

2

Location

Difficulty Of pollutant purification

Emissions per unit of output

Power consumption per unit of output

Social status

Decisionmaking level B

Reducing the load under poor air quality （3）

Fig. 1 The AQI and the comparison of the load

4.2. Multi-objective Genetic Algorithm Based on Pareto Optimum The optimization solution procedure in this paper (which is implemented) by using MATLAB to the “gamultiobj” function in MATLAB calculate and is invoked. 5. Case study In this paper, the air quality index data and electricity load data of enterprise A and B in a certain place are employed as the study data to verify. The enterprise A is a high pollution and high energy-consuming enterprise, which is close to the city, and the contribution of A to the local economy is small. Enterprise B is a high energyconsuming enterprise, which is far from the city and contributes greatly to the local economy. In the calculation of the price adjustment coefficient k, two programs in which the weights of the objective functions were adjusted or not were employed as comparison. Given the current technical problems of AQI forecast, the regulation period was seven days. The original data is in Table 1 below: (original electricity price 1 yuan/KW·h). Table 1. The original data Date/day AQI Load A/MW Load B /MW

1 167 30.7 52.1

2 273 32.4 34.2

3 324 33.6 53.1

4 256 30.1 49.8

5 89 29.6 50.5

6 86 29.1 51.8

7 189 32.3 49.3

(1) Enterprise A:Option 1: Pairwise comparison matrixes were constructed as follows. The weights of objective functions determined by AHP were 1  0.16 , 2  0.39 , 3  0.45 . And the adjustment factors were: k=(0.96, 1.50, 1.98, 1.50, 0.50, 0.50, 0.96). Option 2: The weights of objective functions were not adjusted, the adjustment factors were: k=(0.98, 1.50, 1.99, 1.50, 0.26, 0.26, 0.98).The results of the two options are as Tab2 and Fig 2.

Jingmin Wang et al. / Energy Procedia 142 (2017) 2348–2353 Author name / Energy Procedia 00 (2017) 000–000

2352

1 1/7 1 / 5 1 / 5  1 1 7 1 B  5 1 1 1  1 1 5 1 1 1 / 7 1 / 5 1 / 5

5

 1 5 3   1 1 / 5 2 1 1 / 2 1 / 5 1       1 / 5 1 1 / 2 5 1 5    C C C  1   2   3 2 1 1 / 3 7 1 / 3 2 1  1 / 2 1 / 5 1 5 3 1       5 , 1 1 / 2 1 / 5 1 1 / 3 1 / 5  5     C 4  2 1 1 / 3 C 5  3 1 1 / 2 1 5 3 5 2 1  1   

The result indicates that since the enterprise A is a high pollution and high energy-consuming enterprise, after the regulation of electricity price under the dynamic differential pricing, the load in low AQI (less than 200) was increased while the load in high AQI (more than 200) was decreased. Option 1 performed better in saving energy and reducing emissions, as well as improving the user satisfaction (2) Enterprise B: Option 1: Pairwise comparison matrixes were constructed as follows. The weights of objective functions determined by AHP were 1  0.28 , 2  0.51 , 3  0.21 . And the adjustment factors were: k=(0.99, 1.48, 1.98, 1.48, 0.38, 0.38, 0.99). Option 2: The weights of objective functions were not adjusted, the adjustment factors were: k=(0.97, 1.46, 1.99, 1.46, 0.28, 0.28, 0.97).The results of the two options are as Tab3 and Fig3.  1   3 B  1 / 2  1 / 2  1

1/3 2 2 1

4 4

1/ 4 1 1 1/ 4 1 1 1/3 2 2

 1 1    C 1  1 / 2 3  1 / 5  1 / 2  1  1 / 2  C 4  1 / 2 1   3 

 1 1 / 3 2  1 2 1 / 3 5      1 3 C 2   3 1 5 C 3  1 / 2 1 1 / 3 1 / 2 1 / 5 1  3 3 1  1 / 3 1     1 1 / 3 1 2 1 / 3    1 1 / 3 C 5  3 1 3 1 1 / 3 1 3 1    2

The result indicates that after the regulation of electricity price under the dynamic differential pricing, the load of the two enterprises in low AQI (less than 200) was increased while the load of the two enterprises in high AQI (more than 200) was decreased. The different weights of the two enterprises shows that the power company should be more focused on guiding the enterprise to reduce the production in high AQI when adjust the electricity price for enterprise A. While the power company should be more focused on guiding the enterprise to reduce its total production when adjust the electricity price for enterprise B. In addition, option 1 performed better in saving energy and reducing emissions, as well as improving the user satisfaction. 40

400

80

400

300

20

200

10

100

0

0

1

2

3

4 Date/day

5

6

7

Fig. 2 The AQI and the comparison of the load Table 2. The objective function values F1 F2/MW Original 1 217.8 Option 1 1.17 200.9 Option 2 1.05 207.3

0 8

60

300

40

200

20

100

0

0

1

2

3

4 Date/day

5

6

7

AQI

30

AQI

P0 P1 P2 AQI

Load/MW

Load/MW

P0 P1 P2 AQI

0 8

Fig. 3 The AQI and the comparison of the load

F3/MW 76.1 65.9 66.1

Table 3. The objective function values F1 F2/MW Original 1 340.8 Option 1 1.13 323.8 Option 2 1.03 330.5

F3/MW 137.1 92.8 93.9

6

Jingmin Wang et al. / Energy Procedia 142 (2017) 2348–2353 Author name / Energy Procedia 00 (2017) 000–000

2353

6. Conclusions In view of the current increasingly serious air pollution problems in China, a dynamic differential electricity price for industrial users is proposed in this paper. the development of the dynamic differential electricity price mechanism is aimed at saving energy and reducing emissions. When it is implemented, the satisfaction of users and their own characteristics are taken comprehensive consideration. And the case study verified the validity of the dynamic differential electricity price. 7. Acknowledgements This research was financially supported by project of the National Natural Science Foundation of China (51607068) and the Beijing Natural Science Foundation (3164051). References [1] Lin D．Discussion on the Formation and Prevention Countermeasures of Urban Haze Weather．Guangzhou Chemical Industry，2013，41(10)： 175-177． [2] Chick M．Le tarif vert retrouve： The marginal cost concept and the pricing of electricity in Britain and France， 1945-1970． The Energy Journal， 2002： 97-116． [3] Zhou， Hao，Jian-hua CHEN，and Wei-zhen SUN．"Analysis and Adjustment of Electricity Price in Electricity Market．" Power System Technology 28．6 (2004)： 37-40． [4] Zeng M, Feng Y, Liu D, et al. Electricity Price Forecasting Based on Multi-models Combined by Evidential Theory[J]. Proceedings of the Csee, 2008. [5] Tan Z F, Chen G J, Zhao J B, et al. Optimization Model for Designing Peak-valley Time-of-use Power Price of Generation Side and Sale Side at the Direction of Energy Conservation Dispatch. Proceedings of the Csee, 2009, 29(1):55-62. [6] Zhu K D，Song Y H，Tan Z F，et al．Design Optimization Model for Tiered Pricing of Household Electricity Consumption．East China Electric Power．2011，39(6)：862-866． [7] Zhu K D，Song Y H，Tan Z F，et al．Design Optimization Model for Tiered Pricing of Household Electricity Consumption．East China Electric Power．2011，39(6)：862-866． [8] Zhiwei X U, Zechun H U, Song Y, et al. Coordinated charging strategy for PEV charging stations based on dynamic time-of-use tariffs. Zhongguo Dianji Gongcheng Xuebao/proceedings of the Chinese Society of Electrical Engineering, 2014, 34(22):3638-3646. [9] Wei，Guohui．"Research on Time-of-Use Electricity Pricing Based on Consumers’ Response and Demand Elasticity．" North China Electric Power University．2005． [11] Gao，Yajing，et al．"Power Demand Price Elasticity Matrix Based on Discrete Attraction Model ．" Automation of Electric Power Systems 38．13(2014)：103-102． [12] GAO Yajing L Mengkuo WANG Qiu LIANG Haifeng ZHANG Jiancheng State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University State Grid Hunan Electric Power Compa. Research on Optimal TOU Price Considering Electric Vehicles Charging and Discharging Based on Discrete Attractive Model. Proceedings of the Csee, 2014, 34(22):3647-3653. [13] Gao Y ， Wang C ， Wang Z ， et al ． Research on time-of-use price applying to electric vehicles charging．Innovative Smart Grid Technologies-Asia (ISGT Asia)，2012 IEEE．IEEE， 2012：1-6． [14] Ding，Wei，Jia-Hai Yuan，and Zhao-Guang Hu．"Time-of-use price decision model considering users reaction and satisfaction index ． " Dianli Xitong Zidonghua(Automation of Electric Power Systems) 29 ． 20 (2005)：10-14． [15] Zhenfang，Qin，et al．"Price elasticity matrix of demand in current retail power market．"Automation of Electric Power Systems 5 (2004)：16-24． [16] Xiao X Y, Chen W D, Yang H G, et al. Voltage Sag Frequency Assessment Under the Measure of Interval Data of Customer Satisfaction. Proceedings of the Csee, 2010, 30(16):104-110. [17] Yu G J, Bin E Z Z, Yun S E S Q, et al. Study and Applications of Analytic Hierarchy Process. China Safety Science Journal, 2008, 18(5):148-153.