A Method for Determining Hourly Generation Schedules of a Large

Hydropower Station with Monthly Trading Electricity

Xiaoyong Hu

, Jianjian Shen

2,*

, Xu Han

and Jianpeng Cheng

China Three Gorges Corporation Electricity Market Research Center, 100000, Beijing, China

Institute of Hydropower and Hydroinformatics, Dalian University of Technology,

Dalian 116024, People's Republic of China

Institute of Hydropower and Hydroinformatics, Dalian University of Technology,

Dalian 116024, People's Republic of China

China Yangtze Power Co., Ltd, 100000, Beijing, China

Keywords: Generation Schedule, Hydropower Station, Trading Electricity.

Abstract: The trans-provincial and trans-regional power transmission of the southwest giant hydropower station

involves complex multi-dimensional hourly generation curve decomposition with monthly energy demands.

This needs to consider complex power grid peak shaving and other needs, which is an important problem to

be solved urgently for the monthly power generation and operation of the hydropower station. This study

constructs a monthly contracted electricity curve decomposition model suitable for trans-provincial and

trans-regional power transmission giant hydropower stations. Considering the differences in electricity

prices of three types, i.e. guaranteed quantity and guaranteed price, guaranteed quantity bidding and

marketization, as well as the complex constraints of power grid peak shaving, market and power station

operation, this paper puts forward the secondary planning goal of maximizing the total revenue of multi

provincial and multi variety power generation, The mixed integer linear programming method is used to

solve the model efficiently. The model is verified through two different application scenarios of a real

hydropower station in dry season and flood season. The results show that under the boundary conditions of

peak load regulation that meet the requirements of the power grid, the power generation income of the

power station can be effectively improved by optimizing the multi-scale and multi variety power and output

distribution of the two provinces and giving priority to the distribution of market-oriented power to the peak

load and flat section.

1 INTRODUCTION

Since 2015, China has started a new round of

electricity market reform(Jia, et al., 2022; Shen et al.,

2022). A unified market and a two-level operation

mode of regional and provincial power grids, and

medium - and long-term trading operation

mechanism with sound systems and rules are

initially developed. In this case, how to ensure the

performance of the long-term traded electricity is

very important, especially for hydropower stations.

Due to the uncertainty of inflow and the boundary

restrictions of relevant dispatching constraints(Wang,

et al., 2019), the medium and long-term trading

electricity may not be fully implemented or the

traded electricity may not be traded but the power

plant is over generated. Performing the trading

electricity of hydropower plants is one of the key

issues to participate in the electricity market

transaction.

There are several difficulties in performing

long-term contract electricity. First, there are

restrictions on the proportion of electricity in all

power-receiving provinces, and the distribution

proportion of different types of electricity needs to

be controlled separately. Moreover, the load curves

of different provinces are discrepant, such as the

peak and valley loads. Second, the total distribution

proportion should be considered for the power of

multiple varieties, and the output distribution

process of all varieties should be equal to the total

power generation capacity of the hydropower plant.

In addition, this problem also involves various

complex constraints of hydropower operations, with

strong space-time coupling. Mathematically, it is a

very complex high-dimensional nonlinear

192

Hu, X., Shen, J., Han, X. and Cheng, J.

A Method for Determining Hourly Generation Schedules of a Large Hydropower Station with Monthly Trading Electricity.

DOI: 10.5220/0011956600003536

In Proceedings of the 3rd International Symposium on Water, Ecology and Environment (ISWEE 2022), pages 192-197

ISBN: 978-989-758-639-2; ISSN: 2975-9439

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

optimization problem. At present, some research

works have focused on the decomposition of

transaction electricity curve. For example, some

works considered the short-term operation

constraints of cascade hydropower stations and

relevant constraints of the daily electricity contract

decomposition curve. In this work, the maximum

comprehensive income model of transaction

electricity decomposition is constructed(Xie, et al.,

2021; Xu et al., 2019). Another works developed an

optimization model based on the day-ahead actual

needs of power grids, taking into account the

objectives of power purchase economy, energy

conservation and consumption reduction, and

fairness of bidding and non-bidding unit

dispatching(Cheng, et al., 2020).

This study constructs a monthly contracted

electricity curve decomposition model suitable for

trans-provincial and trans-regional power

transmission giant hydropower stations. Considering

the differences in electricity prices of three types, i.e.

guaranteed quantity and guaranteed price,

guaranteed quantity bidding and marketization, as

well as the complex constraints of power grid peak

shaving, market and power station operation, this

paper puts forward the secondary planning goal of

maximizing the total revenue of multi provincial and

multi variety power generation, The mixed integer

linear programming method is used to solve the

model efficiently. The model is verified through two

different application scenarios of a real hydropower

station in dry season and flood season. The results

show that under the boundary conditions of peak

load regulation that meet the requirements of the

power grid, the power generation income of the

power station can be effectively improved by

optimizing the multi-scale and multi variety power

and output distribution of the two provinces and

giving priority to the distribution of market-oriented

power to the peak load and flat section.

2 CASE STUDY: XILUODU

HYDROPOWER STATION

Xiluodu hydropower station is the third largest

hydropower station in China. It is an important

backbone power supply for the "West to East Power

Transmission" in the main stream of Jinsha River. It

is equipped with 18 700MW-hydropower units,

equally distributed on the left and right banks

respectively, with a total installed capacity of 12600

MW. The power station is located at the junction of

Sichuan and Yunnan. During the wet season, all the

power is transmitted to Guangdong and Zhejiang. In

the dry season, when the electricity demand in

Yunnan and Sichuan is met, all the remaining

electricity will be sent out. According to the national

hydropower integration arrangement, during the wet

season, all the electric power of Xiluodu

Hydropower Station will be consumed by Zhejiang

and Guangdong at the ratio of 1:1. In the dry season,

30% of the retained electricity is consumed by

Yunnan and Sichuan at the ratio of 1:1, and the rest

by Zhejiang and Guangdong at the ratio of 1:1.

Considering the electricity replacement among

cascade hydropower stations in the lower reaches of

the Jinsha River, in the actual operation, 30% of the

retained electricity of Xiluodu hydropower station in

the dry season is consumed by Sichuan and Yunnan

at the ratio of 7:23.

Table 1 Scheme for absorbing energy production of Xiluodu Hydropower Plant.

Province Zhejiang Sichuan Guangdong Yunnan Total

Flood

season

Left bank 50% - - - 50%

Right bank - - 50% 50%

Dry

season

Left bank 35% 7% - - 42%

Right bank - - 35% 23% 58%

A Method for Determining Hourly Generation Schedules of a Large Hydropower Station with Monthly Trading Electricity

193

3 MATHEMATICAL MODEL

3.1 Notation

NMAX

Maximum power at a period

of ten da

Ratio of two power-

receiving

provinces(Guangdong and

Yunnan)

Monthly energy demand

,kv

Trading electricity of variety

v at province

,,kvx

EMAX

,,kvx

EMIN

Maximum and Minimum of

trading electricity of variety

v at province k for each a

eriod of ten da

coef

A coefficient for non-work

,kh

Minimum and maximum of

hourl

coefficien

,kh

coef

A coefficient of normal

price for market electricity

in province

at period

,1k

,2k

,3k

Prices of three types of

electricity, namely, quantity-

price guarantee electricity,

quantity guarantee

electricity, and market

electricit

,,kvd

A daily coefficient for

variety v of province k in

,,,kvxt

A hourly coefficient for

variety v of province k in

eriod t o

the vth ten da

,, ,kvdt

Generation for variety v of

province k in period t of the

dth da

3.2 Objective

With the requirements for contracted electricity and

peak shaving of Guangdong and Yunnan Power

Grids, an hourly generation scheduling model with

maximizing the total power generation income is

developed. By optimizing the hourly generation

curves among provinces, ten days and varieties, the

monthly electricity income is maximized as far as

possible. Therefore, the objective function can be

described by the total income of three varieties, i.e.

quantity-price guarantee electricity, quantity

guarantee electricity, and market electricity.

1 ,1,,,1 ,2,,,2 ,3,,,3 ,

111

ax F ( )

KDT

kdtk kdtk kdtk kt

kdt

n P n P n P coef

===

=⋅+⋅+⋅⋅



(1)

3.3 Constraints

The above objective function is subject to important

operation constraints, listed in the following.

(1) Power balance constraint

(2)

(2) Energy production constraints

(3)

(4)

(5)

(6)

(3) Other operation constraints

(7)

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194

(8)

(9)

,,,

()

kvxt

kt kt

kvxt

Max

≤≤

‘

(10)

(11)

(12)

(13)

(14)

4 SOLUTION METHOD

Mathematically, the above trading electricity

decomposition model is a very complex large-scale

nonlinear optimization problem. It has high

dimensions and many nonlinear constraints, making

it difficult to solve. In order to realize the efficient

solution of the model, it is necessary to treat the

complex constraints equivalently. In the following,

the processing strategies of decomposition

coefficients of daily and hourly generation curves

are proposed, respectively.

Because the daily coefficient has a maximum

function, a temporal variable

introduced to describe the maximum of

at the

current ten days. The daily coefficient of each

province at each ten days should be limited in the

following.

(15)

(16)

(17)

For all provinces and varieties, the sum of

generation on working days should be equal to the

maximum power in current ten days, and there

should be a limit on non-working days.

(18)

(19)

Similarly, the hourly coefficients require

temporal variables

and to make the

model easily solve.

(20)

(21)

(22)

With the equivalent transformation of the above

constraints, the optimization model can be

transformed into a mixed integer quadratic

programming model and solved by a mathematical

solver.

5 CASE STUDY

This study takes the right bank of Xiluodu

hydropower station as the case study. Here, some

actual data are used to test the model. The monthly

trading electricity is set as 9.736*10

kWh. The ratio

of electricity for Guangdong Province and Yunnan

Province is set as 1.5.

According to the above solution method, we can

get hourly generation curve of each type of power

station in the first, middle and last ten days when the

hydropower station sends power to Guangdong and

Yunnan. Figure 1 shows the output process of the

power station in the first, middle and last ten days.

Figure 2 shows the hourly coefficients in ten days

for Guangdong and Yunnan, respectively. Figure 3

shows the generation schedules of the hydropower

station in two different days.

A Method for Determining Hourly Generation Schedules of a Large Hydropower Station with Monthly Trading Electricity

195

Figure 1: Typical generation schedules of the hydropower

station for three ten days.

Figure 2: Hourly coefficients for Guangdong and Yunnan

Figure 3: Generation schedules of different types of

electricity.

Firstly, the rationality is analysed. In the example of

dry season, the total monthly electricity in right bank

of Xiluodu is 973.6 million kWh, and the electricity

delivered to Guangdong and Yunnan is 584million

kWh and 389.6 million kWh respectively, meeting

the given requirements for cross provincial power

transmission ratio; The two priority electricity

varieties of guaranteed quantity and guaranteed price

and guaranteed quantity bidding are 725.6 million

kWh and 248.0 million kWh respectively, and the

results obtained are completely consistent with the

given parameters. At the same time, according to the

distribution proportion requirements of the two

provinces, the distribution results of electricity of

each variety are also consistent with the specified

power transmission proportion; The ratio of

guaranteed quantity, guaranteed price and

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196

guaranteed quantity bidding electricity in each

province and each ten day period is within the

allowable fluctuation range of 20% above and below

the ratio of the monthly guaranteed quantity,

guaranteed price and guaranteed quantity bidding,

meeting the set boundary requirements; The time-

sharing coefficients of ten days in Figure 2 and

figure 3 are within the given purple value range,

meeting the peak shaving requirements proposed by

the power grid.

The efficiency of the model optimization results

is further analysed. According to the electricity

decomposition results in the dry season months, on

the premise of meeting the peak load regulation

requirements of the power grid, the market-oriented

electricity in the first ten days, the middle ten days

and the last ten days is basically distributed in the

load peak and normal sections with relatively high

electricity prices, of which 100% of Guangdong's

market-oriented electricity is distributed in the high

peak hours, because its market-oriented electricity

price is the highest in the peak hours, that is, 1.1

times of the benchmark price, so as to maximize the

power generation income, This distribution method

is reasonable. 63% of the market-oriented electricity

in Yunnan is distributed in the peak load and 37% in

the normal load section. The main reason is that the

market-oriented electricity price in Yunnan is lower

than that in Guangdong. Through the coordinated

distribution of multi varieties across provinces, the

market-oriented electricity will be preferentially

arranged in the peak period in Guangdong, which

has the highest electricity price. In this way, in order

to meet the requirements for the proportion of the

two varieties of electricity in Yunnan, i.e. the

quantity and price guarantee and the quantity and

bidding guarantee, Yunnan needs to arrange a large

proportion of priority electricity during peak hours,

so a part of market-oriented electricity is allocated at

periods with middle loads.

6 CONCLUSION

This paper mainly focuses on the hourly generation

scheduling model of a large hydropower station

when the monthly contract electricity is given. Using

monthly actual data, the following conclusions are

obtained. 1) The hourly generation scheduling model

can adapt to the power decomposition requirements

of multiple provinces, multiple market varieties and

multiple time scales. The dispatching scheme

obtained conforms to the actual production habits,

reflecting good adaptability and practicality. 2)

Taking into account the requirements of

differentiated peak shaving of multiple power grids,

and aiming at maximizing the monthly electricity

revenue of the hydropower station, it can effectively

take into account the interests of power grids and

hydropower enterprises. The hourly coefficients in

the middle and late ten days of the monthly

electricity declaration are consistent with the peak-

valley trend. Within the boundary conditions of peak

shaving, it is reasonable to arrange the market-

oriented electricity in the peak and flat load sections

with higher electricity prices as far as possible.

ACKNOWLEDGEMENTS

This paper is supported by scientific research

projects of China Three Gorges Corporation

(Contract number: 202003252).

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A Method for Determining Hourly Generation Schedules of a Large Hydropower Station with Monthly Trading Electricity

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