XI AN JIAOTONG UNIVERSITY Renewable Energy Trading in Cross-Region Power Market of China Wang Xiuli Xian Jiaotong University Chicago, USA July 2017
Outline 1 Back Ground of Power Market in China 2 Cross-regional Power Trading Method Considering Network Problems 3 Conclusion and Outlook 1
Low Carbonized Goal of China 2020 Goal On November 22th, 2009, State Council of China announced that, By 2020, China s carbon dioxide emission for per unit of GDP decreases by 40%-45%, compared to that in 2005. Medium Term Goal Carbon dioxide emission reaches its peak around 2030 and as early as possible, decreases by 60%-65%, compared to that in 2005. Chinese Attitude It is not only the inherent requirement of sustainable development of China, but also the international obligations and responsibilities of developing countries to promote clean energy transformation and low-carbon development. 2
Carbon Emission Amount (100 million tons) Low Carbonized Goal of China Electricity production is accounting for 40%~50% of total emissions. By 2030, the electricity energy, twice as 2016, will reach to 11000 TWh. The CO 2 emission will reach peak in 2030. Carbon emission in power industry needs slow growth and fast decline. 3
Power Generation of China in 2016 Energy Type Generation Capacity /TW Proportion Generated Energy / TWh Proportion Thermal 1.054 64.0% 4288.6 71.60% Hydro 0.332 20.2% 1180.7 19.71% Nuclear 0.034 2.10% 213.2 3.56% Wind 0.149 9.10% 241.0 4.02% Photovoltaic 0.077 4.7% 66.2 1.11% Total 1.646 5989.7 The power energy produced by thermal power still covers 71.60% while the renewable energy only occupies 5.12%. But the total curtailed energy of wind and photovoltaic power are 49.7 and 7.42 TWh respectively. How to Curb the Mega Clean Energy Waste? 4
Challenges of Renewable Energy Consumption in China With the rapid growth of the installed capacity, the renewable energy consumption problem has become increasingly prominent. In China 2016, the overall curtailed ratio of wind and photovoltaic power reached 17% and 9.6% respectively. Some provinces in the northwest have witnessed extremely serious curtailment, such as Energy Type Curtailed Ratio Curtailed Energy/TWh Gansu Xinjiang Wind 43% 10.4 photovoltaic 31% 2.6 Wind 38% 13.7 photovoltaic 32% 3.1 The renewable energy just covers a relatively low generation share(~5%), but suffers from very serious curtailment. Why? 5
Challenges of Renewable Energy Consumption in China Renewable energy in northwest Hydro energy in southwest Wind Energy Distribution Hydropower Plants Distribution The main load is distributed in the east and south part. The inverse distribution is among the reasons for the curtailment. So long-distance transmission/large-scale consumption of renewable energy is the inevitable choice to achieve low-carbon emission target. 6
Challenges of Renewable Energy Consumption in China There are 7 regional grids in China. Ultra-high Voltage AC/DC Grids over regions have been developed. The bulk transmission system lays a good basis for the cross-regional consumption of renewable energy. 7
Challenges of Renewable Energy Consumption in China Current status and bottlenecks: I. Power source structure is unreasonable, and the system is lack of the ability for peak load regulation. II. III. The consumption of renewable energy by cross-regional transactions is still inadequate. The current market mechanism limits the renewable energy consumption in whole country. We need to send energy from west to east for wide consumption. 8
New Round of Power Industry Reform The publication of Opinions on further deepening of the power industry reform 2015 marked the start of a new round of power system reform. Series of related documents play an active role. Promote the reform of transmission/distribution prices Promote the construction of power markets Build and regulate the power exchange center Deregulate the generation schedule Promote the reform of electricity retail market Strengthen the management of coal-fired captive power plants The renewable energy consumption will be promoted by the market methods especially by the cross-regional transactions. 9
Reform process in China Reform process of power industry Started at 2002 Separate generation and grid companies. transmission and distribution price reform not included New round of power system reform restarted since 2015 Establish provincial transmission and distribution pricing system to customers. Beijing and Guangzhou power exchange centers are erected. More than 6000 power retail companies are funded. There is no unified transmission price of regional power wheeling. 2002-2015 2015-2017 2017-10
Transmission and Distribution Pricing Method in China Calculation Method of Average Transmission and Distribution Price of Provincial Power Grid 电源类型完备 Permitted income Average price = electric power transmitted and distributed by the power grid 11
Transmission and Distribution Pricing Method in China Transmission and distribution price Stamp method Apportioned To transmission and distribution price of different voltage levels transmission and distribution price of different User categories Transmission/distribution price for big industries Transmission/distribution price for other users 12
Reform of Transmission and Distribution Price in China Examples in some provinces( /kwh) Province Beijing Power Grid (North) Shenzhen Power Grid (South) Anhui power grid (East) Shaanxi Power Grid (West) Hubei power grid (Middle) Big industry Others 1-10kV 35kV 110kV 220kV <1kV 10kV 35kV 0.1956 0.1751 0.1508 0.1493 0.4674 0.4505 0.4263 0.1794 0.1354 0.0679 0.0537 0.1794 0.1354 Cost results of the studied cases 0.1784 0.1634 0.1484 0.1384 0.3932 0.3782 0.3632 0.1484 0.1284 0.1084 0.1034 0.3917 0.3717 0.3517 0.1329 0.1131 0.0950 0.0760 0.4862 0.4662 0.4462 Provincial price characteristics: west less than east; traditional load area significantly higher than the power output area. The big industry price should pay extra capacity fee. 13
Outline 1 Back Ground of Power Market in China 2 Cross-regional Power Trading Method Considering Network Problems 3 Conclusion and Outlook 14
Cross-region Renewable Energy Trading The energy resources and demand distribution is not balanced. The cross-provincial/regional transactions are in great need for large-scale renewable resource allocation. Clean energy is in the west of China Main load is in the south east The network structure in China is very complex. There are many optional paths for power transactions. The largescale transmission line The key problem is how to optimize the transaction path, price the energy transmission and finally promote the large scale renewable energy consumption. Cross-regional transaction model to optimize allocation of resources 15
Operation of Beijing/Guangzhou Power Exchange Center Mid/Long-term transactions based on bidding and matchmaking is adopted. The energy transaction is greatly promoted especially the renewable energy transaction. Taking the Beijing Ex. (2016) as an example, Types Amount (TWh) Increase Rate Energy 774.4 7.7% Renewable Energy 362.8 21.9% The main defect is that transaction path capacity limits, transfer cost and transmission loss are not included. We put forward a method to determine the optimal transaction paths between multi-buyer and multi-seller, considering the transfer cost, loss, and capacity limits, to make the clearing match results more feasible and efficient. 16
Model and Methodology Modeling Basic Models in the Cross-regional transaction Cross-regional transaction path model considering under several objectives Theoretical Analysis Cross-regional path transaction model considering fixed clean energy paths The risk analysis of clean energy Centralized matching model under different objectives Graph theory and network flow model Mid/Long-term contracts market Real time balancing market Risk: The bias punitive measures Bidding strategies The bidding strategies Seller Buyer The bidding price Demand side bidding 17
Model and Methodology Step 1 Optimization model of cross-provincial/ regional power transaction To promote the wider cross-regional renewable consumption via market methods provide the transaction path and amount considering transfer losses and cost Step2 Cross-regional power generation right transaction The generation right is the contracted electricity allocated to thermal units with priority. The trade is an effective measure to optimize electric power dispatching and promote clearing energy utilization. Replace the thermal energy by the renewable energy via the generation right transaction according to double-win principle. 18
Model and Methodology Network flow optimization theory Consider the network topology and branch transmission capacity directly Meet the Kirchhoff current law. The bidirectional arc represents the transmission channel, the transmission capacity and other parameters on the correspondingarc are represented by c ij, f ij Equivalence network in network flow model purchase province G, demand province L, the transit province A transmission channel is simplified as the arc the virtual node t connected to all the demand nodes and the virtual node s connected to all the supply nodes s t 19
Model and Methodology Step 1 Cross-provincial/regional power transaction model Objective function: max E = x od od ij b ij (o,d) Z Seller biding Buyer biding p o j = o, i = s, o O b od ij = p d i = d, j = t, d D u ij p j kijp j other O Sellers Set D Buyers Set Constraints: Channel capacity constraint Losses Cost Transmission Cost Node flow balance constraint Forward and backward flow utilization hours constraint 20
Model and Methodology Step 2 - Power generation right transaction Transaction type is power generation right, and the two traders are both generators It is conducive to the consumption of clean energy and the resources allocation Objective function: max E xijlbijl yijlkqijlk i, j i, j l L l L k K The profits from power generation right are shared by both new and old generators. pol i s, j ol bijl pdl i dl, j t uij pdl k pk other pk pol i s, j ol qijl pdl i dl, j t uij pk k pk other L is all the original trading contract set ; K is the set for new generators P k is the power generation right price purchased by new power generation k 21
Model and Methodology Constraints: Contract capacity Channel capacity ijl ijlk l ijl ijlk l j t j t i s i ns i dl i dl j ol j ol k K k K Nodal balance of net flow x y Ds x y Os l L vijtij i, j s, t, ns 0 xijl yijlk cij cij l L l L dij i s(or ns)or j t k K j x x 0 i s, t l L k K ijl y y 0 i ns, t l L k K ijlk j, k j, k Forward and backward flow utilization hours j jil jilk xijl y ijlk xijl y ijlk tij i, j s, t, ns l L vij l L v r ij l L v r ij l L v ij 22
Case Study BUS23 XIN JIANG BUS20 GAN SU BUS22 NING XIA BUS3 SHAN XI BUS1 JING- JIN- TANG BUS2 HE BEI BUS18 LIAO NING BUS17 LIAO NING BUS16 HEI LONG JIANG Here we take the 23-node simplified large-scale China grid as a topology. Each node represents a province grid. BUS21 QING HAI BUS14 SI CHUAN BUS19 SHAAN XI BUS15 CHONG QING BUS12 HE NAN BUS11 HU NAN BUS10 HU BEI BUS13 JIANG XI BUS4 SHAN DONG BUS8 AN HUI BUS9 FU JIAN BUS6 JIANG SU BUS7 ZHE JIANG BUS5 SHANG HAI Transmission Capacity Constraint and Loss Rate Nodes Jiangsu-- Zhejiang Forward Transmission Capacity (MW) Backward Transmission Capacity (MW) Loss Rate 4000 4000 0.01 Hubei--Hunan 2600 1100 0.02 Shaanxi Henan Shaanxi-- Gansu 1000 1000 0.02 2600 2000 0.02 23
Case Study Sale Nodes Shanxi (Bus-3) Sale Power (GW.h) Price (Yuan/MWh) 1931 315 Demand Nodes Jingjintang (Bus-1) Demand Power (GW.h) Price (Yuan/MWh) 857.6 382 Shandong (Bus-5) 2790 397 Hebei (Bus-2) 0 395 Anhui (Bus-8) 1000 398 Shanghai (Bus-5) 5989 462 Fujian (Bus-9) 127.3 422 Jiangsu (Bus-6) 4900 436 Hubei (Bus-10) 10224.6 354 Zhejiang (Bus-7) 3926.1 458 Sichuan (Bus-14) 255 288 Hunan (Bus-11) 53 371 Chongqing (Bus-15) 227 291 Henan (Bus-12) 301.8 358 Heilongjiang (Bus-16) 1435 400 Jiangxi (Bus-13) 585 391 Jilin (Bus-17) 1431.41 376 Liaoning (Bus-18) 321.2 380 Gansu (Bus-20) 220.2 277 Shaanxi (Bus-19) 34.5 297 Qinghai (Bus-21) 418.6 279 Xinjiang (Bus-23) 0 250 Ningxia (Bus-22) 117.6 268 24
Case Study Trading results (without transmission cost) Total transaction number Total traded power Energy(GW.h) Total profit (million yuan) 17 8645.9 771 The optimized trading results and the detailed path of each transaction could be achieved by the model. The trading can be executed more conveniently. It is mid-long period transaction ( e.g. for one month). 25
Case Study Trading results (with transmission cost) Total transaction number Total traded power energy(gw.h) Total profit(million yuan) 11 8592.9 456 The transmission cost is considered. The results show that the trading paths become shorter and trading number decreased. The total amount is not changed so much. 26
Case Study Trading results (with transmission cost k=5%) No Path Traded Amount(GWh) Region 1 Shanxi-Jingjintang 857.6 North China 2 Anhui-Jiangsu 1000 East China 3 Fujian-Zhejiang 127.3 East China 4 Hubei-Jiangxi 103 Central China 5 Sichuan-Chongqing-Hubei-Jiangxi 255 Central China 6 Chongqing-Hubei-Jiangxi 227 Central China 7 Hubei-Shanghai 2700 cross-region 8 Hubei-Jiangsu 2700 cross-region 9 Gansu-Shaanxi-Henan 220.2 cross-region 10 Ningxia-Gansu-Shaanxi-Henan 81.6 cross-region 11 Shanxi-Jingjintang -Liaoning 321.2 cross-region 27
Case Study Impact of transmission cost on cross-regional transactions 18 Transactions Number Transactions Amount(GWh) 800 Total Profit (million yuan) 16 9000 700 14 12 10 8 6 4 8000 7000 6000 5000 4000 3000 2000 600 500 400 300 200 2 1000 100 0 0% 5% 10% 15% 0 0% 5% 10% 15% 0 0% 5% 10% 15% Transmission cost is assumed as an ratio of the demanding province transmission/distribution price The transmission cost will seriously affect the cross-regional transaction. Unreasonable cost are not conducive to allocate the energy optimally. 28
Case Study Power generation right transaction Renewable generation biding data Province Node Amount(GWh) Price(yuan/MWh) Gansu 20 500 100 Qinghai 21 600 120 Henan 12 300 140 Xinjiang 23 700 135 Sichuan 14 1000 130 The bidding price is relatively lower to avoid the curtailment of renewable energy. 29
Case Study Generation right trading results Contract Renewable producers Path Trade amount (GWh) 5 Xinjiang Xinjiang-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Jiangxi 255 6 Xinjiang Xinjiang-Gansu-Shaanxi-Henan-Hubei-Jiangxi 210.8 6 Xinjiang Xinjiang-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Jiangxi 16.2 7 Qinghai Qinghai-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Shanghai 228.8 7 Xinjiang Xinjiang-Gansu-Shaanxi-Henan-Hubei-Shanghai 216.2 7 Sichuan Sichuan-Chongqing-Hubei-Shanghai 1000 9 Henan Henan 218.4 9 Xinjiang Xinjiang-Gansu-Shaanxi-Henan 1.8 10 Henan Henan 81.6 11 Qinghai Qinghai-Gansu-Shaanxi-Henan-Shanxi-Jingjintang-Liaoning 321.2 The renewable energy consumption is promoted which is mainly produced in west China. 30
Outline 1 Back Ground of Power Market in China 2 Cross-regional Power Trading Method Considering Network Problems 3 Conclusion and Outlook 31
Conclusion The proposed cross-region trading method is an useful tool to country s Power Exchange Centre, such as Beijing and Guangzhou. It can give concrete trading amount and flow path between supply and demand sides. The trading can promote the clean energy transportation, help to solve the problem of uneven energy distribution, and achieve power supply with low carbon emission. The proposed model gains advantages on taking the transmission loss and cost into account. The numerical analysis has validates its effectiveness. From the environment and the sustainable development view, the generation right transaction is beneficial to promote low-emission energy to replace high-emission energy. 32
Outlook Energy layouts and power flow patterns in future According to the resource endowment, the large energy bases in the west and northern regions should be developed to promote renewable energy consumptions. The construction of coal power stations in the eastern and central regions should be strictly controlled. Power flow pattern is west-to east and north-to-south. The cross-region power trading and the power generation right transaction is very important for the optimal allocation of resources over a wider area. The country should improve trading mechanisms in electricity market and fully release the cross-provincial/regional transactions. 33
Outlook Big Data Application for Advanced Power Markets The power markets in China is far from mature and mainly for the mid/longterm energy transactions. Even so, great challenges related to big data technique are faced. Extremely large system Power Exchange Massive data for the large system Complex behavior Multiple data sources Participant Model the complex behavior of others via data mining Regulator Multiple data sources to determine transmission prices 34
Outlook Big Data Application for Advanced Power Markets Towards a short-term or real-time power market, the difficulties and challenges will be more complex. We hope that large data technology will help the effective work of the electricity market and the realization of low carbon targets. Great Uncertainty Supporting System Quick Decision Large scale wind and photovoltaic power precise forecast for multiple producers related analysis demanding requirement collect, test, analyze, visualize the data High-speed and reliable management high-frequency transaction advanced techniques supporting fast clearing 35
Outlook Beneficial Result of Renewable Generation in China The renewable generation contributes a lot to the low carbon goal. By the year 2020, The installed capacity of wind power and photovoltaic power is about to reach 210 and 110 GW respectively. The following yearly pollutant and emission reduction can be achieved equivalently. Types SO 2 NO X soot CO 2 Reduced Quantity/Tons 8 million 3 million 4 million 1.2 billion A great but challenging target! 36
Thanks! 37