draft - purdue...

Alternative Structures for China's Electricity Market Policy & Investment Priorities - 2002 to 2012 DRAFT

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A collaborative policy analysis project between Purdue University, United States of America,

& the Energy Research Institute, Peoples Republic of China.

Alternative Structures for China's Electricity Market - Policy & Investment Priorities

- 2002 to 2012

F.T. Sparrow Zuwei Yu

Brian H. Bowen

Institute for Interdisciplinary Engineering Studies State Utility Forecasting Group, SUFG Power Pool Development Group, PPDG

PURDUE UNIVERSITY

December 20, 2001

DRAFT


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F.T. Sparrow et al December 2001

Summary

Investments for new generation within the Chinese electricity sector have been enormous over the past decade involving tens of billions of dollars [1]. The 18.2 GW Three Gorges Dam, to be completed in 2009, and the 15.8 GW Yellow River Hydroelectric Development Corporation are two large initiatives among others. A 1995 forecast for investments in the combined electricity and coal sector was estimated at $240 billion, for the 15-year horizon [4]. Considerable investment is yet to be committed during the next five years. With China's intention to create a unified national power grid and modern power market, various decision support systems will be needed to provide critical quantitative assessment. Purdue University's State Utility Forecasting Group (SUFG) is developing quantitative decision support systems (DSS), which combine expansion planning of both transmission and generation capacity, and wishes to identify research partners, in China, for development of the most suitable electricity market analysis models to assist Chinese planners. Designs of China's electricity market, regional transmission expansions, and the location of new generation capacity options all demand complex quantitative analysis for ensuring minimal cost with economic efficiency. A 1995 analysis shows potential benefits of $6.4 billion on the estimated $240 billion total cost. This benefit (2.6%) appears low. Previous Purdue studies shows the benefits, from free trading of electricity over a wide region, can make savings of 6% to 13% and higher. A comprehensive electricity market model will assist the Chinese Government in dispelling some of the caution of international developers seeing China as having vast opportunities for failure as well as success. Another Chinese electricity investment projection, which assume China to have 20% of the world's global power market by 2007, estimates that $262 billion is needed for investment during 1996 to 2006 [5]. These magnitudes of investment clearly imply that, with the correct market strategy, very large cost savings might also be achieved. Purdue University’s SUFG has 20 years of experience in electricity demand/supply forecasting, analyzing alternative electricity trading arrangements, and strategic investment plans from working with the utilities and government regulatory commissions of the United States’ Midwest. Also over the past five years there has also been a highly successful international collaboration with the 12 nations of the Southern African Power Pool (SAPP), and the newly formed West Africa Power Pool (14 nations of the Economic Community of West African States). The Purdue models, which combine economic, engineering, generation and transmission parameters, have determined the magnitudes of the savings from a regional integration approach for the SAPP. Purdue's collaboration with Chinese partners will create a dynamic modeling team that can demonstrate the major cost savings through appropriate electricity market structures. Phase 1 of the proposal (2002 to 2003), of a planned three year project, will involve establishing the China-Purdue partnerships, appropriately compiling of the latest Chinese electricity data-set, and preparing the preliminary regional electricity market model for specified provinces within China.


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1. PROJECT RATIONALE The People's Republic of China (PRC) has a fast growing electricity rate of demand, (8.3% from 1980 to 1995, and 6% forecast for up to 2020, compared with the USA and Western Europe rate of about 1.9%). The remotely located huge hydropower schemes require new transmission lines, plans to construct combined cycle thermal stations (instead of building new coal fired stations) for environmental and economic effectiveness could mean new gas pipe lines are also needed to be built. These long distances between supply and demand centers and the expectations to speed up harmonization of the PRC electricity market, for operations and capacity expansion planning, therefore raise a number of strategically crucial policy planning problems. Energy by wire or by pipe and the hydro-thermal generation mix prompt provoking policy and planning issues for the PRC. Inevitably with the hydropower and natural gas supplies being located so far from the demand centers the cost of shipment becomes a decisive operational variable. The shipment of coal supplies across the PRC to electricity generating sites has been a constant concern to PRC planners. The costly long HVDC transmission lines will still involve significant line losses over lengthy distances (10% and more) of several hundred km or more. Similarly the cost of gas pipelines will be a parallel problem and one that becomes more complex as harmonization gets considered more seriously. The level of cooperation and increased interconnectivity between the provinces of the PRC must be planned hand in hand with the expected type of pool being developed. The tight (very limited electricity trading) and open (flexible electricity trading is permitted) power pools require very different amounts of transmission capacity. Free trading across a region can cause congestion if insufficient load carrying capability is available interfering with using the grid for reliability. How much integration of these alternative fuel shipment lines should take place? The substitution of gas by wire, planning of reserve requirements, accounting for load diversity across the extents of the PRC, consideration of economies of scale, and joint planning are each an vital issue that will be assessed in the configuration of proposed new pools in the PRC. Substitution of gas by wire policy In any power market, generating electricity on site instead of building natural gas pipelines, can yield more efficient planning and better electricity market structures when both economic and technical issues are considered. The proposed new generation and transmission capacity in each region and network of the PRC will be determined by the expected size of the electricity market to be served. Establishment therefore of power pooling regions within the PRC is of primary importance for a country the size of the PRC and for marketing analysis boundaries. The levels of freedom in flexibility of trade can then be analyzed. Regional boundaries need specifying and then linkages between the several power pooling markets can be considered for a further larger scale trading scenario.


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Lower Reserve Requirements As individual generators represent a smaller fraction of the total system load, their unplanned outages are less likely to result in an overall generation shortage. Thus, more diverse generation sources result in lower reserve requirements. Joint planning for utilities will increase generation diversity, thereby resulting in lower reserve requirements than would occur under separate planning. While lower reserve requirements are a benefit of regional planning, the Purdue long-term (LT) trade models do not implicitly capture that benefit. This benefit would have to be determined outside the model and then the appropriate reserve requirement could be placed in the model. The resulting PRC regional wide-reserve margin, which would be lower than the individual utility/province reserve margins for the reasons stated in this paragraph, would then be used. Load Diversity Not all utilities/provinces experience peak load conditions at the same time of day due to the different characteristics of the customers they serve. Similarly, they experience annual peak demand on different days. Therefore, the chronological sum of the individual utility loads provides a peak that is lower than the sum of the individual peak demands. Since generation capacity must be capable of handling the peak demand during the year, separate planning will result in larger generation requirements than will joint planning. Economies of Scale Generally, it requires less capital to construct one large facility than is required to build an equivalent capacity with several smaller units. Similarly, multiple units at a single site are cheaper to build than the same units at numerous different sites. These economies of scale result from common use of facilities, such as fuel handling, transformers, and transmission lines. Joint planning allows these economies to be captured more frequently than separate planning does by allowing utilities to share a jointly planned unit. There are substantial scale economies in hydro production, and substantial economic benefits in harmonizing the operation of a combined hydro/fossil fuel generation system, where hydro is used during peak hours. This cuts down on the need for building expensive peaking combustion turbine units, even if fuel cost are low, as in the PRC. Joint planning This will allow a utility/province to utilize generation options for both energy and capacity requirements that are otherwise unavailable when planning is done separately. Thus a utility/province with little or no hydro sites available will not have to build a more expensive type of generation. Joint development of the transmission networks increases the reliability of the system, allowing provinces/networks of the PRC to use the collective capacity of the region to insure a reliable electricity supply, rather than each having to construct its own reserves, is another advantage of joint planning trade in reserve. It has the same advantages as trade in energy. Opportunities to use pumped storage on the supply side and integrated resource planning on the demand side to peak shave/valley fill until demand justifies construction of large dammed river hydro facilities are another advantage.


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In addition to reflecting the advantages of regional power pools, the LT model must take account of the extraordinary uncertainty regarding demand growth in the networks/regions of the PRC, as well as uncertainty on the supply side, with the impact of low rain falls, and line or unit failure. Long-run expansion decisions must consider alternative growth and supply scenarios. It is almost a certainty that an expansion plan based on most likely growth and supply scenarios will not be the preferred option, if its performance is measured against all scenarios. Flexible capacity expansion scenarios, ones where the cost of over, or under estimating demand/supply are not catastrophic to the region, are always preferred. An added feature of the LT model will be to allow each province/network participant to decide on the maximum level of dependence on imports expressed as a domestic generation reserve margin (domestic energy production capacity divided by peak demand). This number can be between 0 and 1, depending on each country’s need for security and autonomy. To allow free movement of energy (electricity and natural gas) between the provinces and networks of the PRC an integrated energy grid must exist within the region. The form of regional market for electricity (loose or tight power pool) within the PRC can be critically and analytically assessed. This is done with mathematical and economic modeling combining the generation and transmission facilities across the region (existing and proposed new facilities). Cost minimization techniques are to be used.

Another issue the Purdue models can address is a problem currently facing deregulated United States power purchasers. How can one insure that sufficient competition exists in electricity markets to prevent the exercise of market power by a few suppliers? The Purdue studies show that such abuses can be expected in vulnerable regions of the grid during peak demand hours. Such behavior adds another dimension to the problem of network congestion caused by inadequate systems planning.

Time and again, it has been observed that the rationalization and harmonization of regional economies has started with cooperative ventures such as this that involve specific commodity sectors. The European Common market has its roots in the Iron and Steel Community agreements in the early 1950s; the North American Free Trade Agreement (NAFTA) was preceded by a host of commodity agreements, including electricity. The growing economic integration of the Southern African Development Community (SADC) is both a cause and an effect of the success of the Southern African Power Pool (SAPP). It is hoped that, as in the case of SADC, rationalization of the electricity sector will provide both the model and the impetus for further economic integration within the PRC.


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2. BACKGROUND TO THE CHINESE ELECTRICITY SECTOR The 2001 World Bank discussion paper on China's power markets suggests that a flexible approach is needed in order to develop competitive power markets in China [1]. Both the accommodation of the wide diversity in regional and provincial power systems and the allowance of adequate time for power entities to acquire the management capacity and skills to adapt to competition, will require a very flexible approach towards a more efficient power market orientation. This approach reflects the positive and steady approach of the Chinese authorities towards power sector development. Over the past 15 years the attitudes towards electricity supply within China have made the strategically important shift from viewing it as a social service to seeing it as a commercial business and having it all corporatized. Subsidies have now been all eliminated and electricity prices exceed supply costs. The 1995 Electricity Law formalized private ownership of Chinese power assets. In 1999 the international and domestic stock markets listed 37 power companies in China (installed capacity of 25,000 MW). There were also 40 power projects involving private developers. Market structures of the 1990s showed weakness in the abuse of single buyer's dual monopsony and monopoly power. Discrimination in dispatching power has prevented lower costing and a more efficient market to take shape. Economics of scale and improved trade have not been promoted in a significant way and pollution continues at a very high level, contributed by small inefficient coal plants. Investment, pricing, and environmental policies can all be significantly improved when greater incentives for increased operational efficiency and lower investment costs are made through an enlarged competitive market. The Purdue modeling methodology can assist the Chinese planners in determining the extent and execution for the proposed new electricty sector structures. Over the post couple of years the success of the Zhejiang province pilot electricity market is a leading example of how China is to proceed. The whole Chinese market is so widely dispersed however that it cannot constitute a single national market but regional markets will eventually be defined and the interconnections between the marketing regions can be designed to meet market needs. The Purdue electricity trade models are therefore being considered in a most propitious time. The amount of transmission interconnection within the regions of China is summarized in Table 1 showing the Central Region to have 19% of the nation's total transmission lines (by length). The North China, Northwest China, and East China regions respectively have 14%, 12%, and 14% of the nation's transmission lines. The interconnectivity between the regions and the level to which interconnection should proceed will be determined in accordance with proposed new power marketing structures.


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Table 1 People's Republic of China Circuit Length of Transmission Lines by the Ends of 1995 and 1996

Regions/Provinces Total Length (km) in which

PercentageOf Total

500 kV 330 kV 200 kV 110 kV

1995 1996 1995 1996 1995 1996 1995 1996 1995 1996

North China 80,718 84,475 14% 2,074 2,137 15,024 16,062 23,210 23,897Northeast China 72,454 73,734 12% 1,711 1,711 18,230 18,547 5,179 5,269East China 79,933 83,048 14% 2,619 2,802 15,419 16,219 19,222 20,498Central China 102,333 111,959 19% 2,588 2,585 71 71 16,377 17,493 30,696 33,469Northwest China 46,083 48,529 8% 5,538 6,147 2,953 3,261 15,882 16,491Shandong Prov. 37,222 38,701 6% 737 739 6,201 6,646 9,509 9,881Fujian Prov. 15,954 17,067 3% 3,316 3,357 5,112 5,540Guangdong Prov. 30,556 31,962 5% 1,085 1,085 5,685 5,913 12,257 13,265Guangxi A. Region 24,371 23,864 4% 1,464 407 2,264 2,780 5,112 5,398Guizhou Prov. 7,124 7,825 1% 264 1,900 2,129 3,706 4,267Yunnan Prov. 12,144 13,930 2% 440 440 1,967 2,364 4,577 5,330Sichuan Prov. 40,000 42,509 7% 70 148 5,619 5,813 11,010 12,130Hainan Prov. 3,913 3,340 1% 689 569 1,817 1,350Xinjiang A. Region 13,902 15,832 3% 1,089 1,178 4,671 5,438Nation's Total 566,707 598,962 13,052 13,635 5,609 6,218 96,913 102,417 151,932 162,497

Nation's Total Check: 566,707 596,775 13,052 12,054 5,609 6,218 96,733 102,331 151,960 162,223

Source: Electric Power Industry in China 1997, Table 1 (page 39)


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3. MODELING & ANALYZING OF ELECTRICITY GENERATION &

TRADE OPTIONS

A simple three-panel trade diagram illustrates how trade between low cost and high cost areas can be modeled. Electricity costs in each area (city, district, province, country, called a node in the modeling) will be increased as decisions to build new generation capacity are made. Operational, maintenance and fuel costs, as well as reserve margin requirements, will be higher in one node compared with the others in the region. Purchasing of generation capacity from one node to another can avoid construction of new generation and reduce costs in the process. By virtue of the differences in marginal costs from one node to another then trade will be beneficial to both. To one node the benefit is a cost saving, from reductions in capital and/or operational costs, and to another node there a benefit from increased revenues. Consider Figure 1 below.

Figure 1. The Three-Panel Trade Model.

EXPORTERS TRADE IMPORTERS

S SS

D DD

Px

Pt

Pm

Qx Qt Qm

Excesssupply

Excessdemand

Q

P

The three-panel trade diagram shows supply and demand schedules for a low-cost utility on the left-hand panel, and shows supply and demand schedules for a higher-cost utility on the right-hand panel. Note supply and demand schedules are shown as straight lines on the diagrams, but would be nonlinear in any empirical model. The third panel, in the middle, shows the schedules of excess demand of the importer and excess supply for the exporter, which can be derived from the other two panels.

Each panel reveals the prices and quantities, which would arise under alternative trading arrangements. Px and Pm would be the prices in the exporting and importing regions without trade: at these prices, the supply and demand schedules meet and demanders want to purchase exactly the amount producers want to sell in each node (Qx). If higher prices were to prevail in the export node, then suppliers would offer additional production, demanders would offer to purchase less, and there would be excess supply, in the amount of the horizontal difference


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between the demand curve and the supply curve at that price. In the importing region, there would be excess demand if prices were lower than Pm.

The curves in the middle panel are constructed by tracing out the excess supply schedule from the exporter and excess demand schedule from the importer. With no transaction costs (losses or other costs of transmission), the equilibrium price with trade would be equal to Pt, and quantity Qt would be traded (i.e., produced by the exporter and sold to consumers in the importing region).

Electricity trade, therefore, within integrated systems benefits in practice from the load diversity patterns across the region and also from the economies of scale. In any hour of one day the total peak load over a large power pooling region will always be smaller than the summation of each nations peak demand. Costs can be further reduced when combining costs between two nodes for one large station compared with each node each building a smaller station. Total investments and thermal efficiencies associated with the increase in MW capacity will affect the final cost of each kWh produced.

A long-term simulation model capturing changes over time and space is tailored to the computing facilities specified by the government and utilities managers, by linking short-term models of trade to the various options for generation and transmission expansion. The chronological and spatial nature of this model can make it very large, in the order of tens of thousands of constraints, and near 1000 integer variables in addition to an even larger number of continuous variables.

Multi-regional spatial optimization takes place with generation units and transmission lines whose limited capacity can be expanded at a cost. The cost function (capacity and line expansion costs plus operational costs over 20 years) will minimize the sum of the present value of horizon costs. Generation and transmission data (technical, capital investment, and operational costs) will need to be collected from each province in a national policy analysis. The 31 provinces will entail a very extensive data collection (Figure 3). Figure 2 illustrates how about 70% of all existing Chinese transmission is within the eastern half of the country.

It is expected that during Phase 1 (September 2002 to late spring 2003), of the proposed four year long project, that most modeling attention will be given to the coastal and central region of China. Dialogue between Purdue and China's modeling teams will determine the market policy scenario priorities.

Fi

Ke1223


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gure 2. Regional Percentage Distribution of Transmission Lines in P.R. China (1996) (% of total national value & above 110 kV)

y to Nodes: 1=Anhui, 2=Beijing, 3=Fujian, 4=Guangdong, 5=Gansu, 6=Guangxi, 7=Guizhou, 8=Hubei, 9=Hebei, 10=Henan, 11=Hainan, =Heilongjiang, 13=Hunan, 14=Jilin, 15=Jiangsu, 16=Jiangxi, 17=Liaoning, 18=Mongolia, 19=Ningxia, 20=Qinghai, 21=Sichuan, 22=Shandong, =Shanghai, 24=Shaanxi, 25=Shanxi, 26=Tianjin, 27=Taiwan, 28=Xinjiang, 29=Tibet, 30=Yunnan, 31=Zhejiang

3 - 3% 4 - 5% 6 - 4% 7 - 1% 11 - 1% 21 - 7% 22 - 6% 30 - 2%

14%

Central 19%

East

North East12%

North14%

North West 8%

24

25 26

27

28

29

30

16

1718

13

15

11

10

14

12

1920

21

22

23

2

8

317

9

6 4

5

3

1


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How therefore is electricity trade, which is facilitated through the existence of transmission lines, to be modeled for China? The initial structure of the electricity market models can be considered in three stages of development. A brief outline of the formulation is shown below:- • Stage 1 - Short Run, Power Trade Only • Stage 2 - Short Run, Power & Reserves Traded • Stage 3 - Long-Term Run - With Capacity Expansions Included Purdue has developed various thermal-hydropower dispatch models as well as long-term expansion models [4,5,6]. Full details of the formulation for the basic long-term model are found in the LT model User Manual [10]. Let us now therefore consider the modeling principles. Electricity trade quantities are always to be observed in the modeling notation through the use of the two letters, "PF", standing for power flow. MODEL Stage 1: Short Run, Power Trade Only In the short-run model I the objective is to minimize the total costs that arise from the cost of centralized generation operations (fuel and maintenance), distributed generation and/or unmet demand costs, and the cost of unmet reserve requirements.

t i zmin c(i, z)PG(i, z, t) DG cos tDG(z, t) UM cos tUM(z)+ +∑∑∑

i.e.: Minimizing over all hours, all stations, and all nodes, the sum of fuel costs (cost/MW times MW) plus demands met by distributed generation plus unsatisfied reserve requirements.

(Over-lined expressions are parameters, the rest are variables).

c(i, z) = Fuel Cost/MW at i in z ($) PG(i,z,t) = Centralized Power Generation at i in z during t (MW)

DGcost = Cost of distributed generation supply and/or demand shortage cost ($/MWh)

DG(z,t) = Distributed Generation and/or unmet demand in z during t (MW) UMcost = Cost of unmet reserves ($/MW/year)

UM(z) = Unmet reserve requirement in z (MW) This minimization is subject to the following constraints:

∑ i PG(i,z,t) + ∑ zp PF(zp,z) ( ) }{ zpzPFloss1− + DG(z,t) = ( )t,zD + ∑ zp PF(z,zp) PF(zp,z) = Power Flow from zp to z (MW)


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)( z,zpPFloss = line loss from zp to z (%)

( )t,zD = Demand in z during t (MW) ie: All generation in node z plus all imports from other nodes (adjusted for line loss) is equal to the demand in node z plus exports to all nodes.

( ) ( )PG i,z,t PGinit i,z≤

PGinit(i, z) = initial capacities (MW) ie: The generation at station i, in node z, at any time t, is always less than or equal to the initial generating capacity of that station i in node z.

( ) ( )PF z,zp PFinit z,zp≤

PFinit(i, z) = initial capacities (MW) ie: The power flow from node z to node zp will always be less than or equal to the initial power flow capability along the transmission line connecting node z to node zp.

( )( )

( ) ( )i

PGinit i,zUM z D z,peak

1 res i,z+ ≥

+∑

( )z,ires = reserve requirement for i in z (%)

( )peak,zD = peak demand in z (MW) ie: The total capacity of all the plants in node z, derated by their reserve margins, plus the unmet MW in node z must always exceed or be equal to the peak demand in node z.

∑ ≥i

)peak,z(D)z(A)z,i(PGinit

( )zA = Autonomy factor for z (%) ie: The total capacity at all plants, in node z, must be greater than or equal to the peak demand in node z times the autonomy factor of node z. MODEL Stage 2: Short-Run, Power and Reserves Traded In model 2 the objective is still to minimize the total costs that arise from the cost of operations (fuel and maintenance), distributed generation costs, and the cost of unserved


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MW, except trade in reserve is now allowed. Thus, the only change in the model is in the reserve margin constraint.

( )[ ] [ ]{ } ( )[ ]{ }∑∑ ++zpi

z,zpmaxF)z,i(res1/z,iPGinit + DG(z)

( )peak,zD≥ + ∑zp

)zp,zmax(F

Where Fmax(zp,z) = reserves held by zp for z. ie: Total generating capacity in node z, derated by the appropriate reserve margins plus reserves in other nodes held for node z, plus unmet reserve requirements must be ≥ peak demand plus reserves held by node z for other nodes. MODEL Stage 3: Long-Run Model - With Capacity Expansions Included In the long-run model the objective is to minimize the present value of the total cost of operations (fuel, maintenance), distributed energy (or unmet energy), unmet reserve, and the annualized cost of capacity expansion. There is a time horizon of “y” years and a discount rate in this model.

++

++∑∑∑∑

=y

i z tY

1y )disc1(

)y,z(UMtcosUM)y,t,z(DG)t,z(tcosDG)y,t,z,i(PG)z,i(cmin

∑∑∑∑∑∑∑∑= =τ

τ= =τ

τ +τ

++

τ

z zp

Y

1y

Y

yz i

Y

1y

Y

y )disc1(),zp,zexp(PF))zp,z(tcosexp)(crf(

)disc1()),z,iexp(PG))z,i(tcosexp)(crf(

Where: New variables: PGexp(i,z,y) = MW generation added in y at i in z Pfexp(z,zp,y) = MW transmission between z and zp added in z

New parameters: )z,i(tcosexp = cost/MW of expansion at i in z

disc = discount rate for present value purposes crf = capital recovery factor )zp,z(tcosexp = cost/MW at transmission expansion from z to

zp

This minimization is subject to the following constraints:

• The Model 2 load balance and PF equations with the “y” (yearly) variable added.


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• PG(i,z,t,y) < PGinit(i,z) + ∑τ=1-y PGexp(i,z,τ)

• PF(z,zp,t,y) ∑=τ

τ+≤y

1),zp,zexp(PF)zp,z(PFinit

The power generation at station i, in node z, at hour t, and in year y will be less than or equal to the initial generating capacity at station i in node z plus the sum of all new expansions at the station i for the years up to year y. Power flow from z to zp, in y, must be less than or equal to the initial capacity plus expansion up to year y.

• ∑ ∑∑∑

+≥+++

τ+=τ

zp zpi

y

1 )y,zp,zmax(F)y,peak,z(D)y,z(UM)y,z,zpmax(F)z,i(res1

),z,iexp(PG)z,i(PGinit

ie: The reserve constraint is the same as before, except total generating capacity now includes additions up to and including year y.

• PGinit(i,z) + ∑ PGexp(i,z,τ) > ∑i =τ

y

1

)y,peak,z(D)z(A

ie: The autonomy constraint is the same as before, except total generating capacity now includes additions up to y. Implications of Model Structure on Data:

• The model is a cash flow model; cash outflows entered into the model in the year in which they take place. Capital purchases assumed to be paid for by yearly payments obtained by multiplying the initial cost by a capital recovery factor. (See below)

• No need to collect data on sunk costs (costs of past investments, etc.), only incremental costs.

• Model assumes equipment purchases financed by borrowed money – hence equipment purchase cost shows up as an annualized cost, equal to the capital recovery factor times the Engineering, Procurement, and Construction (EPC) cost, in each year subsequent to the purchase date.

• Plant operating costs (fuel, variable O&M, water costs) should be average incremental costs for each plant, not marginal costs which might be lower due to say, take or pay fuel contracts. Ignore variable heat rates for existing thermal plants – assume heat rate at 100% load.

• Plant equipment costs should be EPC costs, not including financing costs.

• Fixed O&M ($/kW/yr) should be considered only for new plants; they are sunk costs for existing plants (unless plants mothballed).


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• Reserve margins, autonomy factors, discount rate, crf, unserved energy and reserve costs are policy decisions; get them, if you can but don’t spend a lot of time.

• Line losses should be average incremental, not marginal.

• Line capacities should be maximum transfer capability, not maximum capacity.

• Generation capacities should be net effective (dependable) sent out capacity, not nameplate capacity.

• Demands (D(z,t,y)) should include distribution line losses.


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4. CHINA'S ELECTRICITY RESTRUCTURING DEVELOPMENTS Learning from the evaluation of benefits achieved through competition in the generation markets in the Zhejiang Province the State Power Corporation of China has many further market policy decisions to be made in the light of experiences and ambitions for the next ten years. During 2002 to 2010 there are many significant power market decisions and policy changes made for the Chinese power infrastructure. The size of the power markets in China is a major issue to be considered. The USA has the same land area as China and it is divided into ten main reliability or marketing regions: ECAR East Central Area Reliability Coordination Agreement. ERCOT Electric Reliability Council of Texas. FRCC Florida Reliability Coordinating Council. MAAC Mid-Atlantic Area Council. MAIN Mid-America Interconnected Network. MAPP Mid-Continent Power Pool. NPCC Northeast Power Coordinating Council. SERC Southeastern Electric Reliability Council. SPP Southwest Power Pool. WSCC Western Systems Coordination Council. There are potentially many parallel lessons for China to learn from the USA electricity structures and unbundling process. In 1968 the North American Electric Reliability Council (NERC) was formed. It has operated as a "voluntary organization - one dependent n reciprocity, peer pressure, and the mutual self - interest of all those involved" [2]. Is China to establish it's NERC along parallel lines to those of the USA? Is China's equivalent of the USA's Federal Energy Regulatory Commission (FERC) also to determine the future of China's regional networks? These over-riding national issues will have profound consequences on the development of regional power markets. In whatever direction and shape and size the Chinese equivalents of NERC and FERC take it has become a matter of principle in both North American, Europe and other deregulating environments that the principle of independence should stand. The transmission network in a power market should be totally independent for fair trading to take place. The expansion of China's Western Region is similar to the vast expanse of America's WSCC. The smaller MAAC of the USA might be compared with China's Guangdong Provincial Grid (GDPG). Certainly these comparisons are good in terms of geography but differences will soon emerge as we consider the trading patterns and governance of these different regions. Within China the 15 provincial and inter-provincial power networks (Figure 3) are the closest comparison to NERC's ten regional councils. The history of how each power


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network was formed in China is as fascinating as how each NERC region came to be formed in the USA. Ownership of China's generation, transmission, and distribution sectors into different private companies will promote power sector peer pressure such that improvement in sectoral efficiency is predicted. Until China's present generation sector has been dominated by coal generating stations. New thermal stations increasingly will consider the use of natural gas and the new larger hydropower stations will also reduce the dependency on coal. Environmental problems continue to challenge the Chinese power sector especially in the more industrialized eastern provinces.

Table 2. Performance Characteristics of Power Networks

Available Output Coal Consumption (gce/kWh)

Plant Yearly

Power Networks by the end of 1995 Use Utilization (MW) gross net (%) Hours

NCPN North China Network 24,242 360 393 8.36 5428NEPN Northeast China Network 18,479 352 384 7.46 4980ECPN East China Network 22,627 351 377 6.68 5519CCPN Central China Network 19,558 369 403 5.84 5398NWPN Northwest China Network 11,937 374 407 6.14 5323SDPG Shandong Prov. Grid 10,076 359 386 6.88 6457FJPG Fujian Prov. Grid 2,633 419 465 2.96 5359GXPG Guangdxi Prov. Grid 2,183 440 395 3.87 4806SCPG Sichuan Prov. Grid 6,446 379 421 6.66 5136GZPG Guizhou Prov. Grid 3,320 440 439 5.80 5760YNPG Yunnan Prov. Grid 3,681 420 468 3.76 4499Nation's Total/Average 125,621 362 394 6.72 5401

Total/Average Check 125,182 388 413 5.86 5333

Source: [3]

All existing thermal and hydropower generation stations are listed in Appendix I and Appendix II. Table 2 shows the amount of coal used at stations in 1997. A comparison for each region shows heavy usage right across the country and so the transport costs of shipping coal long distances is added to electricity prices. The location of new stations should be optimally located such that while the supply point can be as near as possible to the demand node the shipping distances of coal needs to be kept to a minimum. Freedom of flexibility in electricity trading in a select few of the Chinese power networks is proposed for the first phase of power pool modeling between Purdue and the Chinese Energy Research Institute (CERI). The CERI will identify the networks that should be considered first of all in the marketing and restructuring analysis.


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Figure China’s Provincial & Interprovincial Power Networks , 2001

HNPG

SDPN

NEPN

ECPN

NWPN

NCPN

SCPG CCPN

FJPGGDPG

XJAR 17

24

25 26

27

28

29

16

18

13

15

11

10

14

12

1920

21

22

23

2

8

317

30

GZPG

GXPGYNPG

CCPN Central China Power Network ECPN East China Power Network FJPG Fujian Provincial Grid GDPG Guangdong Provincial Grid GXPG Guangxi Provincial Grid GZPG Guizhou Provincial Grid HNPG Hainan Provincial Grid NCPN North China Power Network SDPG Shandong Provincial Grid NEPN Northeast Power Network XJAR Xinjiang Autonomous Region NWPN Northwest Power Network XZAR Xizang Autonomous Region SCPG Sichuan Provincial Grid YNPG Yunnan Provincial Grid Source: [1]

9

6 4

5

3

1


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5. SUFG’S APPROACH TO ELECTRICITY SYSTEM PLANNING

Purdue University’s State Utility Forecasting Group (SUFG) integrated electricity modeling system projects electricity demand, supply, and price for each electric utility in the state of Indiana, U.S. The modeling system captures the dynamic interactions between customer demand, the utility's operating and investment decisions, and customer rates by cycling through the various bus-models until an equilibrium is attained. The SUFG modeling system is unique among utility forecasting and planning models because of its comprehensive and integrated characteristics (Figure 4).

Publications and information from SUFG can be obtained by fax at 765/494-2351 or at the website: http://IIES.www.ecn.purdue.edu/IIES/SUFG/. Some titles are listed in the references [7-14].

Figure 4. Cost-Price-Demand Feedback Loop.

Cost

Demand

Price

InitialPrices

CustomerEnergy

andDemand

UtilitySupply

UtilityFinance

andRates

EquilibriumPrices

Demand Sub-model The demand sub-model projects the hourly demand for each customer class for each year of the forecast period. The system demand, including peak load, is obtained by simply aggregating hourly demands across all customer sectors. The hourly load impacts of individual DSM programs are aggregated with the corresponding sector’s hourly demand profile. System load impacts of DSM programs are obtained by aggregating the “adjusted” hourly demand profiles of each customer class.


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Major inputs to the demand sub-model include annual energy projections by customer class (developed by the energy sub-model), load profiles for each customer class, and hourly DSM program impacts. Supply Sub-model

The supply sub-model simulates the operation of the generation system: it commits and dispatches generating resources hour-by-hour in a way that minimizes the daily operating costs of meeting the projected system demand. By preserving the chronological order of system demand throughout the simulation, this sub-model captures the time dependencies between customer demand, system operations, and customer rates, i.e., hourly marginal cost detail. Major inputs to the supply sub-model include the system demand profile (developed by the demand sub-model), generating unit characteristics, investment in new plants, and operation and maintenance (O & M) costs.

Generating additions are determined from a statewide as well as individual utility perspective. The modeling system matches supply to demand (peak load plus reserves) on a statewide level by adding capacity whenever demand exceeds supply. These capacity additions are then allocated to individual utilities based on their individual need for generating capacity. Although this approach provides a reasonable basis for estimating future electricity prices for planning purposes, it does not ensure that the resource plans are least cost. Rates Sub-model and Price Iteration The rates sub-model simulates the average cost-of-service for each customer sector. Cost allocation factors, based on hourly customer demands that include the projected load impacts of DSM programs (from the demand sub-model), are used to apportion revenue requirements in each functional category, including DSM program costs, to each customer class.

The energy modeling system cycles through the five integrated sub-models just described: energy, demand, supply, finance, and rates. During each cycle, price changes in the model cause customers to adjust their consumption of electricity, which in turn affects system demand, which in turn affects the utility’s operating and investment decisions. These changes in demand and supply bring forth yet another change in price and the cycle is complete. After each cycle, the modeling system compares the “after” electricity prices from the rates sub-model to the “before” prices input to the energy consumption models. If these prices match, they are termed equilibrium prices in the sense that they balance demand and supply, and the iteration ends. Otherwise, the modeling system continues to cycle through the sub-models until an equilibrium is attained.

The iterative process just described, known as the cost-price-demand feedback loop, is often referred to as “closing the loop” and is illustrated in Figure 10. Many analysts


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believe the major reason the utility industry grossly over forecast electricity demand during the decade following the first Arab oil embargo was because it ignored the price consequences of its investment decisions on customer demand, i.e., it failed to close the loop.

Uncertainty As indicated above, SUFG’s electricity projections are conditional on assumptions, or exogenous variables, such as economic growth, construction costs, and fossil fuel prices. These assumptions are a principal source of uncertainty in any energy forecast. Another major source of load uncertainty is the statistical error inherent in the structure of any forecasting model. To provide an indication of the importance of these sources of uncertainty, scenario-based projections were developed by operating the modeling system under varying sets of assumptions. These low probability, low and high scenarios capture much of the uncertainty associated with economic growth, fossil fuel prices, and statistical error in the model structure.


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6. WORK PLAN AND PERSONNEL

The proposed China electricity marketing analysis project is expected to take three to four years with the first six months determining the specific detailed objectives of the work as well as commencing a substantial up-to-date China data sets of existing and proposed projects (generation and transmission). The second phase of the project will see the construction of the detailed new China models and Phase three will concentrate on policy analysis and model refinement.

Phase 1 (6 months) - Determination of project objectives and start data collection. Phase 2 (18 months) - Detailed China models developed for specified regions. Phase 3 (2 years) - Policy analysis and model refinement. During Phase 1 the team from Purdue will visit the CERI and visa versa at the start of the Phase 1 period. A return visit of the Purdue team will present the Phase 1 report and clarify the work of the main Phases 2 and 3. The size of the Purdue China modeling team is expected to be three to five people working on the project on a 25% to 50% time basis. A similar sized team is expected for participation from the CERI. If more rapid results are essential then a larger team may be necessary.


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REFERENCES [a1] N.Berrah, R. Lamech, J.Zhao, "Fostering Competitin in China's Power Markets",

ESMAP World Bank Discussion Paper No 416, 2001 [a2]. [http:www.nerc.com/about/] [a3] Electric Power Industry in China 1997, Table 3 (page 41) EIA - http://www.eia.doe.gov/emeu/international/chinadata.htm) [a4] M.Kuby, S. Qingqi, T. Watanatada, "Planning China's Coal and Electricity Delivery System", INTERFACES 25, pp. 41-68, January-February 1995.

[a5] Ming Yang, Xin Yu, "China's Power Management", ENERGY POLICY, Volume 24, Number 8, pp. 735-757, 1996

[a6] DOE Energy Information Administration, CHINA Country Analysis Brief, April 2001, http://www/eia.gov/emeu/cabs/china/htm [a7] Zuwei Yu, F.T.Sparrow, Brian H. Bowen, “A New Long Term Hydro Production Scheduling Method

for Maximizing the Profit of Hydroelectric Systems”, IEEE TRANSACTIONS on Power Systems, Volume 13, Number 1, February 1998.

[a8] Brian H. Bowen, F.T. Sparrow, Zuwei Yu, “Modeling Electricity Trade for the Twelve Nations of the Southern African Power Pool (SAPP)”, UTILITIES POLICY 8 (1999), pp 183-197.

[a9] F.T.Sparrow, Brian H. Bowen, Zuwei Yu, “Modeling Long-Term Capacity Expansion Options for the Southern African Power Pool (SAPP)”, Proceedings of the IASTED International Conference, Power and Energy Systems, Las Vegas, Nevada, November 8-10, 1999.

[a10] F.T. Sparrow, Brian H. Bowen, "User Manual for the Long-Term Model, Purdue University, Seventh Edition, November 2000. http://IIES.www.ecn.purdue.edu/IIES/PPDG/SAPP/user-manual.whtml [a11] Zuwei Yu, F.T. Sparrow, Brian H. Bowen, “Developing the Southern African Power Pool –

Its Regional Electricity Trade Undergoes Modeling,” TRANSMISSION & DISTRIBUTION (accepted for publication August 2000).

[a12] F.T. Sparrow, Brian H. Bowen, “The Southern African Power Pool (SAPP) Long-Term Pool Plan and Modeling Improvements,” Johannesburg, South Africa, February 23-25, 2000.

[a13] P. Robinson, F.T. Sparrow, “Wheeling Charges and Loose Power Pools: North American Experience and its Relevance for the Southern African Power Pool,” Sixth Joint Plenary Session of SAPP Sub-Committee Meetings, Harare, Zimbabwe, May13 1997.

[a14] SUFG 90-2, F.T. Sparrow, P. Schmidt, R. McCallister, and W. Lenagh, “Targeting Electric Utility DSM Energy Conservation Programs in the Industrial Sector,” Presented at the Innovations in Pricing and Planning Conference, May 1990.


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Appendix I Thermal Power Plants in China

(Source: EIA - http://www.eia.doe.gov/emeu/international/chinadata.htm)

NO. Name of Location Plant Capacity Unit Capacity Steam Condition Fuel Power Plant (Province,

Municipality Design Existing and number Pressure Temperature

or Autonomous Region) (MW x Nos) (MPa) (Degrees C) Thermal Power Plants in Operation

1 Shijingshan Beijing 1166 1166 200 x 4 12.75 535/535 coal (cogeneration) 200 x 1 12.75 535/535

2 Dagang Tianjin 1280 1280 320 x 4 16.60 538/538 coal, oil 3 Junliangcheng Tianjin 1000 1000 200 x 4 12.75 535/535 coal 4 Jixian (Panshan) Tianjin 1000 1000 500 x 2 23.54 540/540 coal 5 Douhe Hebei 1550 1550 125 x 2 12.75 538/538 coal

250 x 2 16.07 538/538 200 x 4 12.75 535/535

6 Xingtai Hebei 1290 1290 200 x 6 12.75 535/535 coal 7 Shalingzi Hebei 1200 1200 300 x 4 16.67 537/537 coal 8 Qinhuangdao Hebei 1000 1000 200 x 2 12.75 535/535 coal 9 Datong No. 2 Shanxi 1200 1200 200 x 6 12.75 535/535 coal

10 Shentou Shanxi 1300 1300 200 x 2 12.75 540/540 coal 200 x 4 16.20 530/530

11 Shentou No. 2 Shanxi 1000 1000 500 x 4 16.60 535/535 coal 12 Zhangze Shanxi 1040 1040 100 x 2 8.83 535 coal

210 x 4 12.75 535/535 13 Fengzhen Inner Mongolia 1200 1200 200 x 6 12.75 535/535 coal

8.83 12.75

14 Qinghe Liaoning 1300 1300 100 x 5 12.75 535 coal, oil 200 x 4 12.75 535/535

15 Jinzhou Liaoning 1200 1200 200 x 6 16.67 coal 16 Liaoning Liaoning 1050 1050 200 x 2 12.75 535/535 coal 17 Tieling Liaoning 1200 1200 300 x 4 16.67 537/537 coal 18 Fularji No. 2 Heilongjiang 1200 1200 200 x 6 12.75 535/535 coal

200 x 1 535/535 19 Shidongkou Shanghai 1280 1200 300 x 4 16.20 535/535 coal 20 Shidongkou No. 2 Shanghai 1200 1200 600 x 2 24.13 538/538 coal 21 Wujing Shanghai 1000 1000 100 x 1 8.83 535 coal

125 x 1 12.75 535/535 300 x 2 16.67 537/537

22 Wangting Jiangsu 1100 1100 300 x 2 16.20 550/550 coal, oil 300 x 1 16.20 535/535

23 Jianbi Jiangsu 1625 1625 100 x 3 8.83 535 coal 300 x 4 16.20 550/550


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24 Xuzhou Jiangsu 1300 1300 125 x 4 12.75 550/550 coal 200 x 4 12.75 535/535

25 Changshu Jiangsu 1200 1200 300 x 4 16.67 537/537 coal 26 Zhenhai Zhejiang 1050 1050 125 x 2 12.75 550/550 coal

200 x 4 12.75 537/537 27 Pingwei Anhui 1200 1200 600 x 2 16.67 537/537 coal 28 Huaneng Dezhou Shandong 1200 1200 300 x 4 16.67 537/537 coal 29 Longkou Shandong 1000 1000 100 x 2 8.83 535 coal

200 x 4 12.75 535/535 30 Qinling Shaanxi 1050 1050 125 x 2 12.75 550/550 coal

200 x 4 12.75 535/535 31 Weihe Shaanxi 1300 1300 300 x 4 16.67 537/537 coal 32 Yaomeng Henan 1200 1200 300 x 2 16.20 550/550 coal

300 x 2 17.75 540/540 coal 33 Jiaozuo Henan 1224 1224 200 x 6 12.75 535/535 coal 34 Shajiao A Guangdong 1200 1200 200 x 3 12.75 535/535 coal

300 x 2 16.67 537/537 35 Huangpu Guangdong 1100 1100 125 x 4 12.75 550/550 coal, oil

300 x 2 16.20 535/535 36 Shajiao C Guangdong 1980 1980 660 x 3 16.80 540/540 coal

Nuclear Power Plant in Operation 37 Daya Bay Guangdong 1800 1800 900 x 2 PWR nuclear

Thermal Power Plants under Construction 38 Shang'an Hebei 1300 700 350 x 2 16.60 538/538 coal

* 300 x 2 16.67 537/537 39 Xibaipo Hebei 1200 600 300 x 2 16.67 537/537 coal

* 300 x 2 16.67 537/537 40 Hanfeng Hebei 1320 * 600 x 2 16.80 540/540 coal 41 Yangquan No. 2 Shanxi 1200 * 300 x 4 16.67 537/537 coal 42 Taiyuan No. 1 Shanxi 1312 712 300 x 2 16.67 537/537 coal

* 300 x 2 16.67 537/537 43 Yangcheng Shanxi 2100 * 350 x 6 16.60 538/538 coal


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Appendix II Large Hydropower Stations (1000 MW and above) in China

(Source: EIA - http://www.eia.doe.gov/emeu/international/chinadata.htm) NO. Name River Province or Year of Active Dam Dam Design Average Installed Annual

Autonomous Commissioning Storage Type Height Head Discharge Capacity Output Region (Gm^3) (m) (m) (m^3/s) (MW x No. of

Units) (TWh)

1 Baishan Songhuajiang Jilin 1983-1991 3.54 AG 149.5 110 239 300 x 5 2.04 2 Shuikou Minjiang Fujian 1993-1996 0.7 G 101 43.5 1728 200 x 7 4.95 3 Wuqiangxi Yuanshui Hunan 1994-1996 2.02 G 87.5 42 2050 240 x 5 5.37 4 Gezhouba Changjiang Hubei 1981-1989 BA 47 18.61 4300 170 x 2 14.10 125 x 19

5 Geheyan Qingjiang Hubei 1993-1994 2.2 AG 151 99 390 300 x 4 3.04 6 Guangzhou Pumped Tributary of Guangdong 1993-1994 0.01/0.01 R 68/34 523/542 222/273 300 x 4 2.38/3.14

Storage Liuxi River (U.D.) (U/D) (G/P) (G/P) (G/P)7 Guangzhou Pumped Tributary of Guangdong U.C. * 300 x 4 Storage Phase II Liuxi River

8 Yantan Hongshuihe Guangxi 1992-1995 1.53 G 111 55.5 1760 302.5 x 4 5.37 9 Ertan Yalongjiang Sichuan U.C. 3.37 A 240 155 1670 * 550 x 6 17.0

10 Tianshengqiao II Nanpanjiang Guizhou 1992 0.018 G 58.7 176 615 220 x 2 4.92/8.20 Guangxi U.C. * 220 x 4

11 Tianshengqiao I Nanpanjiang Guizhou U.C. 6.701 R 180 110 612 * 300 x 4 5.38 Guangxi

12 Manwan Lancangjiang Yunnan 1993-1995 0.26 G 128 83.5 1200 250 x 5 5.48 13 Liujiaxia Huanghe Gansu 1969-1974 4.15 G 147 100 877 225 x 4 5.58

260 x 1 14 Longyangxia Huanghe Qinghai 1987-1989 19.4 AG 178 120 640 320 x 4 5.98 15 Lijiaxia Huanghe Qinghai U.C. G 175 120 662 * 400 x 5 5.90 16 Xiaolangdi Huanghe Henan U.C. 5.1 R 154 112 312 * 300 x 6 5.10 17 Tianhuangping Zhejiang U.C. * 300 x 6

Pumped Storage 18 Gongzui Daduhe Sichuan 0.096 G 85.5 48 1530 110 x 7 4.12

0.063 G 80 31 1500 150 x 4 3.21 19 Three Gorges Changjiang Hubei U.C. 165 G 175 80.6 14300 * 700 x 26 84.0

Project Notes: U.C. - Under Construction; U/D - Upper Reservoir/Downstream Reservoir; G/P - Generation/Pump. Dam Type:

G - Gravity Dam; E - Earth Dam; A - Arch Dam; B - Buttress Dam; AG - Arch-Gravity Dam; R - Rockvill Dam, * - under construction Ref [3]


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draft - purdue...

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