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Generation and transmission investments in restructured electricity markets

Generation and Transmission Investments in Restructured Electricity Markets

Rajat K. Deb, Pushkar Wagle, and Rafael Emmanuel A. Macatangay §

Abstract

            The efficient expansion of generation and transmission facilities is one of the biggest challenges in restructured electricity markets. Competition has become the mechanism for encouraging generator entry and transmission investment. Market decisions on generation and transmission investments are separately made but affect each other intimately. The impact of generator entry, depending on size, location, and timing, on a power system is different to that of transmission investments. Indeed generator entry outside a region could simultaneously increase congestion on an interface connecting to that region and reduce congestion costs in the entire region. Market players need to understand and model the complex interaction between generation and transmission investment. As a demonstration of the complexity of the modeling challenges, UPLAN, a proprietary engineering economy model of the North American power system, is used to forecast unserved energy and congestion costs in the WSCC for 2005. UPLAN rigorously accounts for merchant plant and transmission line additions as well as for the fundamental drivers of WSCC power markets.

Outline

  1. Generation, transmission, and system reliability in restructured electricity markets
  2. Market signals for investments in generation and transmission
  3. Analyzing and modeling investments in generation and transmission
  4. The impact of generation and transmission investments on unserved energy and congestion costs

1.          Generation, transmission, and system reliability in restructured electricity markets

The nature of the interaction between generation and transmission has been considerably altered under restructuring. In the days of the vertically integrated utility, a single firm made planning decisions on both generation and transmission and, as a consequence, internalized most of the externalities associated with them. The traditional planning process for generation was focused on a particular index of reliability, usually the loss-of-load probability (LOLP), in all planning years and for a given minimum revenue requirement. The logic was quite straightforward: transmission expansion improves reliability and reduces both LOLP as well as the need for generation investment. The reduction in LOLP depends on the size of the new line as well as on the correlation of the loads in either end. The optimal amount of generation investment, therefore, can be calculated, given alternative line investments and with the use of LOLP as a criterion for generation expansion.

Thus, in the traditional planning world, generation and transmission were substitutes, and the substitution decision was made by the vertically integrated utility. Generation and transmission expansions were designed to minimize the total discounted cost of delivering uninterrupted power to the utility’s customers. At each point in the grid, in every hour, and for every reasonable contingency, generating capacity has to be sufficient to meet loads. When the grid is at capacity, however, some loads can be served only by increasing the output of local generation. The planning process aims to compare, given an LOLP target, the cost of delivering new generation to the cost of reinforcing the grid.

A number of models were routinely used to perform reliability analysis. The PICES model, developed at the Oakridge National Laboratory, uses equipment failure rates and specified reserve levels in order to calculate an optimal generation expansion plan. If the LOLP target is not reached, then the reserve-margin criterion may be raised. EGEAS, developed by the Electric Power Research Institute (EPRI), and WASP, developed by General Electric (GE), enables a utility to develop an optimal generation expansion plan with a suitable reserve margin in place (such that LOLP targets are met in each year of the planning period). The “Over/Under” Capacity Model, developed by EPRI in the early 1980s, evaluates the trade-off between reserve capacity and reliability.

            Today, by contrast, the restructuring of the electric power industry has caused the unbundling of generation, transmission, distribution, and supply (or retail sales). Markets govern the operation and expansion of generation and transmission, which have ceased to be perfect substitutes in a world of competition. Dispersed players separately make decisions that collectively affect system adequacy. Potential generation investors are interested in a location’s energy prices that, in future, are influenced by changes in the transmission system. The profit opportunities arising from transmission reinforcements are affected significantly by the location of generator entry. New generation may decrease congestion in certain parts of the system but may increase it in others, and new transmission may decrease the use of already under-utilized lines. Thus, generation and transmission investments alter each other’s revenue expectations.

Most strikingly, LOLP has become a rather limited measure of reliability. It accounts only for generator outages, assumes no uncertainty in demand, and is, by nature, long-term. Under a restructured environment, however, merchant plant additions are driven by commercial considerations, and capacity additions are not dictated solely by reliability measures. In short, in the restructured power industry, markets, not mavens, rule. Clearly a modern approach to reliability analysis has to be based firmly not only on the physics of electric power systems but also on the economics of restructured energy markets.

2.         Market signals for investments in generation and transmission

Under a restructured regime, the appropriate size, timing, and location of generation and transmission investments typically depend on increasingly complex market interactions. For example, reserves in one area can be used to serve another. As a consequence, energy prices could be lower than otherwise, and investments in either generation or transmission could be postponed. The impact of generator entry, depending on size, location, and timing, on a power system is different to that of transmission investment.

The market signals for generation and transmission investment are diverse and continue to evolve. A plant could make money from day-ahead and hour-ahead markets for energy and ancillary services. Price duration and fluctuation have important profit implications (see Figure 1). Maximum profits are achieved from an optimal allocation of a plant’s output to the various cascading markets for power, as well as from correctly bidding a plant’s opportunity cost (the greater of marginal production cost and the expected prices in the different market). In some cases, depending on the market rules for reserve capacity payments, a plant might earn more from ancillary services than from energy. One of the most crucial market signals is in relation to reliability and reserve sharing. Different regions in North America have taken innovative steps. For example, in the restructured electricity systems of the Northeast (New York, PJM, Ontario, and New England), a market has been created specifically for long-term capacity. Load-serving entities can purchase reserves in order to meet their responsibility of carrying monthly reserves. The product in these monthly reserve markets is designed as six-month strips of capacity recently selling for $10/kW to $18/kW.

 

Figure 1. January 2001 duration curves of hourly actual and UPLAN-simulated[1] California PX prices

In addition to long-term capacity and reliability, New York, PJM, New England, and California ISO operate and manage markets for ancillary services, such as regulation, spin, and non-spin reserves, on a day-ahead basis. Sophisticated protocols have been developed to encourage participation as well as to regulate prices. In California, the ISO maintains reliability must-run reserve (RMR) contracts to support reserve margin requirements in generation-deficient but transmission-constrained load-pockets. Texas has created an ancillary services market and is now doing trial runs. In the rest of the US, however, the capacity market for reliability is not fully developed. There is no open market for capacity, although bilateral deals are quite common. Reliability and capacity can also be provided through financial instruments. For example, Enron uses options on capacity and weather as a way of managing the risk of shortfalls in capacity and reliability. Finally, in other markets, reliability is indirectly traded as firm and non-firm contracts.

The emergence of regional transmission organizations (RTOs) considerably increases the need for close coordination between generation and transmission expansions. As described in FERC Order 2000, which is on the functions of an RTO, the reliability issue is no longer a generation issue alone and now includes the impact of both generation and transmission on a wider area than individual utility service territories. The benefits and costs of carrying reserve are distributed across service territories due to network effects. Both federal and state commissions are to develop new protocols for allocating the costs and benefits of reserve margins over dispersed regions. Indeed RTO tariff design affects a merchant generator’s response to load growth in the context of an evolving transmission system. Moreover, periodic shortages in generation typically lead to episodes of market power exercise. The RTO has to contend with the usual results, such as severe and clustered price spikes, inefficiencies in grid operations, and violations of merit-order dispatch.

Finally, transmission expansion is closely related to the growth of loads and resources, but its financing depends on expected congestion and congestion revenues. Transmission expansion, therefore, has to be balanced with generation expansion, given that generator location affects congestion revenues. One of the most important signals for transmission investment is the accumulation of congestion rent, which is a line’s economic value estimated as the difference in energy prices between one end and another. High congestion costs usually signal the need for grid expansion. However, expansions that eliminate those rents would also remove much of the market basis of the expansion in the first place. Eliminating congestion through transmission expansion would require the imposition of usage fees on all generation and load, with no locational price signal. Some grid expansions may remove the need for certain site-specific generation. In the end, the plant fleet could have few and large units (and quite possibly be more efficient than one that has many and small ones).

One key consideration is volatility. The factors influencing the generator entry decision, such as energy and ancillary service prices, load, economic growth, fuel prices, and emission allowance prices, are volatile, and their volatility has to be analyzed rigorously. The factors influencing transmission investments are very similar and thus also volatile. Another key consideration in the decision to invest in transmission lines is the difficulty of acquiring the required planning and related permits. A new plant is to be situated in only one spot, and the markets for obtaining fuel contracts and emission allowances are well developed. By contrast, a new transmission line potentially straddles many areas, and there is usually a huge outcry over the replacement of rolling hills and lush landscapes with monstrous transmission pylons. The direct and indirect costs of compensating stakeholders for the perceived loss of countryside beauty are another form of “market” signal that transmission investors have to receive.

 3.         Analyzing and modeling investments in generation and transmission

            In a restructured electricity industry, the market realities faced by generation and transmission investors are exceedingly complex. Price is determined by a confluence of a large number of different events and factors (see Figure 2). Supply and demand are just one set of factors. The status of deregulation and restructuring, including the design of market rules, is a major determinant of prices. All possible market design loopholes and legal inconsistencies are exploited for profit. The physical features of the grid are also crucial. The status-quo pattern of transmission constraints is usually beneficial to some generation and transmission owners but detrimental to others. Any proposed alteration of the network implies a redistribution of rents, a change in bidding strategies, and, depending on the location of the players, a realignment of alliances. The interaction of the different markets influences capacity deployment. The potential for earning capacity payments in the ancillary service markets is a powerful incentive to withdraw capacity from the energy market, in which payments are purely on energy. Climactic factors are especially important for hydro units. Expectations of drought and unfavorable changes in weather patterns increase the scarcity value of water and worsen any strategic behavior exercised by the hydro unit. Thus, many interacting factors are at work, and any analysis of generation and transmission investment quickly becomes intractable.

 

Figure 2. Price discovery in restructured electricity markets

 

Sophisticated analysis and modeling are evidently needed. The first analytical challenge is an accurate representation of the various markets and the bidding behavior they encourage. Profits are made in energy and ancillary service markets clearing day-ahead, hour-ahead, and in real-time. In maximizing profits, therefore, a plant has to bid its true opportunity cost, which is the greater of its marginal production cost and the expected prices in the different product, temporal, and spatial markets. Another related factor is the expectations of players. Market participants are typically well informed: many of the executives are carry-overs from the days of vertical integration, the technology of power plants is fairly well known, high quality data on the industry are abundantly available, and most importantly, frequent market interaction, on an hourly basis in many markets, facilitates learning. Bidders can be said to have an intimate understanding of the underlying factors driving market outcomes.

A second analytical challenge concerns the locational dimension. The value of energy is different across the power system, and an accurate representation of the physics of power systems is thus quite central. An alternating current (AC) representation of the system is a detailed and accurate picture of the transmission lines, their links to one another, and the flow of power in and around them. For example, an optimal power flow (OPF) is a traditional engineering tool for analyzing the dispatch of plants in order to meet loads across the system. An AC OPF could be used to evaluate two alternative investments, one in generation and another in transmission, and determine how they affect current and future prices across the network and for different products.

A third and final analytical challenge is the inherent uncertainty in complex phenomena. Electricity markets are subject to severe fluctuations in fuel prices (such as oil and natural gas), emission allowance prices (for SOx and NOx), and regulatory interventions. Volatility analysis is crucial to the avoidance of costly investment mistakes. Moreover, investments in generation (and to some extent, transmission) are flexible and, in the event new information is obtained, can be postponed. A real options approach to investment under uncertainty has to be deployed in order to take advantage of the plant’s flexibility and acquisition of fresh commercial information.

In summary, the impact of generation and transmission investments on power prices is not easy to assess, and a large number of factors have to be taken into account. Only specialist models, such as an engineering economy representation of an electric power system, can do a proper job of capturing the key market, physical, regulatory, and climactic drivers underlying market outcomes.

4.         The impact of generation and transmission investments on unserved energy and congestion costs

            The impact of generator entry and transmission expansion on unserved energy and congestion costs in California and the WSCC is forecast using UPLAN, a proprietary engineering economy model (see Figure 3). UPLAN satisfies the steep requirements discussed in the previous section. It has four key components: a market model, an AC OPF, a volatility model, and a merchant plant model. The market model captures the multi-market multi-commodity nature of a restructured power industry. It utilizes opportunity cost bidding and thus captures the optimization behavior of a generator across product, temporal, and locational markets. The AC OPF is a faithful representation of the physical features of the grid. It is accurate for each bus and line in the system. The volatility model allows the calculation of the real option value of the plant for a distribution of market outcomes. It is a powerful approach to the evaluation of risk. Finally, the merchant plant model is a dynamic model of generator entry. Generator investment decisions are endogenously determined by the profit a potential entrant is expected to earn. UPLAN employs a rational expectations approach to the behavior of players: solutions are obtained iteratively, and as a consequence, the information set of each player consists of the underlying determinants of market outcomes. UPLAN, therefore, is a mathematical replica of the US power market. [2]

The analysis of generation and transmission adequacy[3] consists of a three-step simulation of the system: first, a base case, before any generation addition in future years; next, an optimal expansion of generation and transmission; and finally, an analysis of adequacy given the plant additions. The simulation resulted in the addition of about 3,500 MW of capacity at different locations in the WSCC (but all outside California) in 2004 and 2005.

 

  Figure 3. Schematic of UPLAN

Generator entry reduces the level and frequency of unserved energy in the WSCC (see Figure 4). However, there is no significant difference in the flow duration curves[4] for the California-Oregon Interface (COI) and the Path 15 interfaces before and after generation expansion (see Figure 5). All the units added in the simulation are outside California, and thus they do not have a significant impact on the interface between California and Oregon.

Figure 4. UPLAN 2005 forecast of level and frequency of average unserved energy with and without generator entry for selected WSCC zones

 


 Figure 5. Flow Duration Curves With and Without Generation Expansion for WSCC, 2005

The duration and marginal and total annual costs of congestion on selected transmission interfaces in California, given the simulated generation and loads for 2005, are in Table 1. These congestion costs represent the differences, net of the relevant grid-access or wheeling charges, between locational prices on either side of the interface, over the hours during which the interface is constrained. They are, therefore, the marginal value of additional generation. If transmission capacity is added, then the congestion will disappear, and owners of new lines would have to recover their investment through appropriate transmission charges.

Table 1. Transmission Adequacy and Congestion Cost at Selected Interfaces in the WSCC, 2005

(a)    After unit addition

Interfaces

Zones

Average % of Congested Hours

Average number of Congested Hours

Std Dev of Hours Congested

Total Congestion Cost ($)

Std Dev of Total Congestion Cost

Average Congestion Cost ($/KW)

Std Dev of Average Congestion Cost

COI

Oregon

Northern California

2.09

183.18

178.66

91.33

86.87

0.03

0.03

PATH15

Central California

Northern California

7.77

680.55

977.58

571.81

1029.02

0.21

0.38

 (b)   Before unit addition

Interfaces

Average % of Congested Hours

Average number of Congested Hours

Std Dev of Hours Congested

Total Congestion Cost ($)

Std Dev of Total Congestion Cost

Average Congestion Cost ($/KW)

Std Dev of Average Congestion Cost

COI

0.56

48.86

336.79

54.33

481.46

0.01

0.13

PATH15

1.12

97.84

440.03

88.47

514.35

0.03

0.19

The situation for generation investors is quite different. The addition of generating plants increases congestion costs on the COI interface.[5] On average, the COI interface is congested for 183 hours with generation expansion (see Table 1a), but for only 49 hours without (see Table 1b). Generator entrants have shifted the supply curve outwards, and as a result, at the same price, a greater quantity of power can be exported than without generator entry. However, system-wide congestion costs are lower with than without generation expansion (see Figure 6). The additional generation, which in principle is cheaper than existing units, has decreased the price difference between one end of the interface and the other. Thus, generation investments outside California reduce not only the level and frequency of unserved energy in California but also average and total congestion costs in the WSCC.

 

 Figure 6. Duration Curves of Projected Congestion Cost With and Without Generation Expansion in the WSCC, 2005

 In summary, generation and transmission investments have different effects on the system, and the incentives motivating them are also different. In the simulation, generator entry is most profitable outside California, but the reduction in WSCC congestion costs and unserved energy, two region-wide externalities, has not removed the incentive to invest in generation. By contrast, additions to transmission capacity eliminate the congestion rent and, probably with them, the incentive to invest in new lines, unless appropriate transmission charges are designed.

References

Deb, R. K. (1984) “Effective load carrying capability of interties” IEEE/PES Winter Meeting.

Deb, R. K. (2001) “Real option valuation of coal generation and coal R&D – Volume 1: Electric sector analysis” EPRI, Palo Alto, CA and LCG Consulting, Los Altos, CA.

Deb, R. K., R. Albert, L. Hsue, and N. Brown (2000) “How to incorporate volatility and risk in electricity price forecasting” The Electricity Journal Vol. 13 No. 4 (May) pp. 65-75.

Deb, R. K., R. Albert, L. Hsue, and P. Wagle (2001) “Multi-market modeling of regional transmission organization functions” The Electricity Journal (March) pp. 39-54.

Deb, R. K., L. Hsue, A. Ornatsky, and J. Christian (2001) “Surviving and thriving in the RTO revolution” Public Utilities Fortnightly (1st February).

LCG Consulting proprietary reports.

Macatangay, R. E. A. (2001) “Market definition and dominant position abuse under the new electricity trading arrangements in England and Wales” Energy Policy Vol. 29 No. 5 pp. 337-40. 

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