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Description
We derive the optimal prices and investment program for an electric power system when there are price-insensitive retail consumers served by load serving entities that can choose any level of rationing contingent on real-time prices. We then examine the assumptions required for competitive electricity markets to achieve this optimal price and investment program and the implications of relaxing several of these assumptions. We analyze the interrelationships between regulator-imposed wholesale market price caps and generating capacity obligations. The implications of potential network collapses for operating reserve requirements and whether market prices yield generation investments consistent with these reserve requirements are examined.
1. Introduction
* Despite all of the talk about "deregulation" of the electricity sector, there continues to be a large number of non-market mechanisms that have been imposed on the emerging competitive wholesale and retail electricity markets. These mechanisms include wholesale market price caps on energy supplies, generating capacity contracting obligations placed on electricity distribution companies and other load serving entities (LSEs), (1) and system operating reserve requirements.
In some cases the non-market mechanisms are argued to be justified by imperfections in the retail or wholesale markets: in particular, problems caused by the inability of most retail customers to see and react to real time prices with legacy meters, by non-price rationing of demand, by wholesale market power problems and by imperfections in mechanisms adopted to mitigate these market power problems.
Other mechanisms and requirements have been justified by what are perceived to be special physical characteristics of electricity and electric power networks, which in turn lead to market failures that are unique to electricity. These include the need to meet specific physical criteria governing network frequency; voltage and stability that are thought to have public-good attributes; the rapid speed with which responses to unanticipated failures of generating and transmission equipment must be accomplished to continue to meet these physical network requirements; and the possibility that market mechanisms cannot respond fast enough to achieve the network's physical operating parameters under all states of nature.
Much of the economic analysis of the behavior and performance of wholesale and retail markets has either ignored these non-market mechanisms or failed to consider them in a comprehensive fashion. There continues to be a lack of adequate communication and understanding between economists focused on the design and evaluation of alternative market mechanisms and network engineers focused on the physical complexities of electric power networks and the constraints that these physical requirements may place on market mechanisms. The purpose of this article and of Joskow and Tirole (2006a,b) is to start to bridge this gap.
The institutional environment in which our analysis proceeds has competing LSEs that market electricity to residential, commercial, and industrial ("retail") consumers. Some retail consumers served by competing LSEs see and can respond to real-time wholesale energy prices, while others are on traditional meters, which record only their total consumption over some period of time (for instance, a quarter), and therefore do not react to the real-time price. (2) Retail consumers may be subject to non-price rationing to balance supply and demand in real time. The wholesale market is composed of generators who compete to sell power to LSEs. LSEs in turn compete to resell this power to retail consumers using the transmission and distribution delivery infrastructure. The prices for these delivery services are regulated and have been unbundled from power supply services. In what follows we normalize the prices for delivery services to zero. The wholesale market may be perfectly competitive or characterized by market power. Finally, there is an independent system operator (ISO) that is responsible for operating the transmission network in real time to support the wholesale and retail markets for power, including meeting certain network reliability and wholesale market power mitigation criteria.
In Section 2 we first derive the optimal prices and investment program for an electric power system when there is state-contingent demand, at least some consumers do not react to real-time prices, but their LSE can choose any level of rationing it prefers contingent on real-time prices. The latter assumption is important. It implies that even if retail consumers do not have real-time meters and cannot see or react directly to the real-time price, LSEs, which do see real-time prices, can enter into price-contingent priority rationing contracts (e.g., interruptible contracts) with the retail consumers with whom they have power supply contracts (as in Chao and Wilson, 1987). Later, we consider the effect of relaxing the assumption that individual retail consumers' consumption can be physically controlled by the system operator. We discuss the implications of zonal rationing in the presence of retail competition in Proposition 3 below and in Joskow and Tirole (2006a). In this model, consumers are identical, possibly up to a proportionality factor, and therefore all have the same load profile. While the latter significantly constrains the nature of consumer heterogeneity considered, it is consistent with the existing literature (e.g., Borenstein and Holland, 2005). (3) We then derive the competitive equilibrium under these assumptions when there are competing LSEs that can offer two-part tariffs. This leads to a proposition that extends the standard welfare theorem to price-insensitive consumers and rationing; this proposition serves as an important benchmark for evaluating a number of non-market obligations and regulatory mechanisms:
The second-best optimum (given the presence of price-insensitive consumers) can be implemented by an equilibrium with retail and generation (wholesale) competition provided that:
(i) The real-time wholesale price accurately reflects the social opportunity cost of generation.
(ii) Rationing, if any, is orderly, and makes efficient use of available generation.
(iii) LSEs face the real-time wholesale price for the aggregate consumption of the retail customers for whom they are responsible.
(iv) Consumers who can react fully to the real-time price are not rationed. Furthermore, the LSEs serving consumers who cannot fully react to the real time price can demand any level of rationing they prefer contingent on the real-time price. That is, LSEs can enter into price contingent rationing contracts with their retail customers (alternatively we will allow the system operator to interrupt consumers to the best of their (ex ante) interests).
(v) Consumers have the same load profile (4) (they are identical up to a scale factor).
The assumptions underlying this benchmark proposition are obviously very strong: (a) market power on the one hand, and regulator-imposed price caps and other policy interventions on the other hand create differences between the real-time wholesale market price and the social opportunity cost of generation; (b) network collapses, unlike, say, rolling blackouts, have systemic consequences, in that some available generation cannot be used to satisfy load; (c) LSEs do not face the real-time price for their customers if these customers are load profiled; (5) (d) price-sensitive consumers may be rationed along with everyone else who is physically connected to the same controllable distribution circuit; and, relatedly, LSEs generally cannot demand any level of rationing they desire; (e) consumer heterogeneity is more complex than a scaling factor. We examine the implications of relaxing assumptions (a) and (b), while Joskow and Tirole (2006a) focus on retail competition and investigate the failure of assumptions (c), (d), and (e).
Section 3 studies the implications of distorted wholesale prices. We first consider the case where there is a competitive supply of baseload generation; market power in the supply of investment in and production from peaking capacity that runs during peak-demand periods, and a price cap is applied that constrains the wholesale market price of energy to be lower than the competitive price during peak periods. This creates a shortage of peaking capacity in the long run. We show that placing generating-capacity obligations on LSEs, combined with the associated capacity prices paid to generators have the potential to restore investment incentives by compensating generators ex ante for the earnings shortfall they will incur due to the price cap on energy produced from this capacity for the wholesale market. Indeed, with up to two states of nature with market power, the Ramsey optimum can be achieved despite the presence of market power through a combination of a price cap on energy and capacity obligations and associated capacity prices provided that (i) both peak and baseload generating capacity are eligible to meet LSE capacity obligations and receive the associated capacity price and (ii) the demand of all consumers, including price-sensitive consumers, counts for determining capacity obligations and the capacity prices are reflected in the prices paid by all retail consumers. With more than two states of nature with market power, a combination of spot wholesale market price caps and capacity obligations will not achieve the Ramsey optimum. Thus, the regulator faces a trade-off between alleviating market power off-peak (if it is a problem), through a strict price cap, and providing the proper investment incentives to meet peak demand efficiently. He is further unable to provide price-sensitive consumers with the appropriate economic signals. The intuition for this result is that, when more than two prices are distorted by market power, the optimality of a competitive equilibrium cannot be restored with only two instruments--a price cap on energy and a capacity price.
In Section 4 we derive the implications of network collapses and the concomitant need for network support services typically provided by generating plants that the system operator schedules as "operating reserves." Network collapses differ in a fundamental way from other forms of energy shortages and rationing. While scarcity makes available generation (extremely) valuable under orderly rationing, it makes it valueless when the network collapses. An analogy may help understand the distinction between orderly rationing and a collapse: when a mattress manufacturer fails, buyers of mattresses may experience delays; competitors, however, do not suffer and may even gain from the failure. By contrast, a farmer whose cows have contracted mad-cow disease may spoil the entire market for beef. Hence, system collapses, unlike, say, controlled rolling blackouts that shed load to match demand with available capacity, create a rationale for network support services with public-goods characteristics. We derive the optimal level for these network support services and discuss the implementation of the Ramsey allocation through a combination of operating reserve obligations and market mechanisms.
2. A benchmark decentralization result with price-sensitive and price-insensitive consumers (6)
* Model. There is a continuum of states of nature or periods i [member of] [0, l]. The frequency of state i is denoted [f.sub.i] (and so [[integral].sup.1.sub.0] [f.sub.i]di = 1). Let E[*] denote the expectation operator with respect to the density [f.sub.i]. (7) We assume that the (unrationed) demand functions of price-insensitive and price-sensitive consumers, Di and Di, are increasing in i. (8)
Price-insensitive consumers. Price-insensitive consumers are on traditional meters that record only their aggregate consumption over all states of nature, and therefore they do not react to the real-time price (RTP). (9) Consumers are homogeneous, up to possibly a scaling factor, i.e., they have the same load profile. (10) Without loss of generality, they are offered a two-part tariff, with a fixed fee A and a marginal price p. Their demand function in the absence of rationing is denoted [D.sub.i](p), with [D.sub.i] increasing in i. We let [[alpha].sub.i] [less than or equal to] 1 denote the fraction of their demand satisfied in state i. As [[alpha].sub.i] decreases, the fraction of load interrupted (1 - [[alpha].sub.i]) increases. The alphas may be exogenous, as, for example, when the system operator implements rolling blackouts. Alternatively, one could envision situations in which the LSEs would affect the alphas either by demanding that their consumers not be served as the wholesale price reaches a certain level or, conversely, by bidding for priority in situations of rationing; (11) a case in point is that of consumers with interrruptible contracts. We let [D.sub.i](p, [[alpha].sub.i]) denote their expected consumption in that state and [S.sub.i](p, [[alpha].sub.i]) their realized gross surplus, with
[D.sub.i](p, 1) = [D.sub.i](p) and [S.sub.i](p, 1) = [S.sub.i]([D.sub.i](p)),
where [S.sub.i] is the standard gross surplus function (with [S'.sub.i] = p). We assume that [S.sub.i] is concave in [[alpha].sub.i] on [0, 1]: more severe rationing involves higher relative deadweight losses.
In the separable case, the demand [D.sub.i] takes the multiplicative form [[alpha].sub.i] [D.sub.i](p) and the surplus takes the separable form [S.sub.i] ([D.sub.i] (p), [[alpha].sub.i]). (12) More generally, however, the consumer may adjust her demand to the prospect of being potentially rationed. (13)
We will also assume that lost... |
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