Hourly plant-level wind and solar generation output and real-time price data for one year from the California ISO control area is used to estimate the vector of means and the contemporaneous covariance matrix of hourly output and revenues across all wind and solar locations in the state. Annual hourly output and annual hourly revenues mean/standard deviation efficient frontiers for wind and solar resource locations are computed from this information. For both efficient frontiers, economically meaningful differences between portfolios on the efficient frontier and the actual wind and solar generation capacity mix are found. The relative difference is significantly larger for aggregate hourly output relative to aggregate hourly revenues, consistent with expected profit-maximizing unilateral entry decisions by renewable resource owners. Most of the hourly output and hourly revenue risk-reducing benefits from the optimal choice of locational generation capacities is captured by a small number of wind resource locations, with the addition of a small number of solar resource locations only slightly increasing the set of feasible portfolio mean and standard deviation combinations. Measures of non-diversifiable wind and solar energy and revenue risk are computed using the actual market portfolio and the risk-adjusted expected hourly output or hourly revenue maximizing portfolios.

We show that a common regulatory mandate in electricity markets that employ locational marginal pricing (LMP) requiring all electricity retailers to purchase their wholesale electricity at the same quantity-weighted average of these prices can improve the performance of imperfectly competitive wholesale electricity markets. Linking loca- tional markets strengthens the incentive for vertically integrated firms to participate in the retail market, which increases competition in the short-term wholesale market. Sim- ulations based on a stylized model of an electricity supply industry find economically significant price reductions are likely for actual markets that employ this regulatory mandate. Our results imply that a policy designed to address equity considerations associated with implementing a locational marginal pricing market design can also en- hance wholesale market efficiency.

This paper proposes a model of the behavior of an expected profit-maximizing merchant storage owner with the ability to exercise unilateral market power. The resulting non-linear bilevel optimization problem is transformed into a single-level stochastic bilinear program using the Karush-Kuhn-Tucker conditions of the lower-level Independent System Operator dispatch problem. By discretizing the offers and bids of the merchant storage owner, the problem is formulated as a stochastic disjunctive program. Using the disjunctive nature of the derived program, a specialized branch-and-bound algorithm that applies a linear quasi-relaxation of the merchant storage problem is proposed. Our solution algorithm is able to solve the problem in an efficient manner; returning the charge and discharge strategies for the merchant storage owner that yield the highest expected profits. Simulations of test systems reveal the various abilities of the merchant storage owner to exercise unilateral market power. Those include demand withholding, generation withholding and under-usewhich result in an increased congestion in both space and time when compared to the welfare-maximizing use of storage. Factors such as uncertain bids by other players, final state-of-charge requirements and arbitrage by other storage players are investigated. Moreover, numerical results demonstrate the superior computational performance of the proposed solution algorithm when benchmarked against current practices in the literature.

Producers and consumers will make the investments and innovations necessary to transition to a low carbon electricity supply industry only if they are compensated for their efforts. In the absence of explicit government support for these activities, this outcome will occur only if wholesale and retail prices provide this compensation. Efficient wholesale and retail pricing provides compensation for the cost-effective deployment of these innovations. Multi-settlement locational marginal pricing markets set efficient short-term wholesale electricity prices. Marginal cost-based pricing of transmission and distribution networks is increasingly important in regions with solar resources. More efficient wholesale and retail pricing implies significantly greater price volatility, particularly as the share of intermittent renewable generation increases, which requires implementing a number of competition and regulatory safeguards to protect consumers, while still providing the price signals necessary for a least cost transition to a low-carbon electricity supply industry.

Capacity markets provide guaranteed payments to electricity generation unit own- ers for having the “firm capacity” to produce electricity. Historically, these markets are plagued by the weak incentives they provide for plants to be available during high-demand hours. The reliability payment mechanism in the Colombian electricity market provides market-based incentives for plants to produce during periods of system scarcity. This market has served as a model for the design of capacity markets in a number of jurisdictions in North America and Europe. We demonstrate severe shortcomings of this mechanism. By adjusting their price and quantity offers, genera- tors with the ability to exercise unilateral market power can choose whether or not a scarcity condition exists. We find that this mechanism can make it privately profitable for a firms to withhold output and create a scarcity condition. We illustrate this prob- lem using hourly data from the first ten years of operation of the reliability payment mechanism in Colombia. The mechanism not only fails to minimize the cost of meeting electricity demand but also creates perverse incentives for electricity generators that could reduce the reliability of electricity supply. We quantify the cost of the perverse incentives caused by this capacity payment mechanism by computing a counterfactual dynamic oligopoly equilibrium for the 2015–16 El Niño event in Colombia.

Charging full requirements customers for distribution network services using the traditional cents per kilowatt-hour (KWh) price creates economic incentives for consumers to invest in distributed generation technologies, such as rooftop solar photovoltaics, despite the fact that marginal cost of grid-supplied electricity is lower. This paper first assesses the economic efficiency properties of this approach to transmission and distribution network pricing and whether current approach to distribution network pricing implies that full-requirement customers cross-subsidize distributed solar customers. Using data on quarterly residential distribution network prices and distributed solar installations from California’s three largest investor-owned utilities I find that larger amounts of distributed solar capacity and more geographically concentrated solar capacity predict higher distribution network prices and average distribution network costs. This result continues to hold even after controlling for average distribution network costs for the utility, Using these econometric model estimates, I find that 2/3 of the increase in residential distribution network prices for each of the three utilities between 2003 and 2016 can attributed to the growth distributed solar capacity. The paper then investigates the extent of the legal obligation that distributed solar generation customers have to pay for sunk costs of investments in the transmission and distribution networks. The paper closes with a description of an alternative approach to distribution network pricing that is likely to increase the economic signals for efficient electricity consumption and the incentive for cost effective installation of distributed solar generation capacity.

Economists traditionally argue that forward commodity markets allow more efficient risk-sharing and information aggregation. However, there is little empirical evidence that commodity markets provide economic benefits to producers and consumers of the commodity. This paper demonstrates that the introduction of financial trading to California’s electricity market on February 1st, 2011 improved price discovery and lowered production costs. Specifically, we document that the average, standard deviation and maximum of the differences between day-ahead and real-time electricity prices across California fell after 2/1/2011. Moreover, variable input costs (input energy) per MWh fell by 3% (4%) in high demand hours after 2/1/2011.

We report results from a large field experiment that with a few hours prior notice provided Danish residential consumers with dynamic price and environmental signals aimed at causing them to shift their consumption either into or away from certain hours of the day. The same marginal price signal is found to cause substantially larger consumption shifts into target hours compared to consumption shifts away from target hours. Consumption is also reduced in the hours of the day before and after these into target hours and there is weaker evidence of increased consumption in the hours surrounding away target hours. The same into versus away results hold for the environmental signals, although the absolute size of the eects are smaller. Using detailed household-level demographic information for all customers invited to participate in the experiment, both models are re-estimated accounting for this decision. For both the price and environmental treatments, the same qualitative results are obtained, but with uniformly smaller quantitative magnitudes. These selection-corrected estimates are used to perform a counterfactual experiment where all of the retailer’s residential customers are assumed to face these dynamic price signals. We find substantial wholesale energy cost savings for the retailer from declaring into events designed to shift consumption from high demand periods to low demand perio ds within the day, which suggests that such a pricing strategy could significantly reduce the cost of increasing the share of greenhouse gas free wind and solar electricity production in an electricity supply industry.

An increasing number of wholesale electricity markets employ locational pricing mech­anisms where energy prices account for some or all aspects of the transmission network configuration. A major concern of regulators is that suppliers may have the ability to exercise unilateral market power by impacting the extent to which transmission con­straints bind. We extend the residual demand curve as a measure of the ability to exercise unilateral market power from a single price market to residual demand hyper­surfaces in locational pricing markets. We show that accounting for the fact that firms face residuai demand surfaces improves our ability to explain the offer curves submit­ted by strategie suppliers. A supplier's residuai demand surface also explains why the location of a firm's capacity is an important factor in analyzing the extent to which divestment of generation capacity or a transmission network expansion ultimately ben­efits final consumers. 

We extend the competitive benchmark pricing model of Borenstein et al. (2002) to locational-pricing markets. We further extent this model to account for transmis­sion network security constraints as well as technical constraints on thermal power plants that introduce non-convexities in their operating cost functions. We apply both models to assess the performance of the Italian wholesale electricity market for the year 2018. Hourly competitive benchmark locational prices that ignore the impact of non-convexities in generation unit operation fail to provide credible estimates for the intra-day benchmark price profile. Augmenting the model to account for transmission network security constraints and non-convexities resolves this issue. We find that the average day-ahead market-clearing prices throughout the day are close to average com­petitive benchmark prices throughout the day during 2018. However, accounting for the cost of the re-dispatch market that makes final schedules from the day-ahead finan­cial market physically feasible, raises the average hourly cost of serving demand. Our preferred competitive benchmark pricing model implies annual market inefficiencies in the range of 1 to 2 billion Euros in the actual annual cost of serving load in 2018 in ltaly.