Electricity Auctions Research Articles

Optimal capacity allocation in multi-auction electricity markets under uncertainty

The advent of competitive markets confronts each producer with the problem of optimally allocating his energy/capacity so as to maximize his profits. The multiplicity of auctions in electricity markets and the nontrivial constraints imposed by technical and bidding rules make the problem of crucial importance and difficult to model and solve. Further difficulties are represented by the dynamic and stochastic natures that characterize the decision process. We formulate the problem as a multi-stage mixed-integer stochastic optimization model under the assumption that the seller is a price taker. We validate the effectiveness of the proposed model on a representative test problem.

Optimal bid strategies for electricity auctions

This work deals with strategy optimization for electricity producers. We consider two types of auctions used to maintain the real-time ballance for production and consumption of electricity. For both auction types, we calculate the market equilibrium and discuss its properties.

Discriminatory price auctions in electricity markets: low volatility at the expense of high price levels

A “pay-as-offered” or discriminatory price auction (DPA) has been proposed to solve the problem of inflated and volatile wholesale electricity prices. Using the experimental method we compare the DPA with a uniform price auction (UPA), strictly controlling for unilateral market power. We find that a DPA indeed substantially reduces price volatility. However, in a no market power design, prices in a DPA converge to the high prices of a uniform price auction with structural market power. That is, the DPA in a no market power environment is as anti-competitive as a UPA with structurally introduced market power.

Market Power and the Power Market: Multi-Unit Bidding and (In)Efficiency in Electricity Auctions

In this paper we apply auction theory to the modelling of competition in a multi-unit wholesale electricity market. Bidding strategies of multi-plant producers are derived and examined assuming various degrees of market concentration. Unlike previous literature, which mainly focuses on demand uncertainty, we assume both demand and cost uncertainty as well as cost differentials among the two types of generating units considered in the model, namely baseload units and peak units. Results indicate that the System Marginal Price (competitive pricing) rule provides incentives for bid shading on the part of all those generators-units who have a non-null probability of setting the market price. We also show that the extent of the bid shading is positively affected by the differential in the endowments of the baseload generation capacity of multi-plant firms. The inefficiency of the resulting allocation is also examined and some policy implications are briefly discussed.

A Q-Learning-Based Supplier Bidding Strategy in Electricity Auction Market

One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier’s profit in the long run. In this approach, the supplier bidding strategy is viewed as one kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power and all suppliers winning the market are paid based on their own bid prices. The dynamics and the incomplete information of the market are considered. The impact of suppliers’ strategic bidding on the market price is analyzed. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.

A Supplier Bidding Strategy Through Q-Learning Algorithm in Electricity Auction Markets

One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier’s profit in the long run. In this approach, the supplier bidding strategy is viewed as a kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power. The dynamics and the incomplete information of the market are considered. The impacts of suppliers’ strategic bidding on the market price are analyzed under uniform pricing rule and discriminatory pricing rule. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.

An equilibrium model for electricity auctions

This work discusses the process of price formation for electrical energy within an auction{like trading environment. Calculating optimal bid strategies of power producers by equilibrium arguments, we obtain the corresponding electricity price and estimate its tail behavior.

Control of Market Power in Electricity Auctions

In auction spot markets, suppliers with a relatively small percentage of total capacity have been able to exercise extreme market power during high-demand periods. One remedy is a system that prevents suppliers with the capacity and incentive for exercising such unilateral power from having any role in setting the market-clearing price.

Modeling Electricity Auctions

The recent debates over discriminatory versus uniform-price auctions in the U.K. and elsewhere have revealed an incomplete understanding of the limitations of some popular auction models when applied to real-world electricity markets. This has led some regulatory authorities to prefer discriminatory auctions on the basis of reasoning from models which are not directly applicable to any existing electricity market. Vickrey auctions, although often recommended by economists, have also been ignored in these debates.

Network-constrained multiperiod auction for a pool-based electricity market

This paper presents a multiperiod electricity auction market tool that explicitly takes into account transmission congestion and losses as well as intertemporal operating constraints such as start-up costs, ramp rates, and minimum up and down times that may be included in any generating unit's composite bid. This approach, which requires only existing mixed-integer linear solvers, provides the market operator with a valuable tool for scheduling participants in a competitive market where transparency, fairness, and confidentiality of participants' data are of paramount concern. Indeed, under this framework, only network data are of public domain; producers are not required to reveal corporate data, and they have more flexibility in specifying the structure of their composite bid. This paper demonstrates and illustrates, through numerical studies using test systems, that an efficient and fair competitive electricity market can be implemented, taking into account network constraints and losses.

Network-Constrained Multiperiod Auction for a Pool-Based Electricity Market

This paper presents a multiperiod electricity auction market tool that explicitly takes into account transmission congestion and losses as well as intertemporal operating constraints, such as start-up costs, ramp rates, and minimum up and down times that may be included in any generating unit's composite bid. This approach, which requires only existing mixed-integer linear solvers, provides the market operator with a valuable tool for scheduling participants in a competitive market where transparency, fairness, and confidentiality of participants' data are of paramount concem. Under this framework, only network data is of public domain; producers are not required to reveal corporate data, and they have more flexibility in specifying the structure of their composite bid. This paper demonstrates and illustrates through numerical studies using test systems that an efficient and fair competitive electricity market can be implemented taking into account network constraints and losses.

Resource operation in competitive power systems

Restructuring is happening in the electric power industry. Open electricity auction markets in some kind of power exchange (PX) organization is part of this emerging industry. System operations and controls are managed separately through an independent system operator (ISO). Such an industry changes how generation resources are planned and operated. This paper presents an operation mechanism that better identifies emerging requirements of restructured electricity markets. We provide models and implementation details of resource operation within a PX-ISO environment. The economic allocation of generation and demand resources in the power exchange (PX) market is modeled based on their bids while considering constraints of different sorts. Preferred schedules of the power exchange (PX) market and scheduling (multilateral) coordinators are then considered in the formulation of the operator re-dispatch and management of ancillary controls. The criterion is to provide an optimal dispatch of resources and settings of controls to ensure reliability, security and adequacy. A three-bus system is used as an example. The results illustrate feasible separate functional operations to yield an efficient and reliable competitive power system structure.

An Evolutionary Computation for Supplier Bidding Strategy in Electricity Auction Market

The daily bidding strategy in a day-ahead electricity auction market is studied from a supplier's point of view. An improved evolution strategy is developed to evolve the bidding strategy and to maximize the supplier's profit in the long run. A perfectly competitive day-ahead electricity auction market, where no supplier possesses the market power and all suppliers winning the market are paid on their own bids, is assumed here. The dynamic and the incomplete information of the market are emphasized. An agent-based simulation method is presented in this paper. The simulation results show the feasibility of the proposed bidding strategy.

Electricity Auctions

This article examines auctions in the electricity industry. We find that the laws of physics that rule power transmission networks defeat ex post productive efficiency: the cheapest combination of generating plants is not always selected, not even in the optimal auction. Furthermore, neither the pay‐your‐bid nor the uniform‐price auction coincides in general with the optimal auction, nor do they yield productive efficiency. Our analysis also sheds light on behavior observed in existing power markets, and leads to policy recommendations. First, producers protected by transmission constraints must see their bids capped in the short run to curb their ability to extract large rents. Second, producers apparently hurt by the unavailability of transmission capacity may benefit from it. Hence, contrary to common wisdom, policy makers cannot rely on them to finance or advocate transmission expansion.

Electricity Auctions

Electricity Auctions

A study of basic bidding strategy in clearing pricing auctions

This paper models bidding behaviors of suppliers in electricity auction markets under clearing pricing rule and with some simplified bidding assumptions. Optimal bidding strategy of suppliers is derived by solving a set of differential equations that specify the necessary conditions for bidders to maximize their expected payoffs. The derived result indicates that bidders have incentives to mark up their bids above their costs of production. The amount of markup depends on the probability of winning below and on the margin that are computed from the cost distribution of all suppliers, market demand, and the number of suppliers participating in the auction.

An analytical solution to the economic dispatch problem

This letter treats the basic problem of economic operation of power systems and presents a mathematical derivation that proves that the classic economic dispatch (ED) problem, with quadratic-convex cost functions, can be solved analytically, i.e., without any approximations or need for numerical iterative optimization algorithms. Duality theory is employed to determine both the exact primal and exact dual solutions. All this requires, at most, are 2n function evaluations. It is stated, therefore, that the use of an ED model as an optimization-based electricity auction does not cause any conflict of interest.

Collusive Bidding: Lessons from the FCC Spectrum Auctions

The Federal Communications Commission (FCC) spectrum auctions use a simultaneous ascending auction design. Bidders bid on numerous communication licenses simultaneously, with bidding remaining open on all licenses until no bidder is willing to bid higher on any license. With full revelation of bidding information, simultaneous open bidding allows bidders to send messages to their rivals, telling them on which licenses to bid and which to avoid. These strategies can help bidders coordinate a division of the licenses, and enforce the proposed division by directed punishments. We examine solutions to mitigate collusive bidding in the spectrum auctions, and then apply these ideas to the design of daily electricity auctions.

The Efficiency of Multi-Unit Electricity Auctions

Using a complete information game-theoretic model, we analyze the performance of different electricity auction structures in attaining efficiency (i. e., least-cost dispatch). We find that an auction structure where generators are allowed to bid for load "slices" outperforms an auction structure where generators submit bids for different hours in the day.

Daily Repetition: A Neglected Factor in the Analysis of Electricity Auctions

Although most bidding theory deals with one-shot auctions, daily repetition of auctions can have a major effect on how they proceed. An analysis using available theory enhances the appeal of sealed-bid auctions over progressive auctions compared to the single-auction context that is more commonly discussed.