Supply Function Bidding Research Articles

A market mechanism for truthful bidding with energy storage

This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a novel bid structure for storage participation that allows storage units to communicate their cost to the market using energy-cycling functions that map prices to cycle depths. The resulting market-clearing process – implemented via convex programming – yields corresponding schedules and payments based on traditional energy prices for power supply and per-cycle prices for storage utilization. We illustrate the benefits of our solution by comparing the competitive equilibrium – assuming price-taker participants – of the resulting mechanism to that of an alternative solution that uses prosumer-based bids. Our solution shows several advantages over the standard prosumer-based approach that prices energy per slot. It does not require a priori price estimation. It also incentivizes participants to reveal their truthful cost, thus leading to an efficient, competitive equilibrium. Numerical experiments using New York Independent System Operator (NYISO) data validate our findings.

Demand Response Program in Smart Grid Using Supply Function Bidding Mechanism

In smart grid, customers have access to the electricity consumption and the price data via smart meters; thus, they are able to participate in the demand response (DR) programs. In this paper, we address the interaction among multiple utility companies and multiple customers in smart grid by modeling the DR problem as two noncooperative games: the supplier and customer side games. In the first game, supply function bidding mechanism is employed to model the utility companies’ profit maximization problem. In the proposed mechanism, the utility companies submit their bids to the data center, where the electricity price is computed and is sent to the customers. In the second game, the price anticipating customers determine optimal shiftable load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are studied and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulation results demonstrate the superior performance of the proposed DR method in increasing the utility companies’ profit and customers’ payoff, as well as in reducing the peak-to-average ratio in the aggregate load demand. Finally, the algorithm performance is compared with a DR method in the literature to demonstrate the similarities and differences.

Demand Response With Capacity Constrained Supply Function Bidding

We study the problem faced by an operator who aims to allocate a certain amount of load adjustment (either load reduction or increment) to multiple consumers so as to minimize the aggregate consumer disutility. We propose and analyze a simple uniform-price market mechanism where every consumer submits a single bid to choose a supply function from a group of parameterized ones. These parameterized supply functions are designed to ensure that every consumer's load adjustment is within an exogenous capacity limit that is determined by the current power system operating condition. We show that the proposed mechanism yields bounded efficiency loss at a Nash equilibrium. In particular, the proposed mechanism is shown to achieve approximate social optimality at a Nash equilibrium, if the total capacity limit excluding the consumer with the largest one is much larger than the total amount of load to be adjusted. We complement our analysis through numerical case studies.

Greening multi-tenant data center demand response

Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on “greening” demand response in multi-tenant data centers, i.e., colocation data centers, by designing a pricing mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods for EDR. In particular, we propose a pricing mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the pricing mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).

Demand Response Using Linear Supply Function Bidding

In this paper, we consider an abstract market model for demand response where a supply function bidding is applied to match power supply deficit or surplus. We characterize the resulting equilibria in competitive and oligopolistic markets and propose distributed demand response algorithms to achieve the equilibria. We further show that the equilibrium in competitive market maximizes social welfare, and the equilibrium in oligopolistic market has bounded efficiency loss under certain mild assumptions. We also propose distributed demand response algorithms to achieve the equilibria.

Evolutionary neural networks for strategic bidding in electricity markets

PurposeThe purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.Design/methodology/approachThe problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.FindingsThe effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.Practical implicationsStrategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.Originality/valueTo the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.

A two-stage market model for microgrid power transactions via aggregators

In this paper, we propose a market model where microgrids sell their surplus power to a utility via aggregators. This is a scalable model where a utility does not directly interact with a large number of microgrids. Thus, aggregators collect power from microgrids and resell it to the utility. From the microgrids' perspective, aggregators are buyers. From the utility's perspective, aggregators are sellers. In this context, based on the two-stage Stackelberg game, we show how to achieve efficient market equilibrium using the tatonnement process and supply function bidding. We also show that the participation of aggregators may significantly affect the market depending on the supply elasticity of microgrids, which in turn depends on the cost structure of microgrids. For example, when the cost function of microgrids is roughly linear, the aggregators may not make a profit. However, if the cost function of microgrids has a higher order term, aggregators may accumulate a large profit, which potentially raises the issue of the regulator's role in the market.

Parameterized Supply Function Bidding: Equilibrium and Efficiency

We consider a model where a finite number of producers compete to meet an infinitely divisible but inelastic demand for a product. Each firm is characterized by a production cost that is convex in the output produced, and firms act as profit maximizers. We consider a uniform price market design that uses supply function bidding: firms declare the amount they would supply at any positive price, and a single price is chosen to clear the market. We are interested in evaluating the impact of price-anticipating behavior both on the allocative efficiency of the market and on the prices seen at equilibrium. We show that by restricting the strategy space of the firms to parameterized supply functions, we can provide upper bounds on both the inflation of aggregate cost at the Nash equilibrium relative to the socially optimal level, as well as the markup of the Nash equilibrium price above the competitive level: as long as N > 2 firms are competing, these quantities are both upper bounded by 1 + 1/(N − 2). This result holds even in the presence of asymmetric cost structure across firms. We also discuss several extensions, generalizations, and related issues.

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Optimal combinatorial electricity markets

The deregulation of the electricity industry in many countries has created a number of marketplaces in which producers and consumers can operate in order to more effectively manage and meet their energy needs. To this end, this paper develops a new model for electricity retail where end-use customers choose their supplier from competing electricity retailers. The model is based on simultaneous reverse combinatorial auctions, designed as a second-price sealed-bid multi-item auction with supply function bidding. This model prevents strategic bidding and allows the auctioneer to maximise its pay-off. Furthermore, we develop optimal single-item and multi-item algorithms for winner determination in such auctions that are significantly less complex than those currently available in the literature.