Optimizing Demand-Side Bids in Day-Ahead Electricity Markets

2012

The active demand-side response in electricity market would produce benefits not only for individual consumers but also for the market as a whole. This paper proposes a method i.e. price responsive demand shift bidding of distribution companies to reduce congestion and peak locational marginal prices in the pool-based dayahead electricity markets. The market dispatch problem is formulated as to maximize the social welfare of market participants subject to operational security constraints. This bidding mechanism is able to shift the price responsive demand from the periods of high price to the periods of low price in day-ahead electricity markets. The comparison of the price responsive demand shifting bids with conventional price taking bids is presented by solving hourly market dispatch problems on an IEEE 30-bus system for 24-h scheduling period. The effects of the proportion of demand-side participation on the price taking and price responsive consumer are also illustrated.

IEEE Transactions on Smart Grid

We consider the process of bidding by electricity suppliers in a dayahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in locationdependent prices.

IEEE Transactions on Power Systems, 2001

Competition in day-ahead electricity markets has been established through auctions where generators and loads bid prices and quantities. Different approaches have been discussed regarding the market auction design. Multi-round auctions, despite its implementation complexity, allow market participants to adapt their successive bids to market prices considering their operational and economic constraints. However, most of the day-ahead electricity market implementations use noniterative single-round auctions. This paper presents a market simulator to compare both auction models. Different auction alternatives, such as the Spanish single-round auction that takes into account special conditions included in the generator bids, and multi-round auctions with different stopping rules, are analyzed. The results and acquired experience in the simulation of the Spanish market, started in January 1998, are presented. Hourly market prices, average daily price, price/demand correlation and several economic efficiency indicators, such as generator surplus, consumer surplus and social welfare, are compared to derive conclusions regarding the performance of the auction alternatives.

IEEE Transactions on Power Systems, 2000

This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. A genetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.

Energy Systems, 2011

We review some mathematical programming models that capture the optimal bidding problem that power producers face in day-ahead electricity auction markets. The models consider both price-taking and non-price taking assumptions. The models include linear and non-linear integer programming models, mathematical programs with equilibrium constraints, and stochastic programming models with recourse. Models are emphasized where the producer must self-schedule units and therefore must integrate optimal bidding with unit commitment decisions. We classify models according to whether competition from competing producers is directly incorporated in the model.

Electric Power Systems Research, 2009

Large part of liberalized electricity markets, including the Italian one, features an auction mechanism, called day-ahead energy market, which matches producers' and buyers' simple bids, consisting of energy quantity and price pairs. The match is achieved by a merit-order economic dispatch procedure independently applied for each of the hours of the following day. Power plants operation should, however, take into account several technical constraints, such as maximum and minimum production bounds, ramp constraints and minimum up and downs times, as well as no-load and startup costs. The presence of these constraints forces to adjust the scheduling provided by the market in order to obtain a feasible scheduling. The paper presents an analysis of the possibility and the limits of taking into account the power plants technical constraints in the bidding strategy selection procedure of generating companies (Gencos). The analysis is carried out by using a computer procedure based both on a simple static gametheory approach and on a cost-minimization unit-commitment algorithm. For illustrative purposes, we present the results obtained for a system with three Gencos, each owning several power plants, trying to model the bidding behaviour of every generator in the system. This approach, although complex from the computational point of view, allows an analysis of both price and quantity bidding strategies and appears to be applicable to markets having different rules and features.

IEEE Transactions on Power Systems, 2017

2016

This dissertation studies two topics in Demand Response (DR) in electricity markets, with some discussion of retail electricity pricing more broadly. In each of these investigations we posit a model of a consumer, or population of consumers, optimizing their consumption decisions for their private benefit. The first investigation considers the profit maximization problem of a DR aggregator, and the second studies the welfare impacts of existing and hypothetical retail tariffs and DR programs, with a combination of theoretical analysis and simulation experiments. Part I provides a comprehensive introduction to the dissertation. Part II of the dissertation formulates and analyzes the profit maximization problem of an aggregator that owns the production rights to a Variable Energy Resource's (VER) output, and also signs contracts with a population of DR participants for the right to curtail them in real time, according to a contractually specified probability distribution. The aggregator is situated in a market environment in which it bids a day-ahead offer into the wholesale market, and is penalized for deviations of its realized net production-renewable energy bundled with DR-from that offer. We consider the optimization of the aggregator's endto-end problem: designing the menu of DR service contracts using contract theory, bidding into the wholesale market, and dispatching DR consistently with the contractual agreements. In our setting, DR participants have private information about their valuation for energy; and wholesale market prices, VER output, and participant demand are all stochastic, and possibly correlated. In Part III, we study the welfare effects of various dynamic electricity pricing schemes, including Real-Time pricing, Time-of-Use pricing, Critical Peak Pricing, and Critical Peak Rebates (referred to simply as "Demand Response"), by simulating the behavior of rational consumers under a set of historical scenarios drawn from the greater San Francisco Bay Area. Using realistic dynamic consumption models, we gain novel insights into the effects of intertemporal substitution on individual and social surplus. Defining the concept of a baseline-taking equilibrium, we are able to estimate the welfare impact of the perverse incentive to inflate the Demand Response baseline, under the assumption of perfect foresight.

2003

We develop game theoretic models to evaluate strategic behavior in deregulated electricity markets, with particular attention given to the market rules in place in California through the summer of 2000. We prove existence of a Nash equilibrium under two particular sets of market rules used by the CALPX and CAISO respectively. Next we derive a lower bound (strictly above marginal cost) on average equilibrium prices when there is a positive probability that at least one generator is capacity-constrained. Finally, we compare two competing methods for modelling competition in power markets: supply function equilibrium and discrete, multi-unit auctions and illustrate shortcomings of both approaches.

hicss, 2001

Testing the performance of electricity markets using POWERWEB has already shown that relatively inexperienced players can identify and exploit market power in load pockets. When transmission constraints are not binding, however, auctions with six players have been shown to be efficient. There is evidence from operating electricity markets that prices can be driven above competitive levels when the largest supplier controls less than 20% of total installed capacity. This is accomplished by causing price spikes to occur. In experiments, uncertainty about the actual load and paying standby costs regardless of whether or not a unit is actually dispatched contribute to volatile price behavior. The objective of this paper is to investigate characteristics of a market that affect price volatility. The tests consider three different sets of rules for setting price when there are capacity shortfalls, and the following four market structures: 1. Load is responsive to price 2. Price forecasts are made before market settlement 3 A day-ahead market and a balancing market auction 4. Suppliers are paid actual offers (a discriminatory auction)