Showing papers on "Bidding published in 2015"

Energy Pricing and Dispatch for Smart Grid Retailers Under Demand Response and Market Price Uncertainty

Abstract: This paper proposes a two-stage two-level model for the energy pricing and dispatch problem faced by a smart grid retailer who plays the role of an intermediary agent between a wholesale energy market and end consumers. Demand response of consumers with respect to the retail price is characterized by a Stackelberg game in the first stage, thus the first stage has two levels. A risk-aversive energy dispatch accounting for market price uncertainty is modeled by a linear robust optimization with objective uncertainty in the second stage. The proposed model is transformed to a mixed integer linear program (MILP) by jointly using the Karush-Kuhn-Tucker (KKT) condition, the disjunctive constraints, and the duality theory. We propose a heuristic method to select the parameter in disjunctive constraints based on the interpretation of Lagrange multipliers. Moreover, we suggest solving an additional linear program (LP) to acquire a possible enhanced bidding strategy that guarantees a Pareto improvement on the retailer's profit over the entire uncertainty set. Case studies demonstrate the proposed model and method is valid.

Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty

Abstract: With a large-scale introduction of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator, is expected to be responsible for managing the charging of, and for purchasing electricity for, the vehicles. We approach the problem of an aggregator bidding into the day-ahead electricity market with the objective of minimizing charging costs while satisfying the PEVs' flexible demand. The aggregator places demand bids only (no vehicle-to-grid is considered). The aggregator is assumed to potentially influence market prices, in contrast to what is commonly found in the literature. Specifically, the bidding strategy of the aggregator is formulated as a bilevel problem, which is implemented as a mixed-integer linear program. The upper level problem represents the charging cost minimization of the aggregator, whereas the lower level problem represents the market clearing. Since the bids of other market participants are not known to the aggregator ex ante, a simple strategy is used to estimate them, based on historical data. An aggregated representation of the PEV end-use requirements as a virtual battery, with time varying power and energy constraints, is proposed, derived from individual driving patterns. Since driving patterns cannot be perfectly forecasted, we introduce a probabilistic virtual battery model. We compare the results of the proposed bidding strategy with those of a bidding strategy assuming exogenous prices, uncontrolled charging, and a central dispatch of the fleet. We also explore the impacts of different sources of uncertainty. Results show that with the proposed bidding strategy, costs are significantly lower than under inflexible charging and are lower than assuming exogenous prices. Moreover, the approach also performs well under uncertainty. Results also suggest that the aggregator only has limited market power potential at moderate PEV penetrations.

Demand Response Using Linear Supply Function Bidding

Abstract: 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.

Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design

Abstract: Ad exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a prespecified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in ad exchanges and the implications for publishers' decisions. The presence of budgets introduces dynamic interactions among advertisers that need to be taken into account when attempting to characterize the bidding landscape or the impact of changes in the auction design. To this end, we introduce the notion of a fluid mean-field equilibrium FMFE that is behaviorally appealing and computationally tractable, and in some important cases, it yields a closed-form characterization. We establish that an FMFE approximates well the rational behavior of advertisers in these markets. We then show how this framework may be used to provide sharp prescriptions for key auction design decisions that publishers face in these markets. In particular, we show that ignoring budgets, a common practice in this literature, can result in significant profit losses for the publisher when setting the reserve price. This paper was accepted by Dimitris Bertsimas, optimization.

How to Bid the Cloud

Abstract: Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.

Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties

Abstract: In restructured electricity markets, the electricity retailers try to obtain the consumers' electricity demand at the minimum cost of different resources such as self-generating facilities, bilateral contracts and pool market purchases. Hence, more attention should be paid to the demand response programs (DRPs) which aim to electricity procurement cost reduction. Owing to the uncertain nature of pool prices and the price fluctuation in the pool markets, the uncertainty modelling is inevitable for retailers. In this study, a robust optimisation approach is proposed for decision making of electricity retailers. Meanwhile, considering the effect of DRP on total procurement cost, an optimal bidding strategy is proposed of electricity retailers with the time-based model of DRP in the electricity market. For this purpose, a collection of robust mixed-integer linear programming (RMILP) problem should be solved in the proposed method. Rather than using the forecasted prices as inputs, the upper and lower limits of pool prices are considered for the uncertainty modelling. The range of pool prices is sequentially partitioned into successive nested subintervals, which permits formulating the RMILP problems. The results of these problems give sufficient data to obtain an optimal bidding strategy for electricity retailers considering DRP. Detailed analysis is performed to delineate the proposed method.

Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization

Abstract: Electric vehicles (EVs) can be effectively integrated with the power grid through vehicle-to-grid (V2G). V2G has been proven to reduce the EV owner cost, support the power grid, and generate revenues for the EV owner. Due to regulatory and physical considerations, aggregators are necessary for EVs to participate in electricity markets. The aggregator combines the capacities of many EVs and bids their aggregated capacity into electricity markets. In this paper, an optimal bidding of ancillary services coordinated across different markets, namely regulation and spinning reserves, is proposed. This coordinated bidding considers electricity market uncertainties using fuzzy optimization. The electricity market parameters are forecasted using autoregressive integrated moving average (ARIMA) models. The fuzzy set theory is used to model the uncertainties in the forecasted data of the electricity market, such as ancillary service prices and their deployment signals. Simulations are performed on a hypothetical group of 10000 EVs in the electric reliability council of Texas electricity markets. The results show the benefit of the proposed fuzzy algorithm compared with previously proposed deterministic algorithms that do not consider market uncertainties.

Renewable energy gathers steam in South Africa

Abstract: South Africa׳s Renewable Energy Independent Power Producers Procurement Programme (the REI4P) is an extensive initiative to install 17.8 GW of electricity generation capacity from renewables – wind, solar, biomass, biogas and hydropower – over the period 2012–2030. Although at the outset the REI4P seemed an expensive option, designed only to deflect criticism of South Africa׳s high carbon footprint and excessive dependence on coal-based electricity generation, the escalating costs of the latter, the rapidly falling costs of photovoltaic and wind power, and the increasingly competitive bidding process of the REI4P have changed this prospect. At the conclusion of round three, the weighted cost of energy has reached a 23% discount to the cost of new coal-based generation and a 28% discount to global renewable energy prices. The bidders׳ commitments to local employment creation have similarly increased from 11 to 18 jobs/MW. The programme is now well placed to deliver on a broad range of objectives, including regional development and black economic empowerment. However, maximum benefit from the REI4P will not be secured without some revision to aspects of the bidding and procurement process. More specifically, the local content provisions need to be tightened to drive higher levels of local manufacturing.

Optimal Bidding Strategy of a Strategic Wind Power Producer in the Short-Term Market

Abstract: Wind energy is a clean and renewable energy source which is rapidly growing globally. As the penetration level of wind power grows, the system operators need to consider wind power producers as strategic producers whose bidding behaviors will have an impact on the locational marginal prices. This paper proposes a bilevel stochastic optimization model to obtain the optimal bidding strategy for a strategic wind power producer in the short-term electricity market. The upper level problem of the model maximizes the profit of the wind power producer, while the lower level problem represents the market clearing processes of both day-ahead and real-time markets. The uncertainties in the demand, the wind power production, and the bidding strategies of the strategic conventional power producers are represented by scenarios in the model. The conditional value at risk of the selected worst scenarios is included in the objective function for managing the risk due to uncertainties. Using the duality theory and KarushKuhnTucker condition, the bilevel model is transferred into a mixed-integer linear problem. Case studies are performed to show the effectiveness of the proposed model.

A Stochastic Multi-Layer Agent-Based Model to Study Electricity Market Participants Behavior

Abstract: This paper presents a new stochastic multi-layer agent-based model to study the behavior of electricity market participants. The wholesale market players including renewable power producers are modeled in the first layer of the proposed multi-agent environment. The players optimize bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers including plug-in electric vehicle (PEV) owners and consumers who participate in demand response (DR) programs are modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. Due to the uncertainties of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers (WPPs), PEV aggregators and retailers providing DR is considered to demonstrate the usefulness and proficiency of the proposed multi-layer agent-based model.

Strategic Bidding for a Large Consumer

Abstract: The smart grid technology enables an increasing level of responsiveness on the demand side, facilitating demand serving entities—large consumers and retailers—to procure their electricity needs under the best conditions. Such entities generally exhibit a proactive role in the pool, seeking to procure their energy needs at minimum cost. Within this framework, we propose a mathematical model to help large consumers to derive bidding strategies to alter pool prices to their own benefit. Representing the uncertainty involved, we develop a stochastic complementarity model to derive bidding curves, and show the advantages of such bidding scheme with respect to non-strategic ones.

The effect of target-firm accounting quality on valuation in acquisitions

Abstract: We examine whether acquisitions are more profitable for acquirers when the firms they target disclose higher-quality accounting information. If accounting information reduces uncertainty in the value of the target firm by facilitating a more precise valuation, we predict that managers of the acquiring firm can bid more effectively and pay less to acquire a target firm that has high-quality accounting information. Using a large sample of acquisitions of public firms from 1990 to 2010, we find evidence consistent with our prediction. Specifically, when target firms have higher-quality accounting information, acquirer returns around the acquisition announcement are higher and target returns are lower—consistent with acquirers capturing a greater portion of acquisition gains by paying less for target firms. These findings, which are robust to a variety of controls and alternative measures of uncertainty and accounting quality, suggest that higher-quality accounting information leads to better bidding decisions in acquisitions.

Optimal Demand Bidding for Time-Shiftable Loads

Abstract: Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: how can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads and due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed-form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data.

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming

Abstract: There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator NYISO require that battery storage operators place bids into an hour-ahead market although settlements may occur in increments as small as five minutes, which is considered near "real-time". The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming ADP algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process and makes no assumptions regarding a specific real-time price model. We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective.

Risk-Constrained Strategic Bidding of GenCos Considering Demand Response

Abstract: This paper presented a combined scheduling and bidding algorithm for constructing the bidding curve of an electric utility that participated in the day-ahead energy markets. Day-ahead market price uncertainty was modeled using non-probabilistic information gap decision theory (IGDT). The considered utility consisted of generation units and a retailer part; the retailer part of the utility and its demand response program (DRP) could affect the utility's profit, which should be considered in the bidding strategy problem. The bidding strategy algorithm proposed in this paper dispatched units by optimizing the demand response programs of the retailer part. In addition, non-decreasing bidding curve was constructed according to the proposed IGDT-based method. Applicability of the proposed method was demonstrated using an illustrative example with 54 thermal units. Results were verified using after-the-fact actual market data.

Predicting Winning Price in Real Time Bidding with Censored Data

Abstract: In the aspect of a Demand-Side Platform (DSP), which is the agent of advertisers, we study how to predict the winning price such that the DSP can win the bid by placing a proper bidding value in the real-time bidding (RTB) auction. We propose to leverage the machine learning and statistical methods to train the winning price model from the bidding history. A major challenge is that a DSP usually suffers from the censoring of the winning price, especially for those lost bids in the past. To solve it, we utilize the censored regression model, which is widely used in the survival analysis and econometrics, to fit the censored bidding data. Note, however, the assumption of censored regression does not hold on the real RTB data. As a result, we further propose a mixture model, which combines linear regression on bids with observable winning prices and censored regression on bids with the censored winning prices, weighted by the winning rate of the DSP. Experiment results show that the proposed mixture model in general prominently outperforms linear regression in terms of the prediction accuracy.

Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets

Abstract: Intraday energy markets have been established in some power markets mainly because of large-scale wind power integration. Inspired by the Spanish power market, this paper proposes a modified market design which contains day-ahead and intraday energy bidding sections to better accommodate stochastic wind energy. Then coordinated operation of the wind farm (WF) and energy storage system (ESS) is studied. Rolling stochastic optimization formulations for day-ahead, intraday biddings and real-time operations are put forward to obtain the optimal bidding strategy of WF-ESS union in each bidding section to maximize its overall profit. Case studies and sensitivity analyses are carried out on a union of WFs and a pumped storage plant (PSP). Simulation results show that the proposed rolling optimization method can effectively utilize the updated wind power forecast data and regulation capability of ESS, and thus increase profit for the union prominently.

Optimal demand bidding for time-shiftable loads

Abstract: Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: How can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads, and also due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data.

Distributed Smart-Home Decision-Making in a Hierarchical Interactive Smart Grid Architecture

Abstract: In this paper, we develop a comprehensive real-time interactive framework for the utility and customers in a smart grid while ensuring grid-stability and quality-of-service (QoS). First, we propose a hierarchical architecture for the utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: ( $1$ ) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and ( $2$ ) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer’s (microgrid controller’s) profit. The paper also explicitly discusses how this two-step solution approach can be scaled to be suitable for more complicated smart grid architectures beyond the assumed abstract model.

Bid price optimization for truckload carriers in simultaneous transportation procurement auctions

Abstract: We study simultaneous transportation procurement auctions from a truckload carrier’s perspective. We formulate a stochastic bid price optimization model aimed at maximizing the carrier’s expected profit. The model accounts for synergies among lanes and competing carriers’ bid patterns. We develop an iterative coordinate search algorithm to find high-quality solutions. The benefits of employing the bid price optimization technology are demonstrated through computational experiments involving a simulated marketplace.

Valuing innovation in construction and infrastructure: Getting clients past a lowest price mentality

Abstract: Purpose – The purpose of this paper is to continue the discussion about the actual and potential role of clients in driving more innovation in the construction sector through interviews with some of the Australian construction industry’s leading clients, contractors and consultants. Design/methodology/approach – This paper synthesises previously disconnected literature reports interviews with 46 of Australia’s leading clients, contractors and consultants. Findings – The findings confirm the importance of client leadership, yet also shows that lowest price remains the dominant selection criterion in tenders. Many clients lack the insight and tools to play a leadership role and are unwilling and unable to employ strategies to foster better performance and more innovation because of internal governance constraints, a poor understanding of how built assets contribute to core business objectives and a narrow understanding of their central role in driving innovation. The authors conclude that in reality, the po...

An Incentivized Auction-Based Group-Selling Approach for Demand Response Management in V2G Systems

Abstract: Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Modeling the Decision-Making Process Concerning Participation in Construction Bidding

Abstract: Deciding whether to take part in a bidding has a significant influence on the condition of a construction company. Not bidding deprives the contractor of the possibility of completing a given building project, yet the contractor has to consider whether, for example, he or she has the capability of carrying out a particular project. This paper presents a brief description of some chosen models aiding the bid/no bid decision that have been proposed in previous studies. Moreover, the authors designed their own model based on the fuzzy set theory. The basis of the decision model became the previous selection of factors influencing the decision. To achieve this, the authors conducted a study among contractors in 2010–2011 in Southern Poland. One hundred and fifty surveys were sent, providing 41% of answers. Based on the analysis of the surveys, according to the contractors, the top three most vital factors influencing the bid/no bid decision are (1) the type of work, (2) experience in similar projects,...