Flexible Bidding Research Articles

Multivariate probabilistic forecasting of electricity prices with trading applications

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique for a multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ration (by 18%) in the trading application.

An Iterative Procurement Combinatorial Auction Mechanism for the Multi-Item, Multi-Sourcing Supplier-Selection and Order-Allocation Problem under a Flexible Bidding Language and Price-Sensitive Demand

This study addresses the multi-item, multi-sourcing supplier-selection and order-allocation problem. We propose an iterative procurement combinatorial auction mechanism that aims to reveal the suppliers’ minimum acceptable selling prices and assign orders optimally. Suppliers use a flexible bidding language to submit procurement bids. The buyer solves a Mixed Integer Non-linear Programming (MINLP) model to determine the winning bids for the current auction iteration. We introduce a buyer’s profit-improvement factor that constrains the suppliers to reduce their selling prices in subsequent bids. Moreover, this factor enables the buyer to strike a balance between computational effort and optimality gap. We develop a separate MINLP model for updating the suppliers’ bids while satisfying the buyer’s profit-improvement constraint. If none of the suppliers can find a feasible solution, the buyer reduces the profit-improvement factor until a pre-determined threshold is reached. A randomly generated numerical example is used to illustrate the proposed mechanism. In this example, the buyer’s profit improved by as much as 118% compared to a single-round auction. The experimental results show that the proposed mechanism is most effective in competitive environments with several suppliers and comparable costs. These results reinforce the importance of fostering competition and diversification in a supply chain.

Stochastic short-term scheduling of a wind-solar-hydro complementary system considering both the day-ahead market bidding and bilateral contracts decomposition

Complementary operation of hydropower, wind and photovoltaic (PV) power is one of the most cost-efficient solutions for integration of intermittent wind and PV power. In a deregulated market, synergetic optimization of day-ahead (DA) market bids and the decomposition of daily physical bilateral contracts (BCs) can increase profits for a wind-solar-hydro complementary system (WSHCS) by coordinating the spot market and the forward market. However, this short-term scheduling problem is challenging because of complex market mechanisms and uncertainties of multiple energy resources and market prices. A stochastic scheduling model is established to address this issue. First, uncertainties of wind, PV power and DA market prices are characterized by stochastic scenarios simulation with the adoption of the simultaneous backward reduction algorithm. Then, a two-layer nested optimization framework is conducted to optimize both the DA market bidding strategy and the decomposition of daily BCs. A WSHCS in China’s Yalong River Basin is selected as a case study. Results show that: the proposed model can decompose the total energy of the daily BC reasonably by leaving flexible bidding spaces in the DA market according to price variation, thus larger volume of bidding energy can be sold when DA market prices are higher than the contract price. Besides, the optimal decomposition outperforms the average decomposition of daily BCs with larger benefits, less power curtailment, and higher water resources utilization efficiency. These findings verify the effectiveness and applicability of the proposed model for synergetic optimization of both the decomposition of daily BCs and DA market bids for a WSHCS.

An iterative combinatorial auction mechanism for multi-agent parallel machine scheduling

This paper focuses on the multi-agent parallel machines scheduling problem with consumer agents and resource agents. Within the context, all the agents are self-interested aiming at maximising their profits, and have private information, precluding the use of the centralised scheduling approaches that require complete information of all the consumer agents. Therefore, an iterative combinatorial auction mechanism based on a decentralised decision procedure is proposed to generate a collaborative scheduling scheme without violating information privacy. The developed approach adopts flexible bidding strategies to reduce the conflict in resource allocation, and a hybrid auction termination condition is developed to ensure the convergence of the approach while guaranteeing sufficient competition among agents. Experimental results show the developed approach generates high-quality solutions with a small price of anarchy compared with centralised approaches and outperforms the state-of-the-art decentralised scheduling approach in improving social welfare, especially for problems with a large number of consumer agents.

Comparison of flexible bidding and hourly bidding in the electricity market

Flexible bidding is an emerging electricity trading product in the Nordic spot market, which is different from hourly bidding and block bidding. It provides electricity generation enterprises and users with flexible and changeable electricity generation and consumption options and can reflect the electricity generation and demand-side behavior and habits of electricity generation and consumption, providing reference value for Chinese market participants to participate in the electricity market and demand-side response. This paper firstly introduces the two bidding models of hourly bidding and flexible block bidding; secondly, it introduces the market clearance model of flexible bidding; finally, it compares the differences between these two bidding approach.

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model.

Robust Optimization Model for Energy Purchase and Sale of Electric–Gas Interconnection System in Multi-Energy Market

With the increasing coupling of the power system and the natural gas system, the electric–gas interconnection system has become a typical form of comprehensive energy utilization. Through the energy conversion function of the coupling unit, the system can flexibly participate in the bidding for purchasing and selling energy in a power market and a natural gas market on the premise of meeting the internal demand of multiple loads. To solve the internal coordination and optimization problem and the external flexible bidding problem in the multi-energy market, this paper proposes a robust optimization model of energy purchase and sale for the electric–gas interconnection system in a multi-energy market. Firstly, the basic structure of the electric–gas interconnection system is introduced, and the steady-state model of energy flow in the system is built based on the energy hub model. Secondly, considering the uncertainty of energy prices and the output power of renewable energy units in the system, a bidding model for energy purchase and sale of the electric–gas interconnection system in multi-energy market based on the idea of robust optimization is constructed in the framework of the Nordic energy market. Finally, empirical analysis based on the actual data is carried out, and the results prove the validity and superiority of the model. In this paper, aiming at the uncertainty of energy price, a large number of scenes are generated by Latin hypercube sampling (LHS), and then a k-means algorithm is used to reduce the scenes, so as to simulate typical scenes. Aiming at the uncertainty of the output power of the renewable energy unit in the system, a cardinal uncertainty set is used to control deviation between the actual output power and predicted output power, so that the overall robustness of the model can be controlled. The proposed model can make decision-making independent of the accurate probability distribution of uncertainty factors, and is suitable for complex multi energy systems. Meanwhile, the model possesses excellent robustness, which can effectively reduce the risk of bidding loss in the process of energy purchase and sale.

Action in auctions: neural and computational mechanisms of bidding behaviour.

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

Risk‐averse bidding of energy and spinning reserve by wind farms with on‐site energy storage

The increased net-demand uncertainty and volatility observed in power systems with large-scale penetration of intermittent renewables has translated into the deployment of larger volumes of reserve and into the need for procuring new sources of flexibility. In order to cope with increased uncertainty and risk of experiencing low profits, wind farm owners must adopt flexible bidding strategies such as coordinating its operation with energy storage systems (ESS). Besides managing wind imbalances, ESS are also capable of providing ancillary services such as spinning reserve (SPR) and frequency response to improve profitability. In this context, this paper proposes a novel two-stage stochastic mathematical programming model that allows considering different degrees of risk aversion when optimising the day-ahead energy and SPR bidding strategy of a wind farm with on-site ESS. Uncertainty is modelled through prices and wind generation forecasts, while the conditional-value-at-risk metric is used to handle day-ahead profit risk. The developed case studies provide evidence of the value of combined wind farm and ESS bidding not only through increased daily profits but also through reduced offer uncertainty which improves the position of a wind farm in the day-ahead markets.

CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets

An electric vehicle aggregator (EVA) that manages geographically dispersed electric vehicles offers an opportunity for the demand side to participate in electricity markets. This paper proposes an optimization model to determine the day-ahead inflexible bidding and real-time flexible bidding under market uncertainties. Based on the relationship between market price and bid price, the proposed optimal bidding model of EVA aims to minimize the conditional expectation of electricity purchase cost in two markets considering price volatility. Moreover, the penalty cost of the deviation between the bidding quantities is included to avoid large power variation and arbitrage. The conditional expectation optimization model is formulated as an expectation minimization problem with the conditional value-at-risk constraints. Based on the price data in the PJM market, simulation results verify that our model is a decision-making tool in electricity markets, which can help market players comprehend the variants of bid price, expected cost and probability of successful bidding.

Tracking the impacts of farm-to-school purchasing programs.

This study expands upon previous efforts to estimate economic impacts of farm-to-school purchasing in one state, but holds national import. Several large produce firms currently grow sufficient produce for schools, yet sales to schools have been limited. Interviews with stakeholders in three school districts within South Carolina documented that no data trail currently exists sufficient to allow meaningful and comprehensive evaluations of farm-to-school purchasing efforts and subsequent impacts. Purchasing records are kept inconsistently; competing definitions of the term “local food” frustrate efforts to compile meaningful reports that fully evaluate the impact of these programs. Furthermore, bidding processes, prevailing infrastructure, and support systems limit local purchasing in the first place. More flexible bidding contracts and financial incentives may boost local trade. New software systems that allow better recordkeeping will allow program-purchasing goals to be fully developed.

Keyword Management Costs and “Broad Match” in Sponsored Search Advertising

In sponsored search advertising, advertisers bid to be displayed in response to a keyword search. The operational activities associated with participating in an auction, i.e., submitting the bid and the ad copy, customizing bids and ad copies based on various factors (such as the geographical region from which the query originated, the time of day and the season, the characteristics of the searcher), and continuously measuring outcomes, involve considerable effort. We call the costs that arise from such activities keyword management costs. To reduce these costs and increase advertisers’ participation in keyword auctions, search engines offer an opt-in tool called broad match with automatic and flexible bidding, wherein the search engine automatically places bids on behalf of the advertisers and takes over the above activities as well. The bids are based on the search engine’s estimates of the advertisers’ valuations and, therefore, may be less accurate than the bids the advertisers would have turned in themselves. Using a game-theoretic model, we examine the strategic role of keyword management costs, and of broad match, in sponsored search advertising. We show that because these costs inhibit participation by advertisers in keyword auctions, the search engine has to reduce the reserve price, which reduces the search engine’s profits. This motivates the search engine to offer broad match as a tool to reduce keyword management costs. If the accuracy of broad match bids is sufficiently high, advertisers adopt broad match and benefit from the cost reduction, whereas if the accuracy is very low, advertisers do not use it. Interestingly, at moderate levels of bid accuracy, advertisers individually find it attractive to reduce costs by using broad match, but competing advertisers also adopt broad match and the increased competition hurts all advertisers’ profits, thus creating a “prisoner’s dilemma.” When advertisers adopt broad match, search engine profits increase. It therefore seems natural to expect that the search engine will be motivated to improve broad match accuracy. Our analysis shows that the search engine will increase broad match accuracy up to the point where advertisers choose broad match, but that increasing the accuracy any further reduces the search engine’s profits.

Extending the bidding format to promote demand response

We propose an extended bidding structure to allow more realistic demand characteristics and behaviors to be expressed via flexible bids. In today's ISO-run energy markets, demand bid formats are all separable over time. However, a significant and growing segment of demand can be shifted across time and therefore has no way to bid its true valuation of consumption. We propose additional bid types that allow deferrable, adjustable and storage-type loads to better express their value, and thus elicit demand response in the most natural way – via direct participation in the market. We show that the additional bid types are easily incorporated into the existing market with no technological barrier and that they preserve the market's efficiency and incentive-compatibility properties. Using real market data, we give a numerical demonstration that the extended bid format could substantially increase social welfare, and also present additional insight on storage expansion scenarios.

FlexAuc: Serving Dynamic Demands in a Spectrum Trading Market With Flexible Auction

In secondary spectrum trading markets, auctions are widely used by spectrum holders (SHs) to redistribute their unused channels to secondary wireless service providers (WSPs). As sellers, the SHs design proper auction schemes to stimulate more participants and to maximize the revenue from the auction. As buyers, the WSPs determine the bidding strategies in the auction to better serve their end users. In this paper, we consider a three-layered spectrum trading market consisting of the SH, the WSPs, and the end users. We jointly study the strategies of the three parties. The SH determines the auction scheme and spectrum supplies to optimize its revenue. The WSPs have flexible bidding strategies in terms of both demands and valuations considering the strategies of the end users. For this market, we design a novel auction mechanism called FlexAuc to enable dynamic supplies and demands in the auction. We theoretically prove that FlexAuc not only maximizes the social welfare but also preserves other nice properties such as truthfulness and computational tractability.

Optimal Power Market Participation of Plug-In Electric Vehicles Pooled by Distribution Feeder

Electric-vehicle grid integration has the potential to stress distribution network equipment and increase peak consumption, unless properly managed. In this paper, we use dynamic programming to develop a decision support algorithm and market participation policy for a load aggregator (LA) managing the charging of plug-in electric vehicles (PEVs) connecting at the same distribution network feeder. The LA submits inflexible and flexible bids to a liberalized hour-ahead power market, while monitoring localized feeder and PEV rate constraints. Flexible bids, which include a bid price or utility, can be cleared as regulation service, cleared as energy, or rejected by the market operator. These market events are probabilistically included within the modeling framework. A case study, based on New York independent system operator data, found that the market participation policy may reduce daily electricity costs for PEVs significantly more than is expected through forecasted electricity-price-based scheduling.

Management of price uncertainty in short-term generation planning

Price uncertainty is faced by Gencos in the scheduling of their units in competitive power markets. The proper way to deal with the uncertainty depends on the risk preference of the Genco. Two important means to manage the price uncertainty are (i) suitable flexible bids and (ii) the use of hedging tools such as forward contracts. The influence of these factors in the Genco's short-term generation planning and the corresponding profit performances is studied. The market price is represented by lognormal distribution, Genco risk behaviour is represented by exponential utility functions, the bid functions are taken to be flexible and simple contracts for hedging are assumed available. The unit commitment problem is combined with the hedging problem to obtain the optimal solution. Formulation of the problem to maximise profit in the spot market and its extension to incorporate the risk behaviour of the Gencos and the forward contacts for hedging is presented. Solution method based on genetic algorithms is implemented in MATLAB. It is observed through numerical examples that the flexible outputs and the forward contracts can be used to hedge against price risks to achieve desired profit performance according to Gencos' risk behaviour.

Cooperation and competition of agents in the auction of computer bridge

AbstractThe auction in contract bridge is a game with incomplete information. This paper models the process as an interaction among agents with a hypothetical reasoning mechanism. The criterion for the action of each agent is defined as “maximizing gain by cooperating with the partner and minimizing loss by competing with the opponents.” An agent with the following characteristics is created. Based on the course of bidding, the hands of both sides are estimated by hypothetical reasoning. Then flexible bids are made in an effort to obtain the contract with the greatest gain or smallest loss of points. Experiments show that in a game with incomplete information, it is useful not only to send information to the partner, but also to disrupt information exchange by the opponents. The effect of ambiguity of information on the behavior of both sides is discussed. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(12): 76–86, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10144

Partnership bidding for computer bridge

In order to develop a computer bridge system that can play on an equal basis with the expert, various problems which are inherent to the incomplete information game must be solved. The authors intend to develop a new problem-solving technique through the challenge of computer bridge. In the bidding process in bridge, the player makes an effort to arrive at a better contract through collaboration involving the exchange of information by restricted bids. This paper reports a framework which was devised aiming at more humanlike (flexible) bidding than past programs. As the first step, the partnership in the bidding process is modeled as a communication between two agents with common knowledge and hypothetical reasoning mechanisms. The knowledge is divided into expertise and common sense, and a bid of the partner which deviates from the former is handled by the latter. In inferring the hand of the partner, the partner's conception of the player's own hand is also estimated, and a more adequate hypothesis is posed. As a result, a program that can perform flexible bidding according to the response of the partner is constructed. © 2000 Scripta Technica, Syst Comp Jpn, 31(2): 72–82, 2000

Partnership bidding for computer bridge

In order to develop a computer bridge system that can play on an equal basis with the expert, various problems which are inherent to the incomplete information game must be solved. The authors intend to develop a new problem-solving technique through the challenge of computer bridge. In the bidding process in bridge, the player makes an effort to arrive at a better contract through collaboration involving the exchange of information by restricted bids. This paper reports a framework which was devised aiming at more humanlike (flexible) bidding than past programs. As the first step, the partnership in the bidding process is modeled as a communication between two agents with common knowledge and hypothetical reasoning mechanisms. The knowledge is divided into expertise and common sense, and a bid of the partner which deviates from the former is handled by the latter. In inferring the hand of the partner, the partner's conception of the player's own hand is also estimated, and a more adequate hypothesis is posed. As a result, a program that can perform flexible bidding according to the response of the partner is constructed. © 2000 Scripta Technica, Syst Comp Jpn, 31(2): 72–82, 2000