Bidding Framework Research Articles

Decentralized micro-energy storage capacity sharing within the residential community: an enhanced uniform price-based bidding framework

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

Trading strategies of energy storage participation in day-ahead joint market based on Stackelberg game

The goal of "carbon peak, carbon neutral" and the increasing expansion of new energy have helped to advance the development of energy storage. However, since the operating cost of energy storage is high, carbon emission trading and power market trading have emerged, effectively improving the efficiency. In this paper, a trading strategy and bidding framework of energy storage participation in the day-ahead joint market are studied. A market bidding model has been established in a framework based on the Stackelberg game. Finally, the "Day-Ahead and Intra-Day and Carbon Emission Trading (CET)" market clearing model has been constructed. It has been simplified to solve the equivalent mixed-integer linear programming (MILP) problem with equilibrium constraints through the use of the Karush–Kuhn–Tucker (KKT) optimality criterion and duality principle. The proposed model is validated through improved examples to obtain thermal unit output cuts of up to 32.2% during load trough periods, and up to 16.75% increase in clearing prices during peak load periods. The storage life is extended and the storage output variation is minimized.

Smart Blockchain Bidding System

Abstract: The E-auction is a prevalent e-commerce event that enables bidders to place bids directly on the Internet. Similar to sealed deals, intermediaries are required to facilitate transactions, as third parties play a crucial role in negotiating during the auction between buyers and sellers. However, the trustworthiness of third parties is never guaranteed. This project showcases a bidding framework utilizing blockchain technology. The bidding application is developed using the Advanced Encryption Standard (AES) Algorithm, which specifies the number of transformation rounds required to convert the input, known as plain text, into the final output, known as cipher text

Economic evaluation of a power-to-hydrogen system providing frequency regulation reserves: a case study of Denmark

Operating costs are dominant in the hydrogen production of a power-to-hydrogen system. An optimal operational strategy or bidding framework is effective in reducing these costs. However, it is still found that the production cost of hydrogen is high. As the electrolysis unit is characterized by high flexibility, providing ancillary service to the grid becomes a potential pathway for revenue stacking. Recent research has demonstrated the feasibility of providing such a service, but the related economics have not been well evaluated. In this work, we propose a comprehensive operation model to enable participation in the day-head, balancing and reserve markets. Three types of reserves are considered by using different operational constraints. Based on the proposed operation framework, we assess the economic performance of a power-to-hydrogen system in Denmark using plentiful actual market data. The results reveal that providing frequency containment reserve and automatic frequency restoration reserve efficiently raises the operational contribution margins. In parallel, by investing in the cash flows, net present value, and break-even hydrogen prices, we conclude that providing reserves makes the power-to-hydrogen project more profitable in the studied period and region.

Data-Driven Multi-Resolution Probabilistic Energy and Reserve Bidding of Wind Power

The current wind farm control schemes qualify wind power producers (WPPs) to provide balancing services in complement to energy in modern electricity markets. Accordingly, WPPs are responsible for real-time deviations in both energy and reserve market floors, which are settled at different time scales. WPPs should adjust their output to cope with fast wind variations, which are critical in the balancing stage. In this paper, we devise a reliable high-temporal-resolution day-ahead bidding framework for WPPs considering the ultra-short-term wind stochasticity. To that end, the model for the bidding strategy is enriched with a probabilistic constraint controlling the confidence level on reserve bids to enhance the reliability of the offered capacity. Additionally, an original Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is proposed to generate high-temporal-resolution wind speed scenarios to be embedded into the bidding framework. The numerical results firstly confirm the superiority of the proposed ACWGAN over the other GAN-based alternatives. For instance, ACWGAN can reach 30% higher classification accuracy compared to conditional Wasserstein GAN. Then, the effectiveness of the proposed data-driven method over its single-resolution counterpart and other scenario representation methods is verified regarding the minimization of the negative impact of wind variability on WPPs' profit and reliability of offered reserve bids.

Comparisons of Auction Designs Through Multiagent Learning in Peer-to-Peer Energy Trading

Distributed energy resources (DERs), such as solar panels, are growing rapidly and reshaping power systems. To promote DERs, utility companies usually adopt feed-in-tariff (FIT) to pay DER owners (aka prosumers) fixed rates for supplying energy to the grid. As an alternative to FIT, consumers and prosumers can trade energy in a peer-to-peer (P2P) fashion. In this paper, we focus on a P2P market using double auctions, in which the payoffs of energy consumers/prosumers are determined by their bids and auction mechanisms. Special features of a P2P energy auction, however, including zero marginal cost and publicly-known reserve prices, may invalidate many theories on auction design and hinder market development. We discuss the impacts of such features on four specific clearing mechanisms: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -double, Vickrey, McAfee and maximum volume matching (MVM). Furthermore, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning, in which each agent only needs to utilize their own bidding history to determine how to bid in the next round through certain regret-minimizing algorithms. Numerical results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -double and McAfee auction appear to perform better in terms of bidders’ surplus. However, if the auctioneer also requires compensation, MVM can yield the most profit for the auctioneer.

Hybrid optimization for collaborative bidding strategy of renewable resources aggregator in day-ahead market considering competitors' strategies

Fueled by the increasing penetration of renewable energy in the power system, it is a promising way to form renewable energy aggregators so as to improve their competitiveness in the electricity market. This paper focuses on aggregators integrating wind, solar, hydropower and storage, where hydro works to moderate fluctuations and storage helps improve the profitability of the aggregator. The paper establishes a two-phase stochastic information-gap-decision-theory (IGDT) based bidding framework for such aggregator. The interaction between the aggregator and the competitive market is modeled as a stochastic bi-level problem, in which the strategic behaviors of self-interested competitors are fully considered. A density-based K-means clustering algorithm combining seasonal factors is proposed to achieve predicted offering scenarios of rival generators based on their historical market data. The hybrid stochastic-IGDT handles competitors’ offer strategies through scenario-based stochastic programming, and manages uncertainties of wind and solar generation under risk-averse and risk-seeking strategies. The results from Elia’s data demonstrate that the proposed bidding model not only shows better adaptability to the competitors' strategic behaviors, but also generates more profit for the aggregator through collaborative bidding of multiple sources.

Evaluating the emotional bidding framework: new evidence from a decade of neurophysiology

Ten years ago the “emotional bidding framework” (Adam et al., Electronic Markets, 21(3), 197–207, 2011b) was published in this journal. It provided a conceptualization for the role of human emotion in electronic auctions along six propositions on how emotions emerge during the auction process and affect auction outcomes. While the framework emphasized the importance of immediate emotional responses and momentary changes in the bidders’ emotional state, the original article did not include an evaluation of its propositions given the limited data on how bidders experience emotions in the moment that they occur. Ten years on, advances in the growing research field of NeuroIS allow to evaluate the propositions based on neurophysiological evidence. As a rejoinder of the original article, the present paper synthesizes these insights, refines the framework further, and identifies fruitful areas for future research based on remaining gaps in the body of knowledge.

Stochastic Optimal Operation Framework of an Integrated Methane-Based Zero-CO2 Energy Hub in Energy Markets

Energy hubs (EHs) are couplers between different energy carriers in smart grids. The optimal participation of these actors in energy markets (EMs) as active and helpful actors is essential. This paper presents a new structure of methane-based EH considering biomass fuel to participate in the EMs of electricity, heat, and natural gas (NG). For this purpose, we propose an optimal bidding framework for the EH as a MILP stochastic optimization problem. The EH does not inject any CO2 pollution into the air (zero-CO2) and converts it into valuable methane (CH4) fuel using the CH4 production unit. To model uncertain parameters, electricity market price, wind speed, and solar radiation, an LSTM-based model of deep learning is proposed for scenario generation. Moreover, the Kantorovich distance matrix method reduces the generated scenarios. Since the proposed EH structure is compatible with Finland's infrastructure, simulation studies using actual data of this country are performed on selected days. The results show that in addition to profitable operation, high flexibility, environmental friendliness, and high accuracy of uncertainty modeling, the EH has no dependence on the purchase of energy carriers.

Strategic bidding in electricity markets with convexified AC market-clearing process

This paper presents a framework to solve the strategic bidding problem of participants in an electricity market cleared by employing the full AC Optimal Power Flow (ACOPF) problem formulation. Traditionally, the Independent System Operators (ISOs) leveraged DC Optimal Power Flow (DCOPF) problem formulation to settle the electricity market. The main quest of this work is to find what would be the challenges and opportunities if ISOs leveraged the full ACOPF as the Market-Clearing Problem (MCP)? This paper presents tractable mathematical programming with equilibrium constraints for the convexified AC market-clearing problem. Market participants maximize their profit via strategic bidding while considering the reactive power dispatch of generation units. The equilibrium constraints are procured by presenting the dual form of the relaxed ACOPF problem. The strategic bidding problem with an ACOPF-based MCP improves the exactness of the Location Marginal Prices (LMPs) and profit of market participants compared to the one with DCOPF. It is shown that the strategic bidding problem with DCOFP-based MCP is unable to model the limitation of reactive power support. The presented results display cases where the proposed strategic bidding method renders 52.3% more profit for the Generation Company (GENCO) than the DCOPF-based MCP model. The proposed strategic bidding framework also addresses the challenges of coupling real and reactive power dispatch of generation constraints, ramping constraints, demand response implications with curtailable and time shiftable loads, and AC line flow constraints. Therefore, the presented method will help market participants leverage the more accurate ACOPF model in the strategic bidding problem.

An operational bidding framework for aggregated electric vehicles on the electricity spot market

Fluctuating electricity prices offer potential economic savings for the consumption of electricity by flexible assets such as Electric Vehicles (EVs). This study proposes an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market. The framework consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module. In this study we develop and evaluate several regression and machine learning models that forecast the electricity price and EV charging demand. Furthermore, we examine the composition of a most optimal operational bidding framework by comparing the outcome of the bidding module when fed with each of the forecast models. This is determined by considering the day-ahead electricity price and imbalance costs due to forecast errors. The study demonstrates that the best performing self-contained forecast models with the objective of electricity price and EV charging demand forecasting, do not deliver the best overall results when included in the bidding framework. Additionally, the results show that the best performing framework obtains a 26% cost savings compared to a reference case where EVs are charged inflexibly. This corresponds to an achieved savings potential of 92%. Consequently, along with the developed bidding framework, these results provide a fundamental basis for effective electricity trading on the day-ahead market.

Blockchain Based Bidding System

The E-auction, one of the most common e-commerce events, allows bidders to bid directly on the Internet. As with the sealed deal, additional transaction costs are needed with intermediaries, as the third party plays an essential role between buyers and sellers in helping to negotiate both during the auction. In fact, it never confirms that a third party is trustworthy. To solve the problems, we propose the low transaction cost blockchain technology which is used to build the public bid and sealed bid smart contract. The smart contract consists of the Auctioneer address, the start time for the auction, the deadline, the current winner's address, and the current highest price. The project demonstrates the bidding framework with blockchain technology. This bidding application prepared by using Advanced Encryption Standard(AES) Algorithm. This algorithm contains AES cipher specifiers the number of repetitions of transformation rounds, that converts the input called the plain text, then into the final output called cipher text.

Bars Behind Bars: Digital Technology in the Prison System

With little opportunity to engage with technology while behind bars, returning citizens are finding themselves on the far side of the digital divide and increasingly vulnerable to recidivism. Investing in a well-run digital literacy program for our prison system is an innovative solution to a persistent problem and a rare win-win situation for inmates, prison officials, and American taxpayers. We begin by discussing how inmate tablet distribution programs mutually benefit both inmates and prison officials. We then explore prison profiteering by technology companies as a potential obstacle to the successful administration of technology programs, discussing the emergence of virtual monopolies in the prison technology space, their history of controversial pricing practices, and how these practices are perpetuated through prison tablet programs. We then present novel insights into how competitive bidding can be used as a public policy instrument to regulate competition, specifically in the context of prison technology. We argue that a traditional bidding framework is insufficient to act as a policy instrument and propose an alternative incentive-based framework toward this end. We conclude by outlining several practical recommendations that prison officials should consider when administering digital literacy programs in their facilities.

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit–enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Then, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed data-driven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

Learning End-User Behavior for Optimized Bidding and User/Network Association

We study the impact of end-user behavior on service provider (SP) bidding and user/network association in a HetNet with multiple SPs while considering the uncertainty in the service guarantees offered by the SPs. Using Prospect Theory (PT) to model end-user decision making that deviates from expected utility theory (EUT), we formulate user association with SPs as a multiple leader Stackelberg game where each SP offers a bid to each user that includes a data rate with a certain probabilistic service guarantee and at a given price, while the user chooses the best offer among multiple such bids. We show that when users underweight the advertised service guarantees of the SPs (a behavior observed under uncertainty), the rejection rate of the bids increases dramatically which in turn decreases the SPs utilities and service rates. To overcome this, we design a two-stage learning-based optimized bidding framework for SPs. In the first stage, we use a support vector machine (SVM) learning algorithm to predict users’ binary decisions (accept/reject bids), and then in the second stage we use a dynamic programming based optimized bidding (DPOB) algorithm to efficiently arrive at the optimal SP bids. Simulation results and computational complexity analysis validate the efficiency of the bidding framework.

Research on the Optimization of the Purchasing Methods of Chinese State-Owned Enterprises from the Perspective of Supply Chain Procurement

As the backbone of China’s economic development, the procurement of state-owned enterprises has a far-reaching impact on the breadth and depth of the whole procurement field. With the rapid development of China’s economy and the accelerating pace of GPA accession, the role of procurement in the supply chain of Chinese state-owned enterprises has become more prominent. Based on the procurement dilemma of Chinese state-owned enterprises and the characteristics of procurement under the supply chain environment, this paper dialectically analyzes the relationship among supply chain management and procurement of Chinese state-owned enterprises, and compares the advantages and disadvantages of the general framework agreement procurement method and the bidding framework agreement procurement method from the perspective of supply chain procurement. On this basis, it explores that the consignment bidding framework agreement procurement is an effective way to optimize the procurement method of Chinese state-owned enterprises.

Identification and Estimation in First-Price Auctions with Risk-Averse Bidders and Selective Entry

We study identification and estimation in first-price auctions with risk averse bidders and selective entry, building on a flexible entry and bidding framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. This framework extends the AS model of Gentry and Li (2014) to accommodate arbitrary bidder risk aversion, thereby nesting a variety of standard models as special cases. It poses, however, a unique methodological challenge – existing results on identification with risk aversion fail in the presence of selection, while the selection-robust bounds of Gentry and Li (2014) fail in the presence of risk aversion. Motivated by this problem, we translate excludable variation in potential competition into identified sets for AS-RA primitives under various classes of restrictions on the model. We show that a single parametric restriction – on the copula governing selection into entry – is typically sufficient to restore point identification of all primitives. In contrast, a parametric form for utility yields point identification of the utility function but only partial identification of remaining primitives. Finally, we outline a simple semiparametric estimator combining Constant Relative Risk Aversion utility with a parametric signal-value copula. Simulation evidence suggests that this estimator performs very well even in small samples, underscoring the practical value of our identification results.

Constructing a V2G-enabled regional energy Internet for cost-efficient carbon trading

The increasing penetration of plug-in electric vehicles (PEVs) has highlighted the importance of coordinating ubiquitous distributed energy resources (DERs) via the internet of things (IoT). With the help of vehicle-to-grid (V2G) technology, PEVs can be aggregated to behave as a storage system, yielding both economic and environmental benefits. In this paper, we propose an optimal bidding framework for a V2G-enabled regional energy internet (REI) to participate in day-ahead markets considering carbon trading. The REI operator aims to maximize the net profits from day-ahead markets while anticipating realtime adjustments. A detailed battery model is developed to depict the charging and discharging capability of V2G-enabled PEVs. A two-stage stochastic optimization model is formulated to schedule the operation of PEV fleets against various sources of uncertainties, e.g., the arrival and departure time of PEVs, solar power and real-time prices. Case studies undertaken based on realistic datasets demonstrate that the coordination of the V2G-enabled PEVs and other DERs can facilitate the accommodation of renewable energy, thus improving the REI's revenues in energy and carbon markets.

Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots

In this letter, we propose a low-cost, high-efficiency framework for cooperative active pose-graph simultaneous localization and mapping (SLAM) for multiple robots in three-dimensional (3D) environments based on graph topology. Based on the selection of weak connections in pose graphs, this method aims to find the best trajectories for optimal information exchange to repair these weaknesses opportunistically when robots move near them. Based on tree-connectivity, which is greatly related to the D-optimality metric of the Fisher information matrix (FIM), we explore the relationship between measurement (edge) selection and pose-measurement (node-edge) selection, which often occurs in active SLAM, in terms of information increment. The measurement selection problem is formulated as a submodular optimization problem and solved by an exhaustive method using rank-1 updates. We decide which robot takes the selected measurements through a bidding framework where each robot computes its predicted cost. Finally, based on a novel continuous trajectory optimization method, these additional measurements collected by the winning robot are sent to the requesting robot to strengthen its pose graph. In simulations and experiments, we validate our approach by comparing against existing methods. Further, we demonstrate online communication based on offline planning results using two unmanned aerial vehicles (UAVs).

Identification and Inference in First-Price Auctions with Risk Averse Bidders and Selective Entry

We study identification and inference in first-price auctions with risk averse bidders and selective entry, building on a flexible entry and bidding framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming that the econometrician observes either exogenous variation in the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. Given variation in either competition or costs, this characterization implies that risk neutrality is nonparametrically testable in the sense that if bidders are strictly risk averse, then no risk neutral model can rationalize the data. In addition, if both instruments (discrete N and continuous z) are available, then the model primitives are nonparametrically point identified. We then explore inference based on these identification results, focusing on set inference and testing when primitives are set identified. Keywords: Auctions, entry, risk aversion, identification, set inference.