Bidding Model Research Articles

Optimizing Power Market Clearing with Segmented Electricity Prices: A Bilevel Model

The energy sector plays a critical role in reducing emissions by transitioning to renewable energy. However, integrating renewable energy into the electricity market impacts market clearing. This article investigates market clearing decisions of renewable and traditional energy companies, considering electricity price segmentation. We propose an interprovincial bilevel market clearing decision-making model. The upper level optimizes the provincial market through a unilateral bidding model, minimizing the operating cost of the power market as the objective function. Meanwhile, the lower level optimizes power purchases by minimizing the cost of acquiring interprovincial renewable energy. A case study was conducted to determine the optimal interprovincial renewable energy purchasing power for multiple energy generation companies, considering the minimum market operating cost and segmented electricity prices. The proposed bilevel market clearing model captures the interplay between intraprovincial and interprovincial transactions, integrating electricity price and ecological attributes. By solving this model, we can attain optimal interprovincial electricity purchase of renewable energy, minimizing the overall market operating cost in the province. Through simulation, 18% of interprovincial transactions are needed to absorb 7.21 KWH of renewable energy from other provinces. When considering price segmentation, the cross-provincial two-level market clearing model can reduce the market cost by CNY 1,554,700.

A Wasserstein‐distance‐based distributionally robust chance constrained bidding model for virtual power plant considering electricity‐carbon trading

AbstractVirtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach.

A latent auction model of firm establishment location choice in the Greater Toronto Area

We present a maximum bid model for business establishments in the Greater Toronto Area (GTA). The model is based on a latent auction approach, wherein price and utility are jointly modeled in a random utility-maximizing framework. Structural relationships between establishments by their industry classification and type (i.e., headquarters, subsidiary, branch, or single location) are explored through a generalized nesting correlation structure. This structure provides a mechanism for differentiating between branch and single-location establishments, having seemingly similar functions, but differing in their allocation between industry and type nests. Maximum bid models are estimated for the full establishment population in the GTA at the level of individual buildings. The model scope allows us to provide a range of insights into establishment location choice behavior, which was previously infeasible due to data limitations. We find that professional service establishments tend to locate near passenger rail stations (i.e., subway and LRT), while industrial establishments tend to locate near major highway interchanges. We confirm the finding by Kenworthy and Laube (1999) that Canadian cities are less auto-dependent than US cities, albeit from the perspective of establishment location rather than individual vehicle travel data.

Cooperative Power Bidding of Smart Community Grids With an Aggregator-Prosumer-Based Hierarchical Framework

The progressive development of electricity markets envisions the participation of smart community grids in energy trading, but it is still challenging to guarantee global optimality for uncertain power management of multiple entities under a hierarchical framework. To tackle the cooperative strategic bid-ding problem with uncertainties, this paper proposes a hierar-chical three-stage robust optimization (HTS-RO) method. With respect to the bilayer coordination framework involving an ag-gregator gathering multiple prosumers, a novel HTS-RO bidding model is firstly established relying on the canonical centralized two-stage RO model. In the first stage, the aggregator performs power bidding in the upper layer according to the derived power flow information of the shared buses from the lower-layer prosumers. Considering renewable-load uncertainties and pri-vacy preservation, each prosumer conducts its own robust scheduling via the second and third stages based on the bidding prices from the aggregator and submits its optimal results of the shared bus back to the aggregator. For the robust scheduling model of each prosumer, the second stage identifies the basic plans of the nominal scenario, and the third stage checks the fea-sibility of the basic plans under uncertainties. To solve the re-sulting intractable multi-level mixed-integer nonlinear pro-gramming, a tri-loop decomposition-based iterative algorithm is tailored to ensure that the distributed computation converges to the optimal solution with the overall profit, thereby achieving the equivalent split of the centralized optimization into a hierarchical manner. Numerical tests on generic smart community grids with multiple prosumers validate the applicability and superiority of the proposed HTS-RO method for power bidding.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Online Truthful Incentives for Heterogeneous and $K$-Submodular Crowdsensing

Recently, crowdsensing receives extensive attentions to solve complex sensing tasks. However, these existing works mainly focus on the incentive issues of traditional bidding model. In practice, solutions of the incentive issues of the posted-pricing model, are more important for some realistic mobile crowdsensing (MCS) applications. However, only few works focus on the homogeneous MCS applications (each user can perform only a single task). To this end, in this article we propose a posted-pricing incentive mechanism for heterogeneous MCS applications (different users have different limits of the number of assignments). Furthermore, considering more general MCS applications (satisfying <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> -submodular function: 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> -set functions that are submodular in every type <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\in [K]$</tex-math></inline-formula> ), we define the weighted marginal utilities and explore two posted-pricing incentive mechanisms for heterogeneous and <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> -submodular MCS applications, respectively. Moreover, both of the two mechanisms are based not only on heterogeneous or <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> -submodular sensing tasks, but also on heterogeneous sensing types. Finally, performance analysis shows that our mechanisms satisfy the desired properties, such as the truthfulness of acceptance decision, the individual rationality,budget feasibility,computational efficiency, and the regret minimization. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our proposed mechanism.

RETRACTED: Robust bidding strategy of interconnected multi-carrier systems in the electricity markets under the uncertainty in electricity load

This paper presents a robust bi-level model for optimal bidding of interconnected multi-carrier systems (MCSs) in the wholesale electricity markets under the uncertainty in electricity load. The proposed model implements a robust optimization method (ROM) to model the risk-based bidding of interconnected MCSs under the uncertainty in load in the upper-level problem while including the energy and reserve markets in the lower-level problem. The studied MSCs include combined heat and power (CHP) units, boilers, central air conditioning (CAC) systems, and battery storage units to provide local energy and make the MCSs’ operator capable of participating in the reserve market to gain profit. ROM models the electricity demand and provides different bidding strategies (i.e., risk-seeking, risk-neutral, and risk-averse) for the participation of MCSs’ operator in the energy and reserve markets. The studied bi-level model is converted to a single-level mixed-integer linear programming problem using the Karush–Kuhn–Tucker conditions and strong duality and then solved using the CPLEX solver of the General Algebraic Modeling System. Numerical experiments reveal that the interconnection of MSCs, along with the integration of storage units, enables the MSCs’ operator to control the uncertainty in the electricity load and increase the energy efficiency in order to optimally bid in the day-ahead energy and reserve markets. Results show that by enabling the MCSs to locally trade energy, the total operation cost of MCSs in the risk-seeking, risk-neutral, and risk-averse modes decreases by 5.65%, 4.28%, and 3.49%. Moreover, with the integration of battery storage systems into the interconnected operation of MCSs, the total operation cost of systems in the risk-seeking, risk-neutral, and risk-averse modes decreases by 7.65%, 6.99%, and 6.40%, respectively.

Risk-Aware Battery Bidding With a Novel Benchmark Selection Under Second-Order Stochastic Dominance

This paper studies the risk management of a battery bidding in both day-ahead and intraday markets arising from the uncertain nature of electricity prices. To this end, a coherent risk measure, Second-order Stochastic Dominance (SSD), which is capable of expressing battery preferences in the form of a preset fixed benchmark (profit), is incorporated into the bidding model. The SSD serves the decision-maker as a risk-averse optimizer exploring for profit distribution members greater than a preset fixed benchmark. The most challenging facet of SSD-constrained methodologies is how to effectually define the preset fixed benchmark. In this regard, first, a generic approach is offered to find the feasible region for benchmark selection in SSD-constrained optimization problems. Then, a novel benchmark selection technique considering both the decision-maker's regret and out-of-sample profit, leverages the VIKOR method to get the ranking of different solutions and find the compromise benchmark in the risk-aware environment. Consequently, two decisive criteria from both ex- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ante</i> and ex- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">post</i> tests are involved in the benchmark selection procedure, making the bidding problem regret- and consequence-aware. The numerical results of the developed methodology against risk-neutral and deterministic approaches show the efficiency of the proposed model.

A novel load-side settlement mechanism based on carbon emission flow in electricity spot market

In the electricity spot market (ESM), thermal power plants (TPP) are quoted based on fuel costs and carbon costs, which impose carbon costs from the generation side to the load-side. The cost of load-side participation in the ESM will increase further. In addition, the traditional ESM centralized bidding model is based on the marginal clearing price, without considering the line transmission constraints, which may affect the security of the power system. To solve this challenge, firstly, an ESM centralized bidding clearing model including the generation side and the load-side is established based on the transaction mechanism of centralized bidding in the ESM. In this model, the marginal clearing prices of two possible bidding scenarios in the ESM are considered. Secondly, the marginal clearing price of the ESM is designed based on the security constraint of the power flow, which can avoid power transmission exceeding its capacity. Finally, a carbon emission cost settlement mechanism is proposed based on the carbon emission flow (CEF) method, which can reduce the carbon emission cost paid by the load-side. The effectiveness of the method is verified by simulations. Compared with a traditional load-side settlement model, the settlement cost on the load-side is reduced by 30.45 %.

Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties

Multi-energy virtual power plant (MEVPP) can aggregate flexible resources such as energy storage and flexible loads that decentralized in the region to meet the access conditions in the peak-regulation ancillary service market. However, the uncertainties in energy sources and loads bring adverse impact on the operation of MEVPP. Therefore, this paper proposes a day-ahead robust bidding strategy for MEVPP to participate in the peak-regulation market. Firstly, this paper analyzes the impact of uncertainties for MEVPP on the peak-regulation market. On this basis, the operation mechanism for MEVPP in the peak-regulation market is proposed by considering the integrated demand response (IDR). Additionally, the day-ahead two-stage robust bidding model is established to minimize the operation cost of MEVPP. Finally, the case studies show that the day-ahead robust bidding strategy can effectively reduce the peak-regulation deviation penalty compared with traditional deterministic optimization. Specifically, with the proposed robust bidding strategy, the total revenue in the actual operation stage is increased by 5.16% and 8.45% on sunny day and raised by 8.28% and 15.35% on cloudy day when the predicted deviations are respectively 20% and 30%, comparing with traditional deterministic optimization.

Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market

Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.

Transactive Operational Framework for Internet Data Centers in Geo-Distributed Local Energy Markets

Internet data centers (IDCs), which can regulate the spatial and temporal load distribution, and manage on-site energy resources, are promising candidates to enhance the connected distribution network's operation. The recent emergence of local energy trading provides a new opportunity for IDCs to trade energy with end energy customers in low-voltage distribution networks and enhance their operational energy efficiency, and this has not been well investigated in the literature. Motivated by this, this paper proposes a two-stage transactive operation framework for a group of geo-distributed IDCs to engage in local energy markets. In the first stage, an ex-ante bidding model is proposed, which optimally schedules the IDCs' cyber-energy resources (computing requests and on-site battery energy storage system) and determines energy trading prices in the local energy markets. The bidding model aims to minimize the cloud service provider (CSP)'s total cost. In the second stage, a real-time energy balancing model is proposed to adjust the IDCs' energy volumes and faciliate them to offer energy balancing services to the power distribution networks in terms of alleviating energy supply-demand imbalances. The coupling relationship among the IDCs' operation strategies, energy trading prices, and energy balancing signals are modeled in the framework. Extensive numerical case studies are implemented to demonstrate the effectiveness of the proposed framework. The simulation results show that the framework can reduce a considerable proportion of total operation cost for networked IDCs and can facilitate the CSP to effectively assist power distribution networks in balancing the energy supply and demand in real-time operation.

Design of a Structured Parsing Model for Corporate Bidding Documents Based on Bi-LSTM and Conditional Random Field (CRF)

International projects are often realized through the bidding process, but the existence of information exchange barriers in different countries leads to a complex and tedious bidding process. In this paper, the authors study the complex problem of reading bidding documents, combine the artificial intelligence to analyze them in a structured way, and realize the intelligent analysis of bidding documents. Firstly, through the CRF model, the structured analysis of the tender document is carried out according to the title of the tender document, and the extraction of quoted price, technology and commercial part is realized. Secondly, the detailed analysis of quoted price and technology is completed using the Bi-LSTM method, and the main five key feature extraction and analysis are completed. Finally, based on the CRF-Bi-LSTM method, the actual test is carried out, and the correlation coefficient is as high as 0.965. The results show that the structured parsing model proposed has good application prospects.

A Ridepooling Bidding Model Considering Service Quality from the Perspective of Cournot Game Theory

Pricing for ridepooling services may be affected by travel needs, penalties, and ride service quality. Firstly, this paper explores pricing methods based on the perceived value of the ride as compared to the distance–time prices and penalties and proposes a ridepooling travel utility function for pricing strategies. Then this paper develops a combined optimization ridepooling scheduling model integrating order allocation and path optimization (CORM), and designs a hybrid simulated annealing (HSA) heuristic algorithm for solving this model. Based on the Cournot game theory, this paper further develops a ridepooling bidding model (RBM) that can simultaneously optimize the seekers’ utility and the profit of the ridepool platform. The effectiveness of the CORM and HSA are verified, and the superiority of the RBM has also been proved by a numerical simulation example. The results show the following: (1) neither the low distance–time price and high penalty price strategy nor the high distance–time and low penalty price strategy is an absolute advantage strategy for ridepooling passengers and the platform; (2) compared with the CORM, the RBM can not only effectively improve the profit of ridepooling platform, but also improve the success rate of ridepooling.

Modeling for wind-thermal combined bidding considering bilateral tail information

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper.

Energy Storage State-of-Charge Market Model

This paper introduces and rationalizes a new model for bidding and clearing energy storage resources in wholesale energy markets. Charge and discharge bids in this model depend on the storage state-of-charge (SoC). In this setting, storage participants submit different bids for each SoC segment. The system operator monitors the storage SoC and updates their bids accordingly in market clearings. Combined with an optimal bidding design algorithm using dynamic programming, our paper shows that the SoC segment market model provides more accurate representations of the opportunity costs of energy storage compared to existing power-based bidding models. The new model also captures the inherent SoC-dependent operational characteristics of energy storage. We benchmark the SoC segment market model against an existing single-segment model in price-taker and price-influencer simulations. The simulation results show that compared to the existing power-based bidding model, the proposed model improves profits by 10-56% in the price-taker case study; the model also improves total system cost reduction from storage by around 5%, and helps reduce price volatilities in the price-influencer case study.

Day-Ahead Optimal Bidding for a Retailer With Flexible Participation of Electric Vehicles

The existing bidding models for retailers managing electric vehicles (EVs) in distribution-level day-ahead (DA) electricity markets have not fully addressed EVs’ temporal distribution, charging and discharging management, or bidding curves. To address these challenges, this paper proposes a DA optimal bidding model for a retailer with flexible participation of EVs. First, the investigating period for DA optimal bidding is modeled to cover the connecting periods of all EVs that may potentially impact the DA power demand prediction. Then, incentive mechanisms for charging and discharging are proposed to enable retailers to manage EVs under uncertain market prices and EV parameters while also considering the battery degradation cost and the preferences of EV users based on a social survey. The optimal charging model based on incentive mechanisms for charging-discharging helps EVs minimize their energy purchase costs under different price scenarios. Based on the minimum EV energy purchase cost, the optimal bidding model of a retailer aims to achieve the maximum bidding profit considering the conditional value at risk (CVaR), with the step bidding curves and the temporal differences for different time intervals considered. Simulations validate the proposed model under flexible incentive mechanism for charging and incentive mechanism for discharging participation of EVs.

The Winner’s Curse in Dynamic Forecasting of Auction Data: Empirical Evidence from eBay

Problem definition: Dynamic forecasting models in auctions have fallen short on two dimensions: (i) the lack of an equilibrium model for final bids and (ii) the lack of a winner’s curse (i.e., a tendency to overpay conditional on winning the auction) adjustment to allow bidders to account for a common value component in the auction item. In this paper, we develop a methodology to accurately predict equilibrium stage bids from the initial bidding dynamics and quantify the impact of the winner’s curse. This methodology allows us to conduct policy simulations to optimize auction design parameters. Methodology/Results: Dynamic auctions typically have a stage of high exploratory activity, followed by an inactivity period, and then an equilibrium stage of last-minute bids with sharp jumps. With a Kalman filter approach, we use exploratory stage bids to predict an auction item’s valuation distribution. We feed this prediction into an equilibrium model and apply item-specific adjustments for winner’s curse, bidder heterogeneity, and inactivity period. We use the resulting equilibrium model to predict the equilibrium stage bids. Our methodology improves the forecast of equilibrium stage bids by 11.33%, on average, compared with a state-of-the-art benchmark. This improvement is even higher (18.99%) for common value auctions. We also find that (i) significantly more (respectively, fewer) bidders internalize the winner’s curse in common value (respectively, private value) auctions; (ii) bidders in common value auctions decrease their bids by 6.03% because of the winner’s curse; and (iii) the inactivity period has a lesser impact on the equilibrium stage bids in private value auctions. Managerial implications: Our proposed methodology is intended to facilitate the need in academia and practice for real-time bid predictions that encompass different levels of the common value component in auctions. Using our methodology, auction platforms can support their choice of minimum bid increment policies and decide how to allocate resources across different auctions to mitigate the adverse effects of the winner’s curse. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1165 .

A multi-rate hybrid model for real-time iterative bidding coupled with power system dynamics

To maintain the system's real-time balance, mature electricity markets generally tend to raise the frequency of market clearing. Given the shrinking time lag between the price update process and underlying electromechanical dynamics, it inevitably considers the two in a unified, dynamical framework. However, the relationship between market activities and the evolution of electricity networks has received scant attention in pertinent research. Inspired by the primal-dual gradient method and modern control theory, we propose a novel multi-rate hybrid model that simultaneously accounts for the continuous nature of physical system dynamics and the discrete nature of market clearing. To maintain the asymptotical convergence of the proposed model, we derive the explicit constraints on the upper bounds of market clearing and bidding intervals. These bounds can be determined without prior knowledge of the Nash equilibrium, and even the time schedules don't have to be periodic, which dramatically improves the applicability of the proposed model in practical market operation. The whole model provides a new perspective to study economic-physical problems, which can be readily implemented in a distributed way and achieve social welfare maximization along with frequency regulation.

Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid

The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market environment. The present work aims to determine optimal day-ahead market bidding curves for a microgrid comprised of a battery, power generator, photovoltaic (PV) system and an electricity load from a commercial building. Existing day-ahead market bidding models heuristically fix price values for each allowed bidding curve point prior to the optimisation problem or relax limitations set by market rules on the number of price–quantity points per curve. In contrast, this work integrates the optimal selection of prices for the construction of day-ahead market bidding curves into the optimisation of the energy system schedule; aiming to further enhance the bidding curve accuracy while remaining feasible under present market rules. The examined optimisation problem is formulated as a mixed integer linear programming (MILP) model, embedded in a two-stage stochastic programming approach. Uncertainty is considered in the electricity price and the PV power. First stage decisions are day-ahead market bidding curves, while the overall objective is to minimise the expected operational cost of the microgrid. The bidding strategy derived is then examined through Monte Carlo simulations by comparing it against a deterministic approach and two alternative stochastic bidding approaches from literature.