Bidding Method Research Articles

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

AbstractAneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Robust optimization bidding strategy for user-side resource-side participation in the market distribution of electrical energy and peaking ancillary services considering risk expectations and opportunity constraints

Compared to traditional resources, user-side resources are of various types and have more significant uncertainty about their regulatory capacity, leading to difficulties in coordinating decisions about their simultaneous participation in the electric energy and peaking ancillary services markets. This paper proposes a joint bidding decision-making method for the day-ahead electricity energy and peak shaving auxiliary service market based on distributed robust opportunity constraints, which addresses the problem of difficulty in using an accurate probability density distribution to represent the uncertainty process of user-side resources. Firstly, a data-driven method for characterizing the uncertainty of load regulation capacity is investigated, and fuzzy sets are constructed without assuming specific probability distributions of random variables. Then, to minimize the risk expectation of the joint bidding cost on the customer side, a bidding strategy that considers the uncertainty is proposed. Finally, an example simulation verifies the reasonableness and effectiveness of the proposed joint bidding method, and the results show that the constructed model overcomes the problem of over-conservatism of the robust model, and the computational adaptability is better than that of the stochastic model, which achieves a better balance between robustness and economy.

A study of electricity sales offer strategies applicable to the participation of multi-energy generators in short- and medium-term markets

Due to the increasing proportion of renewable energy, a multi-layered and multi-timescale energy market has emerged in many countries such as China. In the meanwhile, power generation companies must develop more intelligent and dynamic offer strategies to adapt to today's intricate energy trading. Because of the difficulty in describing the dynamic trading environment caused by the uncertainty of renewable energy, previous studies have not fully explored the offer strategy especially in both short-term and medium-term electricity markets. In response to this challenge, this research introduces a novel biding strategy framework leveraging a Asynchronous Advantage Actor-Critic (A3C) algorithm, which can effectively address the decision making in dynamic and uncertain energy markets. The framework focuses on intra-monthly transaction clearing mechanisms with the aim of optimally enhancing earnings. The research formulates an offer model both for thermal and renewable power generation enterprises, which is applicable to medium-term monthly and intra-monthly trading. The study then validates this framework through three distinct analyses: the returns of various bid methods under standard scenarios, the offer strategies return of power generation companies with diverse cost profiles, and the impact of varying renewable energy proportions. The multi-angle simulations confirm that the model presented in this paper offers a scientific basis for the development of offer strategies for power generation companies and enable power generating firms to effectively adopt to the current power market.

A Showcase for Resilient and Sustainable District Heating in Denmark

This presentation gives a description of the Danish district heating company, Hvide Sande District Heating, which has become independent of fossil fuels by using wind and solar energy. This has resulted in lower consumer heating prices in a time when other fossil fueled district heating plants are raising their heating prices due to higher fossil fuel prices.Besides participating in the Day-ahead market it also participates in the balancing markets.Hvide Sande is a small fishing town. The district heating plant provides heat to 1,700 consumers. From being a natural gas fired Combined Heat and Power plant, it has in recent years become more resilient by investing in a solar collector, wind turbines, a heat pump, an electrical boiler as well as more large thermal storages, and is now independent of natural gas.The two thermal storages of 2,000 m3 and 1,200 m3, respectively, can store around 200 MWh-heat, which allows flexible market-based productions of the different production units. The heat delivered to consumers can thus be produced many hours or days before delivery.Even in the Day-ahead market, the market-based productions are a challenge to plan. Because of the large thermal storages, the manager must look more days ahead as well as consider the heat amount in the thermal storages, when deciding the hourly bids for tomorrow. Biddings are based on forecasts more days ahead of wind velocity, solar radiation, ambient temperatures, and Day-ahead prices.Besides participating in the Day-ahead market it also participates in the balancing markets. However, to take advantage across more electricity markets of this flexibility, a vast digitalization of the plant using advanced bidding methods has been required. This presentation illustrates examples of the daily earnings the plant has had participating across more electricity markets. It is also important that the manager maintains a digital twin of the plant. The digital twin of the plant will in this presentation be used for simulating the resilience of the plant against large changes in electricity prices and fuel prices.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Multi-Domain Collaborative Task Allocation and Conflict Resolution in Unmanned Systems under Complex Constraints

Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.

Intelligent strategic bidding in competitive electricity markets using multi-agent simulation and deep reinforcement learning

Aiming at the lack of comprehension of agents in Multi-Agent Simulation (MAS) based on classic Reinforcement Learning algorithms of competitive electricity markets, an intelligent strategic bidding method using Deep Reinforcement Learning (DRL) and MAS is proposed in this paper, which not only can provide more intelligent strategies for market participants to maximize their profits, but can enhance the performance of simulation models dealing with high-dimensional continuous data in electricity markets. Firstly, a theoretical framework of intelligent strategic bidding in competitive electricity markets based on MAS and DRL is proposed, and the process of intelligent bidding in electricity markets based on MAS and DRL is described. Then, three MAS models of intelligent strategic bidding are built based on three classic DRL algorithms, including Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Asynchronous n-step Q-learning (Async n-step QL), and three algorithms’ convergence speed, computational efficiency, and response sensitivity are compared and analyzed. Finally, a novel Improved Async n-step QL (IAsync n-step QL) algorithm is proposed, the MAS model based on the IAsync n-step QL algorithm for intelligent strategic bidding is established. Simulation results show that the model using the novel DRL algorithm is more profitable and responsive than the classic DRL algorithms.

A Location-Privacy-Aware Taxi-Hailing System: Adaptive Differential Privacy-Based Dynamic Incentive Method

Nowadays location-based service (LBS) has become an important service in people’s daily life. Online taxi-hailing system (DiDi, Uber, etc.) is one of the most common LBS system. As the scale of online taxi services continues to expand, some problems have gradually emerged. Specifically, the uncertainty of taxis and passengers makes it difficult to match them effectively, and the passengers’ location privacy will be threatened from both internal and external of the system. In this paper, we first proposed a Bayesian-based location privacy inference attack method from external adversary’s point of view. After that, an adaptive differential privacy-based dynamic incentive method was proposed. Firstly, an adaptivity clocking area division method was proposed to resist the internal privacy threat. Secondly, a dynamic incentive bidding method was proposed to deal with the trading issue. Thirdly, an exponential-based matching method was proposed to resist the external inference privacy threat. Further, theoretical proofs show that the proposed method satisfy the privacy properties of k-anonymity, 2μ1ε-differential privacy, and the economic properties of incentive compatibility, individual rationality. Finally, the experimental results show that the inference attack would achieve a maximum attack success rate of 95% while ensuring the accuracy within 150 meters, and the proposed adaptive differential privacy-based dynamic incentive method can not only provide a good economic performance in terms of satisfaction ratio, social welfare and travel distance, but also can resist internal privacy threat with less than 1% of privacy leakage probability and reduce the success rate of external privacy inference attacks to 25%.

A Novel Electric Vehicle Aggregator Bidding Method in Electricity Markets Considering the Coupling of Cross-day Charging Flexibility

A Novel Electric Vehicle Aggregator Bidding Method in Electricity Markets Considering the Coupling of Cross-day Charging Flexibility

The reliability of the standard deviation bid method for awarding construction contracts

This study examines the complex test of observing to time, cost, and quality factors when executing construction projects in developing countries, which frequently becomes more difficult by awarding projects based on low bids. By compared to collusion implications and e-bidding benefits using the standard deviation bid and average bid methods, the study provides light on critical aspects. An examination of contracts in Nepal’s Gandaki province between 2018 and 2022 yields interesting results. When there is no collusion, both bidding methods produce similar results. The average bid method, on the other hand, is subject to collusion, whereas the standard deviation bid method is not. To address the constraints associated with low bids, a cautious recommendation for the average bid method is made, along with vigilance against collusion. The standard deviation bid method, on the other hand, emerges as an effective obstacle to collusive behavior. To enhance the bid procedures, the study encourages for additional qualification criteria such as financial weighting and enhanced performance bonds. These amendments to Nepal’s Public Procurement Act (PPA) and Public Procurement Regulation (PPR) of 2007 are intended to address issues raised by low bids. The importance of e-bidding practices is highlighted for digital transformation and corruption prevention. The study emphasizes the importance of putting in place mechanisms to detect collusion across bid selection methods. The research concludes with an invitation for experimental exploration of the Standard Deviation Bidding Method (SDBM), with an evaluation of its efficiency across time, cost, and quality domains. This investigation has the potential to reveal the best bid selection methodology for construction projects.

ANALISA PEMILIHAN PENYEDIA JASA PELAKSANA KONSTRUKSI PEMERINTAH SUMBER DANA PINJAMAN/HIBAH BANK DUNIA

The implementation of the implementation of the Selection of Construction Work Providers through Public Auction/Direct Selection with Postqualification The National Competitive Bidding (NCB) method is still very rarely used both in ministries and institutions, especially in local governments. Moreover, socialization of this regulation is fairly rare, causing limited information from relevant stakeholders in its implementation, while there are some substantive differences which must be considered and must be implemented. This condition causes the potential implementation of the rugulation is not in accordance with the provisions and causes negative impacts in the implementation of the work/project carried out due to the unpreparedness of the relevant parties. Dominant faktors and correlations to the level of readiness of Stakeholders in the context of implementing the Selection of Construction Work Providers through Public Auction/Direct Selection with Postqualification One Cover Method (National Competitive Bidding/NCB) with. manner. analyze the faktors in the selection of the provider. alone, all at once. Measure .level. readiness of Stakeholders regarding the application of methods. The selection of this provider needs to be done. The research was reviewed in terms of variabel salalt steeped and explained valralibel malsal lallu in a period called the qualitative descriptive method by using falktor alnallisis in correlational alnallysis.

Market bidding method for the inter-provincial delivery of cascaded hydroelectric plants in day-ahead markets considering settlement rules

Chinese electricity market reform poses huge challenges to hydropower operations and electricity trading. This study proposes a scheduling method coupling priority electricity and day-ahead trading for large hydropower plants. The study focuses on complex factors such as tariff uncertainty, different types of electricity settlement rules, and inter-provincial electricity transmission links. Spot market tariff scenarios are determined through the Latin hypercube and the K-means methods. A performance formulation of priority electricity deviation considering settlement assessment rules is established. A transmission description for different sub-plants and a triangular linear interpolation method based on binary independent branching mode are proposed to solve inter-regional transmission connections and hydraulic coupling in cascaded hydropower plants, respectively. Finally, the Big M method is employed to equivalently transform the complex non-linear problem into a mixed-integer linear programming (MILP) model. The method is verified with the day-ahead operation of four large hydropower plants downstream of the Jinsha River in China as a case study. Settlement assessment rules, inter-regional power transmission, and price uncertainty are analyzed in three different cases. Three conclusions are obtained: 1) the priority electricity performance rate and the price are positively correlated, which is useful to guide hydropower plants to actively participate in the market. 2) Introducing the prediction error of electricity price in the model can help avoid market decision risk and improve the expected return by approximately 1.2%. 3) Considering the settlement penalty rule is helpful for power generation enterprises to improve power allocation and thus seek higher revenue compared to traditional methods without considering it.

Improving Real-Time Bidding in Online Advertising using Markov Decision Processes and Machine Learning Techniques

Real-time bidding has emerged as an effective online advertising technique. With real-time bidding, advertisers can position ads per impression, enabling them to optimise ad campaigns by targeting specific audiences in real-time. This paper proposes a novel method for real-time bidding that combines deep learning and reinforcement learning techniques to enhance the efficiency and precision of the bidding process. In particular, the proposed method employs a deep neural network to predict auction details and market prices and a reinforcement learning algorithm to determine the optimal bid price. The model is trained using historical data from the i Pin You dataset and compared to cutting-edge real-time bidding algorithms. The outcomes demonstrate that the proposed method is preferable regarding cost-effectiveness and precision. In addition, the study investigates the influence of various model parameters on the performance of the proposed algorithm. It offers insights into the efficacy of the combined deep learning and reinforcement learning approach for real-time bidding. This study contributes to advancing techniques and offers a promising direction for future research.

CVaR‐based method for optimizing the contract bidding strategy of PV power stations

AbstractWith the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long‐term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi‐order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C‐means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long‐term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue.

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.

A Coordinated Frequency Regulation and Bidding Method for Wind-Electrolysis Joint Systems Participating Within Ancillary Services Markets

A Coordinated Frequency Regulation and Bidding Method for Wind-Electrolysis Joint Systems Participating Within Ancillary Services Markets

Metropolitan water purification facilities towards variability mitigation of the renewable resources: Optimal bid method for small hydropower generators

As carbon neutrality in the power system arises as one of the important issues, numerous nations have been increasing penetration of the renewable resources. However, greater penetration of the renewable resources in the power systems has caused reliability issues due to the innate unpredictable output characteristics. For minimization of unpredictability and its consequential effects on the system reliability, the nations such as the Republic of Korea, Great Britain and Australia have been introduced market-based variability mitigating measures. The incentive policy driven market-based measures were designed to draw voluntary participation from the asset owners capable of providing controllability over the resources aggregated to be a single portfolio. Small hydropower generators in metropolitan water purification facilities can be actively utilized for such mitigation because of their relatively stable output characteristics. However, entities responsible for metropolitan water purification facilities with small hydropower generators have been reluctant to participate in the market with the mitigations incentive since there are no structured methods to acquire dispatch reliability of the water resources considering participation in the energy market. Thus, this paper presents a scheduling algorithm for the aggregated portfolio of renewable resources, utilizing small hydropower generators as one of the tools for variability mitigation. In the results, the portfolio-wide forecast error was reduced to below 2% in the presence of the scheduling algorithm and small hydropower generators as mitigation resources, while the water intake schedule at water purification facilities remained evenly distributed. Small hydropower generators played a key role in mitigating variability in the algorithm, and the revenue generated from the participation of these small hydropower generators contributed to approximately one third of the gross revenue from the portfolio. The algorithm was demonstrated to provide renewable resource owners with an additional revenue stream, beyond what is typically provided by government subsidies.

Historical-based bidding strategy in transactive energy market for household: Data driven analysis

The energy market design is changing to enable distribution-level transactions in smart cities, encouraging end-users to participate efficiently in the market. The Transactive Energy (TE) concept opens the floor for a new market settlement philosophy that can benefit the end-user and Distributed Generators (DGs). The bidding of the customers in the distribution level market raises many concerns in the latest reports due to many factors (e.g., lack of information about the participant’s flexibility). This paper proposes a non-intrusive bidding strategy to enhance the accuracy of the household’s participation in the transactive electricity market. The proposed model utilizes the historical household user energy consumption to build the thermal model and develop the bidding strategy. The proposed approach examines the bidding in the context of user preferences of the total consumption, time at bidding, day of bidding, and ambient temperature. The results prove that the proposed strategy can significantly improve the flexibility’s recognition of the customers’ participation in the transactive market. Finally, the efficacy of the bidding method is examined via numerical analysis of actual data.

Optimum Bidding Method using MOGWO in Open Energy Market for Customers

Optimum Bidding Method using MOGWO in Open Energy Market for Customers

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.