Bidding Decisions Research Articles

Power Supply Coal Consumption Prediction Model Based on Long and Short-term Memory Neural Network

As a measure of the overall economic performance of thermal power plants and an important component of the variable cost of power generation, the prediction of coal consumption of power supply is of great significance for the bidding decision of power generation companies. In this paper, we propose an improved long and short-term memory (LSTM) model for calculating the coal consumption of thermal power units, using a large amount of data stored in the plant monitoring information system (SIS). Firstly, the data are pre-processed using the threshold determination method to filter out the data of stable operating conditions. Secondly, the maximum correlation minimum redundancy (mRMR) algorithm is used to determine the optimal set of special features. The results show that the prediction effect of the improved LSTM-based power supply coal consumption calculation model proposed in this paper is better than other models, and the calculation accuracy is higher, which is suitable for power supply coal consumption calculation.

Examining Motivation Factors on Bidding Decision in Crowdsourcing Platforms: Can Trust Mediates Motivation Factors on Bidding Decision in Crowdsourcing Malaysia: A Conceptual Study in Malaysia

Crowdsourcing in Malaysia has been recognised as an emerging industry that can be used as a platform to generate more income and offer businesses the chance to conduct their operations more innovatively. Crowdsourcing has become an efficient method for businesses to offer work opportunities to the general public so that they can apply their skills and earn more money. It is estimated that about 258,482 Malaysian digital talents that participate in crowdsourcing. The crowdsourcing system depends a lot on how many people use the platform. This is important for the success of the platform. However, the factors that motivate crowds to remain on the platform remain unclear, and there is very little literature on the subject. This paper is a conceptual paper that generates research propositions based on the comprehensive literature review and several theories regarding decision-making in crowdsourcing. Despite this, the paper is conceptual. Still, the claims made are based on theory, but they need to be tested in the proposed setting and context. In the future, researchers could add to the conceptual framework by validating the hypotheses more and adding mediators to the relationships that have been proposed. Other consequences and suggestions were also discussed.

A State-Based Potential Game-Theoretic Approach for Massive Residential Consumers Participating in Demand Response

In order to realize massive residential consumers participating in demand response (DR), load aggregator (LA) can be employed to aggregate flexible loads. In this article, a distributed optimization approach based on state-based potential game (SPG) is proposed to describe the bidding behavior among LAs in day-ahead DR market for load shifting amount. Under the consideration of DR resource's energy consumption satisfaction, profit-oriented bidding decision problem is modeled from the perspective of individual LA and DR market. Basically, the establishment and proof of an SPG model is presented in details. Then, the best-strategy-response iterative algorithm is proposed to search for the Nash equilibrium in a distributed way. Finally, simulation is carried out to verify the efficiency and effectiveness of the proposed approach. Simulation results show that the whole profit of all LAs increases 0.15%, while the running time of the algorithm reduces 59.53% compared with the conventional algorithm in the noncooperative game.

MRL-Based Model for Diverse Bidding Decision-Makings of Power Retail Company in the Wholesale Electricity Market of China

Power retail companies in the electricity market make profits through buying and selling power energy in the wholesale and retail markets, respectively. Traditionally, they are assumed to bid in the wholesale market with the same objective, i.e., maximize the profit. This paper proposes a multiagent reinforcement learning (MRL)-based model to simulate the diverse bidding decision-making concerning various operation objectives and the profit-sharing modes of power retail companies in China’s wholesale electricity market, which contributes to a more realistic modeling and simulation of the retail companies. Specifically, three types of operation objectives and five types of profit-sharing modes are mathematically formulated. After that, a complete electricity market optimization model is established, and a case study with 30 retail companies is carried out. The simulation results show that the proposed method can effectively model the diverse bidding decision-making of the power retail companies, which can further assist their decision-making and further contribute to the analysis and simulations of the electricity market.

Offense and defence against adversarial sample: A reinforcement learning method in energy trading market

The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless, the security problem raised by artificial intelligence technology has been paid more and more attention. For example, the neural network has been proved to be able to resist adversarial example attacks, thus affecting its training results. Considering that reinforcement learning is also widely used for training by neural networks, the security problem can not be ignored, especially in scenarios with high security requirements such as smart grids. To this end, we study the security issues in reinforcement learning-based bidding strategy method facing by the adversarial example. First of all, regarding to the electric vehicle double auction market, we formalize the bidding decision problem of EVs into a Markov Decision Process, so that reinforcement learning is used to solve this problem. Secondly, from the perspective of attackers, we have designed a local Fast Gradient Sign Method which affects the environment and the results of reinforcement learning by changing its own bidding. Then, from the perspective of the defender, we designed a reinforcement learning training network containing an attack identifier based on the deep neural network, so as to identify malicious injection attacks to resist against adversarial attacks. Finally, comprehensive simulations are conducted to verify our proposed method. The results shows that, our proposed attack method will reduce the auction profit by influencing reinforcement learning algorithm, and the protect method will be able to completely resist such attacks.

Bidding and Charging Scheduling Optimization for the Urban Electric Bus Operator

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.

A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets

This paper proposes a bilevel game-theoretic model for multiple strategic retailers participating in both wholesale and local electricity markets while considering customers’ switching behaviors. At the upper level, each retailer maximizes its own profit by making optimal pricing decisions in the retail market and bidding decisions in the day-ahead wholesale (DAW) and local power exchange (LPE) markets. The interaction among multiple strategic retailers is formulated using the Bertrand competition model. For the lower level, there are three optimization problems. First, the welfare maximization problem is formulated for customers to model their switching behaviors among different retailers. Second, a market-clearing problem is formulated for the independent system operator (ISO) in the DAW market. Third, a novel LPE market is developed for retailers to facilitate their power balancing. In addition, the bilevel multi-leader multi-follower Stackelberg game forms an equilibrium problem with equilibrium constraints (EPEC) problem, which is solved by the diagonalization algorithm. Numerical results demonstrate the feasibility and effectiveness of the EPEC model and the importance of modeling customers’ switching behaviors. We corroborate that incentivizing customers’ switching behaviors and increasing the number of retailers facilitates retail competition, which results in reducing strategic retailers’ retail prices and profits. Moreover, the relationship between customers’ switching behaviors and welfare is reflected by a balance between the electricity purchasing cost (i.e., electricity price) and the electricity consumption level.

Multi-Market Bidding Behavior Analysis of Energy Storage System Based on Inverse Reinforcement Learning

The bidding behaviors of the energy storage systems (ESS) are complicated due to time coupling and market coupling limited by their capacity states. The existing research is mainly based on optimization models and reinforcement learning (RL) models, which are idealized with analytical objective functions, rational decisions, and virtual historical data. This leads to mismatches between theoretical results and actual bidding behaviors, and the obtained results cannot be generalized to a real-world market. In recent years, the disclosure of market data has enabled data-driven analysis, while the complexity of actual data makes the analysis process challenging and calls for more suitable methods. On the basis of our investigation of ESS bidding behaviors and market data, we propose a novel inverse RL (IRL)-based framework to identify the bidding decision objective function of ESS in coupled multi-market through their historical bidding records and operation status. The identification results can be used as the model parameters to help RL-based simulation models available for a real-world market. Based on the proposed framework, we conduct an empirical analysis on the real-world data of a battery ESS in the Australian electricity market. The results show how the ESS balances its objective among different markets and maintains its state of charge (SOC) to gain more profits. This framework can help us model ESS behaviors by relying on historical data to better understand the ESS bidding decision-making mechanism, which will facilitate market equilibrium analysis and mechanism design.

Challenges in Conducting Strategic Decisions in Tendering among Contractors in Malaysia.

Malaysia’s construction industry is rapidly growing from time to time. The bidding decision of construction projects is a very complex decision-making process which affected by several factors. Usually, to obtain good quality, cost, time, and safety is the primary goal of the construction project, which the contractors will compete in the tendering process to get a project, and most of the construction work in Malaysia was awarded based on competitive tendering. With the limited information and time, the contractor has to decide whether to tender or vice versa. The paper aims to explore the challenges in conducting strategic decisions in tendering among contractors in Malaysia. A set of questionnaires are used to collect the data for the study. The sampling method used in this study was random sampling and quota sampling. The study only focuses on the contractors in Klang Valley as Klang valley is known as the heartland of Malaysia’s industry and commerce. The data was gathered and analysed by using SPSS. The result of this study was in the form of ranking with the mean value as the ranking indicator. As a result of the questionnaire, most respondents agree that the biggest Challenges in conducting strategic decisions in tendering are the need of the contractor to win a project. The contractor must have sufficient data in deciding to tender. Adequate information regarding the project is vital as it acts as a reference for an estimator to estimate the project cost. Estimating is a critical factor that influences the contractor’s success in the tendering and ensures that the contractor can do the work diligently as stated in the contract.

Break the Cycle of Collusion: Simulation to Influence Mechanism of Cognitive Bias on To-Collude Decision Making

Collusion is an all-pervading illegal market behavior that can undermine the sustainable development of the construction industry. It is acknowledged that collusive bidding decision making is influenced by conspirators’ cognitive bias. Nevertheless, the understanding of such an influence mechanism remains vague in the literature. This study aims to examine the mechanism of conspirators’ to-collude decision making by establishing a system dynamic model. The model development is based on the theories of cognitive biases, collusive bidding, and complex adaptive system. Multiple scenarios were simulated in the context of the Chinese construction industry. Three most influential cognitive bias are overconfidence, the illusion of control, and cognitive dissonance. The simulation results reveal conspirators’ intrinsic mechanisms to decide whether they deserve to participate in collusive bidding. The evolution of to-collude decision making is characterized by nonlinearity, multiplier, and stimulus enhancement effects. Collusion motivation and enterprise network relationships expand conspirators’ to-collude decision making. The increase of government regulation intensity and enterprise performance inhibit conspirators’ to-collude decision making. This study provides an insight into the cycle of collusion emergence from a complex system perspective and implies that antitrust authorities can launch carrot-and-stick measures for better regulation.

Coupling Effects of Economic, Industrial, and Geographical Factors on Collusive Bidding Decisions

Coupling Effects of Economic, Industrial, and Geographical Factors on Collusive Bidding Decisions

Ranking of Contractor by AHP Method

The construction branch is one of the important branches for achieving the country's economy. So it is obligatory to find out a competent contractor among all, for successfully complete work. Construction bidding is the process of offering a proposal to undertake a construction project. And it is one of the methods for the selection of contractors. bidding strategies vary from contractor to contractor and each contractor has a different view of the factors affecting their bid decisions. This study aims to find an effective strategy for the selection of competent contractors and bid evaluation. There are many factors that influence the bidding strategy. These factors influence depend on the situation when the research is done.

Operations Research Helps the Optimal Bidding of Virtual Power Plants

As distributed energy resources (DERs) continue to emerge, a new cloud-based information technology platform business model, the virtual power plant (VPP), is being introduced into the electricity market. The competitiveness of VPPs mainly depends on data analytics and operational technologies. Among the several operational problems, we focus on the optimal bidding decision problem in the day-ahead market. The bidding decision is a VPP’s commitment to supply the market with electricity from uncertain DERs, thereby affecting the VPP’s profits. Based on a collaboration with a VPP company in South Korea, H Energy Co. Ltd., we formulate a Markov decision process model for the problem and use a stochastic dynamic programming-based solution approach. This is the first study under the incentive-based market structure. To describe the uncertainty in the power supply from DERs, we build frameworks to generate scenario trees or lattices. Additionally, we apply heuristic techniques to reduce the computational burden. Through a pilot test based on real data, we verify the performance and practicality of our proposed model and solution approach. The case company has begun implementing the model and solution approach on its platform and has found that performance has improved after using advanced forecasting models for DERs.

Factors Affecting Selection of an Eligible Contractor

The construction branch is one of the important branches for achieving the economy of the country. So, it is obligatory to find out a competent contractor among all, for the successful completion of the work. Construction bidding is the process of offering a proposal to undertake a construction project. And it is one of the methods for the selection of a contractor. Bidding strategies vary from contractor to contractor and each contractor has a different view of the factors affecting their bid decisions. This study aims to find an effective strategy for the selection of competent contractors and bid evaluation. There are many factors that influence the bidding strategy. These factors influence depend on the situation when the research is done.

Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market

In the power sector, competitive strategic bidding optimization has become a major challenge. Digital plate-form provides a superior technical base as well as backing for the optimization’s execution. The state-of-the-art frameworks used for simulating strategic bidding decisions in deregulated electricity markets (EM’s) in this article are bi-level optimization and neural networks. In this research, we provide HHO-NN (Harris Hawk Optimization-Neural network), a novel algorithm based on Harris Hawk Optimization (HHO) that is capable of fast convergence when compared to previous evolutionary algorithms for automatically searching for meaningful multilayered perceptron neural networks (MPNNs) topologies for optimal bidding. This technique usually demands a considerable amount of time and computer resources. This method sets up the problem in multi-dimensional continuous state-action spaces, allowing market players to get precise information on the effect of their bidding judgments on the market clearing results, as well as implement more valuable bidding decisions by utilizing a whole action domain and accounting for non-convex operating principles. Due to the use of the MPNN, case studies show that the suggested methodology delivers a much larger profit than other state-of-the-art methods and has a better computational performance than the benchmark HHO technique.

Factors Influencing Bidding Strategy

The construction branch is one of the important branches for achieving the country's economy. So it is obligatory to find out a competent contractor among all, for successfully complete work. Construction bidding is the process of offering a proposal to undertake a construction project. And it is one of the methods for the selection of contractors. Bidding strategies vary from contractor to contractor and each contractor has a different view of the factors affecting their bid decisions. This study aims to find an effective strategy for the selection of competent contractors and bid evaluation. There are many factors that influence the bidding strategy. These factors influence depend on the situation when the research is done.

Framework to Facilitate Bid/No Bid Decision Making of the Contracting Organisations in Sri Lanka

Framework to Facilitate Bid/No Bid Decision Making of the Contracting Organisations in Sri Lanka

Modeling bidding decisions and bid markup size for construction projects: A fuzzy approach

Previous studies of construction contract auction bidding have mainly focused on finding factors affecting the markup and decision to bid (d2b), without considering expert weight and critical factors as input variables for estimating the size of markup needed. This study develops a 3-step Mamdani-type of Fuzzy Inference System (FIS) identifying critical factors and predicting markups for construction projects. The first step models the wights of 31 construction experts based on their characteristics (i.e., experience and academic qualifications). The second step models frequency, severity, and importance weights for each factor to find the rank and priority of the factors, revealing the critical factors to be current workload, project (contract) size, need for work, availability of labor and staff required, project owner, and duration. The third step takes the importance weight of each factor from the previous step and the contractor’s evaluation of the frequency of the factors as input variables to predict the bid markup. The model is demonstrated and tested with two actual construction projects having actual markups of 20% and 45%, and the predicted markups are found to be 26.3% and 42% respectively, which ensures reliable outcomes in assessing contractors’ bidding decisions. It is a novel model that can simultaneously identify critical factors and predict an optimal markup in assisting contractors’ d2b for the construction auctions and developing risk management plans. Future research can optimize this model by incorporating competitors’ bids and enhancing prediction accuracy to guarantee the lowest price with a reasonable profit margin.

Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants

Participants in energy auction markets must simultaneously decide on the quantity and price of their offers while attempting to ensure bid acceptance through the market clearing process. To participate profitably in these markets, energy resource aggregators require accurate knowledge about the costs and quantity of electricity that can be produced and consumed, i.e., knowledge about scheduling decisions. When scheduling and bidding decisions are combined, complex interdependencies arise. Integrated scheduling and bidding with pay-as-bid remuneration has long been treated as a non-linear problem. However, by sampling stochastic parameter distributions, a practical and useful mixed-integer linear programming formulation for the integrated problem can be derived. Within the sampled distribution, the integrated model produces a priori optimal expected profit decisions. Recently, such a linear formulation for a price-taker with pay-as-bid remuneration was proposed for the first time. Our contribution completes that formulation to guarantee non-anticipativity, standardizes it with uniform remuneration, and extends it to integrated scheduling and bidding of energy and control reserve. An additional focus is placed on reserve provision through storage plants. A novel reserve scheduling model with reserve availability guarantees is introduced. This allows for leveraging the full flexibility of storage plants and, simultaneously, reveals information about the direction of reserve power flows. The integrated model is validated in a case study based on real-world data by investigating trade-offs for scheduling and bidding decisions through analysis of the Pareto front of three competing objectives. The results show that a large proportion of bids are placed at price levels with high acceptance probability, which reduces risk. Decreasing storage utilization rates increase risk and reduce solution robustness because of lost flexibility, and result in increased reserve market participation.

Network-secure bidding strategy for aggregators under uncertainty

The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.