Banking on decoupling

A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances

Summary Public cloud providers, such as the Microsoft Azure and the Amazon Web Services (AWS), offer a wide variety of virtual machines, with different specifications, and prices, that are enabling users to run high-performance programs without buying specialized and expensive hardware. Moreover, some providers, such as AWS, allow the user to bid for lower cost virtual machines, called Spot Instances. Nonetheless, these machines may be terminated within a few minutes of warning at the provider discretion. In this work, we leveraged the SPITS programming model to implement a high-performance and fault-tolerant seismic processing application that is proper for execution on Spot instances and analyzed how different virtual machines from the AWS may affect the performance and the price of the computation. Our experimental results indicate that Spot instances have similar performance to regular instances but are roughly three times less expensive. Finally, we show that AWS groups virtual machines in Availability Zones and that selecting virtual machines from different zones may also affect the total execution cost.

In the new competitive electricity market, bidding plays a significant role in the area of power trading. The participants are partaking in the trading procedure for a fixed amount of power. The price at which a single buyer bids a block of power influences both the net volume of power cleared and the market-clearing price of electricity traded in the entire network. In this paper, different bidding strategies are defined and simulated. The defined strategies show the dependency on the bid price for clearing a bid. The net earnings of the buyers and sellers depend on the bid price. The selection of bid price for each block of power is studied such that the total volume of power cleared is equal to the participating buyers’ total power demand. The study focuses on determining the optimal bid to result in maximum societal benefit. In addition to the bid price effect on the volume of power cleared, the bid price prediction is also performed in this work using linear regression. The predicted Locational Marginal Price (LMP) of the buyers for different volumes of power cleared is estimated. The results are further compared with LMP obtained using an ordinary load flow problem through which a margin change in the net earnings is observed. An IEEE-30 bus system is simulated with different bidding strategies using MATLAB, and the predicted LMP shows a significant increase in net earnings.

In this paper, a multi-agent model of an electricity market is proposed using the agent-based computational economics (ACE) methodology. The proposed methodology for modeling the bidding price behavior of Generation Companies (GENCOs) is based on a reinforcement learning algorithm (Q-learning) that uses some soft computing techniques to face the discovery of a complex function among bidding prices, states and profits. The proposed model also comprise the power system operation of a large-scale system by simulating optimal DC power flows (DCOPF) in order to obtain real dispatches of agents and a mapping from action space (bidding strategies) to quantities dispatched. In this model, agents are provided with learning capabilities so that they learn to bid depending on market prices and their risk perception so that profits are maximized. The proposed methodology is applied on colombian power market and some results about bidding strategies dynamics are shown. In addition, a new index defined as rate of market exploitation is introduced in order to characterize the agents bidding behavior.

Real-time bidding (RTB) is one of the most striking advances in online advertising, where the websites can sell each ad impression through a public auction, and the advertisers can participate in bidding the impression based on its estimated value. In RTB, the bidding strategy is an essential component for advertisers to maximize their revenues (e.g., clicks and conversions). However, most existing bidding strategies may not work well when the RTB environment changes dramatically between the historical and the new ad delivery periods since they regard the bidding decision as $\boldsymbol {a}$ static optimization problem and derive the bidding function only based on historical data. Thus, the latest research suggests using the reinforcement learning (RL) framework to learn the optimal bidding strategy suitable for the highly dynamic RTB environment. In this paper, we focus on using model-free reinforcement learning to optimize the bidding strategy. Specifically, we divide an ad delivery period into several time slots. The bidding agent decides each impression's bidding price depending on its estimated value and the bidding factor of its arriving time slot. Therefore, the bidding strategy is simplified to solve each time slot's optimal bidding factor, which can adapt dynamically to the RTB environment. We exploit the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to learn each time slot's optimal bidding factor. Finally, the empirical study on a public dataset demonstrates the superior performance and high efficiency of the proposed bidding strategy compared with other state-of-the-art baselines.

As independent economic entity, each power generation company in China participates in current electric power market competition by price bidding. Multi-population replicator dynamics of evolutionary game theory is applied to model and simulate power generation companiespsila discriminatory and bounded rationality bidding strategies according to different generating set status in a power market. The spontaneous forming process of the generation companiespsila bidding strategies and evolutionarily stable strategies are both analyzed. A practical case is set to verify the modelpsilas ability and reliability. The results not only demonstrate the multi-population replicator dynamics model can describe the dynamic process of generation companiespsila bidding quite well, but also suggest reasonable price competition rules must be constituted so as to ensure and promote the healthy development of the generation-side power market.

This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.

The operation of power-to-X systems requires measures to control the cost and sustainability of electricity purchased from spot markets. This study investigated different bidding strategies for the day-ahead market with a special focus on Sweden. A price independent order (PIO) strategy was developed assisted by forecasting electricity prices with an artificial neural network. For comparison, a price dependent order (PDO) with fixed bid price was used. The bidding strategies were used to simulate H2 production with both alkaline and proton exchange membrane electrolysers in different years and technological scenarios. Results showed that using PIO to control H2 production helped to avoid the purchase of expensive and carbon intense electricity during peak loads, but it also reduced the total number of operating hours compared to PDO. For this reason, under optimal conditions for both bidding strategies, PDO resulted in an average of 10.9% lower levelised cost of H2, and more attractive cash flows and net present values than PIO. Nevertheless, PIO showed to be a useful strategy to control costs in years with unexpected hourly price behaviour such as 2018. Furthermore, PIO could be successfully demonstrated in a practical case study to fulfil the on-demand requirement of an industrial captive customer.

With the development of the national economy and the change of policies, the development of the electric power industry continues to expand. The New Electricity Reform proposes that electricity price is the core of electricity reform, and the power generation side is the main member of the market. Its quotation strategy affects the listing price in the electricity market. Therefore, this paper summarizes the electricity market trading models and bidding strategies on the power generation side, and discusses the bidding strategy based on electricity price prediction. As the core of electricity price forecasting, load forecasting is studied and discussed in detail in this paper. Finally, the future trend of bidding strategy in electricity market is forecasted.

This study aims to reduce the cost of using computing services on AWS. Cost reduction is needed because there is a possibility that the total cost of using cloud services exceeds the estimated budget. One type of EC2 that offers a large discount is the Spot Instance. The downside of this type of EC2 is that AWS reserves the right to stop it at any time. The proposed solution is an automation system to select and run EC2 Spot Instance types based on price, discount, amount of memory, and vCPU usage from previous instances. The automation system is built with the implementation of fuzzy inference system and Best-Worst Method (BWM). All input data is obtained using the Boto3 SDK. System deployment is done in Lambda functions. This Lambda function is automatically executed whenever a Spot Instance is terminated by AWS. The EventBridge service will catch the event and then trigger the Lambda to run. System testing was run for 4 (four) days with event simulation using the Send Events feature. From these tests it is known that the automation system can select the appropriate instance and generate a total cost of $3.85 (USD). After calculating the total cost with regular EC2 estimation (On Demand), the cost is reduced by 71.28%. This number proved to be 4.28% greater than previous similar studies.

This paper examines bidding strategies in a bilateral market in which generating companies submit bids to loads. A load accepts electricity delivery from the generator with the lowest bid at its bid price as long as this price is not higher than the load's willingness to pay. Necessary and sufficient conditions of Nash equilibrium (NE) bidding strategy are derived based on a generic generating cost matrix and the loads' willingness to pay vector. The study shows that in any NE, efficient allocation is achieved. Furthermore, all Nash equilibria are revenue equivalent for the generators. Based on the necessary and sufficient conditions, this problem is formulated as an optimal assignment problem. Network optimization techniques are applied to calculate NE bid prices for the generators.

In a competitive energy market, instead of, or in addition to, a centralized unit commitment, individual generation owners will make independent unit commitment decisions. They will seek to maximize their profits against the predicted market clearing price. Their unit commitment strategy will be expressed in their bids, so that they shut-down or start-up when the market price indicates such activity. In this chapter, we develop a unit commitment based price-taking (UCPT) bidding strategy with a simple price prediction mechanism and explore it using a market simulator. Simulation results show that an individual generator has higher profits with UCPT bidding than with simple price-taking bidding, and that the cost of supplying price-inelastic loads achieved by the market is lower when all generators use UCPT bidding. It appears that UCPT bidding gives results similar to those from a Lagrangian relaxation unit commitment (LRUC), without a fix-up step, and it has problems with convergence and feasibility similar to LRUC. We observe cyclic behavior in market prices with UCPT bidding, and we show that it depends on the price prediction mechanism. Alternative price prediction mechanisms can reduce cyclic behavior. Finally, we conceptually explore potential strategic behavior and market power arising from unit commitment constraints.KeywordsUnit CommitmentBidding StrategyMarket Clearing PriceFuture ProfitUnit Commitment ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

A contractor’s ability to prepare a competitive bid for a construction tender is crucial for its survival on the market. The bid price estimation strategy should promote the probability of winning a sufficient amount of tenders but, at the same time, ensure the economic stability and development of the company. This paper aims to address this issue in the area of Czech public construction procurement. The opinions, experiences and practices of contractors were collected through a questionnaire survey, and the data were evaluated with the support of statistical methods. This revealed that Czech contractors mostly base their multicriteria bidding strategy on cost-oriented pricing while considering various aspects such as the risks and attractiveness of the tender. The Czech construction market is generally perceived as oriented toward low costs, and with a relatively common occurrence of abnormally low bids. The findings presented in this paper may help contractors improve their current bidding strategies in public construction procurement.

In this article, we have simulated an agent-based model to investigate the bidding behavior of generation companies (GenCos) in the electricity market under different pricing rules. In a real electricity market, they do not have complete information about the behavior of competitors and therefore make their own decisions based on information that exists on the market-clearing price (MCP) from the past. So considering of high ability of reinforcement learning (RL) algorithms for making decisions on issues with incomplete information, the behavior of companies has been simulated with RL algorithms. In addition, to remove the maximization bias in usual algorithms, we have used the double learning technique for unbiased estimation. The results have shown that companies using Double Q-learning and Double SARSA algorithms, can gain optimal values with unbiased estimation. Furthermore, we investigated market-clearing mechanisms with different pricing rules under an equal rationing policy, in terms of competitiveness in the market and total profit of GenCos. The results showed that in terms of competitiveness, the uniform pricing rule causes more competitive bid prices. However, from the side of the total profit of GenCos, it was seen that with changing from the uniform pricing to the pay-as-bid (P AB), the total profit of companies would reduce.