Competitive Bidding Strategies for Online Linear Optimization with Inventory Management Constraints

Cloud providers are auctioning their excess capacity using dynamically priced virtual instances. These spot instances provide significant savings compared to on-demand or fixed price instances. The users willing to use these resources are asked to provide a maximum bid price per hour, and the cloud provider runs the instances as long as the market price is below the user's bid price. By using such resources, the users are exposed explicitly to failures, and need to adapt their applications to provide some level of fault tolerance. In this paper, we expose the effect of bidding in the case of virtual HPC clusters composed of spot instances. We describe the interesting effect of uniform versus non-uniform bidding in terms of both the failure rate and the failure model. We propose an initial attempt to deal with the problem of predicting the runtime of a parallel application under various bidding strategies and various system parameters. We describe the relationship between bidding strategies and programming models, and we build a preliminary optimization model that uses real price traces from Amazon Web Services as inputs, as well as instrumented values related to the processing and network capacities of cluster instances on the EC2 services. Our results show preliminary insights into the relationship between non-uniform bidding and application scaling strategies.

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO 2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm

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The paper describes a procedure developed to assist a generating company in choosing the most convenient bidding strategies for a day-ahead electricity energy market. According to the proposed method, the profit maximization problem is transformed into a minimization problem that can be solved by a traditional hydro-thermal unit commitment program after implementing a few modifications. The paper describes the modifications introduced in a unit commitment program based on the Lagrangian relaxation approach and on a disaggregated Bundle method for the solution of the dual problem. It also presents some results obtained for a realistic data set of hydrothermal power plants. The results are discussed in order to emphasize how the method can be applied to assess the bidding strategy choice of a given company.

This paper presents a pricing model considering the simultaneous interaction of pool, bilateral and reserve markets in a power system. The model is adapted to work through a 'pay as bid' (PAB) pricing approach that is currently being considered as an option for pricing in some actual systems. The combined market is settled through an AC optimal power flow (OPF) that accounts for power generation (pool and bilateral contracts) and availability of reserve services. As a result, prices of energy and reserve services incorporate the influences of the transmission network such as topology, voltage levels, losses, generation and transmission capacity limits. Results show that with this model agents can better plan their portfolios based on prices that reflect the costs of resources considering several operation scenarios and bid strategies.

In the one-way trading problem, a seller has some product to be sold to a sequence σ of buyers u 1, u 2, …, u σ arriving online and he needs to decide, for each u i , the amount of product to be sold to u i at the then-prevailing market price p i . The objective is to maximize the seller’s revenue. We note that most previous algorithms for the problem need to impose some artificial upper bound M and lower bound m on the market prices, and the seller needs to know either the values of M and m, or their ratio M/m, at the outset. Moreover, the performance guarantees provided by these algorithms depend only on M and m, and are often too loose; for example, given a one-way trading algorithm with competitive ratio Θ(log(M/m)), its actual performance can be significantly better when the actual highest to actual lowest price ratio is significantly smaller than M/m.This paper gives a one-way trading algorithm that does not impose any bounds on market prices and whose performance guarantee depends directly on the input. In particular, we give a class of one-way trading algorithms such that for any positive integer h and any positive number ε, we have an algorithm A h,ε that has competitive ratio O (logr ∗ (log(2) r ∗ ) … (log(h − 1) r ∗ )(log(h) r ∗ )1 + ε ) if the value of r ∗ = p ∗ /p 1, the ratio of the highest market price p ∗ = max i p i and the first price p 1, is large and satisfy log(h) r ∗ > 1, where log(i) x denotes the application of the logarithm function i times to x; otherwise, A h,ε has a constant competitive ratio Γ h . We also show that our algorithms are near optimal by showing that given any positive integer h and any one-way trading algorithm A, we can construct a sequence of buyers σ with log(h) r ∗ > 1 such that the ratio between the optimal revenue and the revenue obtained by A is at least Ω(logr ∗ (log(2) r ∗ ) …(log(h − 1) r ∗ ) (log(h) r ∗ )).

Liquid air energy storage: Price arbitrage operations and sizing optimization in the GB real-time electricity market

In this paper, we consider the profit-maximizing demand response of an energy customer in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy customer, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon continuous-state Markov decision process (MDP) problem. Through rigorous analysis, we show that the optimal actual demand policy exhibits a threshold structure, which can solve the MDP without the need of discretizing the state and action spaces. We demonstrate through extensive simulations that the proposed demand response strategy not only maximizes the profit of the energy customer, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed demand response strategy increases the energy customer's profit by 53.8% and saves the bills of other utilities by 80.4% comparing with the benchmark algorithms.

In this paper, we augment online algorithms for the knapsack problem using the total weight information. The conventional optimal online algorithm achieves the ln(U/L)+1 competitive ratio where L and U are the upper and lower bounds of the value-to-weight ratio. However, it does not consider that decision makers can know the total weight information or obtain it through machine-learned predictions. To fill this gap, we first propose the Known Weight Algorithm (KWA) which uses the exact total weight information to achieve a competitive ratio of W((U-L)/(eL))+1, where W denotes the Lambert-W function. We prove that it is optimal and tight. After that, we extend KWA to the Predicted Weight Algorithm (PWA), a learning-augmented online algorithm that uses predicted total weight. We show the consistency and robustness of PWA, and prove that its competitive ratio degrades gracefully as the prediction error grows. Finally, we introduce the Limited Volume Algorithm (LWA), which achieves a better competitive ratio than ln(U/L)+1 when the total weight is less than twice the capacity.

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.

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

Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

Portfolio management and performance improvement with Sharpe and Treynor ratios in electricity markets