Welfare and Profit Maximization with Production Costs

Blum, Avrim; Gupta, Anupam; Mansour, Yishay; Sharma, Ankit

doi:10.1109/focs.2011.68

Cited by 38 publications

(88 citation statements)

References 22 publications

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“…However, the problem has not been studied in the online setting to the best of our knowledge. In a different context, a recent work considered the problem of pricing a set of items with general production cost to a sequence of buyers to maximize social welfare or profit [3]. The approach does not apply to expiring resources like electricity and does not consider the time elasticity of demand.…”

Section: Introductionmentioning

confidence: 99%

Online welfare maximization for electric vehicle charging with electricity cost

Zheng

Shroff

2014

Proceedings of the 5th International Conference on Future Energy Systems

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The accelerated adoption of EVs in the last few years has raised concerns that the power grid can get overloaded when a large number of EVs are charged simultaneously. A promising direction is to implement large scale automated scheduling of EV charging at public facilities, by exploiting the time elasticity of charging requests. In this work, we study the problem of online EV charging for maximizing the total value of served vehicles minus the energy cost incurred. In contrast to most previous works that assume a fixed capacity constraint while ignoring the electricity cost, we adopt a convex cost model for the system operator together with a concave valuation model for the vehicle owners. We design an online algorithm for balancing the two and prove a bound on its competitive performance for a general class of valuation and cost functions.

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Section: Introductionmentioning

confidence: 99%

Online welfare maximization for electric vehicle charging with electricity cost

Zheng

Shroff

2014

Proceedings of the 5th International Conference on Future Energy Systems

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“…The results in (21) and (22) show that the energy provider can set the price approximately when the number of consumers is sufficiently large. In this case, the energy control algorithm can be implemented with low communication overhead, because the energy provider does not need to acquire the individual parameters of the consumers.…”

Section: Control Algorithm Implementationmentioning

confidence: 90%

Distributed Energy Consumption Control via Real-Time Pricing Feedback in Smart Grid

Spanos

2014

IEEE Trans. Contr. Syst. Technol.

172

140

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This brief proposes a pricing-based energy control strategy to remove the peak load for smart grid. According to the price, energy consumers control their energy consumption to make a tradeoff between the electricity cost and the load curtailment cost. The consumers are interactive with each other because of pricing based on the total load. We formulate the interactions among the consumers into a noncooperative game and give a sufficient condition to ensure a unique equilibrium in the game. We develop a distributed energy control algorithm and provide a sufficient convergence condition of the algorithm. The energy control algorithm starts at the beginning of each time slot, e.g., 15 min. Finally, the energy control strategy is applied to control the energy consumption of the consumers with heating ventilation air conditioning systems. The numerical results show that the energy control strategy is effective in removing the peak load and matching supply with demand, and the energy control algorithm can converge to the equilibrium.

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“…For recent related research on revenue maximization that allows price setting, we mention the auction problem [4,13] and the pricing problem [1][2][3][5][6][7]. For the auction problem, there are bidders competing for the products by sending their bids to the auctioneer, and the auctioneer chooses some bidders, and determines the price and amount of products to be sold to each chosen bidder.…”

Section: Previous Resultsmentioning

confidence: 99%

Competitive Algorithms for Unbounded One-Way Trading

Chin

Fu²,

Jiang

et al. 2014

Algorithmic Aspects in Information and Management

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In the one-way trading problem, a seller has L units of 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 all 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.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(log r * (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 satisfies 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 Ω(log r * (log (2) r * ) . . . (log (h−1) r * )(log (h) r * )). A special case of the one-way trading is also studied, in which the L units of product is comprised of L items, each of which must be sold atomically (or equivalently, the amount of product sold to each buyer must be an integer).Furthermore, a complementary problem to the one-way trading problem, say, the oneway buying problem, is studied in this paper. In the one-way buying problem, a buyer wants to purchase one unit of product through a sequence of n sellers v 1 , v 2 , . . . , v n arriving online, and she needs to decide the fraction to purchase from each v i at the then-prevailing market price p i . Her objective is to minimize the cost. The optimal competitive algorithms whose performance guarantees depend only on the lowest market price p * = min i p i , and one of M and φ, the price fluctuation ratio, are presented.

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Welfare and Profit Maximization with Production Costs

Cited by 38 publications

References 22 publications

Online welfare maximization for electric vehicle charging with electricity cost

Online welfare maximization for electric vehicle charging with electricity cost

Distributed Energy Consumption Control via Real-Time Pricing Feedback in Smart Grid

Competitive Algorithms for Unbounded One-Way Trading

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