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Long-Run Equilibrium Modeling of Alternative Emissions Allowance Allocation Systems in Electric Power Markets
A question in the design of carbon dioxide trading systems is how allowances are to be initially allocated: by auction, by giving away fixed amounts, or by allocating based on output, fuel, or other decisions. The latter system can bias investment, operations, and pricing decisions, and increase costs relative to other systems. A nonlinear complementarity model is used to investigate long-run equilibria that would result under alternative systems for power markets characterized by time varying demand and multiple generation technologies. Existence of equilibria is shown under mild conditions. Solutions show that allocating allowances to new capacity based on fuel use or generator type can distort generation mixes, invert the operating order of power plants, and inflate consumer costs. The distortions can be smaller for tighter CO2 restrictions, and are somewhat mitigated if there are also electricity capacity markets or minimum-run restrictions on coal plants
Distributed Optimal Frequency Control Considering a Nonlinear Network-Preserving Model
This paper addresses the distributed optimal frequency control of power
systems considering a network-preserving model with nonlinear power flows and
excitation voltage dynamics. Salient features of the proposed distributed
control strategy are fourfold: i) nonlinearity is considered to cope with large
disturbances; ii) only a part of generators are controllable; iii) no load
measurement is required; iv) communication connectivity is required only for
the controllable generators. To this end, benefiting from the concept of
'virtual load demand', we first design the distributed controller for the
controllable generators by leveraging the primal-dual decomposition technique.
We then propose a method to estimate the virtual load demand of each
controllable generator based on local frequencies. We derive incremental
passivity conditions for the uncontrollable generators. Finally, we prove that
the closed-loop system is asymptotically stable and its equilibrium attains the
optimal solution to the associated economic dispatch problem. Simulations,
including small and large-disturbance scenarios, are carried on the New England
system, demonstrating the effectiveness of our design
Online Station Assignment for Electric Vehicle Battery Swapping
This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs' locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters
Online Station Assignment for Electric Vehicle Battery Swapping
This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs' locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters
Networked Cournot Competition in Platform Markets: Access Control and Efficiency Loss
This paper studies network design and efficiency loss in open and discriminatory access platforms under networked Cournot competition. In open platforms, every firm connects to every market, while discriminatory platforms limit connections between firms and markets to improve social welfare. We provide tight bounds on the efficiency loss of both platforms; (i) that the efficiency loss at a Nash equilibrium under open access is bounded by 3/2, and (ii) for discriminatory access platforms, we provide a greedy algorithm for optimizing network connections that guarantees efficiency loss at a Nash equilibrium is bounded by 4/3, under an assumption on the linearity of cost functions
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