1,478 research outputs found

    A unified pricing of variable annuity guarantees under the optimal stochastic control framework

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    In this paper, we review pricing of variable annuity living and death guarantees offered to retail investors in many countries. Investors purchase these products to take advantage of market growth and protect savings. We present pricing of these products via an optimal stochastic control framework, and review the existing numerical methods. For numerical valuation of these contracts, we develop a direct integration method based on Gauss-Hermite quadrature with a one-dimensional cubic spline for calculation of the expected contract value, and a bi-cubic spline interpolation for applying the jump conditions across the contract cashflow event times. This method is very efficient when compared to the partial differential equation methods if the transition density (or its moments) of the risky asset underlying the contract is known in closed form between the event times. We also present accurate numerical results for pricing of a Guaranteed Minimum Accumulation Benefit (GMAB) guarantee available on the market that can serve as a benchmark for practitioners and researchers developing pricing of variable annuity guarantees.Comment: Keywords: variable annuity, guaranteed living and death benefits, guaranteed minimum accumulation benefit, optimal stochastic control, direct integration metho

    A Reassessment of the Potential for Loss-framed Incentive Contracts to Increase Productivity: A Meta-analysis and a Real-effort Experiment

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    Substantial productivity increases have been reported when incentives are framed as losses rather than gains. Loss-framed contracts have also been reported to be preferred by workers. The results from our meta-analysis and real-effort experiment challenge these claims. The meta-analysis\u27 summary effect size of loss framing is a 0.16 SD increase in productivity. Whereas the summary effect size in laboratory experiments is a 0.33 SD, the summary effect size from field experiments is 0.02 SD. We detect evidence of publication biases among laboratory experiments. In a new laboratory experiment that addresses prior design weaknesses, we estimate an effect size of 0.12 SD. This result, in combination with the meta-analysis, suggests that the difference between the effect size estimates in laboratory and field experiments does not stem from the limited external validity of laboratory experiments, but may instead stem from a mix of underpowered laboratory designs and publication biases. More- over, in our experiment, most workers preferred the gain-framed contract and the increase in average productivity is only detectable in the subgroup of workers (20%) who preferred the loss-framed contracts. Based on the results from our experiment and meta-analysis, we believe that behavioral scientists should better assess preferences for loss-framed contracts and the magnitude of their effects on productivity before advocating for greater use of such contracts among private and public sector actors

    An Efficient Framework for Improving Microgrid Resilience against Islanding with Battery Swapping Stations

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    In this paper, an efficient bi-level framework is proposed to enhance the resilience of microgrids (MGs) against islanding due to low probability-high impact events by incorporating battery swapping stations (BSSs). In the emergency condition, MG solves the upper-level of the proposed model to report the desired energy transaction including surplus energy and unsupplied loads during the islanding period to the BSSs coordinator. The lower-level problem will be solved with an iterative algorithm by BSSs coordinator to report different plans of energy transactions and their prices to the MG during the emergency period. The price of each energy transaction plan is determined based on a bonus mechanism. Finally, MG will choose the best plan of energy trading considering a new proposed perspective of resilience improvement. Furthermore, a new formulation for BSS operation with fewer variables in comparison to the previous works is proposed in this paper. Simulations are carried out on an MG with two BSSs to verify the proposed model

    An economic market for the brokering of time and budget guarantees

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    Grids offer best effort services to users. Service level agreements offer the opportunity to provide guarantees upon services offered, in such a way that it captures the users’ requirements, while also considering concerns of the service providers. This is achieved via a process of converging requirements and service cost values from both sides towards an agreement. This paper presents the intelligent scheduling for quality of service market-oriented mechanism for brokering guarantees upon completion time and cost for jobs submitted to a batch-oriented compute service. Web Services agreement (negotiation) is used along with the planning of schedules in determining pricing, ensuring that jobs become prioritised depending on their budget constraints. An evaluation is performed to demonstrate how market mechanisms can be used to achieve this, whilst also showing the effects that scheduling algorithms can have upon the market in terms of rescheduling. The evaluation is completed with a comparison of the broker’s capabilities in relation to the literature

    Decarbonising Future Power Systems by Demand Side Management in Smart Grid

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    Carbon emission reduction is an urgent global task. Renewable energy sources integration can promote the transformation of cleaner and greener power system. But the time-varying nature of these sources causes indeterminacy problems. Smart grid is a powerful tool that can deal with these problems in electricity aspect. One of the key smart grid technologies is demand side management. How to use demand side management to regulate and decarbonise the power system is the main point of this thesis. In order to integrate renewable energy sources, a day-ahead electricity market scheme is proposed, involving the utility, the demand response aggregator and customers. This model leads to a multiobjective optimization problem, which is solved by an artificial immune algorithm. The simulation results confirm the feasibility and robustness of the proposed model. All participants can benefit from it, and the system power peak to average ratio can be reduced. In order to realize the carbon emission reduction, a system model for annual fuel sources scheduling and operational policy making of electricity generation is established, considering the economic, environmental and social aspects. A minimum Manhattan distance approach is proposed to select the final solution. The impacts of carbon tax and renewable obligation on carbon emission, generation cost and electricity bill are examined. These can reveal the proper strategy for deciding renewable energy source and carbon emission related policies. After that, a carbon emission flow model is introduced to facilitate the analysis and assessment of demand side management’s impacts on carbon emission reduction. The time sensitivity of carbon emission in both generation side and customer side are obtained. The daily case and seasonal case are presented. The simulation results show that the load curtailment and load shift approaches can effectively reduce the carbon emission

    HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

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    Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation

    Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

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    Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

    The 2016 Power Trading Agent Competition

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    This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many o
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