Day-ahead market bidding for a Nordic hydropower producer: taking the Elbas market into account

In many power markets around the world the energy generation decisions result from two-sided auctions in which producing and consuming agents submit their price-quantity bids. The determination of optimal bids in power markets is a complicated task that has to be undertaken every day. In the present work, we propose an optimization model for a price-taker hydropower producer in Nord Pool that takes into account the uncertainty in market prices and both production and physical trading aspects. The day-ahead bidding takes place a day before the actual operation and energy delivery. After this round of bidding, but before actual operation, some adjustments in the dispatched power (accepted bids) have to be done, due to uncertainty in prices, inflow and load. Such adjustments can be done in the Elbas market, which allows for trading physical electricity up to one hour before the operation hour. This paper uses stochastic programming to determine the optimal bidding strategy and the impact of the possibility to participate in the Elbas. ARMAX and GARCH techniques are used to generate realistic market price scenarios taking into account both day-ahead price and Elbas price uncertainty. The results show that considering Elbas when bidding in the day-ahead market does not significantly impact neither the profit nor the recommended bids of a typical hydro producer.     

Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer

From the point of view of a price-taking hydropower producer participating in the day-ahead power market, market prices are highly uncertain. The present paper provides a model for determining optimal bidding strategies taking this uncertainty into account. In particular, market price scenarios are generated and a stochastic mixed-integer linear programming model that involves both hydropower production and physical trading aspects is developed. The idea is to explore the effects of including uncertainty explicitly into optimization by comparing the stochastic approach to a deterministic approach. The model is illustrated with data from a Norwegian hydropower producer and the Nordic power market at Nord Pool.     

Privacy impact on generalized Nash equilibrium in peer-to-peer electricity market

We consider a peer-to-peer electricity market, where agents hold private information that they might not want to share. The problem is modeled as a noncooperative communication game, which takes the form of a Generalized Nash Equilibrium Problem, where the agents determine their randomized reports to share with the other market players, while anticipating the form of the peer-to-peer market equilibrium. In the noncooperative game, each agent decides on the deterministic and random parts of the report, such that (a) the distance between the deterministic part of the report and the truthful private information is bounded and (b) the expectation of the privacy loss random variable is bounded. This allows each agent to change her privacy level. We characterize the equilibrium of the game, prove the uniqueness of the Variational Equilibria and provide a closed form expression of the privacy price. Numerical illustrations are presented on the 14-bus IEEE network.     

Electricity market clearing with improved scheduling of stochastic production

In this paper, we consider an electricity market that consists of a day-ahead and a balancing settlement, and includes a number of stochastic producers. We first introduce two reference procedures for scheduling and pricing energy in the day-ahead market: on the one hand, a conventional network-constrained auction purely based on the least-cost merit order, where stochastic generation enters with its expected production and a low marginal cost; on the other, a counterfactual auction that also accounts for the projected balancing costs using stochastic programming. Although the stochastic clearing procedure attains higher market efficiency in expectation than the conventional day-ahead auction, it suffers from fundamental drawbacks with a view to its practical implementation. In particular, it requires flexible producers (those that make up for the lack or surplus of stochastic generation) to accept losses in some scenarios. Using a bilevel programming framework, we then show that the conventional auction, if combined with a suitable day-ahead dispatch of stochastic producers (generally different from their expected production), can substantially increase market efficiency and emulate the advantageous features of the stochastic optimization ideal, while avoiding its major pitfalls.     

Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity

We develop a multi-stage stochastic programming approach to optimize the bidding strategy of a virtual power plant (VPP) operating on the Spanish spot market for electricity. The VPP markets electricity produced in the wind parks it manages on the day-ahead market and on six staggered auction-based intraday markets. Uncertainty enters the problem via stochastic electricity prices as well as uncertain wind energy production. We set up the problem of bidding for one day of operation as a Markov decision process (MDP) that is solved using a variant of the stochastic dual dynamic programming algorithm. We conduct an extensive out-of-sample comparison demonstrating that the optimal policy obtained by the stochastic program clearly outperforms deterministic planning, a pure day-ahead strategy, a benchmark that only uses the day-ahead market and the first intraday market, as well as a proprietary stochastic programming approach developed in the industry. Furthermore, we study the effect of risk aversion as modeled by the nested Conditional Value-at-Risk as well as the impact of changes in various problem parameters.     

A dynamic Cournot–Nash game: a representation of a finitely repeated feedback game

This paper studies market outcome equivalence of two dynamic production-capital investment games under uncertainty. One is played under complete information, while the other, feedback (FB) game, is played under incomplete information about the opponents’ costs and market demand. The FB game structure may be observed in some newly initiated industries, in which a homogeneous good is exchanged via an auction mechanism. In that case, the FB game setting may predict the complete information equilibrium market outcomes.      

Equilibrium prices supported by dual price functions in markets with non-convexities

The issue of finding market clearing prices in markets with non-convexities has had a renewed interest due to the deregulation of the electricity sector. In the day-ahead electricity market, equilibrium prices are calculated based on bids from generators and consumers. In most of the existing markets, several generation technologies are present, some of which have considerable non-convexities, such as capacity limitations and large start-up costs. In this paper we present equilibrium prices composed of a commodity price and an uplift charge. The prices are based on the generation of a separating valid inequality that supports the optimal resource allocation. In the case when the sub-problem generated as the integer variables are held fixed to their optimal values possess the integrality property, the generated prices are also supported by non-linear price functions that are the basis for integer programming duality.     

Optimal production in a competitive market under yield uncertainty

Motivated by the U.S. influenza vaccine market, we study the impact of random yield and production capacity on the number of firms, total supply, consumer surplus and social welfare in a market with identical suppliers. We formulate a two-stage game with endogenous entry, where each entering firm aims to maximize its profit under yield uncertainty subject to a production capacity constraint. We show that if firms produce full capacity in the equilibrium, then there are fewer firms in the equilibrium compared to the social optimum even for small levels of yield uncertainty. Furthermore, we prove that if firms do not produce full capacity in the equilibrium, they will not produce full capacity in the social optimum.     

Dynamic price dispersion in Bertrand–Edgeworth competition

This paper studies a dynamic oligopoly model of price competition under demand uncertainty. Sellers are endowed with one unit of the good and compete by posting prices in every period. Buyers each demand one unit of the good and have a common reservation price. They have full information regarding the prices posted by each firm in the market; hence, search is costless. The number of buyers coming to the market in each period is random. Demand uncertainty is said to be high if there are at least two non-zero demand states that give a seller different option values of waiting to sell. Our model features a unique symmetric Markov perfect equilibrium in which price dispersion prevails if and only if the degree of demand uncertainty is high. Several testable theoretical implications on the distribution of market prices are derived.     

Asset pricing under ambiguous information: an empirical game-theoretic analysis

In a representative agent model, the behavior of a social system is described in terms of a single aggregate decision maker. Such models are popular in economic and finance research, largely due to their analytic tractability, but fail to account for real-world agent heterogeneity, and may ignore effects of market microstructure. Agent-based simulation models naturally incorporate agent heterogeneity, and can account for any particular market microstructure; however, such models have gained only limited acceptance by the mainstream economic research community, due to concerns over how much general insight can be gleaned from simulating a particular configuration of agent behaviors. We address such concerns by employing game-theoretic criteria in behavior selection. We present a case study investigating a recent model from the finance literature proposed by Epstein and Schneider (ES), and its ability to explain the classic equity premium puzzle in risky asset pricing. For all market configurations that we examined, ambiguity-averse pricing was played with little or no probability in equilibrium. Moreover, none of the market configurations exhibited significant equity premia. Both our use of strategic equilibrium as a market composition concept, and the actions of our simulated market microstructure contribute to removing any equity premium. These findings underscore the value of checking that results from abstract representative-agent models are supportable in a higher-fidelity model where heterogeneity and strategic interactions are taken into account.     

Optimally-informative path planning for dynamic Bayesian classification

An agent, consisting of an unmanned aerial vehicle (UAV) carrying strapped-down sensors, is to examine a number of unidentified objects within a given search area, collect information, and utilize that information to classify the objects. The problem is challenging because the mission time is often limited, the agent is only provided with partial a priori information, and the amount of information that the sensor can measure is dependent on the relative position of the agent with respect to the object. Our technical approach is three-fold. First, we model the motion of the agent using a kinematic model with constant altitude. Second, we use a performance prediction model that gives the probability of target discrimination as a function of the range from the sensor to the object. Third, a linear classifier that utilizes Bayes?? theorem diagnoses the status of the objects of interest while an information-theoretic measure is used to quantify the uncertainty in classification. We pose an optimal control problem that minimizes the classification uncertainty while taking differential constraints and the time history of the agent??s steering decisions as the control input. We investigate whether maximizing information by choosing informative paths always minimizes the classification uncertainty.     

Robust Optimal Portfolio Choice Under Markovian Regime-switching Model

We investigate an optimal portfolio selection problem in a continuous-time Markov-modulated financial market when an economic agent faces model uncertainty and seeks a robust optimal portfolio strategy. The key market parameters are assumed to be modulated by a continuous-time, finite-state Markov chain whose states are interpreted as different states of an economy. The goal of the agent is to maximize the minimal expected utility of terminal wealth over a family of probability measures in a finite time horizon. The problem is then formulated as a Markovian regime-switching version of a two-player, zero-sum stochastic differential game between the agent and the market. We solve the problem by the Hamilton-Jacobi-Bellman approach.      

带有风能的智能电网多周期买电策略研究

本文研究带有风能随机供给的智能电网中传统能源的多周期买电问题,假设存在一个能源运营商集中负责智能电网传统能源的购买和消费。通过构建并求解动态规划模型,找到能源运营商在风能供给不确定性下的传统能源最优多周期买电策略。在最优买电策略下,能源运营商只有在当期电价足够小时才购买传统能源,其买电量与风能分布、价格信息和时间信息有关。在实际数据的基础之上,提供详实的数值实验对比研究了本文的最优买电策略和其他两种策略(实践中只考虑风能估计的策略和放弃利用风能的策略)在最小化总成本方面的效果,并验证了本文的最优买电策略在真实风能数据中的鲁棒性。     

Robust optimization of consumption with random endowment

We consider the problem of optimal consumption for an investor who is risk and uncertainty averse. We model these preferences of the investor with the help of a convex risk-measure. Apart from consumption the agent has the possibility to invest initial capital and random endowment in a market where stock-prices are semimartingales. We formulate this as a maximin problem that will be solved by duality methods.     

关于大经济金融市场

在Aumann大经济框架下，本文给出具有可测空间经纪人的大金融市场模型；讨论了资产结构完全时均衡的存在性；给出了以经纪人代表效用表出形式的金融资产定价公式，它推广[3]中相关内容。模型更贴近于具有对称信息充分竞争的涵义．     

非对称信息证券交易的跨期动态博弈均衡解析

揭示了不对称信息条件下证券市场均衡的基本特征.Grossman和Stiglitz模型依据不知情交易者的弱理性,解析了证券交易的静态均衡状态.O'Hara模型增强了不知情交易者的理性,强调了市场均衡时的风险定价,但其命题的成立条件是相互矛盾的.认为不知情交易者信息收集和处理能力的提高会使决策更为理性,证券市场的均衡本质上是交易者的动态博弈均衡.依此思路,运用不完美信息的跨期动态博弈模型解析了非对称信息条件下证券交易者的精炼贝叶斯纳什均衡.结论显示出,市场失效的主要原因是交易者之间的信息分析能力不平衡,而不是信息不对称;市场流动性的决定因素不是信息不对称风险而是知情交易者与不知情交易者所研判的无风险收益率的差别.     

Robust game theory

We present a distribution-free model of incomplete-information games, both with and without private information, in which the players use a robust optimization approach to contend with payoff uncertainty. Our ``robust game' model relaxes the assumptions of Harsanyi's Bayesian game model, and provides an alternative distribution-free equilibrium concept, which we call ``robust-optimization equilibrium,' to that of the ex post equilibrium. We prove that the robust-optimization equilibria of an incomplete-information game subsume the ex post equilibria of the game and are, unlike the latter, guaranteed to exist when the game is finite and has bounded payoff uncertainty set. For arbitrary robust finite games with bounded polyhedral payoff uncertainty sets, we show that we can compute a robust-optimization equilibrium by methods analogous to those for identifying a Nash equilibrium of a finite game with complete information. In addition, we present computational results. The research of the author was partially supported by a National Science Foundation Graduate Research Fellowship and by the Singapore-MIT Alliance. The research of the author was partially supported by the Singapore-MIT Alliance.     

Cournot model with investments to change the market size

We present a new deterministic dynamical model on the market size of Cournot competitions, based on Nash equilibria of R&D investment strategies to increase the size of the market of the firms at every period of the game. We compute the unique Nash equilibrium for the second subgame and the profit functions for both firms. Adding uncertainty to the R&D investment strategies, we get a new stochastic dynamical model and we analyse the importance of the uncertainty to reverse the initial advantage of one firm with respect to the other. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On computational complexity of the electric power flow optimization problem in market environment

Under consideration is the electric power flow optimization problem for an electric power system which typically arises in calculation of electrical power auctions in the “day-ahead” and balancing markets. It was established that the problem of finding a feasible flow in the balancing market is NP-hard in the strong sense even in case of one generator. The problem of finding an optimal flow in the day-ahead market is proved to be NP-hard even with one generator and without controlled cuts.     

On the convergence to Walrasian prices in random matching Edgeworthian economies

We show that for a specific class of random matching Edgeworthian economies, the expectation of the limiting equilibrium price coincides with the equilibrium price of the related Walrasian economies. This result extends to the study of economies in the presence of uncertainty within the multi-period Arrow-Debreu model, allowing to understand the dynamics of how beliefs survive and propagate through the market.