Electricity price modeling and asset valuation: a multi-fuel structural approach

We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies per fuel type and for the possibility of future changes in the merit order of the fuels. The derived spot price process captures important stylized facts of historical electricity prices, including both spikes and the complex dependence upon its underlying supply and demand drivers. Furthermore, under mild and commonly used assumptions on the distributions of the input factors, we obtain closed-form formulae for electricity forward contracts and for spark and dark spread options. As merit order dynamics and fuel forward prices are embedded into the model, we capture a much richer and more realistic dependence structure than can be achieved by classical reduced-form models. We illustrate these advantages by comparing with Margrabe’s formula and a simple cointegration model, and highlight important implications for the valuation of power plants.     

Feasibility principles for Downstream Demand Inference in supply chains

Many companies are adopting strategies that enable Demand Information Sharing (DIS) between the supply chain links. Recently, a steady stream of research has identified mathematical relationships between demands and orders at any link in the supply chain. Based on these relationships and strict model assumptions, it has been suggested that the upstream member can infer the demand at the downstream member from their orders. If this is so, DIS will be of no value. In this paper, we argue that real-world modelling requires less restrictive assumptions. We present Feasibility Principles to show that it is not possible for an upstream member to accurately infer consumer demand under more realistic model assumptions. Thus, we conclude that DIS has value in supply chains. We then move our focus to the supply chain model assumptions in the papers arguing that there is value in sharing demand information. Using a simulation experiment, we show that the value of sharing demand information in terms of inventory reductions will increase under more realistic supply chain model assumptions.     

Asymmetric Supply Function Equilibria with Forward Contracts

We consider markets in which firms offer supply functions, rather than a quantity or price alone: the most important examples are wholesale electricity markets. The equilibria in such markets can be hard to characterize. In many cases, whole families of supply function equilibria occur so there are difficulties in determining which equilibrium will be chosen. In this paper, we consider supply function equilibria, when firms hold forward contracts, which is common in electricity markets. Under the assumption that contract positions have been fixed in advance, we characterize the families of supply function equilibria in a duopoly. The existence of forward contracts implies a tightening of the conditions for an equilibrium, and a greater likelihood that no equilibrium solution exists. In the case of three firms, there can be at most one supply function equilibrium, provided that the lowest demand be small enough.     

Semi-Lagrangean approach for price discovery in markets with non-convexities

From standard economic theory, the market clearing price for a commodity is set where the demand and supply curves intersect. Convexity is a property that economic models require for a competitive equilibrium, which is efficient and well-behaved and provides equilibrium prices. However, some markets present non-convexities due to their cost structure or due to some operational constraints that need to be addressed. This is the case for electricity markets where the electricity producers incur costs for shutting down a generating unit and then bringing it back on. Non-convex cost structures can be a challenge for the price discovery process, since the supply and demand curves may not intersect, or if they intersect, the price found may not be high enough to cover the total cost of production. We apply a Semi-Lagrangean approach to find a price that can be applied in the electricity pool markets where a central system operator decides who produces and how much they should produce. By applying the model to an example from the literature, we found prices that are high enough to cover the producer’s total costs, and follows the optimal solution for achieving mining cost in production. The prices are an alternative solution to the price discovery problem in non-convexities economies; in addition, they provide nonnegative profits to all the generators without the use of side-payments or up-lifts, and closes the integrality gap.     

Interactive High-low Search: The Case of Lost Sales

We consider the problem of supplying perishable goods to disloyal customers. As in traditional stock-control literature, a penalty is incurred whenever there is a stockout. However, in contrast to mainstream models, loss of goodwill is explicitly treated by incorporating the behavioural assumption that a fixed proportion of unsatisfied demand is lost forever after each stockout. The problem consists of finding supply levels which minimize costs of over- and underproduction, given unknown but deterministic demand. We derive optimal adaptive search procedures under varying assumptions of a priori knowledge about demand. Optimal strategies are compared to myopic strategies. Our methodology extends the mathematical theory of ‘high-low search’ for a hidden point to incorporate the ‘Heisenberg principle’: the position of the hidden point (demand) is directly influenced by the actions (supply levels) of the searcher.     

Spatial dependencies of wind power and interrelations with spot price dynamics

Wind power has seen strong growth over the last decade and increasingly affects electricity spot prices. In particular, prices are more volatile due to the stochastic nature of wind, such that more generation of wind energy yields lower prices. Therefore, it is important to assess the value of wind power at different locations not only for an investor but for the electricity system as a whole. In this paper, we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas, incorporated into a supply and demand based model for the electricity spot price. This model is calibrated with German data. We find that the specific location of a turbine – i.e., its spatial dependence with respect to the aggregated wind power in the system – is of high relevance for its value. Many of the locations analyzed show an upper tail dependence that adversely impacts the market value. Therefore, a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases. This effect becomes more important for increasing levels of wind power penetration and may render the large-scale integration into markets more difficult.     

带有环境指示因子的闭环供应链网络均衡的互补模型研究

以非线性互补理论为基本工具,提出带有环境指示因子且由原料供应商、制造商、零售商、需求市场及回收中心组成的闭环供应链网络均衡。鉴于对可持续发展的日益关注,环境保护部门通过实施对制造商的排放进行惩罚的机制以遏制其污染行为,同时通过对回收中心的回收实行奖励的机制以激励其回收行为。排放惩罚与回收奖励应大于相应的影子价格,这些影子价格作为环境指示因子由模型决定且有益于政府部门制定相应环保政策。在描述不同决策者优化行为的基础上推导最优性条件,得到对应于闭环供应链网络均衡问题的非线性互补模型,证明解的存在性进而构造数值算例以验证模型的合理性。     

动态供应链网络均衡模型构建与分析

基于单一商品流,考虑了时间变量和库存问题,建立了三层动态供应链网络结构模型.对制造商、零售商和需求市场的多期独立决策行为及其相互作用进行了分析,应用变分不等式构建了各层均衡模型和整个供应链网络均衡模型.最后与相关文献的模型进行了比较.     

Supply Function Equilibrium in Electricity Spot Markets with Contracts and Price Caps

In electricity wholesale markets, generators often sign long term contracts with purchasers of power in order to hedge risks. In this paper, we consider a market where demand is uncertain, but can be represented as a function of price together with a random shock. Each generator offers a smooth supply function into the market and wishes to maximize his expected profit, allowing for his contract position. We investigate supply function equilibria in this setting, using a model introduced by Anderson and Philpott. We study first the existence of a unique monotonically increasing supply curve that maximizes the objective function under the constraint of limited generation capacity and a price cap, and discuss the influence of the generators contract on the optimal supply curve. We then investigate the existence of a symmetric Nash supply function equilibrium, where we do not have to assume that the demand is a concave function of price. Finally, we identify the Nash supply function equilibrium which gives rise to the generators maximal expected profit. This work was supported by the Australian Research Council Grant RMG1965The authors would like to thank three anonymous referees for their helpful comments     

Market selection decisions for inventory models with price-sensitive demand

In the majority of classical inventory theory literature, demand arises from exogenous sources upon which the firm has little or no control. In many practical contexts, however, aggregate demand is comprised of individual demands from a number of distinct customers or markets. This introduces new dimensions to supply chain planning problems involving the selection of markets or customers to include in the demand portfolio. We present a nonlinear, combinatorial optimization model to address planning decisions in both deterministic and stochastic settings, where a firm constructs a demand portfolio from a set of potential markets having price-sensitive demands. We first consider a pricing strategy that dictates a single price throughout all markets and provide an efficient algorithm for maximizing total profit. We also analyze the model under a market-specific pricing policy and describe its optimal solution. An extensive computational study characterizes the effects of key system parameters on the optimal value of expected profit, and provides some interesting insights on how a given market’s characteristics can affect optimal pricing decisions in other markets.     

Supply chain networks with global outsourcing and quick-response production under demand and cost uncertainty

This paper develops a modeling and computational framework for supply chain networks with global outsourcing and quick-response production under demand and cost uncertainty. Our model considers multiple off-shore suppliers, multiple manufacturers, and multiple demand markets. Using variational inequality theory, we formulate the governing equilibrium conditions of the competing decision-makers (the manufacturers) who are faced with two-stage stochastic programming problems but who also have to cooperate with the other decision-makers (the off-shore suppliers). Our theoretical and analytical results shed light on the value of outsourcing from novel real option perspectives. Moreover, our simulation studies reveal important managerial insights regarding how demand and cost uncertainty affects the profits, the risks, as well as the global outsourcing and quick-production decisions of supply chain firms under competition.     

Modelling the effects of interconnection between electricity markets subject to uncertainty

Interconnecting distinct electricity markets by adding a new transmission line affects the outcomes in these markets in a complicated way when there is uncertainty in demand or participant behaviour. We use market distribution functions to examine the effects of interconnection using a single transmission line under the assumption that this line has a differentiable loss function and agents in each of the interconnected markets do not change their behaviour in response to the interconnection. We also show how the case with capacity constraints on flows can be represented with appropriately formulated loss functions. We give analytical formulae for computing market outcomes when the uncertain events in the markets being connected are statistically independent, and show by example how to compute these outcomes when these events are correlated.     

Contract design and supply chain coordination in the electricity industry

In this article we propose a model of the supply chain in electricity markets with multiple generators and retailers and considering several market structures. We analyze how market design interacts with the different types of contract and market structure to affect the coordination between the different firms and the performance of the supply chain as a whole. We compare the implications on supply chain coordination and on the players’ profitability of two different market structures: a pool based market vs. bilateral contracts, taking into consideration the relationship between futures and spot markets. Furthermore, we analyze the use of contracts for differences and two-part-tariffs as tools for supply chain coordination. We have concluded that there are multiple equilibria in the supply chain contracts and structure and that the two-part tariff is the best contract to reduce double marginalization and increase efficiency in the management of the supply chain.     

Supply function equilibria for uniform price auction in oligopolistic markets

We consider game-theoretic models related to the supply function auction for electricity markets. We determine the set of supply function equilibria (SFE), introduced by Klemperer and Mayer (Econometrica 57:1243–1277, 1989), for a symmetric oligopoly with linear demand, fixed marginal cost and capacity constraint. This set depends on the maximum random shock of the demand function. We also study the best response dynamics and show that in general it does not converge to any SFE. We find out sufficient conditions for the convergence and conclude on the optimal parameters of the auction.     

多商品流多期供应链网络均衡模型

针对具有多种商品流的三层供应链网络结构模型,考虑时间变量和库存问题,分别对制造商、零售商和需求市场的决策者的多期独立决策行为及其相互作用进行了分析,利用变分不等式构建了各层均衡模型以及系统均衡模型,得到了系统达到均衡的条件,给出了具体算例并进行了求解,最后与相关文献的模型进行了比较.     

On the existence of supply function equilibria

Supply function equilibria are used in the analysis of divisible good auctions with a large number of identical objects to be sold or bought. An important example occurs in wholesale electricity markets. Despite the substantial literature on supply function equilibria the existence of a pure strategy Nash equilibria for a uniform price auction in asymmetric cases has not been established in a general setting. In this paper we prove the existence of a supply function equilibrium for a duopoly with asymmetric firms having convex non-decreasing marginal costs, with decreasing concave demand subject to an additive demand shock, provided that the second derivative of the demand function is small enough and not increasing. The proof is constructive and also gives insight into the structure of the equilibrium solutions.     

大规模风电并网的双供应源电力供应链牛鞭效应分析

中国当前的电力供应链除具有部分垄断特征外,还由于大规模风电并网使得电力供给也出现随机性,它与随机需求一起影响了供应链信息的准确传递,在电力供应链产生了牛鞭效应,但对这类问题的研究极少。本文在分析中国电力供应链特点的基础上,构建了由煤炭供应企业、发电厂(火力发电和风力发电)和用户组成的多级电力供应链模型,揭示了牛鞭效应在单/双供应源两种供应链类型下的变化。研究结果表明,大规模风电并网形成的双供应源电力供应链牛鞭效应较大且波动剧烈,尤其当下游用户需求较平稳时,供应链会出现牛鞭效应与反牛鞭效应共存现象,而预测技术的选择、风电场合理规划等有助于抑制牛鞭效应,保证电力安全并减小资源浪费。     

随机需求下考虑风险行为的闭环供应链网络均衡模型

以变分不等式和均衡理论为基本研究工具,研究了随机需求与再制造率不确定条件下多个竞争型的供应商、制造商、零售商及消费市场的行为及均衡条件。对所建立的多级闭环供应链网络均衡模型,通过拟牛顿算法求解变分不等式,并仿真分析了再制造率、回收率以及风险因素对闭环供应链网络均衡结果的影响。结果表明:制造商提高再制造率能实现供应链成员利润的增加、产品价格的降低以及回收量的增加;制造商基于风险最小化和利润最大化相结合的原则进行决策能增加产品的交易量及企业的利润。     

Superiority–inferiority modeling coupled minimax-regret analysis for energy management systems

In this study, a superiority–inferiority-based minimax-regret analysis (SI-MRA) model is developed for supporting the energy management systems (EMS) planning under uncertainty. In SI-MRA model, techniques of fuzzy mathematical programming (FMP) with the superiority and inferiority measures and minimax regret analysis (MMR) are incorporated within a general framework. The SI-MRA improves upon conventional FMP methods by directly reflecting the relationships among fuzzy coefficients in both the objective function and constraints with a high computational efficiency. It can not only address uncertainties expressed as fuzzy sets in both of the objective function and system constraints but also can adopt a list of scenarios to reflect the uncertainties of random variables without making assumptions on their possibilistic distributions. The developed SI-MRA model is applied to a case study of long-term EMS planning, where fuzziness and randomness exist in the costs for electricity generation and demand. A number of scenarios associated with various alternatives and outcomes under different electricity demand levels are examined. The results can help decision makers identify an optimal strategy of planning electricity generation and capacity expansion based on a minimax regret level under uncertainty.