An optimization problem in the electricity market

New types of optimization problems are faced by the generating companies that operate on deregulated electricity markets. The characteristics of these problems depend on the various market structures. In the framework of the recently settled Italian electricity market, one of these new problems is the transition from hourly energy programs, defined by the market, to more detailed power generation dispatches, defined for intervals of 15 min. Such a more detailed plan is needed on the one hand by the national system operator (Terna, Rete Elettrica Nazionale) for the assessment of power system stability and security, and on the other hand by the power plant operators for its implementation. The transition procedure should respect the hourly energy constraints and take the main operating constraints of the generating units into account. The paper presents possible solutions of the problem through linear optimization models and reports computational results on real-world instances.      

Stable modeling in energy risk management

High price volatility in energy markets compels the companies to adopt and implement policies for measurement and management of the energy risk. A popular measure of risk exposure is the Value at Risk (VaR). Traditional methods of estimation of VaR used by major energy companies fail to capture the heavy tails and asymmetry of energy returns distributions. We suggest the use of stable distributions for modeling energy return distributions. The results of our study demonstrate that stable modeling captures asymmetry and heavy-tails of returns, and, therefore, provides more accurate estimates of energy VaR.     

Solving stochastic complementarity problems in energy market modeling using scenario reduction

In this paper, we analyze market equilibrium models with random aspects that lead to stochastic complementarity problems. While the models presented depict energy markets, the results are believed to be applicable to more general stochastic complementarity problems. The contribution is the development of new heuristic, scenario reduction approaches that iteratively work towards solving the full, extensive form, stochastic market model. The methods are tested on three representative models and supporting numerical results are provided as well as derived mathematical bounds.     

Stochastic Stability of Some Mechanical Systems

We discuss the behavior, for large values of time, of two linear stochastic mechanical systems. The systems are similar mathematically in that they contain a white noise in their parameters. The initial data may be random as well but are independent of white noise. The expected energy is calculated in both cases. It is well known that for free nonstochastic mechanical systems with viscous damping, the energy approaches zero as time increases. We check that this behavior takes place for the stochastic systems under consideration in the case when the initial data are random but the parameters are not. When the parameters contain a random noise the expected energy may be infinite, approach zero, remain bounded, or increase with no bound. This regime is similar to but more interesting than the known regime for the solutions of differential equations with time dependent periodic coefficients that describes the behavior of a mechanical system with characteristics that are periodic functions of time. We give necessary and sufficient conditions for stability of both systems in terms of the structure of the set of roots of an auxiliary equation.     

The forward dynamics in energy markets – infinite-dimensional modelling and simulation

In this paper an infinite-dimensional approach to model energy forward markets is introduced. Similar to the Heath–Jarrow–Morton framework in interest-rate modelling, a first-order hyperbolic stochastic partial differential equation models the dynamics of the forward price curves. These equations are analysed, and in particular regularity and no-arbitrage conditions in the general situation of stochastic partial differential equations driven by an infinite-dimensional martingale process are studied. Both arithmetic and geometric forward price dynamics are studied, as well as accounting for the delivery period of electricity forward contracts. A stable and convergent numerical approximation in the form of a finite element method for hyperbolic stochastic partial differential equations is introduced and applied to some examples with relevance to energy markets.     

Financial networks with socially responsible investing

In this paper, we develop a framework for the modeling, analysis, and computation of solutions to multitiered financial network problems with socially responsible investment in which both the sources of financial funds as well as the intermediaries are multicriteria decision-makers. We assume that these decision-makers seek not only to maximize their net revenues but also minimize risk with the risk being penalized by a variable weight. Furthermore, we assume that the intermediaries are socially responsible companies, who want to maximize their social responsibility levels. We make explicit the behavior of the various decision-makers, including the consumers at the demand markets for the financial products. We derive the optimality conditions, and demonstrate that the governing equilibrium conditions of the financial network economy can be formulated as a finite-dimensional variational inequality problem. Qualitative properties of the equilibrium financial flow and price pattern are provided. A numerical example is used to illustrate the model.     

Stochastic differential reinsurance games with capital injections

This paper investigates a class of reinsurance game problems between two insurance companies under the framework of non-zero-sum stochastic differential games. Both insurers can purchase proportional reinsurance contracts from reinsurance markets and have the option of conducting capital injections. We assume the reinsurance premium is calculated under the generalized variance premium principle. The objective of each insurer is to maximize the expected value that synthesizes the discounted utility of his surplus relative to a reference point, the penalties caused by his own capital injection interventions, and the gains brought by capital injections of his competitor. We prove the verification theorem and derive explicit expressions of the Nash equilibrium strategy by solving the corresponding quasi-variational inequalities. Numerical examples are also conducted to illustrate our results.     

Climate change and optimal energy technology R&D policy

Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.     

Regularity of Solutions to Bilinear Stochastic Wave Equations

Abstract

The paper is concerned with strong solutions of bilinear stochastic wave equations in ? ^d , of which the coefficients contain semimartingale white noises with spatial parameters. For the Cauchy problems, the existence and spatial regularity of solutions in Sobolev spaces are proved under appropriate conditions. The dependence of solution regularity on the smoothness of the random coefficients is ascertained. The proofs are based on stochastic energy inequalities, the semigroup method and certain submartingale inequalities. Regularity results are also obtained for the special case of Wiener semimartingales.     

A heuristic for the long-term electricity generation planning problem using the Bloom and Gallant formulation

Long-term power planning is a stochastic problem often confronted by electrical utilities in liberalized markets. One can model it for profit maximization—using market-price estimation functions for each interval—by posing it as a quadratic programming problem with some linear equalities and an exponential number of load-matching linear inequality constraints.     

Explosive solutions of stochastic nonlinear beam equations with damping

This study considers a class of damped stochastic nonlinear beam equations driven by multiplicative noise. By an appropriate energy inequality, we provide sufficient conditions such that the local solutions of the stochastic equations blow up with a positive probability or are explosive in an L²$L^2$ sense. We also derive estimates of the upper bound of the blow-up time.     

Modelling energy consumption in manufacturing industry

The major effort in energy modelling has been directed to national and global consumption.This paper describes how the techniques of multivariate statistical analysis can be used to model energy consumption in a production environment with varying levels of production activity and weather conditions.The author discusses how the methods were applied by the Operational Research Department at Lancaster University to a manufacturing group of six companies involved in making various products for the engineering and construction industry. The paper identifies the practical issues that arose through the project and describes how the energy models are used for budgeting monthly fuel consumption and for evaluating new energt policies.     

Flexible Energy Management and Heat Exchanger Network Design

The design of heat exchanger networks (HEN) is a well-studied problem in process synthesis and an ideal test base to benchmark methods and techniques in the field. Despite a significant number of relevant publications, networks are still designed under assumptions of fixed operating conditions. Significant variations in supply and demand, alongside a need for efficient management in energy markets (energy grids, deregulated markets), impose limitations to this practice. Networks, designed with thermodynamic and economic efficiency under nominal operation, are known to have their efficiency dissipated and wasted in a context of similar though different conditions and demands. In a process plant, operational changes are common but designers still favor the staged approach of Pinch Technology (i.e., targeting-network development) where flexibility is not addressed properly and systematically. Alternatively, superstructure methods offer formulations with complexities hard to address by conventional algorithms. In this work, flexibility is addressed in a context amenable to targeting and network development stages, offering opportunities to visualise solutions and review options. For targeting, a dual approach is proposed that follows the framework of Hypertargets by Briones and Kokossis (1999a, 1999b, 1999c). The conceptual screening involves (i) the selection of cost-effective (primal) matches, and (ii) a model-based approach to assess the flexibility of the design options. Models and procedures are employed to assess trade-offs between operating cost (energy), capital cost (area), and the options' ability to handle variations (flexibility). Primal matches are automatically developed into network configurations with the use of mathematical models. A rigorous, superstructure-based approach is next applied to ensure the development of networks capable of handling operational variations without a need to consider exhaustive combinations of scenarios. The iterative approach incrementally augments the mathematical formulation by constraints and vertex conditions that guarantee consistency. The procedure is illustrated with two industrial problems and reports important improvements over conventional techniques.     

Currency Denomination in Long-Term Debt Financing and Refinancing: A Cross- Hedging Paradigm

Under conditions of chronic exchange rate overshooting and mildly segmented capital markets, optimal currency denomination decision rules for international debt financing are derived for risk-neutral and risk-averse borrowers. For the latter, an inter-temporal expected utility framework yields the risk-adjusted cost of foreign debt, which allows for the pricing of currency cross-hedging effects in multi-currency debt portfolios, artificial currency unit-denominated debt instruments as well as currency swaps.     

Dynamic asset allocation for varied financial markets under regime switching framework

Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008. Portfolio optimization is the most developed method to examine the optimal decision for asset allocation. We employ the hidden Markov model to identify regimes in varied financial markets; a regime switching model gives multiple distributions and this information can convert the static mean–variance model into an optimization problem under uncertainty, which is the case for unobservable market regimes. We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem. In addition, we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling-horizon simulation. We conclude that the regime information helps portfolios avoid risk during left-tail events.     

Strategic bidding of offer curves: An agent-based approach to exploring supply curve equilibria

We model a market in which suppliers bid step-function offer curves using agent-based modeling. Our model is an abstraction of electricity markets where step-function offer curves are given to an independent system operator that manages the auctions in electricity markets. Positing an elementary and computationally accessible learning model, Probe and Adjust, we present analytic results that characterize both the behavior of the learning model and the properties of step-function equilibria. Thus, we have developed a framework for validating agent-based models prior to using them in situations that are too complicated to be analyzed using traditional economic theory. In addition, we demonstrate computationally that, by using alternative policies, even simple agents can achieve monopoly rewards for themselves by pursuing more industry-oriented strategies. This raises the issue of how participants in oligopolistic markets actually behave.     

Stochastic Wave Equations with Memory

The authors show the existence and uniqueness of solution for a class of stochastic wave equations with memory. The decay estimate of the energy function of the solution is obtained as well.     

Stochastic load modelling for electric energy distribution applications

Summary The problem of electric load modelling for low aggregation levels is addressed in this paper. The objective is to obtain good “demand” and “response” behaviour models of any group of loads in an electric energy distribution system for any of the functional applications that are beeing considered in the framework of the Distribution Management Systems, aimed to improve the energy efficiency, reliability and quality of the system. A brief critical revision of the methodologies used for that purpose is in the paper, and the advantages of using approaches where physical knowledge about the load characteristics is used, are stated and demonstrated.     

Incomplete financial markets and contingent claim pricing in a dual expected utility theory framework

This paper investigates the price for contingent claims in a dual expected utility theory framework, the dual price, considering arbitrage-free financial markets. A pricing formula is obtained for contingent claims written on n underlying assets following a general diffusion process. The formula holds in both complete and incomplete markets as well as in constrained markets. An application is also considered assuming a geometric Brownian motion for the underlying assets and the Wang transform as the distortion function.     

Open versus closed loop capacity equilibria in electricity markets under perfect and oligopolistic competition

We consider two game-theoretic models of the generation capacity expansion problem in liberalized electricity markets. The first is an open loop equilibrium model, where generation companies simultaneously choose capacities and quantities to maximize their individual profit. The second is a closed loop model, in which companies first choose capacities maximizing their profit anticipating the market equilibrium outcomes in the second stage. The latter problem is an equilibrium problem with equilibrium constraints. In both models, the intensity of competition among producers in the energy market is frequently represented using conjectural variations. Considering one load period, we show that for any choice of conjectural variations ranging from perfect competition to Cournot, the closed loop equilibrium coincides with the Cournot open loop equilibrium, thereby obtaining a ‘Kreps and Scheinkman’-like result and extending it to arbitrary strategic behavior. When expanding the model framework to multiple load periods, the closed loop equilibria for different conjectural variations can diverge from each other and from open loop equilibria. We also present and analyze alternative conjectured price response models with switching conjectures. Surprisingly, the rank ordering of the closed loop equilibria in terms of consumer surplus and market efficiency (as measured by total social welfare) is ambiguous. Thus, regulatory approaches that force marginal cost-based bidding in spot markets may diminish market efficiency and consumer welfare by dampening incentives for investment. We also show that the closed loop capacity yielded by a conjectured price response second stage competition can be less or equal to the closed loop Cournot capacity, and that the former capacity cannot exceed the latter when there are symmetric agents and two load periods.