Modelling and optimisation of electricity,steam and district heating production for a local Swedish utility

District heating may help reduce environmental impact and energy costs, but policy instruments and waste management may influence operations. The energy system optimisation model MODEST has been used for 50 towns, regions and a nation. Investments and operation that satisfy energy demand at minimum cost are found through linear programming. This paper describes the application of MODEST to a municipal utility, which uses several fuels and cogeneration plants. The model reflects diurnal and monthly demand fluctuations.Several studies of the Linköping utility are reviewed. These indicate that the marginal heat cost is lower in summer; a new waste or wood fired cogeneration plant is more profitable than a natural-gas-fired combined cycle; material recycling of paper and hard plastics is preferable to waste incineration from an energy-efficiency viewpoint; and considering external costs enhances wood fuel use. Here, an emission limit is used to show how fossil-fuel cogeneration displaces CO₂ from coal-condensing plants.     

Multicriteria energy policy investments and energy market clearance via integer programming

The deregulation of energy markets has created a framework for policy making, still under evolution, which is much more complex than the previous one. As a consequence, new requirements need to be met, concerning both technical design and financial management. This framework renders the use of multicriteria techniques attractive. Here, the investments in suppliers, depending on the policy implemented, are formulated as an integer programming problem, which consists of different sub-problems according to the assumptions made and the market’s regulations. The equivalent relaxed problem is a mixed integer programming problem that can represent the clearance of the energy market by considering several criteria besides price and quantity. Nonlinearities are reformulated by inserting additional binary variables so that the solution algorithms are more effective and efficient in most realistic cases. The feasible solutions and the optimal solution that maximizes every time the market regulator’s gain are obtained, after imposing some thresholds on the criteria used to evaluate the different energy technologies, thus creating a decision support system for the regulator.     

Valuing the switching flexibility of the ethanol-gas flex fuel car

Renewable energy sources are becoming more important as the world’s supply of fossil fuels decrease and also due to environmental concerns. Since 2003, when the ethanol-gasoline flex fuel car became commercially available in Brazil, the growth of this market has been significant, to the point where currently more than 50% of the fuel consumption of cars in Brazil is from renewable biofuels (ethanol). This has been made possible due to the success of the flex fuel car, which can run on ethanol, gasoline, or any mix of these in the same fuel tank, and which is sold at a premium over the non-flex models.     

Robust time series analysis

This paper considers the problem of obtaining estimates of seasonal and trend components of time series. It proposes a simple computational procedure for finding those estimates which minimize the sum of absolute errors for an additive time series model. It also examines a multiplicative model with a compound relative error criterion. The proposed method, which yields estimates which are insensitive to outlying members of the series, is ideally suited to a management science context, especially when large numbers of time series must be analysed on a regular basis.     

Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

Reducing the fuel load in fire-prone landscapes is aimed at mitigating the risk of catastrophic wildfires but there are ecological consequences. Maintaining habitat for fauna of both sufficient extent and connectivity while fragmenting areas of high fuel loads presents land managers with seemingly contrasting objectives. Faced with this dichotomy, we propose a Mixed Integer Programming (MIP) model that can optimally schedule fuel treatments to reduce fuel hazards by fragmenting high fuel load regions while considering critical ecological requirements over time and space. The model takes into account both the frequency of fire that vegetation can tolerate and the frequency of fire necessary for fire-dependent species. Our approach also ensures that suitable alternate habitat is available and accessible to fauna affected by a treated area. More importantly, to conserve fauna the model sets a minimum acceptable target for the connectivity of habitat at any time. These factors are all included in the formulation of a model that yields a multi-period spatially-explicit schedule for treatment planning. Our approach is then demonstrated in a series of computational experiments with hypothetical landscapes, a single vegetation type and a group of faunal species with the same habitat requirements. Our experiments show that it is possible to fragment areas of high fuel loads while ensuring sufficient connectivity of habitat over both space and time. Furthermore, it is demonstrated that the habitat connectivity constraint is more effective than neighbourhood habitat constraints. This is critical for the conservation of fauna and of special concern for vulnerable or endangered species.     

A transient stochastic simulation–optimization model for operational fuel planning in-theater

Army fuel planners are responsible for developing daily loading plans that specify which tankers to load, with what fuel, and where to send the loaded tankers. The tools used to accomplish this task are custom built spreadsheets which require large amounts of time and effort to use, update, and keep free of errors. This research presents a transient stochastic simulation–optimization model of the in-theater bulk fuel supply chain, where the simulation model is used to simulate the performance of the fuel supply chain under a particular fuel distribution policy and the optimization portion is used to update the policy so that it results in the performance desired by the Army fuel planner. The fuel distribution policy can then be used to derive the daily loading plan. Due to the multi-objective nature of the problem, the set of policies that form the efficient frontier are all candidate policies for the Army fuel planner to select from. Results of experimentation with a wide variety of supply chain scenarios indicate that, for a given supply chain scenario, the optimization portion of the model identifies a set of fuel distribution policies that address the objectives of the Army fuel planner. In addition, the simulation–optimization model comfortably solves the largest supply chain scenarios the Army fuel planner would reasonably be expected to encounter.     

Circle and divisor problems,and double series of Bessel functions

In approximately 1915, Ramanujan recorded two identities involving doubly infinite series of Bessel functions. The identities were brought to the mathematical public for the first time when his lost notebook was published in 1988, and are connected with the classical, long-standing circle and divisor problems, respectively. We provide a proof of the first identity for the first time by analytically continuing a new kind of Dirichlet series. Delicate estimates of exponential sums are needed, and the new methods we introduce may be of independent interest.     

“双积分”政策下考虑燃油车减排的汽车制造商价格博弈研究

考虑供给侧为双寡头车企组成的汽车卖场,以没有“双积分”政策约束作为参照条件,构建有“双积分”政策约束时燃油车是否减排的模型,分别对其进行Bertrand博弈求解分析,从定价视角探讨“双积分”政策对燃油车和新能源汽车发展的作用机制,并通过数值分析进一步直观验证“双积分”政策的作用效果。研究表明:每台新能源汽车积分值设置过大不利于促进燃油车减排水平的提高,且存在合理的新能源汽车积分值区间,可以实现“既鼓励先进,又制约落后”的双重目的。“双积分”政策对涉及燃油车生产的制造商不利,当每台新能源汽车积分值较小时,其获得的利润较无“双积分”政策约束时减小;对仅生产新能源汽车的制造商有利,其获得的利润总是较无“双积分”政策约束时增大。     

Developments in time preference and their implications for medical decision making

The field of time preference is developing rapidly. It concerns important concepts for many economic issues. One important domain of application is health economics. This paper reviews several empirical and theoretical developments for time preference with special attention to the health economics field. In addition, the implications for medical decision making, long-term health-care planning and health economic evaluation are discussed. Recognition of this empirical evidence in health-care policy making is recommended, as well as a more transparent process of the framing and analysis of, and deliberation on, public policy.     

Is energy self sufficiency a realistic goal for Canada?

Canada uses more energy per capita than any other major nation. The primary reasons are geography, climate, energy intensive industries and low energy prices. The country has substantial primary energy resources. This paper utilizes a dynamic energy policy model and a wide range of scenarios to investigate the possibility for long run energy self sufficiency and the related questions of timing, export possibilities and maintaining domestic energy prices below world levels.     

Temporal aggregation in structural VAR models

This paper examines the effects of temporal aggregation on the estimated time series properties of economic data. Theory predicts that temporal aggregation loses information about the underlying data processes. We derive low frequency, quarterly and annual, models implied by high frequency, monthly, structural vector autoregressive (SVAR) models and we find that these losses in information are substantial. It is shown that the accuracy of both the estimates and the forecasts of this class of models improve substantially when monthly data are used. Moreover, the aggregated data show more long-run persistence than the underlying disaggregated data. © 1998 John Wiley & Sons, Ltd.     

基于两步法带宽选择的金融时间序列长记忆参数估计

金融时间序列长记忆参数的半参数估计方法以频域分析为主,带宽选择是其中必不可少的关键环节。不同的带宽可能给出差异明显的长记忆参数估计值,甚至产生矛盾的结论,进而影响时间序列平稳性的判断。本文提出一种两步法,用于金融时间序列长记忆估计的半参数方法的带宽选择,并进一步对长记忆参数进行估计:首先,为了克服半参数方法忽略短期结构的不足,通过信息准则判断ARFIMA(p,d,q)过程的短记忆结构;其次,用短记忆模型拟合差分后的序列,根据拟合效果确定选择带宽及长记忆参数估计值。数值模拟显示以长记忆参数估计值均方根误差最小为标准,两步法优于其他方法。经上证50指数已实现波动率日数据的实证检验,两步法在长记忆模型中的预测误差最小;与短记忆模型相比,两步法在中期提前预测步长上具有优势。     

Nuclear fuel cycle optimization using multi-objective stochastic linear programming

The share of electricity produced by nuclear power plants is still increasing in many countries. If the head-end of the nuclear fuel cycle, regarding all operations before leaving the reactor, has now reached maturity, many pathways are still possible in the back-end of the cycle, dealing with the spent fuel. The long-term consequences of any decision regarding subjects such as fuel reprocessing, waste disposal, etc., require the use of strategic planning methods, in which provision is made for multiple objectives and for uncertainties.An interactive multicriteria approach using stochastic linear programming is provided by the code Strange, which has already been applied to different energy strategy problems, also outside the nuclear field. The application presented in the paper illustrates the search for a best fuel cycle policy including four criteria: production costs, the supply of raw material, the commercial balance and employment. The concept of scenarios is used to describe future uncertainties.     

From evidence-based policy making to policy analytics

This paper aims at addressing the problem of what characterises decision-aiding for public policy making problem situations. Under such a perspective it analyses concepts like “public policy”, “deliberation”, “legitimation”, “accountability” and shows the need to expand the concept of rationality which is expected to support the acceptability of a public policy. We then analyse the more recent attempt to construct a rational support for policy making, the “evidence-based policy making” approach. Despite the innovation introduced with this approach, we show that it basically fails to address the deep reasons why supporting the design, implementation and assessment of public policies is such a hard problem. We finally show that we need to move one step ahead, specialising decision-aiding to meet the policy cycle requirements: a need for policy analytics.     

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。     

矿山环境恢复治理保证金制度中公众参与的博弈分析:基于合谋与防范的视角

矿产资源开采行为容易造成矿区生态环境的恶化,国内外目前主要采取矿山环境恢复治理保证金制度以强制矿山企业对矿区开采后生态环境进行一系列修复。在缺少公众监督的情况下,保证金制度容易出现道德风险,即矿山企业和地方政府监管者的合谋行为。目前,我国各地推行的矿山环境恢复治理保证金制度均没有让公众参与进来,这就需要对这一制度缺陷进行弥补与修正。本文基于公众参与理论和博弈论构建了公众、矿山企业和政府监管者三方博弈模型,并利用对其混合纳什均衡解的分析,得出结论为:通过提高公众监督概率和公众监督有效概率、降低公众监督成本来防范和控制合谋行为。最后,根据以上分析提出了相关的政策建议。     

Consistency of perturbation analysis for a queue with finite buffer space and loss policy

The subject of discrete-event dynamical systems has taken on a new direction with the advent of perturbation analysis (PA), an efficient method for estimating the gradients of a steady-state performance measure, by analyzing data obtained from a single-simulation experiment in the time domain. A crucial issue is whether PA gives strongly consistent estimates, namely, whether average time-domain-based gradients converge, over infinite horizon, to the steady-state gradients. In this paper, we investigate this issue for a queue with a finite buffer capacity and a loss policy. The performance measure in question is the average amount of lost customers, as a function of the buffer's capacity, which is assumed to be continuous in our work. It is shown that PA gives strongly consistent estimates. The analysis uses a new technique, based on busy period-dependent inequalities. This technique may have possible extensions to analyses of consistency of PA for more general queueing systems.     

Finite element analysis of a class of stress-free martensitic microstructures

This work is concerned with the finite element approximation of a class of stress-free martensitic microstructures modeled by multi-well energy minimization. Finite element energy-minimizing sequences are first constructed to obtain bounds on the minimum energy over all admissible finite element deformations. A series of error estimates are then derived for finite element energy minimizers.

     

TIMBER MARKETS AND FUEL TREATMENTS IN THE WESTERN U.S.

ABSTRACT. We developed a model of interrelated timber markets in the U.S. West to assess the impacts of large‐scale fuel reduction programs on these markets, and concomitant effects ofthe market on the fuel reduction programs. The linear programming spatial equilibrium model allows interstate and international trade with western Canada and the rest of the world, while accounting for price effects of introducing softwood logs to the market. The model maximizes area treated, given fire regime‐condition class priorities, maximum increases in softwood processing capacity, maximum rates of annual treatments, prohibitions on exports of U.S. and Canadian softwood logs from public lands and a fixed annual treatment budget. Results show that the loss to U.S. private timber producers is less than the gains for timber consumers (mills). States receiving more treatments when spending is not constrained by state proportions include Idaho, Montana, New Mexico and Oregon. When only the wildland‐urban interface is treated, California, Oregon and Washington receive more treatments. Utah and Colorado receive more treatments when low risk stands are included.     

自适应有限元方法在凹角域线性椭圆方程的应用(续)

作为序列文章自适应有限元方法在凹角域线性椭圆方程的应用的第三篇,在本文我们将给出并详细论证一个重要结论即 |?(u(x)-U(x))|≤Ch(x)|x|^β-2,|x|≥C′h且进一步分析说明在本序列文章的第一部分和地二部分得出方法都是以此为基础作出的。