OR and energy: Problems of modelling

Attention is focused on three problem areas in energy modelling: (1) identifying the essential elements of the system, (2) coping with multiple criteria, (3) incorporating learning in the system. These aspects are illustrated through examples in current energy systems research, involving the oil market, power systems planning, and the role of nuclear energy.In modelling the word oil market, too heavy emphasis is placed on economic forces, but practically non on the political forces. However, certain economics-oriented studies indicate that financial gains of OPEC may actually be intensitive to the oil pricing policy followed. If that is indeed the case, the significant (political) elements and their motives have to be captured in the model in order to arrive at consistent results.Modelling the different objectives in power systems planning is an area where advances are urgently needed. A method is proposed, where decision alternatives are generated a posteriori, in contrast to recent approaches involving a priori articulation of preferences, or interactive methods. A multicriterion dynamic linear programming model and a fast algorithm are used in generating efficient solutions, which are then grouped, based on the clustering in objective values.The problem associated with changing system objectives is discussed and the nuclear programme is given as an example of how the system objectives move from costs to perceived risks. In line with the real system that ‘learns’ from its experience, we need models that change their objective functions as a result of their own outputs at prior times.     

Decision support for strategic energy planning: A robust optimization framework

Optimization models for long-term energy planning often feature many uncertain inputs, which can be handled using robust optimization. However, uncertainty is seldom accounted for in the energy planning practice, and robust optimization applications in this field normally consider only a few uncertain parameters. A reason for this gap between energy practice and stochastic modeling is that large-scale energy models often present features—such as multiplied uncertain parameters in the objective and many uncertainties in the constraints—which make it difficult to develop generalized and tractable robust formulations. In this paper, we address these limiting features to provide a complete robust optimization framework allowing the consideration of all uncertain parameters in energy models. We also introduce an original approach to make use of the obtained robust formulations for decision support and provide a case study of a national energy system for validation.     

A multicriteria decision aid approach for energy planning problems: The case of renewable energy option

In the past, the choice among alternative energy policies at regional level was based only on cost minimization. Lately, it is widely recognized that regional energy planning forms a multi-actor and multicriteria problem. This is particularly true in the case of power generation in regions presenting high rates of increase in electricity demand, together with a significant potential of renewable energy sources, since several and often conflicting points of view must be taken into account. In this paper, the contribution of a Multicriteria Decision Aid method — namely ELECTRE III — addressing in such problems is investigated through the examination of a particular case study in a Greek island. Through the presentation of the procedure followed (identification of actors, selection of criteria, formulation of alternative strategies, application of the specific method and analysis of results and actors' reactions), an effort is made to highlight those aspects that are crucial in reaching a compromise in regional energy planning problems.     

用最少的虚工序构建等效多阶段工序网络

运用网络计划可以直观地表示项目管理中的诸多疑难问题, 便于分析和求解. 但是它也存在明显的缺点, 如, (1) 工序网络的有向无回路性表明很多时候适合运用动态规划法, 但它在通常情况下的无阶段性使得该方法无法直接应用; (2) 任意构建的工序网络容易表现得错综复杂, 不利于研究; (3) 用最少的虚工序表示双代号网络是NP-难问题, 因此对一个工序系统可能构建出多个差别迥异的工序网络, 有碍于进度计划管理研究, 等等. 如果能将工序网络构建成等效的多阶段网络, 各工序分别表示在相应的阶段中, 无疑有助于上述问题的解决. 构建等效多阶段工序网络需要添加虚工序. 通过添加最少的虚工序将工序网络构建成等效多阶段网络, 从而有助于建立更合理的工序网络表示法.     

Stochastic optimization for power system configuration with renewable energy in remote areas

This paper presents a stochastic mixed integer programming model for a comprehensive hybrid power system design problem, including renewable energy generation, storage device, transmission network, and thermal generators, for remote areas. Given the complexity of the model, we developed a Benders’ decomposition algorithm with two additional types of cutting planes: Pareto-optimal cuts generated using a modified Magnanti-Wong method and cuts generated from a maximum feasible subsystem. Computational results show significant improvement in our ability to solve this type of problem in comparison to a state-of-the-art professional solver. This model and the solution algorithm provide an analytical decision support tool for the hybrid power system design problem.     

Forecasting for inventory planning: a 50-year review

Forecasting and planning for inventory management has received considerable attention from the Operational Research (OR) community over the last 50 years because of its implications for decision making, both at the strategic level of an organization and at the operational level. Many influential contributions have been made in this area, reflecting different perspectives that have evolved in divergent strands of the literature, namely: system dynamics, control theory and forecasting theory (both statistical and judgemental). Although this pluralism is healthy in terms of knowledge advancement, it also signifies the fragmentation of the OR discipline and the lack of cross-fertilization of ideas to develop more comprehensive approaches towards the resolution of the same issues. In this paper, the relevant literature is reviewed and synthesized to promote some convergence between these different approaches to inventory forecasting and planning. The review concludes with an inter-disciplinary agenda for further research.     

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.     

Stochastic and Risk Management Models and Solution Algorithm for Natural Gas Transmission Network Expansion and LNG Terminal Location Planning

Due to the increasing demands for natural gas, it is playing a more important role in the energy system, and its system expansion planning is drawing more attentions. In this paper, we propose expansion planning models which include both natural gas transmission network expansion and LNG (Liquified Natural Gas) terminals location planning. These models take into account the uncertainties of demands and supplies in the future, which make the models stochastic mixed integer programs with discrete subproblems. Also we consider risk control in our models by including probabilistic constraints, such as a limit on CVaR (Conditional Value at Risk). In order to solve large-scale problems, especially with a large number of scenarios, we propose the embedded Benders decomposition algorithm, which applies Benders cuts in both first and second stages, to tackle the discrete subproblems. Numerical results show that our algorithm is efficient for large scale stochastic natural gas transportation system expansion planning problems.     

Strategic planning decisions in an R & D setting

Strategic planning, by nature, is concerned with the organization's interaction with its environment and is normative because it deals with organizational motives and self image. The salient problem confronting strategic planning is to reflect the abstract and implicit value system held by management within a disciplined and informed analytical methodology.The Gas Research Institute has developed a strategic planning process, centered upon the annual development of a projection of the U.S. energy/economic situation. The process, which requires collaboration of top management and analytical staff, brings current environmental data and sophisticated methodology to bear on the outlook for the gas industry and gas-related technologies, but it ensures that analytical judgements will be influenced and, if necessary, overriden by current management values. Contradictions between managament perceptions and objective analysis are raised and resolved in an explicit process.     

基于畅通可靠性的开放小区交通流分配及优化决策

开放小区内部道路可增加城市道路网的密度和可达性,从而达到缓解城市道路拥堵的目的。通过对出行者进行问卷调查,得知其路径选择行为主要考虑出行时间及时间波动性两方面因素,基于此对CONTRAM费用函数进行改进,得到适用于小区开放后的广义出行费用,该费用考虑了畅通可靠性对路径选择的影响;结合小区开放后出行者选择准则,建立考虑广义出行费用的随机用户最优模型(SUO),并采用逐次平均算法(MSA)进行求解,从而得到更为精确的路段交通流量;基于小区道路开放后需承担的交通量,结合城市道路规划相关规范,对横断面、出入口等方面提出开放小区内部道路规划的建议。     

地市级可再生资源能源利用优化

对地市级可再生能源管理问题,建立了在确保有限的传统能源和可再生能源能满足地区各个部门在规划期内的能源总需求条件下,使整个系统的运作成本最低的可再生能源利用优化模型,并根据实际情况假设模型中除太阳能和风能的可利用量为随机变量外,其他参数均为区间数时,利用处理区间数的可能度方法和满意度方法推导该类问题的确定性等价模型．     

An Expert System Framework for Economic Evaluation of Machining Operation Planning

Recognising the importance of combining manufacturing and management systems for machining operation planning, this paper presents a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satisfies the required specifications, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, fixed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the defined goal. The proposed methodology has been applied in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan. This optimal plan integrates considerations from both managers and production engineers, and balances their needs for efficient machining of a quality product.     

能源效率提升的管理能力评价模型研究

首先构建了能源效率提升的管理能力评价指标体系,其次运用G1法、熵值法、AHP法、标准离差法确定每一项指标的主观与客观权重,并根据相对熵的原理,依据所确定的各项指标主客观权重来计算各项指标的综合权重,并依据灰色系统理论构建了能源效率提升的管理能力评价模型.然后,选取中国东部地区为研究对象,对其能源效率提升的管理能力进行实证评价研究,结果发现:中国东部地区能源效率提升的管理能力综合评价分值为3.9724,处于接近较高能力的水平,发展潜力较大.各分项能力评价的分值排序依次为管理决策能力、制度管理能力、技术管理能力、能源调控能力、平台建设能力、知识管理能力、创新管理能力.其中管理决策能力、制度管理能力、技术管理能力三项的评价值均高于能力均值,而其他四项的评价值均低于能力均值.最后,针对各项管理能力提出了能源效率提升的相关举措和建议.     

Integrated production scheduling in large-scale flexible electronic parts manufacturing

This study reports the development of a production scheduling system for the integrated management of production in large-scale, high-volume electronic assembly lines. The development of the system incorporates control and planning considerations by addressing the interaction of various subsystems. Stochastic and deterministic aspects of the problem environment are appropriately handled via relevant simulation and analytic models. By effecting a hierarchical breakdown of the problem environment, the system produces information used in practical decision making for production planning and scheduling. Procedures used encompass and address considerations for management of work-in-process, optimization of the various subsystems' performance, minimization of setup time effect, and inventory carrying costs.     

Decision analysis in the energy sector

An optimization model has been developed primarily for the planning of the national energy system. It is also shown, however, that the model can be used as a tool for decision analysis.

The dynamic linear programming mini-model includes four primary energy sources and three forms of secondary energy. Decision variables include investment as well as production activities in the refining, conversion, and transmission of energy. The model contains 186 columns and 226 rows. The objective function is the discounted sum of costs of fuels, investments, operating costs, and under-utilized capacity. The model has been used to analyse decisions in the Turkish energy sector for the years 1960-1975, and possible developments for the period 1980-1995.

Through the use of this model, the underlying assumptions concerning the import prices of petroleum have been analysed, as well as the impact of a delay in the construction of a large hydro project. Furthermore, the effect of the shadow costs of foreign currency, the consequences of rapid industrialization, rapid economic growth, and substitution among fuels have been investigated. The model is currently being used for the analysis of possible nuclear strategies for Turkey.     

Bilevel programming in traffic planning: Models,methods and challenge

Well-founded traffic models recognize the individual network user's right to the decision as to when, where and how to travel. On the other hand, the decisions concerning management, control, design and improvement investments are made by the public sector in the interest of the society as a whole. Hence, transportation planning is a characteristic example of a hierarchical process, in which the public sector at one level makes decisions seeking to improve the performance of the network, while at another level the network users make choices with regard to route, travel mode, origin and destination of their travel. Our objective is to provide a review on the current state of research and development in bilevel programming problems that arize in this context, and attract the attention of the global optimization community to this problem class of imense practical importance.     

军队知识管理模糊神经网络评估

根据军队知识管理的特点,构建了军队知识管理水平评价指标体系,指标体系从组织结构、人力、技术及知识系统4个一级指标和12个二级指标体系,运用四层模糊神经网络确定模糊综合评价中的权重值,同时采用改进的反向传播算法,用样本对网络进行训练,逐步修正网络的连接权值,使权重值更符合实际情况,最后用训练好的样本对六个单位的知识管理进行评估,结果表明,训练好的模糊神经网络可以对军队的知识管理进行评估,并得到很好的结果。     

A cooperative differential game model based on network throughput and energy efficiency in wireless networks

Network throughout and energy efficiency are of paramount importance for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal network throughout and energy efficiency simultaneously. So it is very necessary to find a solution based on tradeoff between network throughput and energy efficiency, which have not yet been well studied in existing works. In this paper, we present a cooperative differential game model and find a time-consistency solution by Shapley value which determines a fair distribution of the total cooperative cost among players.     

Dynamic energy management for a novel hybrid electric system based on driving pattern recognition

This paper focuses on the dynamic energy management for Hybrid Electric Vehicles (HEV) based on driving pattern recognition. The hybrid electric system studied in this paper includes a one-way clutch, a multi-plate clutch and a planetary gear unit as the power coupling device in the architecture. The powertrain efficiency model is established by integrating the component level models for the engine, the battery and the Integrated Starter/Generator (ISG). The powertrain system efficiency has been analyzed at each operation mode, including electric driving mode, driving and charging mode, engine driving mode and hybrid driving mode. The mode switching schedule of HEV system has been designed based on static system efficiency. Adaptive control for hybrid electric vehicles under random driving cycles with battery life and fuel consumption as the main considerations has been optimized by particle swarm optimization algorithm (PSO). Furthermore, driving pattern recognition based on twenty typical reference cycles has been implemented using cluster analysis. Finally, the dynamic energy management strategy for the hybrid electric vehicle has been proposed based on driving pattern recognition. The simulation model of the HEV powertrain system has been established on Matlab/Simulink platform. Two energy management strategies under random driving condition have both been implemented in the study, one is knowledge-based and the other is based on driving pattern recognition. The model simulation results have validated the control strategy for the hybrid electric vehicle in this study in terms of drive pattern recognition and energy management optimization.