智能电网下考虑风险的供电企业购电优化模型

针对智能电网带给供电企业购电决策的影响,提出了一种考虑风险的购电优化决策方法。智能电网建设并开展运营,发电侧考虑接纳更多的可再生能源发电,用电侧智能用电设备的使用导致主动负荷的出现等,这一系列变化给智能电网环境下供电企业购电决策带来一定程度的风险。首先,考虑了智能电网下负荷与风电出力不确定性给供电企业经营带来的风险,采用风险元传递理论与多目标规划理论,建立智能电网购电优化模型。然后,提出采用约束多目标粒子群优化算法(CMOPSO)对模型进行求解思路;最后,算例说明该模型的可行性,研究成果为我国智能电网运营风险管理提供新方法、新思路。     

以费用为独立变量的随机机会约束规划模型研究

美军提出的"以费用为独立变量(CAIV)"方法从论证阶段开始,就强调需求、性能与费用三者之间的平衡,并采用费用-效能综合权衡优化模型进行系统费用和性能参数的最优决策.但是该模型没有考虑采办过程中的不确定因素,且由于决策者往往很关心权衡结果中费用与性能的置信水平问题,因此提出要将采办中的不确定性因素作为随机变量引入权衡模型,建立一种综合权衡优化的随机机会约束规划模型,并进行了示例验证.     

需求导向的容量设施竞争选址问题研究

客户意愿与容量限制是竞争设施选址问题中两个重要的影响因素,在考虑客户意愿与设施容量共同作用条件下,建立了最小化企业总成本以及每个客户费用为目标的竞争设施选址问题优化模型,通过设计需求导向服务分配机制解决设施与客户之间服务关系分配问题,结合模拟退火思想提出了求解模型的算法。最后利用数值例子分析了需求导向服务分配机制以及目标权重、预算限额等参数对于选址决策的影响,其中考虑需求导向因素会适当增加企业的总成本,但可以减少客户所付出的费用从而增强对客户的吸引力;另外企业的预算限额对于企业的设施选址决策有着重要的影响,企业所能获取的市场份额与其选址预算限额呈正相关的关系;而客户所需付出的总费用与企业提供服务的总成本两者之间则呈负相关的关系,因此需要通过服务质量与成本之间的权衡实现最理想的选址决策。     

基于模糊——随机规划的WEEE回收物流网络优化研究

针对废旧电子电气设备(WEEE)绿色回收问题,根据实际需求刻画其回收物流网络结构;在模型构建中.考虑参数为随机和模糊共存的情况,提出应用随机机会约束规划和模糊机会约束规划相结合的方法来建模;设模型参数是相互独立的,合理利用转换定理将不确定规划转变为常规数学规划,并借助LINGO软件求解最优方案.     

考虑需求侧响应的风电并网电力调度研究

为提高风电并网效率、增强风电企业竞争力,以需求侧碳目标导入方式为切入点,将电力需求侧市场与风电并网结合起来,开展了考虑需求侧响应的风电并网电力调度研究.首先,将需求侧响应作为虚拟机组参与到电力调度中,通过激励方式刻画需求侧响应成本.以传统发电成本、风电并网成本、需求侧响应成本作为系统运行的优化目标,同时兼顾发电平衡约束、需求侧响应约束,建立了考虑需求侧响应的风电并网电力调度模型.接着,分别从需求侧响应的碳目标强度和响应时段出发,借助模型分析了需求侧响应对发电侧风电并网效率的影响.最后得到了一些结论:需求侧碳目标导入对于风电并网效率的影响具有双重性,需要综合考虑需求侧碳目标强度的不同水平;当需求侧响应时段选择在电力负荷较大、风电出力较少时,可以有效调节供给侧和需求侧之间的差值,提高风电并网效率以及需求侧响应的参与度.所提出的理论和方法可以为碳目标在电力需求侧市场的推广提供参考依据,同时具有一定的政策导向意义.     

基于模糊机会约束规划的最优产量决策

研究了模糊环境下,双寡头市场中两企业的最优产量决策问题。在四种博弈结构下,结合Cournot模型、Stackelberg模型以及模糊可能性理论,建立了模糊机会约束规划模型来确定两企业的最优产量水平。最后通过一个算例说明,基于市场的最大收益,最优决策应为两企业均为追随者。     

考虑中间库存缓冲区的多目标设备不完美预防维修策略研究

针对考虑库存缓冲区的多目标设备维修问题,以设备维修能力为约束条件,获得随机故障设备的不完美预防维修策略。首先,利用准更新过程,表示出设备的随机故障次数。其次,结合设备故障次数表达式,以最大设备可用度和最小生产总成本为多目标构建不完美预防维修模型,使用粒子群算法求解,优化设备可用度与生产总成本,获得更新周期内的库存量和预防维修周期两个决策变量的最优值。最后,通过算例分析,验证了多目标不完美预防维修模型的可用性。     

碳排放约束下政府与企业的stackelberg决策分析

在碳排放约束额不能由政府单独决定的条件下,本文通过Stackelberg博弈模型分析了非集中决策系统中政府与企业对绿色技术的决策行为。对政府而言,在考虑以社会福利为目标的基础上引入了社会对环境的关注度,并分析了该因素对政府决策的影响。对企业而言,分析结果表明企业对绿色技术的选择并不随着约束值单调变化。当考虑技术成本对博弈双方决策的影响,发现企业对绿色技术的选择是由技术成本和产品经济效益对成本的补偿程度决定的。最后,综合考虑博弈双方的决策,本文讨论了政府能否通过适当的约束值来促使企业选择更好的绿色技术,并给出了建议。     

基于电能节约的E-DEA模型及其应用

按照全要素能源效率的概念,重点考虑电能投入约束,构造了基于电能节约的E-DEA模型,其目标函数为极大化产出比例和电能投入比例之差,约束条件中除考虑一般投入量约束外,还同时强调电能投入径向节约和产出径向增加。根据模型最优解,给出了相应的有效、非有效、弱有效、用电规模收益状态的判断准则,以及相应于不同有效性情况下决策单元的改进。以合肥市通用制造业规上企业所属21个行业为研究对象,从第二次经济普查中选择年均资产、从业人员、电力、非电力能源、二氧化碳排量为投入指标,主营业务收入为产出指标,对行业电能利用效率进行实证分析,通过分析潜在电能可节约量和主营业务收入可增加量,明确了各行业改进目标。     

Copula-CVaR下考虑随机成本和需求的单周期订货决策研究

不确定环境下的单周期最优订货量决策具有重要且广泛的应用价值。与传统的仅考虑需求不确定性的报童模型不同,本文考虑市场价格恒定,但成本和需求随机变化且相关联下的报童决策问题。为此,采用Copula函数构建成本和需求之间的关联,考虑决策者可能具有的风险态度,建立了相应的Copula-CVaR模型,证明了模型解的存在性和唯一性,并将模型离散化为易求解的线性规划问题。最后,通过不同的风险水平和多种Copula函数下的仿真,分析了随机成本与需求的相关性和波动性对最优决策结果影响,并得到相关结论,为相关企业决策提供依据。     

On stochastic auctions in risk-averse electricity markets with uncertain supply

This paper studies risk in a stochastic auction which facilitates the integration of renewable generation in electricity markets. We model market participants who are risk averse and reflect their risk aversion through coherent risk measures. We uncover a closed form characterization of a risk-averse generator’s optimal pre-commitment behaviour for a given real-time policy, both with and without risk trading.     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

A multi-period stochastic portfolio optimization model applied for an airline company in the EU ETS

This paper aims to set up and solve a multi-period stochastic portfolio optimization model from an airline company’s point of view, considering all the specific European Union Emissions Trading Scheme (EU ETS) regulatory, managerial and trading constraints (i.e. physical constraints). Our contribution to existing academic literature is multiple. As the first ever case, we apply this technique to the aviation sector, a newly included sector within the EU ETS. More than mainly incorporating physical and technical (‘engineering’) features and focusing on short-term planning issues, we particularly address financial features and focus on mid-term planning issues. Therefore, instead of using spot prices, we run Monte Carlo simulations of correlated geometric Brownian motions (GBM) for traded futures prices of various emission allowance types for different CO₂ delivery time periods. We thereby specifically refer to the existing exchange-traded emission allowance types EU Emission Allowance (EUA) and Certified Emission Reduction (CER). By implementing actually valid and real-world-oriented regulatory constraints for EU ETS, namely managerial and trading constraints, our model implies a real-life application. We also highlight the possibility of banking and borrowing of emission allowances between CO₂ compliance periods, which is a crucial regulatory feature of EU ETS.     

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.     

Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty

A dynamic (multi-stage) stochastic programming model for the weekly cost-optimal generation of electric power in a hydro-thermal generation system under uncertain demand (or load) is developed. The model involves a large number of mixed-integer (stochastic) decision variables and constraints linking time periods and operating power units. A stochastic Lagrangian relaxation scheme is designed by assigning (stochastic) multipliers to all constraints coupling power units. It is assumed that the stochastic load process is given (or approximated) by a finite number of realizations (scenarios) in scenario tree form. Solving the dual by a bundle subgradient method leads to a successive decomposition into stochastic single (thermal or hydro) unit subproblems. The stochastic thermal and hydro subproblems are solved by a stochastic dynamic programming technique and by a specific descent algorithm, respectively. A Lagrangian heuristics that provides approximate solutions for the first stage (primal) decisions starting from the optimal (stochastic) multipliers is developed. Numerical results are presented for realistic data from a German power utility and for numbers of scenarios ranging from 5 to 100 and a time horizon of 168 hours. The sizes of the corresponding optimization problems go up to 200000 binary and 350000 continuous variables, and more than 500000 constraints.     

A stochastic model for a visit to the doctor’s office

This paper presents a stochastic model of an individual patient’s experience during a visit to a doctor’s office. The stochastic model is based on tracking the visit of patients at a local family practice clinic. A rigorous, iterative procedure for model development allows the stochastic model to be constructed, evaluated, and validated to establish consistency with both the theoretical stochastic assumptions and the clinic’s actual operating environment. This model extends the use of stochastic models in health care in two important respects. First, the stochastic model represents an application of semi-Markov processes in outpatient health care settings. Second, through the use of the infinitesimal generator associated with the transition probability matrices governing patient flow, numerical predictions for first passage times are easily obtained.     

电力零售商引导下智能电网多方利益均衡策略

文提出以电力零售商为主导的智能电网系统运行策略,电力零售商根据发电商供电价和发电量以收益最大化为目标输出用户实时电价。在供能侧对不同类型供电商上网顺序进行优化,考虑风电光伏能源出力不确定的鲁棒性分析,在维持平衡供需的基础上优化供电商利益。在需求侧根据用电规模对用户进行分类,按供电重要性进行二次分类,使用粒子群算法求解用户最优用电量。仿真结果显示电力零售商在电力系统中维护各方利益均衡,对供电商和用户都存在潜在的好处。     

Robust Optimization for Power Systems Capacity Expansion under Uncertainty

We develop a robust optimization model for planning power system capacity expansion in the face of uncertain power demand. The model generates capacity expansion plans that are both solution and model robust. That is, the optimal solution from the model is ‘almost’ optimal for any realization of the demand scenarios (i.e. solution robustness). Furthermore, the optimal solution has reduced excess capacity for any realization of the scenarios (i.e. model robustness). Experience with a characteristic test problem illustrates not only the unavoidable trade-offs between solution and model robustness, but also the effectiveness of the model in controlling the sensitivity of its solution to the uncertain input data. The experiments also illustrate the differences of robust optimization from the classical stochastic programming formulation.     

Short-term electricity procurement: A rolling horizon stochastic programming approach

This paper addresses the problem faced by a large electricity consumer in determining the optimal procurement plan over a short-term time horizon. The inherent complexity of the problem, due to its dynamic and stochastic nature, is dealt by means of the stochastic programming modeling framework. In particular, a two-stage problem is formulated with the aim of establishing the optimal amount of electricity to be purchased through bilateral contracts and in the Day-Ahead Electricity Market. Recourse actions are used to hedge against uncertainty related to future electricity prices and consumer’s needs. The optimal plan is defined so to minimize the overall cost and to control risk, which is measured in the form of violation of budget constraints. The stochastic model is dynamically solved in a rolling horizon fashion by iteratively considering more and more recent information and a planning horizon of decreasing length. Extensive numerical experiments have been carried out to assess the performance of the proposed dynamic decision approach. The results collected considering a real test case are very encouraging and provide evidence of the superiority of the approach also in comparison with other alternative procurement strategies.