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。     

一般工商业零售电价套餐适应性评估与优化方法

在电力体制改革的大背景下,合理评估零售电价套餐适应性,对控制电网经营风险和推进售电侧改革有重要意义。针对我国电力市场以及一般工商业的特点,首先从竞争、用户以及市场环境角度出发建立了一般工商业零售电价套餐评估指标体系;其次将层次分析法和改进的灰色白化权函数相结合,对电价套餐进行适应性评估;最后针对该评估方法建立了基于蚁群算法的优化模型,以最小成本得到提高电价套餐适应性等级的优化方案,并验证了该方法具有良好的鲁棒性,具有一定的参考意义。     

基于双曲型谱风险度量的大用户购电策略

直购电模式正在推行,大用户与电网公司的风险偏好却各不相同。本文将风险偏好纳入结算策略,建立了基于双曲型谱风险的购电优化模型,并用PJM日前市场的数据进行了实证分析。探讨了风险厌恶因子的敏感范围,将大用户划分为积极、稳健和保守三种类型,分别讨论了其购电策略。结果表明:无论风险偏好如何,大用户总愿意为获得高收益而承担更高的风险;风险偏好是购电策略的重要影响因素;当风险偏好既定时,大用户在远期合同市场和日前市场的购电比例可由谱风险值确定。随着谱风险值的增加,大用户会减少远期合同市场的购电量,更倾向于在日前市场购电。     

Impact of compressor failures on gas transmission network capability

National Grid, the gas operator in the United Kingdom, has experienced challenges in evaluating the capability of its gas transmission network to maintain function in the event of risks particularly to withstand the impact of compressor failures. We propose a mathematical programming model to support the operator in dealing with the problem. Several solution techniques are developed to solve the various versions of the problem efficiently. In the case of little data on compressor failure, an uncertainty theory is applied to solve this problem if the compressor failures are independent; while a robust optimisation technique is developed to solve it when they are not. Otherwise, when there are data on compressor failure, Monte Carlo simulation is applied to find the expected capability of the gas transmission network. Computational experiments, carried out on a case study at National Grid, demonstrate the efficiency of the proposed model and solution techniques. A further analysis is performed to determine the impact of compressor failures and suggest efficient maintenance policies for National Grid.     

Dynamic preventive maintenance scheduling of the modules of fighter aircraft based on random effects regression model

Proper maintenance of fighter aircraft is an important issue to control theairpower. Typical maintenance policy applied is based on the constant schedulefor a given module. This kind of maintenance does not take into account varyingcharacteristics of the module over time. In this paper, we utilize the randomeffects Weibull regression model for non-constant MTBF (mean time betweenfailure) and MTTR (mean time to repair) in order to provide a dynamic preventivemaintenance schedule reflecting the module's varying characteristics in atimely manner. Our study is expected to contribute to ROKA (Republic of KoreaAirforce) in terms of improving the level of combat readiness of fighteraircraft.     

Day‐ahead price forecasting based on hybrid prediction model

Short‐Term Price Forecast is a key issue for operation of both regulated power systems and electricity markets. Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, nonstationary, and time variant behavior of electricity price time series. So, in this article, the forecast model includes wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN) is presented. Also, an intelligent algorithm is applied to optimize the RBFN structure, which adapts it to the specified training set, reduce computational complexity and avoids over fitting. Effectiveness of the proposed method is applied for price forecasting of electricity market of mainland Spain and its results are compared with the results of several other price forecast methods. These comparisons confirm the validity of the developed approach. © 2016 Wiley Periodicals, Inc. Complexity 21: 156–164, 2016     

Modelling locational price spreads in competitive electricity markets; applications for transmission rights valuation and replication

The restructuring of the electric utilities industry has forcedindustry participants to rethink their approach to a numberof decision processes. To manage risk and plan investment ingeneration assets, as well as to examine the efficient expansionof the current transmission grid, one needs to have a clearunderstanding of the interaction between the grid propertiesand the behaviour of the regional power markets. In this paperwe discuss a fundamental modelling approach which extracts thestochastic properties of electricity prices by modelling theimpact of physical and economic drivers affecting the production,delivery, and consumption of electricity. If the fundamentalinputs are directly observable, we can use historical data tocalibrate the model parameters. In the case of electricity,this simple and abundant set of training data can make a crucialdifference. We present the bid-based stochastic model (BSM) and look intoits application to valuing of financial derivatives, especiallyoptions based on the locational spread in electricity pricebetween two markets. The advantage of the bid-based model isthat one is able to link the capacity of the transmission line,in megawatts, directly to the correlation between electricityprices at the end nodes. This leads us to a valuation methodfor a locational spread option, the financial equivalent ofa physical transmission right. The model represents an improvementover standard spread option formulation in that it accountsfor the effect of the nonlinear flows in the transmission networkon the correlation and distribution of locational prices. Wealso address the question of whether financial transmissionrights can be replicated with a dynamic portfolio of forwardcontracts at the end nodes. This poses the possibility of model-based arbitrage betweenexisting forward markets and the emerging transmission rightsmarkets. Furthermore, it allows users to simulate the effectof transmission outages or expansion. For example, a for-profittransmission provider who is contemplating addition of a newtransmission line between two markets needs to know whetherhe will be able to recover the fixed cost of investing in theline by selling transmission rights to market participants.By calibrating the bid-based model according to current pricelevels and adding the capacity of the new transmission line,the transmission owner can simulate future cash flows and estimatethe profitability of the investment.     

A shortage probability metric for battery depletion risk

In this paper, we propose a battery depletion risk (BDR) metric that evaluates the probability of depleting an energy storage system in the context of a centralized power system with stochastic demand and supply for electricity across multiple consecutive time periods. This newly proposed reliability metric informs battery sizing decisions to mitigate outage risks. We demonstrate the applicability of this metric by embedding it in an economic dispatch model with thermal generation units and a centralized energy storage system.     

基于鲁棒均值-下半偏差模型的供电公司购电组合策略

电力市场中,日前市场购电电价的随机波动,给供电公司的投资带来了一定的收益风险,因而供电公司需要在不同的市场中合理分配购电电量分散投资,以实现自身收益率尽可能大的同时承受的风险最小.供电公司在多市场中购电电价呈随机波动的特性,本文用均值-下半偏差作为购电风险测度,并用鲁棒优化处理电价的不确定性,建立了供电公司鲁棒均值-下半偏差(Robust Mean Semi-Deviation)购电策略优化模型.最后利用广西电网公司提供的数据进行实证分析,验证了模型的有效性和适用性,表明此模型对供电公司的投资组合决策具有一定的参考价值和指导意义.     

基于改进型GERT网络的供应链突发事件风险传递研究

突发事件会对供应链运作产生巨大影响,甚至会导致整个网络断裂。针对供应链系统中突发事件的风险传递问题,分别构建了基于GERT网络的供应链正常交付模型、延期交付模型和突发事件风险传递模型,并依据模型结构,推导了相应的矩母函数和传递函数表达式。在模型解析的基础上,设计了供应链突发事件风险传递的定量化分析策略。通过对比供应链正常交付模型和延期交付模型的求解结果,得到各个环节的最终延迟时间;再通过突发事件风险传递模型的拟合计算,进一步定量化描述风险在逐级传递过程中的叠加程度。最后,通过一个汽车供应链网络案例,验证了模型和方法的适用性。     

VaR约束下均值-方差模型在基金资产配置的应用

随着我国开放式基金的迅猛发展以及证券市场的波动，如何识别和控制基金风险这一问题越显重要。VaR模型是一种有效的风险计量和管理工具，本文刻划VaR约束下均值-方差模型及其优化模型，并运用基于VaR约束下的均值——方差模型，定量地分析投资基金的投资组合收益和风险，提出开放式基金最优资产配置，使投资组合收益最大。     

Profit-based generation resource planning

This paper presents an optimal generation resource planningmodel based on an expected level of revenue, operation and maintenancecosts, transmission charges, load curtailment costs, and theexpected level of system reliability. The model considers thevolatility of market prices for electricity and fuel, variousoptions for securing investment loans, construction lead time,expected load growth, and transmission congestion costs as majorincentives for adding generating capacity to power systems.The proposed planning algorithm will analyse possible sitesand markets for new generators, various unit types and capacities,operating constraints, planned and forced outages, timing forthe addition of new units, and steps for the decommissioningof old generators. The solution approach is based on the extendedBender decomposition technique. A modified IEEE 30-bus casestudy is presented and discussed to exhibit the effectivenessof the proposed resource planning approach.     

A discrete model for optimal operation of fossil-fuel generators of electricity

This paper presents a new discrete approach to the price-based dynamic economic dispatch (PBDED) problem of fossil-fuel generators of electricity. The objective is to find a sequence of generator temperatures that maximizes profit over a fixed-length time horizon. The generic optimization model presented in this paper can be applied to automatic operation of fossil-fuel generators or to prepare market bids, and it works with various price forecasts. The model’s practical applications are demonstrated by the results of simulation experiments involving 2009 NYISO electricity market data, branch-and-bound, and tabu-search optimization techniques.     

Tacit collusion games in pool-based electricity markets under transmission constraints

Harrington et al. (Math Program Ser B 104:407–435, 2005) introduced a general framework for modeling tacit collusion in which producing firms collectively maximize the Nash bargaining objective function, subject to incentive compatibility constraints. This work extends that collusion model to the setting of a competitive pool-based electricity market operated by an independent system operator. The extension has two features. First, the locationally distinct markets in which firms compete are connected by transmission lines. Capacity limits of the transmission lines, together with the laws of physics that guide the flow of electricity, may alter firms’ strategic behavior. Second, in addition to electricity power producers, other market participants, including system operators and power marketers, play important roles in a competitive electricity market. The new players are included in the model in order to better represent real-world markets, and this inclusion will impact power producers’ strategic behavior as well. The resulting model is a mathematical program with equilibrium constraints (MPEC). Properties of the specific MPEC are discussed and numerical examples illustrating the impacts of transmission congestion in a collusive game are presented.     

Electricity consumption prediction using a neural-network-based grey forecasting approach

Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.     

Investment in electricity networks with transmission switching

We consider the application of Dantzig-Wolfe decomposition to stochastic integer programming problems arising in the capacity planning of electricity transmission networks that have some switchable transmission elements. The decomposition enables a column-generation algorithm to be applied, which allows the solution of large problem instances. The methodology is illustrated by its application to a problem of determining the optimal investment in switching equipment and transmission capacity for an existing network. Computational tests on IEEE test networks with 73 nodes and 118 nodes confirm the efficiency of the approach.     

Static versus dynamic DEA in electricity regulation: the case of US transmission system operators

In this paper a dynamic DEA-model (data envelopment analysis) is developed that takes up Nemoto and Goto’s (Econ Lett 64(1):51–56, 1999) concept of quasi-fixed inputs but can dispense with price-information as it solely deals with technical efficiencies that are derived in an additive-model type of setting. This model is applied to the data of 50 of the largest US electric transmission system operators in the period 2000–2006 in order to show that the static DEA-models that are usually employed by electricity regulators can lead to wrong conclusions about the enterprises’ efficiency since they ignore the short-run fixity of essential inputs like transmission lines.     

What are the actual costs of cyber risk events?

Cyber risks are high on the business agenda of every company, but they are difficult to assess due to the absence of reliable data and thorough analyses. This paper is the first to consider a broad range of cyber risk events and actual cost data. For this purpose, we identify cyber losses from an operational risk database and analyze these with methods from statistics and actuarial science. We use the peaks-over-threshold method from extreme value theory to identify “cyber risks of daily life” and “extreme cyber risks”. Human behavior is the main source of cyber risk and cyber risks are very different compared with other risk categories. Our models can be used to yield consistent risk estimates, depending on country, industry, size, and other variables. The findings of the paper are also useful for practitioners, policymakers and regulators in improving the understanding of this new type of risk.     

基于双因子CIR强度式定价的信用债券投资组合优化

信用风险和利率风险是相互关联影响的。资产组合优化不能将这两种风险单独考虑或简单的相加,应该进行整体的风险控制,不然会造成投资风险的低估。本文的主要工作:一是在强度式定价模型的框架下,分别利用CIR随机利率模型刻画利率风险因素“无风险利率”和信用风险因素“违约强度”的随机动态变化,衡量在两类风险共同影响下信用债券的市场价值,从而构建CRRA型投资效用函数。以CRRA型投资效用函数最大化作为目标函数,同时控制利率和信用两类风险。弥补了现有研究中仅单独考虑信用风险或利率风险、无法对两种风险进行整体控制的弊端。二是将无风险利率作为影响违约强度的一个因子,利用“无风险利率因子”和“纯信用因子”的双因子CIR模型拟合违约强度,考虑了市场利率变化对于债券违约强度的影响,反映两种风险的相关性。使得投资组合模型中既同时考虑了信用风险和利率风险、又考虑了两种风险的交互影响。避免在优化资产组合时忽略两种风险间相关性、可能造成风险低估的问题。     

一个基于线性规划的输电阻塞管理优化模型

本文利用线性规划给出了输电阻塞管理的一个最优化模型.