Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

基于电子路票征收及补偿的新型离散路网设计与算法

在城市交通网络设计中,可交易电子路票系统是一种新型且更加公平的拥挤收费方法。本文将具有征收与补偿机制的0分配可自由交易电子路票和路网离散改造设计结合起来共同研究城市道路交通网络设计与管理问题。建立的新型路网设计与管理模型同时考虑了0分配电子路票系统与离散路网设计。采用Logit随机用户均衡原理模拟出行者的路线选择行为,并设计具有路段容量和电子路票可行约束的随机均衡问题的有效算法。由于该模型是一个具有不动点约束的数学规划问题,求解比较困难,因此采用常见的群体智能优化算法求解该模型,数值实验验证了模型和算法的有效性和可行性。     

Dynamic pricing of call rates: Bayesian approach

In this paper, we present different cases and their possible solutions in the telecommunications market by incorporating dynamically changing call rates over the channel depending upon the network congestion. Since dynamic pricing of call rates is beneficial from both the perspectives of subscribers and service providers, our solution can significantly help to adapt this pricing mechanism in real market scenario. In order to deploy this scheme, we have incorporated the competing network provider's strategy into the mechanism of deciding dynamic price. Establishment of Nash equilibrium with the competing network provider has stabilized our pricing mechanism.     

Combining Energy Networks

Electricity markets depend on upstream energy markets to supply the fuels needed for generation. Since these markets rely on networks, congestion in one can quickly produce changes in another. In this paper we develop a combined partial equilibrium market model which includes the interactions of natural gas and electricity networks. We apply the model to a stylized representation of Europe??s electricity and natural gas markets to illustrate the upstream and downstream feedback effects which are not obvious on first sight. We find that both congestion and loop-flow effects in electricity markets impact prices and quantities in markets located far from the initial cause of the market changes.     

Reliable Intermodal Freight Network Expansion with Demand Uncertainties and Network Disruptions

This paper develops a robust Mixed-Integer Linear Program (MILP) to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model is to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Additional considerations by the model include a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and time window constraints for shipments. A hybrid Genetic Algorithm (GA) is developed to solve the proposed MILP. It utilizes a column generation algorithm to solve the freight flow assignment problem and a multi-modal shortest path label-setting algorithm to solve the pricing sub-problems. An exact exhaustive enumeration method is used to validate the GA results. Experimental results indicate that the developed algorithm is capable of producing optimal solutions efficiently for small-sized intermodal freight networks. The impact of uncertainty on network configuration is discussed for a larger-sized case study.     

Congestion management in dynamic security constrained open power markets

The greatest challenge facing deregulated and unbundled electricity supply industry is the operation of the grid in a non-discriminatory and equitable manner. Transmission pricing and congestion management have been at the center of the debate over facilitating greater competition of electric power generation. This paper studies dynamic security constrained congestion management in an unbundled electric power system. It is possible to reschedule the real power generation along with curtailment of real power loads/transactions to make the system dynamically secure after a fault. A conceptually reasonable and computationally feasible approach for the solution of this problem has been developed for a system with a mix of pool and contract dispatches.     

Evolutionary decision-makings for the dynamic weapon-target assignment problem

The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.     

Master problem approximations in Dantzig–Wolfe decomposition of variational inequality problems with applications to two energy market models

In this paper, a modification to Dantzig–Wolfe (DW) decomposition algorithm for variational inequality (VI) problems is considered to alleviate the computational burden and to facilitate model management and maintenance. As proposals from DW subproblems are accumulated in the DW master problem, the solution time and memory requirements are increasing for the master problem. Approximation of the DW master problem solution significantly reduces the computational effort required to find the equilibrium. The approximate DW algorithm is applied to a time of use pricing model with realistic network constraints for the Ontario electricity market and to a two-region energy model for Canada. In addition to empirical analysis, theoretical results for the convergence of the approximate DW algorithm are presented.     

Network Topology and Locational Market Power

This paper studies the effect of network topology and production constraints on the locational market power of generators. A market power spectrum is considered where one end has infinitely large production capacity but the usual network constraints while the other end has infinitely large network capacity but the usual production constraints. First, we analyze the locational market power function mathematically. Then, we use a real world example of the Portland, Oregon electrical market and determine its position on the market power spectrum. We find the Portland market to be primarily production constrained rather than network constrained. We also identify the local and global threshold generation capacities for each generator beyond which it cannot influence the individual and total locational market power, respectively. This study facilitates the understanding of the economic and physical determinants of locational market power. It can help regulators make informed decisions when it comes to the choice of enhancing the physical infrastructure, or adding more generation capacity to the market.     

基于网络图的产品生产、运输、定价综合决策模型

针对大型垄断厂商在商品生产、运输、定价决策中独立考虑生产定价和运输两个环节的弊端, 提出一种将产量决策、运输决策和定价决策3 个环节作为一个系统进行考虑的综合决策模型. 借助利润网络图, 构建同时考虑生产及运输成本以及市场需求的商品生产、运输和定价综合决策模型, 并探讨模型的解法及其理论基础. 通过给出的应用案例表明, 相比传统决策模型, 利用综合决策模型可以提高15.50% 的利润.

     

A note on the optimal pricing and production decisions with price‐driven substitution

Recently, Kim and Bell ( 2011 ) developed a revenue managemnent pricing model with price‐driven substitution. The authors considered production decisions under unlimited production capacity and investigated the impact of price‐driven substitution on a firm's pricing and production decisions. The authors modeled the consumer demands for each market segment as linear additive demand function based on exogenous variables, where demand substitution occurred as a function of price differences between the two products. In this article, we extend this work to examine the impact of a production capacity constraint on the firm's joint pricing and inventory decisions. Based on this extended model, we investigate the impact of price‐driven substitution on a firm's pricing and production decisions where there is a limit on total capacity. We show how revenue managers should adjust prices and production levels to take into account price‐driven substitution under a capacity constraint setting. Both deterministic and stochastic models are developed, and the impact of price‐driven substitution and a capacity constraint on the optimal prices, production levels, and revenues is illustrated.     

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.      

Fuzzy-set-based decision making through energy-aware and utility agents within wireless sensor networks

Multi-agent systems (MAS) through their intrinsically distributed nature offer a promising software modelling and implementation framework for wireless sensor network (WSN) applications. WSNs are characterised by limited resources from a computational and energy perspective; in addition, the integrity of the WSN coverage area may be compromised over the duration of the network’s operational lifetime, as environmental effects amongst others take their toll. Thus a significant problem arises—how can an agent construct an accurate model of the prevailing situation in order that it can make effective decisions about future courses of action within these constraints? In this paper, one popular agent architecture, the BDI architecture, is examined from this perspective. In particular, the fundamental issue of belief generation within WSN constraints using classical reasoning augmented with a fuzzy component in a hybrid fashion is explored in terms of energy-awareness and utility.     

OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

基于随机模型预测控制的含大规模风电接入的电力系统优化调度

风电是重要的清洁可再生能源,将其引入智能电网中对节能减排有着重要的意义.为降低大规模风电不确定性给电网调度带来的影响,提出一种基于随机模型预测控制的风电与传统机组协调调度方法.考虑了部分传统机组需要人工调度而无法频繁、连续操作的情况,并引入可调负荷以增加系统可调度能力.构建基于混合整数二次规划(MIQP)的风电调度目标函数,以及包括机组最大可调节次数、最小运行/停机时间、可调度负荷总能量需求一致性、风电切负荷比例等约束.提出两阶段场景缩减方法以实现典型场景的快速筛选.通过与传统开环调度方法的性能对比表明所提出方法的可行性与有效性,并在此基础上进一步分析机组启停次数和可调度负荷对系统运行的影响.     

A Nested Benders Decomposition Approach to Locating Distributed Generation in a Multiarea Power System

A nested Generalized Benders decomposition scheme is used to solve a mixed-integer stochastic programming model. The model evaluates central station and distributed power generation, storage, and demand management assets on a linearized electric power transmission network. It considers temporal and spatial variations in the marginal cost of power, which are captured in the Benders cuts in the solution scheme. These variations are caused not only by differences in generating unit operating expenses and capacity expansion costs, but also by physical transmission constraints that can alter minimum cost dispatch and siting of these units. The transmission constraints addressed include limits on MW power flows and both of Kirchhoff's laws via a linearized DC load flow representation. The model consists of three modules: a stochastic linear production costing model for operating central system generation, a nonlinear program for planning central system generation and transmission, and a mixed-integer program for evaluation of local area distributed resources. Generalized Benders decomposition is applied twice to coordinate these modules. The production costing model is a subproblem to the central system planning model, which is in turn a subproblem to the distributed resource model. The coordination scheme is described in detail, including the calculation of marginal costs. An application shows the effects of marginal cost variations on capacity expansion decisions.     

基于效用模型的网络控制

近年来，国外的研究者开始尝试着将微观经济学研究方法引入网络控制，为这一领域问题的解决提供了崭新的思路。该文重点介绍微观经济学中的效用模型在接入控制、资源分配、拥塞计费和SLA管理中的应用，并指出当前网络控制存在的问题以及下一步的工作。     

A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts

We study the long-term generation capacity investment problem of an independent power generation company (GenCo) that functions in an environment where GenCos perform business with both bilateral contracts (BC) and transactions in the day-ahead market (DAM). A fuzzy mixed integer linear programming model with a fuzzy objective and fuzzy constraints is developed to incorporate the impacts of imprecision/uncertainty in the economic environment on the calculation of the optimal value of the GenCo’s objective function. In formulating the fuzzy objective function we also include the potential impacts of climate change on the energy output of hydroelectric power plants. In addition to formulating and solving the capacity planning/investment problem, we also performed scenario-based (sensitivity) analysis to explore how investment decisions of the GenCos change when fuzziness (tolerance) in the maximum energy output of hydroelectric units and/or drought expectation increases. The proposed model is novel and investigates the effects of factors like drought expectations of climate changes, hydroelectric power plant investments, and other power generation technology investment options.     

物联网数据接入最优分流算法设计与仿真

采用传统方法对物联网数据接入进行分流时,易导致网络拥塞和节点瘫痪,存在网络拥塞率较高、最大上传带宽较低和网络平稳运行时间较短的问题.为解决上述问题,设计了物联网数据接入最优分流算法.通过"流"的局部性定义,确定数据接入分流的约束条件,并构建分流过程中的能量消耗模型.运用该模型和空间重构方法对物联网数据做特征提取,获取物...     

Wind Derivatives: Modeling and Pricing

Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed, the wind direction and the wind duration. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. Our results indicate that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures written on two indices, the cumulative average wind speed index and the Nordix wind speed index.