An ICA based approach for solving profit based unit commitment problem market

With the advent of paralleling and implementation of restructuring in the power market, some routine rules and patterns of traditional market should be accomplished in a way different from the past. To this end, the unit commitment (UC) scheduling that has once been aimed at minimizing operating costs in an integrated power market, is metamorphosed to profit based unit commitment (PBUC) by adopting a new schema, in which generation companies (GENCOs) have a common tendency to maximize their own profit. In this paper, a novel optimization technique called imperialist competitive algorithm (ICA) as well as an improved version of this evolutionary algorithm are employed for solving the PBUC problem. Moreover, traditional binary approach of coding of initial solutions is replaced with an improved integer based coding method in order to reduce computational complexity and subsequently ameliorate convergence procedure of the proposed method. Then, a sub-ICA algorithm is proposed to obtain optimal generation power of thermal units. Simulation results validate effectiveness and applicability of the proposed method on two scenarios: (a) a set of unimodal and multimodal standard benchmark functions, (b) two GENCOs consist of 10 and 100 generating units.     

A New Well-Solvable Class of PNS Problems

A manufacturing system consists of operating units which convert their input materials into their output materials. In the problem of designing a process network, we have to find a suitable network of operating units which produces the desired products from the given raw materials. If we consider this process network design problem from a structural point of view, then we obtain a combinatorial optimization problem called the Process Network Synthesis or (PNS) problem. It is known that the PNS problem is NP-complete. Here, a new method is presented to reduce the solution of some more difficult PNS problems to the solution of simpler ones, and using this method, a new well-solvable class of PNS problems is established. Received February 12, 1999; revised October 24, 2000     

Solution to profit based unit commitment problem using particle swarm optimization

In this paper, an algorithm to solve the profit based unit commitment problem (PBUCP) under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique to maximize the GENCOs profit. Deregulation in power sector increases the efficiency of electricity production and distribution, offer lower prices, higher quality, a secure and a more reliable product. The proposed algorithm has been developed from the view point of a generation company wishing to maximize its profit in the deregulated power and reserve markets. UC schedule depends on the market price in the deregulated market. In deregulated environment utilities are not required to meet the demand. GENCO can consider a schedule that produce less than the predicted load demand and reserve but creates maximum profit. More number of units are committed when the market price is higher. When more number of generating units are brought online more power is generated and participated in the deregulated market to get maximum profit. This paper presents a new approach of GENCOs profit based unit commitment using PSO technique in a day ahead competitive electricity markets. The profit based unit commitment problem is solved using various PSO techniques such as Chaotic PSO (CPSO), New PSO (NPSO) and Dispersed PSO (DPSO) and the results are compared. Generation, spinning reserve, non-spinning reserve, and system constraints are considered in proposed formulation. The proposed approach has been tested on IEEE-30 bus system with 6 generating units as an individual GENCO. The results obtained are quite encouraging and useful in deregulated market. The algorithm and simulation are carried out using Matlab software.     

New mixed integer approach to solve a multi-level capacitated disassembly lot-sizing problem with defective items and backlogging

The disassembly process has attracted mounting interest due to growing green concerns. This paper addresses the capacitated dynamic lot-sizing problem with external procurement, defective and backordered items, setup times, and extra capacity. The problem is to determine how many end-of-life products to disassemble during each period. We propose a new mixed-integer programming (MIP) approach to formulate the problem under study in order to maximize the disassembly-process gain, which is obtained as the difference between the revenue achieved by resale of the items recovered after disassembly and the costs tied to operating the disassembly tasks. Several numerical tests using the well-known CPLEX solver proved that this new model can find the optimal disassembly schedule for most test instances within an acceptable computational time. Furthermore, we led sensitivity studies on disassembly capacity, setup time and procurement cost. Test results validate the power of the suggested model and provide helpful insights for industry practitioners.     

Unit commitment problem using enhanced particle swarm optimization algorithm

This paper proposes an enhanced PSO (EPSO) approach to solve the unit commitment (UC) problem in electric power system, which is an integrated improved discrete binary particle swarm optimization (DBPSO) with the Lambda-iteration method. The EPSO is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. The implementation of EPSO for UC problem consists of three stages. First, the DBPSO based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Second, heuristic search strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. To verify the advantages of the EPSO method, the EPSO is tested and compared to the other methods on the systems with the number of units in the range of 10 to 100. Numerical results demonstrate that the EPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.     

Multiobjective target setting in data envelopment analysis using AHP

In this paper two new target setting DEA approaches are proposed. The first one is an interactive multiobjective method that at each step of the process asks the decision maker (DM) which inputs and outputs he wishes to improve, which ones are allowed to worsen and which ones should stay at their current level. The local relative priorities of these inputs and outputs changes are computed using the analytic hierarchy process (AHP). After obtaining the candidate target, the DM can update his preferences for improving, worsening or maintaining current inputs and outputs levels and obtain a new candidate target. Thus continuing, until a satisfactory operating point is computed. The second method proposed uses a lexicographic multiobjective approach in which the DM specifies a priori a set of priority levels and, using AHP, the relative importance given to the improvements of the inputs and outputs at each priority level. This second approach requires solving a series of models in order, one model for each priority level. The models do not allow for worsening of neither inputs nor outputs. After the lowest priority model has been solved the corresponding target operating point is obtained. The application of the proposed approach to a port logistics problem is presented.     

The Berkeley UNIX Consultant Project

UC (UNIX Consultant) is an intelligent, natural-language interface thatallows naive users to learn about the UNIX operating system. UC wasundertaken because the task was thought to be both a fertile domain forArtificial Intelligence research and a useful application of AI work inplanning, reasoning, natural language processing, and knowledgerepresentation. The current implementation of UC comprises the followingcomponents: A language analyzer, called ALANA, that produces arepresentation of the content contained in an utterance; aninference component called a concretion mechanism that furtherrefines this content; a goal analyzer, PAGAN, that hypothesizes theplans and goals under which the user is operating; an agent, calledUCEgo, that decides on UC's goals and proposes plans for them; adomain planner, called KIP, that computes a plan to address the user'srequest; an expression mechanism, UCExpress, that determines thecontent to be communicated to the user, and a language productionmechanism, UCGen, that expresses UC's response in English. UC alsocontains a component called KNOME that builds a model of the user'sknowledge state with respect to UNIX. Another mechanism, UCTeacher,allows a user to add knowledge of both English vocabulary and factsabout UNIX to UC's knowledge base. This is done by interacting with theuser in natural language. All these aspects of UC make use of knowledgerepresented in a knowledge representation system called KODIAK. KODIAKis a relation-oriented system that is intended to have widerepresentational range and a clear semantics, while maintaining acognitive appeal. All of UC's knowledge, ranging from its most generalconcepts to the content of a particular utterance, is represented inKODIAK.     

A new approach for UC security concurrent deniable authentication

Deniable authentication protocols allow a sender to authenticate a message for a receiver, in a way which the receiver cannot convince a third party that such authentication ever took place. When we consider an asynchronous multi-party network with open communications and an adversary that can adaptively corrupt as many parties as it wishes, we present a new approach to solve the problem of concurrent deniable authentication within the framework of universally composable （UC） security. We formulate a definition of an ideal functionality for deniable authentication. Our constructions rely on a modification of the verifiably smooth projective hashing （VSPH） with projection key function by trapdoor commitment. Our protocols are forward deniable and UC security against adaptive adversaries in the common reference string model. A new approach implies that security is preserved under concurrent composition of an unbounded number of protocol executions; it implies non-malleability with respect to arbitrary protocols and more. The novelty of our schemes is the use of witness indistinguishable protocols and the security is based on the decisional composite residuosity （DCR） assumption. This new approach is practically relevant as it leads to more efficient protocols and security reductions.     

Efficient task scheduling for runtime reconfigurable systems

Recent research indicates the promising performance of employing reconfigurable systems to accelerate multimedia and communication applications. Nonetheless, they are yet to be widely adopted. One reason is the lack of efficient operating system support for these platforms. In this paper, we address the problem of runtime task scheduling as a main part of the operating systems. To do so, a new task replacement parameter, called Time-Improvement, is proposed for compiler assisted scheduling algorithms. In contrast with most related approach, we validate our approach using real application workload obtained from an application for multimedia test remotely taken by students. The proposed online task scheduling algorithm outperforms previous algorithms and accelerates task execution from 4% up to 20%.     

抽汽供热机组RB控制逻辑优化研究

辅机故障减负荷(RB)控制在机组发生部分主要辅机故障跳闸时,可将机组负荷快速降到当前辅机实际所能达到的相应出力,并调节机组参数至允许范围内,以保障机组持续运行、避免非停事故发生。然而,大量纯凝机组在完成供热或工业供汽改造后,并没有对其RB控制逻辑进行相应修改,导致主要辅机故障时,机组实际负荷仍然低于当前辅机所能达到的相应出力。这进而会导致RB控制拒动,增加了机组非停风险。为了解决上述问题,在分析供汽改造后RB控制逻辑无法触发的原因基础上,首次提出了有效的机组发电负荷实时预测方法。通过采用预测负荷取代机组当前实际负荷,修正了RB控制逻辑触发的判定条件,确保RB控制能够及时正确触发。该方法具有适用广泛、抗干扰性强等优点。     

Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called “type-ahead search” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.     

ASTERIX: towards a scalable,semistructured data platform for evolving-world models

ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal—the storage and analysis of data pertaining to evolving-world models. We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead.     

Planning and meta-planning (MOLGEN: Part 2)

The selection of what to do next is often the hardest part of resource-limited problem solving. In planning problems, there are typically many goals to be achieved in some order. The goals interact with each other in ways which depend both on the order in which they are achieved and on the particular operators which are used to achieve them. A planning program needs to keep its options open because decisions about one part of a plan are likely to have consequences for another part.This paper describes an approach to planning which integrates and extends two strategies termed the least-commitment and the heuristic strategies. By integrating these, the approach makes sense of the need for guessing; it resorts to plausible reasoning to compensate for the limitations of its knowledge base. The decision-making knowledge is organized in a layered control structure which separates decisions about the planning problem from decisions about the planning process. The approach, termed meta-planning, exposes and organizes a variety of decisions, which are usually made implicitly and sub-optimally in planning programs with rigid control structures. This is part of a course of research which seeks to enhance the power of a problem solvers by enabling them to reason about their own reasoning processes.Meta-planning has been implemented and exercised in a knowledge-based program (named MOLGEN) that plans gene cloning experiments in molecular genetics.     

Supervisory control of families of linear set-point controllers. 2.Robustness

A simply structured high-level controller, called a “supervisor”, has recently been proposed in part I of this article (ibid., vol.41, 1996) for the purpose of orchestrating the switching of a sequence of candidate set-point controllers into feedback with an imprecisely modeled SISO process so as to cause the output of the process to approach and track a constant reference input. The process is assumed to be modeled by an SISO linear system whose transfer function is in the union of a number of subclasses, each subclass being small enough so that one of the candidate controllers would solve the set-point tracking problem, if the process transfer function was to be one of the subclass members. This paper proves that without any further modification, the same supervisor described in Part I can also perform this function in the face of norm-bounded unmodeled dynamics, and moreover that none of the signals within the overall system can grow without bound in response to bounded disturbance and noise inputs, whether they are constant or not     

Mixed Biogeography-Based Optimization for GENCOs’ Maintenance Scheduling in Restructured Power Systems

Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs’ objective function.     

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.      

Stochastic unit commitment problem

The electric power industry is undergoing restructuring and deregulation. We need to incorporate the uncertainty of electric power demand or power generators into the unit commitment problem. The unit commitment problem is to determine the schedule of power generating units and the generating level of each unit. The objective is to minimize the operational cost which is given by the sum of the fuel cost and the start‐up cost. In this paper we propose a new algorithm for the stochastic unit commitment problem which is based on column generation approach. The algorithm continues adding schedules from the dual solution of the restricted linear master program until the algorithm cannot generate new schedules. The schedule generation problem is solved by the calculation of dynamic programming on the scenario tree.     

Process design in SISO systems with input multiplicity using bifurcation analysis and optimisation

This paper presents an approach using continuation and optimisation methods for modifying a process design to avoid control difficulties caused by input multiplicity. The approach assumes an initial design, with a preassigned SISO control structure, has been obtained and is useful where there is an input multiplicity in the operating region. The condition for input multiplicity is obtained by inflating the state space model with a state representing the locus of the point of zero gain. The multiplicity condition is determined using the bifurcation analysis package, AUTO, which allows the study of the influence of operating conditions and parameters on input multiplicity behaviour to obtain an expression for the point of zero gain as a function of the design and disturbance variables. A process modification problem is formulated within an optimisation framework and solved to determine the minimal design parameter changes necessary to avoid input multiplicity given an assumed maximal disturbance. Results are presented for the application of the algorithm to a CSTR system demonstrating that small changes in some design variables can avoid input multiplicity problems in this case, and that the method can determine the changes necessary.     

Intelligent supervision approach based on multilayer neural PCA and nonlinear gain scheduling

This paper is mainly aimed at developing an off-line supervision approach geared to complex processes. This approach consists of two parts: the first part is the fault detection and isolation and the second one is the process control. The first part is devoted to the implementation of the multilayer neural PCA which combines the advantage of data reduction provided by the principal component analysis and the power of neural network linearization. The transition to control is conditioned by the absence of faults in the process; if there is a defect, it must be isolated by identifying the defected variables. The second part rests on the combination of two control tools: both the gain scheduling and the feedback linearization yield a new approach called nonlinear gain scheduling. To have our work validated, we applied it to a photovoltaic system and it gave effective results.

     

基于信息浓缩的隐私保护支持向量机分类算法

支持向量机(SVM)的分类决策过程涉及到对原始训练样本的学习,容易导致数据中隐私信息的泄漏。为解决上述问题,提出一种基于信息浓缩的隐私保护分类方法IC-SVM。该算法首先根据样本的邻域信息,通过模糊C均值(FCM)聚类算法进行聚类分析;接着,使用信息浓缩准则对聚类中心进行处理,得到浓缩点组成的新样本;最后,使用新样本进行训练并得到决策函数,并用它去进行分类测试,可以较好地保护数据的隐私。在UCI真实数据和PIE人脸数据上的实验结果表明,IC-SVM方法既能保护数据信息的安全,又有较高的分类准确率。