Earticle

본 연구의 목적은 초등학생들의 ‘계절의 변화’에 대한 Jigsaw 협동학습이 과제집착력 및 창의적 인성에 미치는 효과를 알아보는 데 있다. 연구 대상은 B시의 G초 등학교 6학년 2개 반 48명을 대상으로 연구집단 24명, 비교집단 24명으로 구성하였다. 초등과학 6학년 ‘계절 의 변화’ 단원을 중심으로 12주간 실험처치를 하였다. 그리고 연구집단과 비교집단에서 사전-사후검사로 과 제집착력 검사, 창의적 인성 검사를 하였다. Jigsaw 협 동학습을 통해 자연스럽게 과제집착력 향상과 창의적 인성 함양이 될 수 있도록 연구절차를 설정하여 연구를 진행하였다. 이에 대한 연구 결과는 다음과 같다. 첫째, 초등학생들의 Jigsaw 협동학습은 과제집착력 향상에 효과가 있었다. 둘째, 초등학생들의 Jigsaw 협 동학습은 창의적 인성 함양에 효과가 있었다. 셋째, 초 등학생들의 Jigsaw 협동학습에 대한 인식은 긍정적으 로 나타났다. 이러한 연구 결과를 통하여 Jigsaw 협동 학습이 과제집착력과 창의적 인성에 효과가 있으며 긍정적인 영향을 미친다는 것을 확인할 수 있다.

The purpose of this study is to investigate the effect of jigsaw cooperative learning strategy on elementary students’ task commitment and creative personality. This study utilized experimental method of comparing the effect of jigsaw strategy between treatment and comparative group. Twenty four 6th grade students in treatment group experienced the jigsaw cooperative learning for twelve-weeks during 2019 school year whereas students in comparetive group did not. Task commitment and creative personality test result were collected from both groups as main data sources and mixed-method was used to analyze the data. The results of the study were as follows. First, the treatment group students’ task commitment score was significantly higher than comparative group of students’ score(p<.05). Second, the treatment group students’ creative personality efficacy score was significantly higher than comparative group students’ (p<.05). Third, students who experienced the jigsaw cooperative learning-based science lesson had positive perceptions about the lesson.

This paper presents a method to solve the optimal generation and dispatch of electrical power with multiple fuel options at different power levels. In general, the cost function for each generator is considered by a single quadratic function of power, for the optimal generation and dispatch problem. However, it is more realistic to represent the generation cost function for fossil fired plants by non smooth cost function i.e. the total generation cost function has non differentiable points such as segmented piece-wise quadratic function and valve point loading. Some generating units utilize multiple fuels sources (such as gas, coal, oil etc) are faced with the problem of determining which is the most economical fuel to be selected. This problem has been solved by Hota & Das [17] considering equal incremental cost. In this method the program has to be run for a number of times for fuel combinations for different units and then to select the best one out of all combinations. This is a complicated process. Also if some other type of nonlinear cost function is considered along with this, the method will not be applicable. In view of the above mentioned problems of economic load dispatch (ELD) of multiple fuel generation, a more general heuristic method known as Particle Swarm Optimization (PSO) is considered in this paper to solve the problem in which either type of nonlinearity in cost function can be considered. For solution of this problem dynamic PSO is proposed. It is dynamic in the sense that the velocity bound or limit is updated in each iteration. This proposed method is applicable to the generating units that can use multiple fuels through valve at different generation levels as well as other problems which results in multiple intersecting continuous and discontinuous cost curves for any unit. The advantage of the method is that the method is applicable to both continuous and discontinuous cost curves of systems. Also it does not require selection of unit and type of fuel to be used after computation for a number of combination which is a cumbersome task as used in ref [17]. The complete method is explained and validated by taking an example. The simulation is carried out using MATLAB software. It has been shown that the method is simple, direct, and practical. It can be used for real time implementation and operation.

Many other unit commitment performed by complex mathmatical problems and algorithmm. but fuzzy logic is not concerned with mathmatical formulations so unit commitment is scheduled with ease. and fuzzy can express many constraints which has uncertanity feasibly because it use lingustic expressions. In this paper MATLAB is used as a simulation tool. Fuzzy Toolbox is made to make fuzzy control systems. Using this Toolbox easily and effectly we can make good unit commitment.

This paper proposes a unit commitment scheduling method based on Genetic Algorithm(GA). Due to a variety of constraints to be satisfied, the search space of the UC problem is highly nonconvex, so the UC problem cannot be solved efficiently only using the standard GA To efficiently deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up and down time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of other constraints arc handled by integrating penalty factors. To show the effectiveness of the GA based unit commitment scheduling, test results for system of 5 units are compared with results obtained using Lagrangian Relaxation and Dynamic Programming.

The non-separable quadratic penalty terms create an inherent difficulty when applying the standard augmented Lagrangian relaxation(ALR) method for decomposing the unit commitment problem into independent subproblems. This paper presents a separable augmented Lagrangian relaxation method for solving the unit commitment problem. The proposed method is designed to have a separable structure by introducing the quadratic terms with additional auxiliary terms in the augmented Lagrangian function. Numerical results are presented to validate the effectiveness of the proposed method.

In this study, a new Unit Commitment (UC) algorithm is proposed to consider the uncertainty of a daily load profile. The proposed algorithm calculates the UC results with a lower load level than that generated by the conventional load forecast method and the greater hourly reserve allocation. In case of the worst load forecast, the deviation of the conventional UC solution can be overcome with the proposed method. The proposed method is tested with sample systems, which indicates that the new UC algorithm yields a completely feasible solution even when the worst load forecast is applied. Also, the effects of the uncertain hourly load demand are statistically analyzed, particularly by the consideration of the average over generation and the average under generation. Finally, it is shown that independent power producers participating in electricity spot-markets can establish bidding strategies by means of the statistical analysis. Therefore, it is expected that the proposed method can be used as the basic guideline for establishing bidding strategies under the deregulation power pool.

This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

본 논문에서는 전력 소비자 입장에서 전력가격에 큰 비중을 차지하는 System Marginal Cost(SMP)를 줄이는 방법으로 ESS를 포함하여 Unit Commitment(UC)를 수행하는 방법과 그에 따른 효과를 분석하였다. 먼저 본 논문에서는 일반적인 UC를 Lagrangian Relaxation(LR) 기법으로 얻은 최적의 결과 값을 토대로 ESS를 추가하였다. 기존의 LR알고리즘으로부터 얻은 결과 값에 ESS를 추가하는 방법으로는 Dynamic Programming 방법의 한 종류인 Label Correcting Methods[]를 사용하였다. 기존의 UC 알고리즘 결과 값을 토대로 ESS를 추가하여 SMP 감소효과 분석을 위해 실 계통에서 사용되는 종류별 발전기 10대와 임의적으로 설정한 24시간의 부하 값을 토대로 MATLAB을 이용해서 구현하였고, 결과 값을 통해 실질적인 SMP 감소효과 유무를 확인할 수 있었다.

The eventual goal of this paper is to help the generating companies and load-serving entities to choose appropriate relative levels of interconnected system versus bilateral trades while considering risk, and economic performance. In competitive power markets, electricity prices are determined by balance between demand and supply in electric power exchanges or bilateral contracts. The problem formulation is bilateral contract incorporated into Multi-area unit commitment with import/export and tie-line constraints. This proposed method considers maximizing own profit or minimize the operating cost among the generating companies in multi-area system. The feasibility of the proposed algorithm has been demonstrated using IEEE system with four areas and experimental results shows that proposed method is reliable, fast and computationally efficient

This paper proposes a new generation scheduling algorithm which can consider total quantity of contamination and energy limits based on Lagrangian Relaxation. First, we formulate a Lagrangian function which consider quantity of contamination including substitution of fuel, and quantity of fuel. Second, we developed Multi-state S.U.D.P. which can treat multiple fuels. Third, we propose an effective generation scheduling algorithm including Multi-state S.U.D.P.. As a results, unit commitment and generating power can be obtained to meet the environmental and fuel constraints with fuel substitution. By applying it to the test system, effectiveness of the method is verified.

A mathematical model using dynamic programming approach is applied to an optimal unit commitment problem. In this study, the units are treated as stages instead of as state dimension, and the time dimension corresponds to the state dimension instead of stages. A considerable amount of computer time is saved as compared to the normal approach if there are many units in the basin. A case study on the Lower Colorado River Basin System is presented to demonstrate the capabilities of the optimal scheduling of hydropower units.

Coordinating operation between large-scale wind power and thermal units in multiple time scale is an important problem to keep power balance, especially for the power grids mainly made up of large coal-fired units. The paper proposes a novel operation mode of multi-scale unit commitment (abbr. UC) that includes mid-term UC and day-ahead UC, which can take full advantage of insufficient flexibility and improve wind power accommodation. First, we introduce the concepts of multi-scale UC and then illustrate the benefits of introducing mid-term UC to the wind-coal intensive grid. The paper then formulates the mid-term UC model, proposes operation performance indices and validates the optimal operation mode by simulation cases. Compared with day-ahead UC only, the multi-scale UC mode could reduce the total generation cost and improve the wind power net benefit by decreasing the coal-fired units' on/off operation. The simulation results also show that the maximum total generation benefit should be pursued rather than the wind power utilization rate in wind-coal intensive system.

Recent deregulation of Korean electricity industry has made each power generation company pay more attention to maximizing its own profit instead of minimizing the overall system operation cost while guaranteeing system security. Electricity power generation problem is typically defined as the problem of determining both the on and off status and the power generation level of each generator under the given fuel constraints, which has been known as Profit-Based Unit Commitment (PBUC) problem. To solve the PBUC problem, the previous research mostly focused on devising Lagrangian Relaxation (LR) based heuristic algorithms due to the complexity of the problem and the nonlinearity of constraints and objectives. However, these heuristic approaches have been reported as less practical in real world applications since the computational run time is usually quite high and it may take a while to implement the devised heuristic algorithms as software applications. Especially when considering long-term planning problem which spans at least one year, the complexity becomes higher. Therefore, this paper proposes an explicit column generation algorithm using power generation patterns and the proposed algorithm is successfully applied to a Korean power generation company. The proposed scheme has a robust structure so that it is expected to extend general PBUC problems.

The electrical vehicles (EV) with vehicle-to-grid (V2G) capability can be used as loads, energy sources and energy storage in MicroGrid integrated with renewable energy sources. The output power of generators will be reallocated in the considering of V2G. An intelligent unit commitment (UC) with V2G for cost optimization is presented in this paper. A new constraint of UC with V2G is considered to satisfy daily use of EVs. A hybrid optimiza-tion algorithm combined Binary Particle Swarm Optimization (BPSO) with Lagrange Mul-tipliers Method (LMM) is proposed. The difference between results of UC with V2G and UC without V2G is presented.

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

This paper presents a new approach with artificial immune system algorithm to solve the profit based unit commitment problem. The objective of this work is to find the optimal generation scheduling and to maximize the profit of generation companies (Gencos) when subjected to various constraints such as power balance, spinning reserve, minimum up/down time and ramp rate limits. The proposed hybrid method is developed through adaptive search which is inspired from artificial immune system and genetic algorithm to carry out profit maximization of generation companies. The effectiveness of the proposed approach has been tested for different Gencos consists of 3, 10 and 36 generating units and the results are compared with the existing methods.

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

Lagrangian relaxation is the most widely adopted method for solving unit commitment (UC) problems. It consists of two steps: dual optimization and primal feasible solution construction. The dual optimization step is crucial in determining the overall performance of the solution. This paper intends to evaluate two dual optimization methods - one based on subgradient (SG) and the other based on the cutting plane. Large-scale UC problems with hundreds of thousands of variables and constraints have been generated for evaluation purposes. It is found that the evaluated SG method yields very promising results.

한국전력산업의 탈규제화의 영향으로 각 개별 발전회사들은 자사의 이익을 최대화하기 위한발전계획 수립에 큰 관심을 가지게 되었다. 발전계획은 주어진 연료제약 하에 발전수익과 유지보수비용을 고려하여 시간대별 발전기의 기동, 정지 및 발전출력을 결정하는 문제로서Profit-Based Unit Commitment (PBUC) 문제로 알려져 있다. PBUC 문제는 문제 자체의 복잡성과 비선형 제약식의 특성으로 인하여 과거 연구는 대부분 비선형 제약식 처리를 위한 Lagrangian Relaxation (LR) 기반 휴리스틱 접근법에 초점이 맞추어져 왔다. 하지만, 실제현업 적용에 있어 계산시간이 많이 소요되고 알고리즘의 구현에 많은 기간이 소요되어 실용성은 낮은 것으로 보고되었다. 특히 연료도입 및 저장제약을 고려하기 위한 1년 단위 장기 발전계획 수립은 문제의 범위가 더욱 넓어짐으로 인하여 복잡성이 매우 크게 증가하고, 이에 따라 기존 접근법에 한계가 있어 왔다. 이에 본 연구에서는 국내가스발전소의 사례를 중심으로 발전패턴개념의 도입 및 다양한 발전패턴 생성을 통한 Explicit Column Generation 기반 최적화 접근법을 제안한다. 발전패턴은 Column Generation 접근법의 각 Column에 해당하는 각주별 발전기 기동정지계획을 의미한다. 즉, 미리 유효한 발전패턴의 Pool을 최대한 확보한 후 Explicit Column Generation Formulation을 통하여 주별 최적의 발전패턴을 찾아내는 알고리즘으로 구성이 된다. 본 알고리즘은 실제 가스발전소의 장기 발전계획 수립과정에 적용되어 효과적으로 운용되고 있으며 연간 수십억원의 추가적인 이익을 실현할 것으로 분석되었다. 본 알고리즘을 확장 적용할 경우 PBUC 문제 해결을 위한 새로운 해법으로도 그 효용성이 클 것으로 예상된다.자료이기 때문에 통계적 활용의 범위가 방대하다. 특히 개인, 가구, 사업체 등 사회 활동의 주체들이 어떻게 변화하는지를 추적할 수 있는 자료를 생산함으로써 다양한 인과적 통계분석을 할 수 있다. 행정자료를 활용한 인구센서스의 이러한 특징은 국가의 교육정책, 노동정책, 복지정책 등 다양한 정책을 정확한 자료를 근거로 수립할 수 있는 기반을 제공한다(Gaasemyr, 1999). 이와 더불어 행정자료 기반의 인구센서스는 비용이 적게 드는 장점이 있다. 예를 들어 덴마크나 핀란드에서는 조사로 자료를 생산하던 때의 1/20 정도 비용으로 행정자료로 인구센서스의 모든 자료를 생산하고 있다. 특히, 최근 모든 행정자료들이 정보통신기술에 의해 데이터베이스 형태로 바뀌고, 인터넷을 근간으로 한 컴퓨터네트워크가 발달함에 따라 각 부처별로 행정을 위해 축적한 자료를 정보통신기술로 연계${cdot}$통합하면 막대한 조사비용을 들이지 않더라도 인구센서스자료를 적은 비용으로 생산할 수 있는 근간이 마련되었다. 이렇듯 행정자료 기반의 인구센서스가 많은 장점을 가졌지만, 그렇다고 모든 국가가 당장 행정자료로 인구센서스를 대체할 수 있는 것은 아니다. 행정자료로 인구센서스통계를 생산하기 위해서는 각 행정부서별로 사용하는 행정자료들을 연계${cdot}$통합할 수 있도록 국가사회전반에 걸쳐 행정 체제가 갖추어져야 하기 때문이다. 특히 모든 국민 개개인에 관한 기본정보, 개인들이 거주하며 생활하는 단위인 개별 주거단위에 관한 정보가 행정부에 등록되어 있고, 잘 정비되어 있어야 하며, 정보의 형태 또한 서로 연계가 가능하도록 표준화되어있어야 한다. 이