送電損失と線路過負荷を考慮した火力発電機起動停止計画手法

Unit commitment is an efficient method to lower the total cost of the power plant by scheduling units' generation and meeting the constraint condition. However, in practice, few power plants implement the algorithm for fear that the load response in the power plant would not be fast enough to track the unscheduled load demand from the power grid if some units of the power plant are shut down. This paper proposes a new unit commitment method that tries to solve this problem effectively. The proposed method guarantees the desired speed limit by setting the plant-level load tracking speed. In this method a ‘one-at-a-time’ unit commitment is accomplished. The most expensive unit (which is determined by computing its cost) is optimized, the unit commitment schedule is updated, and the load dispatch result is re-computed. The result will be repeatedly optimized until the spinning reserve or load tracking speed is lack. The optimization loop will breaks out as well if the unit schedules of two consecutive iterations over the time period remain unchanged. The test results from 10-machine system shows that the proposed method is easy to understand and quick to solve, and is of practical use.

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

This paper presents a new approach for unit commitment (UC); where a large scale wind power exists and the wind speed has a fuzzy characteristic; by using particle swarm optimization method (PSO). In this approach, the system reserve requirements, the requirement of having a load balance, and the wind power availability constraints are realized. A proper modeling of these constraints is an important issue in power system scheduling. Since these constraints are ldquofuzzyrdquo in nature, any crisp treatment of them in this problem may lead to over conservative solutions. In this paper, a fuzzy optimization-based method is developed to solve power system UC problem with a fuzzy objective function and its constraints. This fuzzy mid term UC problem is, at first, converted to a crisp formulation and then is solved by PSO. This method is applied to unit commitment of a 12-unit test system and the results of the particle swarm optimization method are compared with the results of the conventional numerical methods such as mixed integer nonlinear programming (MINLP). Numerical tests results show that near optimal schedules are obtained, by application of this method. Also this method provides a balance between the costs and the constraints satisfaction.

Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment (SCUC) problems. In response to the “curse of dimension” problem of dynamic programming, the approach solves the Bellman’s equation of SCUC approximately by solving a sequence of simplified single stage optimization problems. An extended sequential truncation technique is proposed to explore the state space of the approach, which is superior to traditional sequential truncation in daily cost for unit commitment. Different test cases from 30 to 300 buses over a 24 h horizon are analyzed. Extensive numerical comparisons show that the proposed approach is capable of obtaining the optimal unit commitment schedules without any network and bus voltage violations, and minimizing the operation cost as well.

This paper proposes a new method for solving economic dispatch problems in electrical power systems through the application of multithread programming. Multi-Thread Interior Point Barrier Algorithm, an efficient quadratic programming algorithm, is used to solve economic dispatch problem. The barrier algorithm also caters for any combination of polynomial functions such as polynomial cost functions. Nonlinear characteristics of electrical power system such as generators limitations, transmission losses and nonlinear cost functions are considered in the economic dispatch formulation. Exact transmission loss formula is used in the equality constraint in the economic dispatch equations. The transmission loss coefficients are calculated by using a multi-thread programming. To ensure the optimal dispatch satisfies the line flow limits, line flow constraints has been added to formulation as a security constraint. This optimization problem enable us to solve economic dispatch problems faster, so it is suitable for unit commitment problems and the other power system problems which need to get reasonable speed. IEEE 30, 57, 118 bus case studies are presented and tested with the proposed method.

A heuristic solution for short-term thermal unit commitment is presented. The algorithm has been developed as a FORTRAN-based production-type program. Tests of this algorithm on data provided by a midwest utility have demonstrated satisfactory results. The proposed algorithm produces the same unit commitment schedule as a standard dynamic programming (DP)-based algorithm in one fourth the computation time for the DP-based algorithm. The algorithm is nearly linear with the number of unit periods. A unit period is defined as the online duration time versus operation level for each unit. Operating system restrictions limit the prototype to 30 thermal power units as implemented on a PS/2 model 50 personal computer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The fast-increasing introduction of renewable energy sources (RESes) leads to some problems in electrical power network due to fluctuating generated power. A power system must be operated with provision of various reserve powers like governor free capacity, load frequency control and spinning reserve. Therefore, the generator’s schedule (unit commitment schedule) should include the consideration of the various power reserves. In addition, it is necessary to calculate the annual operational costs of electric power systems by solving the unit commitment per week of thermal power generators and pumped storages in order to compare and examine the variance of the operational costs and the operating ratio of the generators throughout the year. This study proposes a novel annual analysis for the thermal power generator and pumped storages under a massive introduction of RESes. A weekly unit commitment schedule (start/stop planning) for thermal power generator and pumped storages has been modeled and calculated for one year evaluation. To solve the generator start/stop planning problem, Tabu search and interior point methods are adopted to solve the operation planning for thermal power generators and the output decision for pumped storages, respectively. It is demonstrated that the proposed method can analyze a one-year evaluation within practical time. In addition, by assuming load frequency control (LFC) constraints to cope with photovoltaic (PV) output fluctuations, the impact of the intensity of LFC constraints on the operational cost of the thermal power generator has been elucidated. The increment of the operational cost of the power supply with increasing PV introduction amount has been shown in concrete terms.

Scale effects on runoff and erosion losses from arable land under conservation and conventional tillage: The role of residue cover

A layered matrix encoding cascade genetic algorithm and particle swarm optimization approach (GA-PSO) for unit commitment and economic load dispatch problem in a thermal power system is presented in this paper. The tasks of determining and allocating power generation to different thermal units in a way that the total power production cost is at the minimum subject to equality and inequality constraints makes unit commitment and economic load dispatch challenging. A case study, based on the thermal power generation problem presented in [1], is used to demonstrate the effectiveness of the proposed method in generating a cost-effective power generation schedule. The schedule obtained is compared with that of Linear Programming (LP) as reported in [1]. The results show that the proposed GA-PSO approach outperforms LP in solving the unit commitment and economic load dispatch problem for thermal power generation system in this case study.

A systematic review of supply and demand side optimal load scheduling in a smart grid environment

In this paper, first time ever artificial colony bee search is applied for thermal unit commitment. The main aim of doing thermal unit commitment scheduling is to optimize running while ensuring security and reliability. In recent era, lot of attention is given to the environmental conditions. All developed and few developing countries are worrying about emission of carbon dioxide from thermal generating stations. In first section of this paper unit commitment and Artificial Bee Colony (ABC) is discussed. In next section methodology for adopting ABC and general constraints of unit commitment are discussed in great detail. In last section application of ABC for unit commitment is implementation of proposed algorithm is shown and obtained results are presented. It is observed that ABC produces good results for unit commitment problem and easy to implement.

Penghu Island is a small isolated power system with 70MW peak load and 30MW off-peak load. According to the operation records of the installed wind farms, Penghu has bettter wind conditions than any other site in Taiwan. The total capacity of wind farms in Penghu reaches up to 10.2MW, which means 35% penetration on wind power is possible during minimum load. The high level of wind power generation in the Penghu power system makes the decision of diesel unit commitment and control of the power system more difficult due to the high variability of wind power output in a time frame ranging from seconds to hours. This work investigates the significant factors that affect the system transient stability as high penetration of wind power is integrated. Additionally, the possible impact and coping strategies for the high wind power penetration are studied, and several specific suggestions are proposed to Taiwan Power Company. These strategies include revising the load shedding methods, unit commitment scheduling, wind turbine operation, the suitable energy storage system planning, and wind turbine protection.

In this paper, we present a large-scale spatial model of the European electricity market including both generation and the physical transmission network (DC Load Flow approach). The model was developed to analyze various questions on market design, congestion management, and investment decisions, with a focus on Germany and Continental Europe. It is a bottom-up model combining electrical engineering and economics: its objective function is welfare maximization, subject to line flow, energy balance, and generation constraints. The model provides simulations on an hourly basis, taking into account variable demand, wind input, unit commitment, start-up costs, pump storage, and other details. Various forms of spatial price discrimination can be implemented, such as locational marginal pricing (“nodal pricing”), or zonal pricing. With over 2,000 nodes and over 3,000 lines, this is one of the largest models developed to date, and allows a highly differentiated spatial analysis. We report modeling results regarding efficient congestion management for Germany and Europe, optimal network expansion under the aspect of increased wind energy production, and the impact of network constraints on location decisions of generation investments.

The authors propose a rigorous method to treat multiarea generation scheduling with tie line limits. An expert system was used for obtaining the initial solution. As the generation scheduling problem involves unit commitment and economic dispatch, the method adopts an iterative procedure to deal with these two phases. The hourly load demand and the area power generation will cause the tie flows to change. To maintain the operation security in every area, the spinning reserve should comply with the area power generation rather than its load demand. After economic dispatch, it is necessary to adjust the unit commitment in each area for preserving the spinning reserve requirements. Heuristics were used to modify the generation unit combinations. The objective is to find an economic generation schedule for a multiarea system. The interchange transactions among areas represent the transportation problem, embedded within the nonlinear optimization process. The equivalent system concept is adopted, and the transmission losses are included in this study. A four-area system with each area consisting of 26 units was used to test the efficiency of the proposed algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).