计及配电网馈线约束的电动汽车优化调度研究

随着配电网的迅速发展,其以电动汽车为代表的柔性负荷大量并入配电网,给配电网的优化运行带来挑战,电动汽车的无序充电行为会导致电力损失、电压跌落和馈线过载等一系列问题。以电动汽车充电费用和电池衰减成本以及系统发电成本最低为目标,并考虑配电网的馈线容量耦合约束,提出了一种基于交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)的电动汽车分散式优化调度策略。在仿真案例中验证了所提算法的最优性,并表明了该方法能够有效地解决配电网馈线过载问题。     

考虑安全约束及电量执行的年度发电与检修联合优化模型

针对年度发电计划与年度检修计划在系统发电能力、电网潮流约束等方面具有强耦合性的特点,构建了考虑安全约束及月度电量执行的年度发电与检修联合优化模型。年度发电计划模型以天为计算时段,提出以历史负荷率统计与正备用约束选择相结合的电量估算方法,降低了计划规模,提升了优化效率。为充分考虑检修对电网运行的影响,通过引入支路开断潮流分布因子,在优化模型中可同时考虑发电计划与检修计划对潮流的综合影响,提升发电计划和检修计划的联合优化能力。将所提模型运用于中国某省的实际电网,验证了其有效性。     

A decentralized scheduling method for flowshop problems with resource constraints

In this paper, we propose a decentralized scheduling method for flowshop scheduling problems with resource constraints using the Lagrangian decomposition and coordination approach. When a flowshop scheduling problem with resource constraints is decomposed into machine‐level subproblems, the decomposed problem becomes very difficult to solve so as to obtain the optimal solution, even when the production sequence of operations is given. In this study, the decomposed subproblems are solved by a simulated annealing algorithm combined with dynamic programming. By decomposing the problem into single machine subproblems, the changeover cost can easily be incorporated in the objective function. In order to reduce the computation time, a heuristic algorithm for calculating the starting times of operations is also proposed. The performance of the proposed method is compared with that of the simulated annealing method by which the schedule of the entire machine is successively improved. Numerical results have shown that the proposed method can generate better solutions than the conventional method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 149(1): 44–51, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10364     

A new dynamic security enhancement method via genetic algorithms integrated with neural network based tools

In this paper, a new dynamic security assessment and generation rescheduling method utilizing genetic algorithms (GAs) which are integrated with probabilistic neural networks (PNNs) and adaptive neuro fuzzy inference systems (ANFISs) is proposed for the preventive control of large power systems against transient instabilities. By the proposed approach, PNNs are employed in a feasible manner to calculate the security regions accurately during the assessment and control. The security constrained generation rescheduling is implemented through a GA which optimizes the total fuel cost or the generation shifting during the preventive control. The steady-state solutions of the variables required for the GA are smoothly performed by the use of an ANFIS. The proposed methods are demonstrated on the 17-generator 163-bus Iowa power system and on the 50-generator 145-bus IEEE test system successfully and the effectiveness of the approaches is discussed.     

A rule based comprehensive approach for reconfiguration of electrical distribution network

This paper proposes a rule based comprehensive approach to study distribution network reconfiguration (DNRC). The DNRC model with line power constraints is set up, in which the objective is to minimize the system power loss. In order to get the precise branch current and system power loss, a power summation based radiation distribution network load flow (PSRDNLF) method is applied in the study. The rules that are used to select the optimal reconfiguration of distribution network are formed based on the system operation experiences. The proposed rule based comprehensive approach is implemented in distribution network in Guiyang South Power Supply Bureau. For the purpose of illustrating the proposed approach, two distribution network systems are tested and analyzed in the paper.     

A bi-level probabilistic transmission planning with intermittent generations based on life cycle cost

With the growing development of intermittent renewable energy sources, such as wind and solar, transmission planners are faced with uncertainly varying generations and resultant probabilistic power flow. A bi-level programming model is proposed to coordinate the process of decision making and reliability assessment. Based on the concept of life cycle cost (LCC), its minimization can be defined as the objective function of a transmission planner. This upper level problem needs to be solved jointly with the lower probabilistic optimal power flow problem of minimizing the load shedding in the system. Hence the bi-level problem is transformed into a Mathematical Programming with Equilibrium Constraints (MPEC) with Karush-Kuhn-Tucker conditions. Due to the nonlinearity nature of MPEC, disjunctive inequalities and Generalized Benders Decomposition methods are used to solve this problem. Results of both Garver’s 6-bus test system and a realistic 63-bus system are used to illustrate the rationality and validity of the proposed method.     

A new approach to security-constrained generation scheduling of large-scale power systems with a piecewise linear ramping model

This paper presents a new and efficient approach to determine security-constrained generation scheduling (SCGS) in large-scale power systems, taking into account dispatch, network, and security constraints in pre and post-contingency states. A novel ramp rate limit is also modeled as a piecewise linear function in the SCGS problem to reflect more practical characteristics of the generating units. Benders decomposition is applied to this constrained solution process to obtain an optimal SCGS problem based on mixed-integer nonlinear programming (MINLP). The formulation can be embedded in two stages. First, a MIP is formulated in the master problem to solve a unit commitment (UC) problem. This stage determines appropriate on/off states of the units. The second stage, the subproblem, is formulated as a NLP to solve a security-constrained economic dispatch (SCED) problem. This stage is used to determine the feasibility of the master problem solution. It provides information to formulate the benders cuts that connect both problems. The proposed approach is tested in the IEEE 118-bus system to show its effectiveness. The simulation results are more realistic and feasible, whilst assuring an acceptable computation time.