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.     

Optimal power flow with expected security costs

This paper discusses the expected-security-cost optimal power flow (ESCOPF) problem. The objective of the problem is to minimize the expected total cost of system operation subject to power system constraints and expected security costs. The probabilities of operating in the pre-contingency operating state and the probabilities of operating in all possible post-contingency states are considered. In addition, the ESCOPF problem includes the cost of interrupting customer load and the cost of generator ramping, which may be necessary when a contingency occurs. The solution to the ESCOPF problem gives information regarding the marginal value of spinning reserve and the marginal value of interruptible load. A graphical tool is used to view the problem solution.     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

基于电量共享的梯级水光蓄联合发电系统优化调度策略

为对偏远地区的含有抽水蓄能、梯级水电、光伏电站联合运行的多能发电系统进行互补式经济优化,搭建多乡镇级用电体之间的日前电量共享协调优化模型,通过区域运营商对构成乡镇联合体的各用电体进行协调管控,实现用电计划的经济高效制定.通过调用抽水蓄能进一步平滑水光出力波动,对乡镇级用电体、区域电网与主网之间的能量流动进行优化,并按参...     

Hydroelectric generation scheduling with an effective differentialdynamic programming algorithm

An effective multiplier method-based differential dynamic programming (DDP) algorithm for solving the hydroelectric generation scheduling problem (HSP) is presented. The algorithm is developed for solving a class of constrained dynamic optimization problems. It relaxes all constraints but the system dynamics by the multiplier method and adopts the DDP solution technique to solve the resultant unconstrained dynamic optimization problem. The authors formulate the HSP of the Taiwan power system and apply the algorithm to it. Results demonstrate the efficiency and optimality of the algorithm for this application. Computational results indicate that the growth of the algorithm's run time with respect to the problem size is moderate. CPU times of the testing cases are well within the Taiwan Power Company's desirable performance; less than 30 minutes on a VAX/780 mini-computer for a one-week scheduling     

Security-constrained optimal generation scheduling for GENCOs

This paper presents an approach for maximizing a GENCO's profit in a constrained power market. The proposed approach considers the Interior Point Method (IPM) and Benders decomposition for solving the security-constrained optimal generation scheduling (SC-GS) problem. The master problem represents the economic dispatch problem for a GENCO which intends to optimize its profit. The formulation of the master problem does not bear any transmission network constraints. The subproblem will be used by the same GENCO to check the viability of its proposed bidding strategy in the presence of transmission network constraints. In this case if the subproblem does not yield a certain level of financial return for the GENCO or if the subproblem results in an infeasible solution of the GENCO's proposed bidding strategy, the GENCO will modify its proposed solution according to the Benders cuts that stem out of the subproblem. The study shows a more flexible scheduling paradigm for a GENCO in a competitive arena. The proposed approach proves practical for modeling the impact of transmission congestion on a GENCO's expected profit in a competitive environment.     

Allocation of fixed costs in distribution networks with distributed generation

In this paper, we propose a method for the allocation of fixed (capital and nonvariable operation and maintenance) costs at the medium voltage (MV) distribution level. The method is derived from the philosophy behind the widely used MW-mile methodology for transmission networks that bases fixed cost allocations on the "extent of use" that is derived from load flows. We calculate the "extent of use" by multiplying the total consumption or generation at a busbar by the marginal current variations, or power to current distribution factors (PIDFs) that an increment of active and reactive power consumed, or generated in the case of distributed generation, at each busbar, produces in each circuit. These PIDFs are analogous to power transfer distribution factors (PTDFs). Unlike traditional tariff designs that average fixed costs on a per kWh basis across all customers, the proposed method provides more cost-reflective price signals and helps eliminate possible cross-subsidies that deter profitable (in the case of competition) or cost-effective (in the case of a fully regulated industry) deployment of DG by directly accounting for use and location in the allocation of fixed costs. An application of this method for a rural radial distribution network is presented.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Optimal scheduling of hydraulically coupled power plants with runoff prediction

This paper discusses an optimal short-range scheduling of multireservoir hydrothermal power systems considering runoff prediction. Using Prasad's rainfall-runoff model and the Kalman filter-based, second-order filtering technique, the hour-by-hour characteristic is estimated for a given short-range forecasted rainfall pattern. Unlike the usual hydroscheduling problem in which a single-reservoir operating condition is specified at the end of the scheduling period, various end point conditions are examined to deal with the predicted runoff characteristic and to avoid possible water spillages from reservoirs. Dynamic Programming Successive Approximations are applied successfully to obtain optimum operating strategies for many end point conditions at a time. A rapid convergence characteristic coupled with a simple computation algorithm makes the proposed method one of the attractive approaches for scheduling of complicated multireservoir hydrothermal systems considering runoff prediction.     

面向灵活爬坡服务的高比例新能源电力系统可调节资源优化调度模型

随着高比例新能源接入电网,新能源出力和负荷的波动性及不确定性对系统的灵活爬坡能力提出更高的要求。提出了一种面向灵活爬坡服务的针对高比例新能源电力系统中常规发电机组、储能、柔性负荷等可调节资源的优化调度模型。首先,提出一种考虑日前预测净负荷不确定性的灵活爬坡需求估计方法,根据爬坡起始和结束时刻净负荷及其安全裕度,量化估计电力系统灵活爬坡需求。其次,基于生成的净负荷不确定性场景,提出了考虑系统灵活爬坡需求的基于两阶段混合整数线性规划的可调节资源优化调度模型,从日前-实时两个阶段实现可调节资源的优化调度。最后,基于IEEE-RTS-24节点系统,在4种典型方案场景下对比验证所提优化调度模型。结果表明所提模型能有效提升系统灵活性,降低系统运行成本。     

Thermal power generation scheduling by simulated competition

This paper presents a new method for solving the unit commitment problem by simulation of a competitive market where power is traded through a power exchange (PX). Procedures for bidding and market clearing are described. The market clearing process handles the spinning reserve requirements and power balance simultaneously. The method is used on a standard unit commitment problem with minimum up/down times, start-up costs and spinning reserve requirement taken into account. Comparisons with solutions provided by Lagrangian relaxation, genetic algorithms and Chao-an Li's unit decommitment procedure demonstrate the potential benefits of this new method. The motivation for this work was to design a competitive electricity market suitable for thermal generation scheduling. However, performance in simulations of the proposed market has been so good that it is presented here as a solving technique for the unit commitment problem     

Electricity generation scheduling with large-scale wind farms using particle swarm optimization

Large-scale integration of wind power in the electricity system presents some planning and operational difficulties, which are mainly due to the intermittent and difficult nature of wind prediction process. Therefore it is considered as an unreliable energy source.     

Computation of power generation system production costs

This paper considers the problem of computing the expected value of generating system production costs. First, using a stochastic model for the frequency and duration of generation outage, it provides an efficient Monte Carlo procedure for the evaluation of the Baleriaux formula. Secondly, it points out the need for using a stochastic model for estimating production costs with time-dependent constraints via Monte Carlo. Lastly, it considers a model where the chronological load also has a random component and obtains an expression for expected production costs for this situation     

A new method for unit commitment with ramping constraints

Unit commitment with ramping constraints is a very difficult problem with significant economic impact. A new method is developed in this paper for scheduling units with ramping constraints within Lagrangian relaxation framework based on a novel formulation of the discrete states and the integrated applications of standard dynamic programming for determining the optimal discrete states across hours, and constructive dynamic programming for determining optimal generation levels. A section of consecutive running or idle hours is considered as a commitment state. A constructive dynamic programming (CDP) method is modified to determine the optimal generation levels of a commitment state without discretizing generation levels. The cost-to-go functions, required only for a few corner points with a few continuous state transitions at a particular hour, are constructed in the backward sweep. The optimal generation levels can be obtained in the forward sweep. The optimal commitment states across the scheduling horizon can then be obtained by standard dynamic programming. Numerical testing results show that this method is efficient and the optimal commitment and generation levels are obtained in a systematic way without discretizing or relaxing generation levels.     

含抽水蓄能电站的可再生能源电网优化调度策略

由于可再生能源电网发电功率的不确定性十分突出,以功率平衡为首要任务的电网调度控制必须采取有针对性的应对措施。在电网储纳运行机制的基础上,建立含抽水蓄能电站的日前调度及实时调度的数学模型。将电网备用分为波动性备用(即调峰备用)和不确定性备用:平衡波动性功率的备用容量主要由常规电源承担,平衡预测误差的不确定性备用主要由抽水蓄能电站承担,2类电源分工互济实现电网功率平衡。提出一个风电上网功率稳定性评价指标,用于衡量不同调度策略补偿失衡功率的优劣。算例分析验证了所提调度策略是可行的。     

Optimal security-constrained power scheduling by Benders decomposition

This paper presents a Benders decomposition approach to determine the optimal day-ahead power scheduling in a pool-organized power system, taking into account dispatch, network and security constraints. The study model considers the daily market and the technical constraints resolution as two different and consecutive processes. The daily market is solved in a first stage subject to economical criteria exclusively and then, the constraints solution algorithm is applied to this initial dispatch through the redispatching method. The Benders partitioning algorithm is applied to this constraints solution process to obtain an optimal secure power scheduling.     

Optimal scheduling and operation of battery/wind generation system in response to real‐time market prices

This paper addresses the scheduling and operation problem of a combined battery and wind generation system (BWGS) in real‐time market. The proposed operation scheme consists of two control mechanisms: (i) offline scheduling according to the estimation of the market prices and the stochastic information of wind generation, and (ii) online dispatching based on the updated system state in real time. The analysis in this paper is different from the previous studies by taking into account the expense of battery energy storage under a newly defined term: cost of lifetime losses (CLLs). Thus the optimal scheduling and operation of BWGS not only gives maximum profit to the owner but also avoids the abusive operation of the battery. The case study provides a comparison of the proposed operation scheme with other approaches. It is shown that the proposed scheme considering the CLLs can significantly change the optimal charging/discharging of battery energy storage, and the increase in profit obtained in this case is about 9%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Daily generation scheduling optimization with transmissionconstraints: a new class of algorithms

The authors describe a novel class of algorithm dealing with the daily generation scheduling (DGS) problem. These algorithms have been designed by adding artificial constraints to the original optimization problem; handling these artificial constraints by using a dual approach; using an augmented Lagrangian technique rather than a standard Lagrangian relaxation technique; and applying the auxiliary problem principle which can cope with the nonseparable terms introduced by the augmented Lagrangian. To deal with the DGS optimization problem these algorithms are shown to be more effective than classical ones. They are well suited to solve this DGS problem taking into account transmission constraints     

Optimal generation dispatch with renewable energy embedded using multiple objectives

Traditional economic dispatch focuses mainly on minimizing the total operation cost of the power system. With the appearance of energy crisis and environmental pollution becoming a public issue, environmental effect of generator should be taken into consideration through the dispatch process. In this paper a multi-objective dispatch problem considering the integration of wind power is solved whose objectives include the generation cost, the reserve capacity and the environmental emission. To compromise different objectives, a coordination degree combined with a satisfaction degree are introduced in order to transform the multi-objective dispatch problem into a single-objective one, i.e., the optimal generation dispatch (OGD) model. Then the OGD is solved by a particle swarm optimization algorithm on an IEEE 30-bus system, with wind power generation and coal-fired generation embedded. The simulation results show that better results in terms of all the three objectives can be obtained from the OGD model, in comparison with two other multi-objective dispatch models. The simulation results also show that the integration of wind power will cause the increase of both the generation cost and the reserve capacity but will decrease the environmental emission.     

检修周期对发电成本经济性影响的分析

针对电厂机组采用不同检修周期对发电成本的影响进行了研究.通过对某机组的检修方案进行优化计算分析后发现,适当、合理地延长机组大修周期,可减少检修成本,降低发电成本.该研究成果可以在国产600 MW燃煤机组的检修工作中推广和应用,也可以为其他大型机组提供参考,对提高电力企业自身的市场竞争能力有实际指导作用.