Long‐Term Generation Scheduling Method with Constrained Numbers of Unit Commitment and Fuel Constraints on Thermal Units

The long‐term generation scheduling in power utilities is aimed at maintaining power supply sufficiency and estimating fuel consumption. Multiperiod constraints, such as the allowable number of unit commitments of thermal units and fuel consumption with a given total amount of fuel, must be taken into appropriate consideration for practical economic scheduling in the long‐term scheduling horizon. Due to the large size and complexity of such scheduling problems, it is difficult to obtain solutions collectively in terms of stability and processing time in operational situations. We propose a new scheduling method which consists of “preparative processing” and “detailed optimization processing.” The former acts to divide the long‐term problem with multiperiod constraints into time units of a week according to quick and simplified scheduling results, and the latter acts to minimize the total operation cost on the basis of those weekly partitioned problems. The processing starts with an optimal scheduling result excluding the aforementioned two multi‐period constraints and proceeds to sequentially resolve the violation of each constraint with quantitative consideration of interrelationships between them. This paper describes the validation and effectiveness of the proposed method through the real world example of Chubu Electric Power Company.     

平抑机组出力波动的发电计划模型及其应用

在编制基于发电成本最小的发电计划时,机组出力曲线经常会出现来回波动的情况。针对这一问题,引入了机组出力变化惩罚因子,并根据不同容量机组的生产特性,分别建立了机组出力变化的线性惩罚模型和分段惩罚模型。采用某省级电力系统算例对所建立的模型进行测试。算例结果表明,模型能有效平抑大中型机组的出力波动,保证出力平稳;在一定程度上可以改善小型机组的出力特性,便于在实际中进行控制;满足电网安全约束,保证安全断面不越限。     

MINIMUM EMISSIONS HYDRO-THERMAL POWER FLOW

ABSTRACT

Minimum NO_x emission optimal power flow scheduling for a hydro-thermal power system is considered. Computational results using Newton's method and Powell's penalty function approach arc obtained for the 57 bus and 118 bus hypothesized IEEE hydro-thermal power systems. Based on a comparison with the minimum cost solution, it is concluded that regardless of which scheduling strategy is used, increasing the hydro generation results in decreasing both emissions and thermal fuel costs. The results obtained indicate that system N O _x emissions are influenced by bus voltage lower limits, reactive power generation and the hydro unit water conversion factor.     

电力市场环境下负荷分配问题的罚函数法

电力市场环境下的负荷分配问题由于报价曲线的分段特性使问题更加难以解决。针对报价曲线的分段特性进行处理，将每台机组出力等值为几台机组出力和的形式，并增加机组功率平衡约束，以罚函数方法和SQP方法解决后来形成的0，1变量整数规划问题及其线性的连续变量规划问题，本算法经过一个小系统检验证明非常有效。     

Group search optimization for economic fuel scheduling

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.     

计及风光不确定性的虚拟电厂多目标随机调度优化模型

为缓解风电和光伏发电不确定性对虚拟电厂稳定运行的影响,引入鲁棒随机优化理论,建立了计及不确定性和需求响应的虚拟电厂随机调度优化模型。首先,风力发电、光伏发电、燃气轮机发电,以及储能系统和需求响应集成为虚拟电厂,然后最大化虚拟电厂运营收益、最小化系统运行成本和弃能成本被作为目标函数,建立虚拟电厂调度优化模型。再应用鲁棒随机优化理论来转换光伏发电以及风力发电不确定性变量的约束条件,建立了虚拟电厂随机调度模型。最后,选择中国国电云南分布式电源示范工程为实例分析对象。分析结果显示：所提模型能够降低系统运行成本,双重鲁棒系数的引入能够为不同风险态度决策者提供灵活的虚拟电厂调度决策工具,协助应对风电和光伏发电的随机特性。储能系统能够借助自身充放电特性,替代燃气轮机发电机组为风电和光伏发电提供备用服务,促进风电和光伏发电并网。将需求响应纳入虚拟电厂能够实现发电侧与用电侧联动优化目标,平缓化用电负荷曲线,系统整体运营效益达到最佳。     

Unit commitment by genetic algorithm with specialized search operators

An approach for solving the unit commitment problem based on genetic algorithm with new search operators is presented. These operators, specific to the problem, are mutation with a probability of bit change depending on load demand, production and start-up costs of the generating units and transposition. The method incorporates time-dependent start-up costs, demand and reserve constraints, minimum up and down time constraints and units power generation limits. Repair algorithms or penalty factors in the objective function are applied to the infeasible solutions. Numerical results showed an improvement in the solution cost compared to the results obtained from genetic algorithm with standard operators and other techniques.     

市场条件下的机组启动费问题

机组在启动过程中需要耗费高额的启动费，这部分启动费是应由电网公司在制定日计划时予以考虑，还是应由发电公司在报价时自行考虑？针对这一问题，作者以对比机组启动费用与发电效益为出发点，提出了根据机组的启机效率，使用寿命以及国家现阶段的政策导向等多方面因素，由电力市场调度交易中心和发电公司以及市场监管部门兼顾各方利益，共同核定机组的含有技术和经济双重性的参数－“最小经济运行小时数”，并由发电公司依据机组自身特性和市场状况，自行处理启动费；而调度交易中心在制定竞价上网发电计划及结算时则不再单独考虑和支付机组启停费用的观点：文中同时还提出了分摊机组启动费的两种方法－平均分摊法和负荷比例分摊法，并对比了这两种分摊方法对双方经济性的影响。     

Optimal generation scheduling with ramping costs

In this paper, a decomposition method is proposed which relates the unit ramping process to the cost of fatigue effect in the generation scheduling of thermal systems. The objective of this optimization problem is to minimize the system operation cost, which includes the fuel cost for generating the required electrical energy and starting up decommitted units, as well as the rotor depreciation during ramping processes, such as starting up, shutting down, loading, and unloading. According to the unit fatigue index curves provided by generator manufacturers, fixed unit ramping-rate limits, which have been used by previous studies, do not reflect the physical changes of generator rotors during the ramping processes due to the fatigue effect. By introducing ramping costs, the unit on/offstates can be determined more economically by the proposed method. The Lagrangian relaxation method is proposed for unit commitment and economic dispatch, in which the original problem is decomposed into several subproblems corresponding to the optimization process of individual units. The network model is employed to represent the dynamic process of searching for the optimal commitment and generation schedules of a unit over the entire study time span. The experimental results for a practical system demonstrate the effectiveness of the proposed approach in optimizing the power system generation schedule     

考虑环保成本和风电接入影响的动态经济调度模型

风电具有动态随机性,传统的经济调度模型已不再适用。考虑机组的环境效益,引用了环境惩罚成本;基于风电的随机性及其给系统安全造成的影响,引入利润备用容量惩罚成本,应用随机规划理论构建了含风电机组的动态经济调度模型。在求解模型时,采用蒙特卡罗模拟和遗传算法的混合智能算法,以蒙特卡罗模拟的随机变量模拟结果作为遗传算法的输入参数。以IEEE30系统为例,对传统的经济调度模型和建立的调度模型进行了比较分析,验证了该模型的合理性与有效性。     

A Study on Influence of Ramp Event of Aggregated Power Output of Photovoltaic Power Generation on Electric Power System Frequency

This paper examines the influence of aggregated power output fluctuation of photovoltaic power generation system (PVS) on the power system frequency focusing on ramp events of aggregated PVS power output. A numerical simulation model of economic load dispatching control (EDC) and load frequency control (LFC) is used together with a PVS power output forecasting model and a unit commitment (UC) scheduling model developed in our preceding study. As a result, in the case of ramp event with long duration and high ramp rate, the frequency violation occurs when the power output of controllable generators with high load‐following capability reaches to upper/lower limit even if the power output of low load‐following capability generators is still available. If the load dispatching scheme is tentatively changed from the conventional EDC using an equal incremental fuel cost rule to, for example, a dispatching policy based on the capacity without the consideration of fuel cost, the aggregated load‐following capability can be kept, avoiding the frequency violation.     

A study of a unit commitment problem with transmission constraints

Unit commitment problem is an optimization problem to determine the start‐up and shut‐down schedule of thermal units while satisfying various constraints, for example, generation‐demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently. Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start‐up/shut‐down time and demand‐generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained. The proposed method has been applied to the IEEE 118‐bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10187     

计及含氢储能与电价型需求响应的能量枢纽日前经济调度

为了解耦冷热电联供(combined cool,heat and power system,CCHP)机组“以热定电”的运行约束,提高风电消纳能力,降低社会碳排放,提出了计及含氢储能与电价型需求响应的能量枢纽日前经济调度模型。源侧利用冷、热、电、氢4种储能装置,打破CCHP机组热电耦合约束;荷侧引入电价型需求响应改变用户用电行为,通过优化各机组出力与电负荷曲线,增加风机出力。该模型以系统日运行成本最低为目标,引入弃风惩罚成本增加风电消纳,综合功率平衡等约束,调用Gurobi求解器进行优化求解。对不同场景下能量枢纽的优化结果进行分析,并计算燃油汽车的碳排放量以量化氢燃料电池汽车节约的社会碳排放。结果表明:在电价型需求响应策略下,考虑CCHP与含氢储能的能量枢纽系统在增加风电消纳能力的同时降低了系统的日运行总成本,减少了社会碳排放。     

Cost/environmentally compromised dispatch for cascaded hydrothermal system using artificial bee colony algorithm

This paper evaluates the robustness of the artificial bee colony (ABC) algorithm while allocating optimal power generation in a hydrothermal power system at the level of minimum fuel cost and minimum pollutant emission impacts on the environment subjected to physical and technical constraints. The hydrothermal scheduling (HTS) is devised in a bi‐objective framework so as to optimize both objectives of fuel cost and emission release, individually and simultaneously subjected to a verity of intricate equality and inequality constraints. Initially, all feasible solutions are obtained through random search, and then the ABC algorithm is used for the exploration and exploitation processes together in the search space, thereby discovering the optimal hourly schedule of power generation in the hydrothermal system. Meanwhile, a dependent hydro‐discharge computation handles the equality constraints; especially, the reservoir end volume and slack thermal generating unit for each sub‐interval handle the power balance equality constraint. The performance of the proposed approach is illustrated on a multi‐chain interconnected hydrothermal power system with due consideration of the water transport delay between connected reservoirs and transmission loss of system load. The results obtained from the proposed technique are compared with those of other techniques. The results demonstrate that the ABC algorithm is feasible and efficient for solving the HTS problem. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于动态场景集和需求响应的二阶段随机规划调度模型

风电及负荷场景集作为随机规划调度的输入,对决策结果影响大。为使场景集刻画出风电及负荷的波动性,采用考虑随机变量相关性的动态场景法生成风电、负荷及净负荷的场景集,并从与调度接轨的角度提出相应的指标对场景质量进行评价。建立了一种基于here-and-now和waitand-see(HN-WS)的二阶段随机规划调度模型。该模型包含了日前与实时的关联性,决策过程融入了对实时场景已实现情况下的考虑,优化目标涵盖了日前阶段的燃料成本及实时阶段的平衡矫正期望成本。此外,为减少弃风,在调度模型中引入了激励型需求响应,与火电机组的备用容量进行协同优化。最后,采用爱尔兰电网的风电数据、Elia电网的负荷数据及改进的IEEE-118节点系统验证了所提调度模型的有效性。     

抽水蓄能-火电调峰优化对污染物处理成本影响分析

受经济下行影响,火电平均负荷率不断刷低,调峰能耗随之陡增。对抽水蓄能与火电机组联合调峰对污染物处理成本影响定性分析,建立了考虑污染物处理成本的抽水蓄能与火电机组联合调峰优化模型,依据混合整数规划原理,对仿真计算模型进行了求解。结果表明：一个日调峰周期内,抽水蓄能与火电机组两班制联合调峰运行可有效降低脱硝、脱硫、烟尘处理成本以及碳排放成本和发电总成本,与低负荷调峰方式相比,可有效解决因脱硝系统入口温度过低而退出运行的问题,故对其成本影响更加明显,证明了在充分考虑污染物处理成本的前提下,抽水蓄能与火电两班制联合调峰方式可有效解决当前火电机组调峰能耗较高的问题,验证了所建模型及方法的合理性和实用性以及区域系统调峰期污染物超低排放的可达性。     

A Two‐Stage Stochastic Mixed‐Integer Programming Approach to the Smart House Scheduling Problem

A “smart house” is a highly energy‐optimized house equipped with photovoltaic (PV) systems, electric battery systems, fuel cell (FC) cogeneration systems, electric vehicles (EVs), and so on. Smart houses are attracting much attention recently because of their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC cogeneration system, and an EV. We formulate the problem as a mixed‐integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand, and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real‐life demands and PV power generation to show the effectiveness of our method. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 48–58, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22336     

Hydro unit start-up costs and their impact on the short termscheduling strategies of Swedish power producers

This paper is about start-up costs of hydro power units. In the introduction we address three questions about start-up of hydro power units: what causes the costs in the start-up? How much does a start-up cost? How do start-ups effect the short-term scheduling strategies of power producers in Sweden? In order to answer these questions, we have interviewed employees working with generation planning at the eight largest power producers in Sweden. We found five aspects causing start-up costs: loss of water during maintenance; wear and tear of the windings due to temperature changes during the start-up; wear and tear of mechanical equipment during the start-up; malfunctions in the control equipment during the start-up; and loss of water during the start up. The aspects causing the largest costs are maintenance due to the wear and tear and the unavailability and personnel costs due to malfunctions in the control equipment. The cost of lost water is usually small. Our conclusions are the following: start up cost will depend on the nominal power of the unit and the unit model; the majority of the power producers consider start-ups in their planning; there is a need for better knowledge about start-up cost; and there is a need for planning software which considers start-up costs of hydro units     

基于两阶段优化的风储联合发电系统日前发电计划模式

考虑风电日波动性对常规机组启停的影响,为准确刻画系统运行经济性与风电消纳能力的关系,设计了风电最大出力模式、经济运行模式和运行成本控制模式3种风储联合发电系统日前发电计划模式,提出了不同模式的两阶段优化模型及快速求解算法。第1阶段通过储能系统和常规机组协调调度,实现经济性最优或弃风量最小的目标,第2阶段优化则按照经济调度和"三公"调度的原则分配常规机组和风电场的有功出力。提出快速算法削减了模型的寻优空间,大幅提高了优化模型的求解效率。基于IEEE 30节点系统进行仿真分析,验证了所述模型和算法的优越性。     

Generation scheduling in smart grid environment using global best artificial bee colony algorithm

Generation scheduling is an important concern of the current power system which is suffering from many obstacles of limited generation resources, grown energy demand and fuel price, inconsistent load demand and fluctuations of available wind power in case of the thermal–wind system. Smart grid system has a great potential of tumbling existing power system difficulties with intelligent infrastructure and computation technologies. Three different distributed energy resources, namely, distributed generation, demand response and gridable vehicles are used in this paper to overcome the power system hitches. The classical generation scheduling is solved with insertion of the cost of demand response and the cost model pertaining to underestimation and overestimation of fluctuating wind power. The modified optimization problem is solved using an efficient Global best artificial bee colony algorithm for 10 generating units test system. Generation scheduling in the smart grid environment yields a significant reduction in the total cost.