An investigation of the effect of changes of planning criteria on power system expansion planning with a case study of the Jordanian power system

Many factors contribute to the planning process of power systems. In the context of expansion planning, focus is paid to selection criteria that enable the optimization of related factors that will result in the best performance. This is described as meeting demand whilst reducing costs and maintaining minimal risk in operation. In this paper, different criteria used in the planning of power system expansion studies are investigated with the objective of identifying their impact on the expansion plan. The results of these criteria on the expansion study of the Jordanian power system are presented. Results show good correspondence to the actual adopted solutions. The spinning reserve is the most influential planning criterion on the overall system expansion cost. This is followed by the peak load changes, and the forced outage rate of the candidate units used for capacity additions to meet future expected demand. Finally, the loss of load expectation and cost of energy not served have the least effect on the overall system expansion cost. These results highlight the importance to be placed on performing sensitivity analyses to determine the most cost effective and acceptable expansion plan of the electric power system. There is a need to continually update the planning criteria to cater for changes and developments in the power system and the economic situation. Finally, the methodology of this study can be generalized to other power systems.     

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning. In this approach, the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning. A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle. In this study, the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model, thus improving the efficiency and accuracy of the solution. The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible. Moreover, they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.     

An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants

This paper presents an interactive fuzzy satisfying method based on Hybrid Modified Honey Bee Mating Optimization (HMHBMO). Its purpose is to solve the Multi-objective Optimal Operation Management (MOOM) problem which can be affected by Fuel cell power plants (FCPPs). Minimizing total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources and deviation of bus voltages are the objective functions in this method. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy targets for each of the objective functions. Through the interaction with the DM, the fuzzy goals are quantified by eliciting the corresponding membership functions. Considering the current solution, the DM updates the reference membership values until the best solution can be obtain. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Therefore, evolutionary methods can be used to solve this problem since they are independence of objective function’s type and constraints. Recently researchers have presented a new evolutionary method called Honey Bee Mating Optimizations (HBMO) algorithm. Original HBMO often converges to local optima and this is a disadvantage of this method. In order to avoid this shortcoming we propose a new method. This method improves the mating process and also combines the modified HBMO with a Chaotic Local Search (CLS). Numerical results on a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

燃机电厂9F型机组热电负荷优化分配研究

电厂热电负荷优化分配是指在全厂总调度负荷下,根据各机组的热力性能确定各机组应承担的热电负荷,使得全厂效益最大或能耗最小的一种最优化问题.不同于燃煤热电厂,燃机电厂9F型机组由于设计为燃气轮机加蒸汽轮机的组合方式运行,因此在联合循环热力性能模型建立上较为复杂.提出了将余热锅炉新蒸汽参数作为中间变量,建立了机组天然气燃料消耗与电负荷、热负荷之间的关系模型,确定了优化计算的目标函数和边界约束条件,并采用非线性规划方法求解.模拟与实际运行结果均表明,该优化分配方法能有效降低燃机电厂燃料消耗水平,可以为同类型燃机电厂热电负荷优化分配提供参考.     

Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant

The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server.     

垃圾焚烧热电联产蒸汽参数选择

针对垃圾焚烧热电联产时,采用中温中压及中温次高压蒸汽参数对全厂投资及经济性的影响进行研究。以日处理规模为600 t·d^-1的生活垃圾焚烧设施作为研究对象,从主机设备参数、主机设备投资额、经济指标、营业收入及投资回收期等5个方面进行分析。研究结果表明,采用中温次高压参数时,垃圾焚烧设施热电联产全厂热效率较采用中温中压参数时提高1.7%,热电联产时全年总收入较采用中温中压参数时提高11.5%,且经济性更好,静态投资回收期约为4.73年。     

Basic chemically recuperated gas turbines—power plant optimization and thermodynamics second law analysis

One of the proposals to increase the performance of the gas turbines is to improve chemical recuperated cycle. In this cycle, the heat in the turbine exhaust gases is used to heat and modify the chemical characteristics of the fuel. One mixture of natural gas and steam receives heat from the exhaust turbine gases; the mixture components react among themselves producing hot synthesis gas. In this work, an analysis and nonlinear optimization of the cycle were made in order to investigate the temperature and pressure influence on the global cycle performance. The chemical composition in the reformer was assumed according to chemical equilibrium equations, which presents good agreement with data from literature. The mixture of hot gases was treated like ideal gases. The maximum net profit was achieved and a thermodynamic second law analysis was made in order to detect the greatest sources of irreversibility.     

Online optimal power distribution between units of a hydro power plant

The paper presents a novel methodology for the operation of those hydro power plants provided with a single penstock by the optimal distribution of the dispatched power among its available generating units, aiming at the maximum efficiency of the whole power plant energy conversion. While previous optimization methods made use of off-line static curve and parameters or expensive flow meters, the proposed method is on-line in nature and uses a single pressure meter at the end of the power plant penstock. The method was applied to a power plant and has resulted in a higher efficiency operation under several conditions.     

An interval-parameter minimax regret programming approach for power management systems planning under uncertainty

In this study, an interval-parameter minimax regret programming (IMRP) method is developed for supporting the power management systems planning under uncertainty. This method incorporates techniques of interval linear programming (ILP) and minimax regret programming (MRP) within a general optimization framework. The developed IMRP could deal with multiple policy scenarios associated with different costs and risk levels without making any assumptions. It can analyze various economic consequences for all of the possible scenarios through minimizing the maximum cost regret values. The IMRP approach can successfully reduce the worst regrets incurred under the pre-regulated targets. Moreover, it can deal with uncertainties and complexities expressed as interval numbers. A case study of power management systems planning is then presented for demonstrating applicability of the developed approach. The results indicate that many decision alternatives are generated based on the interval solutions which can help decision makers identify the desired system designs with minimized economic cost loss and system-failure risk under uncertainty. The trade-off between system regret and security-failure risk can be handled effectively through this method. And the generated solutions can also provide multiple electric power generation patterns and capacity expansion schemes under the optimal strategy obtained through the developed IMRP method. It is indicated that the proposed method is efficient to provide the decision makers with available plans in actual operation of power management systems.     

Solar multiple optimization for a solar-only thermal power plant, using oil as heat transfer fluid in the parabolic trough collectors

Usual size of parabolic trough solar thermal plants being built at present is approximately 50 MW_e. Most of these plants do not have a thermal storage system for maintaining the power block performance at nominal conditions during long non-insolation periods. Because of that, a proper solar field size, with respect to the electric nominal power, is a fundamental choice. A too large field will be partially useless under high solar irradiance values whereas a small field will mainly make the power block to work at part-load conditions.This paper presents an economic optimization of the solar multiple for a solar-only parabolic trough plant, using neither hybridization nor thermal storage. Five parabolic trough plants have been considered, with the same parameters in the power block but different solar field sizes. Thermal performance for each solar power plant has been featured, both at nominal and part-load conditions. This characterization has been applied to perform a simulation in order to calculate the annual electricity produced by each of these plants. Once annual electric energy generation is known, levelized cost of energy (LCOE) for each plant is calculated, yielding a minimum LCOE value for a certain solar multiple value within the range considered.     

A hybrid model for the optimum integration of renewable technologies in power generation systems

The main purpose of this work is to assess the unavoidable increase in the cost of electricity of a generation system by the integration of the necessary renewable energy sources for power generation (RES-E) technologies in order for the European Union Member States to achieve their national RES energy target. The optimization model developed uses a genetic algorithm (GA) technique for the calculation of both the additional cost of electricity due to the penetration of RES-E technologies as well as the required RES-E levy in the electricity bills in order to fund this RES-E penetration. Also, the procedure enables the estimation of the optimum feed-in-tariff to be offered to future RES-E systems. Also, the overall cost increase in the electricity sector for the promotion of RES-E technologies, for the period 2010–2020, is analyzed taking into account factors, such as, the fuel avoidance cost, the carbon dioxide emissions avoidance cost, the conventional power system increased operation cost, etc. The overall results indicate that in the case of RES-E investments with internal rate of return (IRR) of 10% the cost of integration is higher, compared to RES-E investments with no profit, (i.e., IRR at 0%) by 0.3–0.5 €c/kWh (in real prices), depending on the RES-E penetration level.     

含分布式发电并网虚拟发电厂鲁棒优化经济调度方法

文章基于鲁棒优化理论建立了虚拟发电厂最优经济调度模型。首先以虚拟发电厂发电净收益最大为目标函数,计及出力计划约束、机组运行约束、机组启停约束、储能运行约束等必要约束条件建立系统优化运行模型;然后考虑风光出力区间不确定性,以风光出力为自然决策者,以虚拟发电厂为系统决策者,分别制定博弈策略和支付,建立虚拟发电厂最优经济调度鲁棒优化模型,并对其Nash均衡点进行分析;基于两阶段松弛法将所建立的鲁棒优化模型转化为有限可解的步骤;最后通过一个算例验证了所建立的模型在制定虚拟发电厂运行计划方面的经济优势。     

Dual-fluid-hybrid power plant co-powered by low-temperature geothermal water

Three variants of power plants fuelled or co-fuelled by geothermal water have been assessed, with the aim of making the best use of the energy contained in a stream of 80–120 °C geothermal water. Heat-flow calculations for three power plant types, namely an Organic Rankine Cycle (ORC) power plant, a dual-fluid-hybrid power plant and a single-fluid hybrid-fuelled power plant, are presented. The analysis shows the thermodynamic benefits, in terms of the extent of using the thermal energy of low-temperature geothermal water, that arise from utilizing hybrid and dual-fluid-hybrid power plants rather than ORC power plants. The dual-fluid plant optimizes the use of the geothermal water, but the hybrid plant makes the best overall utilization of the energy compared to separate ORC and fuel-fired plants.     

Impact of heat,power and hydrogen generation on optimal placement and operation of fuel cell power plants

In this paper, a stochastic model is proposed for planning the location and operation of Fuel Cell Power Plants (FCPPs) as Combined Heat, power, and Hydrogen (CHPH) units. Total cost, emissions of FCPPs and substation, and voltage deviation are the objective functions to be minimized. Location and operation of FCPPs as CHPH are considered in this paper while their investment cost is not taken into account. In the proposed model, indeterminacy refers to electrical and thermal loads forecasting, pressure of oxygen and hydrogen, and the nominal temperature of FCPPs. In this method, scenarios are produced using roulette wheel mechanism and probability distribution function of input random variables. Using this method, the probabilistic problem is considered to be distributed as some scenarios and consequently probabilistic problem is considered as combination of some deterministic problems. Considering the nature of objective functions, the problem of locating and operating FCPPs as CHPH is considered as a mixed integer nonlinear problem. A Self Adaptive Charged System Search (SACSS) algorithm is employed for determining the best Pareto optimal set. Furthermore, a set of non-dominated solutions is saved in repository during simulation procedure. A 69-bus distributed system is used for verifying the beneficiary proposed method.     

Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis

Electricity is essential in the economic development of a nation. Due to the rapid growth of economy and industrial development in Taiwan, the demand for use of electricity has increased rapidly. This study evaluates the power-generation efficiency of major thermal power plants in Taiwan during 2004–2006 using the data envelopment analysis (DEA) approach. A stability test was conducted to verify the stability of the DEA model. According to the results, all power plants studied achieved acceptable overall operational efficiencies during 2004–2006, and the combined cycle power plants were the most efficient among all plants. The most important variable in this DEA model is the “heating value of total fuels”. Findings from this study can be beneficial in improving some of the existing power plants and for more efficient operational strategies and related policy-making for future power plants in Taiwan.     

Risk‐based optimal energy management of virtual power plant with uncertainties considering responsive loads

This paper proposes a stochastic scheduling model to determine optimal operation of generation and storage units of a virtual power plant (VPP) for participating in a joint energy and regulation service (RS) market under uncertainty. Beside electricity, the VPP provides required RSs according to the probability of delivery request in the electricity market. A new model for providing RS is introduced in which the dispatchable generation units are financially compensated with their readiness declarations and will be charged/paid for their real‐time down/up regulations. Besides, the VPP sets up incentive price‐quantity curves to benefit from the potential of demand side management in both energy and RS market. Within the model presented here, the VPP consists of two types of generation units: wind turbine and standby diesel generator; the latter is modeled by considering CO₂‐emission penalty costs. The given uncertainties are divided into two parts. Firstly, the uncertainties from the energy market price are simulated using information gap decision theory to evaluate the risk‐based resource scheduling for both risk‐taker and risk‐averse VPP. Other uncertainties affecting decision making such as wind turbine generation, load, regulation up/down calling probabilities, and regulation market prices are modeled via scenario trees. Three typical case studies are implemented to validate the performance and effectiveness of the proposed scheduling approach.     

A model for optimal sizing of solar thermal hydroelectric power plant

This paper presents the model for optimal sizing of a Solar Thermal (ST) power plant with parabolic collectors, which operates with Pump Storage Hydroelectric (PSH), all for the purpose of providing full energy independence of an isolated consumer. The sustainability of such system is based exclusively on solar energy input (without hybridization with any fossil fuel), as a renewable and pure energy resource, and the use of hydro energy, due to the possibility of its continuous production of energy. The feasibility and characteristics of the ST-PSH power plant were tested on power supply of the Island of Vis in Croatia, and the results show that the proposed model describes the operation of the power plant very well. For average solar irradiation of about 1500 kW h/m²/a, precipitation 644 mm/a, evaporation 1444 mm/a, volume of PSH upper reservoir of 20 h m³, electric energy consumption of 18 GV A h/a and reserve in the system for 3-4 months, the obtained power of the ST power plant is 22 MW, which can produce unit value of the annual thermal energy of 459 kW h/m²/a and electric energy of 160 kW h/m²/a, while the total collector aperture in the observed case is about 16 ha. These results show that ST-PSH plants can be successfully applied on locations with relatively low irradiation, wherein the key element that ensures continuous production of energy is precisely the PSH technology that can in the best way, in economic-technical, and especially in ecological sense, balance the relatively large summer surpluses and winter energy shortages.     

考虑光热电站参与的新能源热电联供型微网运行优化

针对传统热电联供型微网运行存在的问题,文章引入光热电站,并结合风力发电、光伏发电、电加热器、储能系统构成热电联供型微网,提出了一种计及微网运行成本的新能源热电联供型微网运行优化策略。该优化策略综合考虑与外部电网交互成本、各设备维护成本、储能老化成本及热电功率平衡约束等因素,建立了热电联供型微网运行优化模型,并采用YALMIP工具箱进行求解。结果表明:该模型运行成本降低了6.2%,电加热器配合光热电站可以提高微网的运行灵活性,实现电-热能量的双向流动,光热电站在一定范围内增大了发电规模,可有效降低微网运行成本。     

基于Matlab/Simulink的火电厂热力系统模块化建模方法

本文以常规热平衡法为基础,引入模块建模的思想,阐述了模块化建模的开发过程,提出了一种火电厂热力系统模块化建模的方法。通过对热力系统分解、模块化,Matlab／Simulink为开发工具建立了火电厂常用设备的模型库,并给出实例验证了所提方法的正确性和可行性。     

Future carbon regulations and current investments in alternative coal-fired power plant technologies

We analyze how uncertain future US carbon regulations shape the current choice of the type of power plant to build. Our focus is on two coal-fired technologies, pulverized coal (PC) and integrated coal gasification combined cycle technology (IGCC). The PC technology is cheapest—assuming there is no need to control carbon emissions. The IGCC technology may be cheaper if carbon must be captured. Since power plants last many years and future regulations are uncertain, a US electric utility faces a standard decision under uncertainty. A company will confront the range of possible outcomes, assigning its best estimate of the probability of each scenario, averaging the results and determining the power plant technology with the lowest possible cost inclusive of expected future carbon related costs, whether those costs be in the form of emissions charges paid or capital expenditures for retrofitting to capture carbon. If the company assigns high probability to no regulation or to less stringent regulation of carbon, then it makes sense for it to build the PC plant. But if it assigns sufficient probability to scenarios with more stringent regulation, then the IGCC technology is warranted. We provide some useful benchmarks for possible future regulation and show how these relate back to the relative costs of the two technologies and the optimal technology choice. Few of the policy proposals widely referenced in the public discussion warrant the choice of the IGCC technology. Instead, the PC technology remains the least costly. However, recent carbon prices in the European Emissions Trading System are higher than these benchmarks. If it is any guide to possible future penalties for emissions in the US, then current investment in the IGCC technology is warranted. Of course, other factors need to be factored into the decision as well.