A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

The purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout.     

石化企业变参数蒸汽动力系统的优化设计及运行研究

针对影响蒸汽系统的不确定因素以及不确定性参数问题直接建模求解困难等问题,提出了将不确定性参数在其可行域内进行离散化,将不确定性问题转化为确定性问题的方法,建立了参数不确定条件下的蒸汽动力系统的混合整数线性规划模型(MILP),该模型综合考虑了设备的启停费用,并将锅炉和汽轮机的模型根据实际运行情况进行了合理的线性化。该模型既保证了系统运行优化求解精度,又避免了非线性模型求解困难的问题,并结合工程实例,利用优化软件LINGO求解,得到了经济性和可操作性都较好的运行计划方案,证明了模型的可行性和合理性。     

A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems

In 2015, 195 countries signed the Paris Agreement under the United Nations Framework Convention on Climate Change. To achieve the ambitious greenhouse gas-reduction targets therein, the electric power sector must be transformed fundamentally. To this end, we develop a two-stage stochastic optimization model. The proposed model determines the optimal mix of generation and transmission capacity to build to serve future demands at least cost, while respecting technical constraints and climate-related considerations. The model uses a mix of AC and high-voltage DC transmission lines, conventional and renewable generation, and different types of energy-storage units to meet these objectives. Short- and long-term uncertainties are modeled using operating conditions and scenarios, respectively.We demonstrate the model using a case study that is based on the Texas power system, with 2050 as the target year of the analysis. We include explicit carbon-emissions constraints. Doing so allows us to examine the effect of carbon-reduction targets and deep decarbonization of electricity production on investment decisions. As expected, we find that thermal-dominated power systems must transition toward having a renewable-dominated generation mix.     

A novel optimization sizing model for hybrid solar-wind power generation system

This paper develops the Hybrid Solar-Wind System Optimization Sizing (HSWSO) model, to optimize the capacity sizes of different components of hybrid solar-wind power generation systems employing a battery bank. The HSWSO model consists of three parts: the model of the hybrid system, the model of Loss of Power Supply Probability (LPSP) and the model of the Levelised Cost of Energy (LCE). The flow chart of the HSWSO model is also illustrated. With the incorporated HSWSO model, the sizing optimization of hybrid solar-wind power generation systems can be achieved technically and economically according to the system reliability requirements. A case study is reported to show the importance of the HSWSO model for sizing the capacities of wind turbines, PV panel and battery banks of a hybrid solar-wind renewable energy system.     

Design optimization under uncertainty of hybrid fuel cell energy systems for power generation and cooling purposes

Reliance on point estimates when developing hybrid energy systems can over/underestimate system performance. Analyzing the sensitivity and uncertainty of large-scale hybrid systems can be a challenging task due to the large number of design parameters to be explored. In this work, a comprehensive and efficient sensitivity/uncertainty methodology is applied on two fuel cell hybrid systems to help analysts to investigate hybrid systems more efficiently. This methodology also includes a step-by-step approach to perform design optimization under uncertainty of energy systems. The two hybrid systems are: molten carbonate fuel cell with thermoelectric generator (MCFC-TEG) and phosphoric acid fuel cell with refrigerator (PAFC-REF). Various sensitivity and uncertainty methods are utilized to analyze the design parameters and their effect on the performance of these two systems. These methods perform local and regression-based sensitivity analysis, Monte Carlo uncertainty propagation, parameter screening, and variance decomposition. Detailed approach is adopted to identify and rank the influential design parameters for each system. Results demonstrate that the optimum power output of the MCFC-TEG has 10% uncertainty, driven mainly by the operating temperature, cahtode activation energy, TEG figure of merit, and TEG thermal conductivity. However, PAFC-REF is more reliable with larger power output and 1.4% uncertainty, driven by the charge transfer coefficient, heat transfer in the refrigeration cycle, cold reservoir temperature, and operating temperature. Based on this identification, design optimization under uncertainty is performed using these sensitive parameters to improve the system performance through increasing the power output and reducing its uncertainty.     

基于LINGO对参数不确定蒸汽动力系统的优化运行研究

首先将蒸汽动力系统的不确定性参数在其可行域内进行离散化,把不确定性问题转化为确定性问题;然后建立参数不确定条件下的蒸汽动力系统的混合整数线性规划模型(MILP),该模型综合考虑了设备的启停费用,并将锅炉和汽轮机的模型根据实际运行情况进行合理的线性化,这样既保证了系统运行优化求解精度,又避免了非线性模型求解困难。结合工程实例,采用提出的问题处理策略,利用优化软件LINGO求解,得到了经济性和可操作性都较好的运行方案,证明了模型的可行性和合理性,为企业运行计划人员提供了计划调度指导。     

Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system

To allow a real penetration of the huge dispersed naturally renewable resources (wind, sun, etc.) intermittent and more or less easily predictable, optimal sizing of hybrid renewable power generation systems prove to be essential. This paper recommends an optimal sizing model based on iterative technique, to optimize the capacity sizes of different components of hybrid photovoltaic/wind power generation system using a battery bank. The recommended model takes into account the submodels of the hybrid system, the Deficiency of Power Supply Probability (DPSP) and the Levelised Unit Electricity Cost (LUEC). The flow chart of the hybrid optimal sizing model is also illustrated. With this incorporated model, the sizing optimization of grid-independent hybrid PV/wind power generation system can be accomplished technically and economically according to the system reliability requirements. A case study is conducted to analyze one hybrid project, which is designed to supply residential household located in the area of the CDER (Center for Renewable Energy Development) situated in Bouzaréah, Algeria (36^° 48′N, 3^° 1′E, 345 m).     

Stability simulation and parameter optimization of a hybrid wind-diesel power generation system

In this paper a systematic method of choosing the gain parameter of the wind turbine generator pitch control is presented using the Lyapunov technique that guarantees stability. A comprehensive digital computer model of a hybrid wind–diesel power generation system including the diesel and wind power dynamics with a superconducting magnetic energy storage (SMES) unit for stability evaluation is developed. The effect of introducing an SMES unit for improvement of stability and system dynamic response is studied. Analysis of stability has further been explored using an eigenvalue sensitivity technique. The eigenvalues of the system with and without an SMES unit are studied and the effect of variation of the SMES unit parameters on eigenvalue locations are plotted. The dynamic response of the power system to random load changes with optimal gain setting is also presented.     

IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty

The development of optimization models for energy systems planning has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the planning process and the complex interactions among various uncertain parameters are challenging the capabilities of these developed tools. Therefore, the objective of this study is to develop a hybrid interval-fuzzy two-stage stochastic energy systems planning model (IFTEM) to deal with various uncertainties that can be expressed as fuzzy numbers, probability distributions and discrete intervals. The developed IFTEM is then applied to a hypothetical regional energy system. The results indicate that the IFTEM has advantages in reflecting complexities of various system uncertainties as well as dealing with two-stage stochastic decision problems within energy systems.     

Assessing incentive policies for integrating centralized solar power generation in the Brazilian electric power system

This study assesses the impacts of promoting, through auctions, centralized solar power generation (concentrated solar power – CSP, and photovoltaic solar panels – PV) on the Brazilian power system. Four types of CSP plants with parabolic troughs were simulated at two sites, Bom Jesus da Lapa and Campo Grande, and PV plants were simulated at two other sites, Recife and Rio de Janeiro. The main parameters obtained for each plant were expanded to other suitable sites in the country (totaling 17.2 GW in 2040), as inputs in an optimization model for evaluating the impacts of the introduction of centralized solar power on the expansion of the electricity grid up to 2040. This scenario would be about USD$ 185 billion more expensive than a business as usual scenario, where expansion solely relies on least-cost options. Hence, for the country to incentivize the expansion of centralized solar power, specific auctions for solar energy should be adopted, as well as complementary policies to promote investments in R&D and the use of hybrid systems based on solar and fuels in CSP plants.     

考虑多源负荷不确定性的电力系统安稳风险优化

针对多源负荷的波动性和不确定性影响电力系统安稳运行,导致系统运行可靠性降低等问题,文章提出了一种考虑多源负荷不确定性的电力系统安稳风险优化方法;建立了考虑多源负荷不确定性的电力系统元件停运概率模型;提出了考虑多源负荷不确定性的电力系统安稳风险评估方法、安稳风险评估指标及其评估流程,对多源负荷波动下的电力系统安稳风险进行了评估。以系统运行过程安稳风险最低、负荷削减总量最小和系统经济损失后果最小为目标,建立电力系统安全稳定风险优化模型,对电力系统安稳风险进行优化。文章以IEEE-39节点算例进行仿真,验证了所建立模型的有效性。     

The optimization of a holographic system for solar power generation

A holographic device has been developed that greatly improves the efficiency of solar energy conversion. The single-element hologram focuses light, spectrally splits it and diverts unwanted infrared heat away from the solar cells. The output appears as a thin concentrated line, focused perpendicular to the hologram and displaced to the side. Solar cells are placed along this line such that each cell absorbs only the wavelengths which it can efficiently convert to electric power.The theoretical and experimental development of this system are discussed, as well as its application in space and on Earth. The system is excellent for space applications since the holograms are single element, very lightweight, and require minimal cooling. For terrestrial purposes, the projected costs of the system are nearly a factor of two lower per kWh than other solar concentrator systems; thus it is competitive with conventional power generation systems. Other state of the art holographic solar power generation systems are also discussed.     

基于GPRS的风光互补发电无线远程监测系统

采用ATmega128单片机以及SIM300模块设计了基于通用分组无线业务(GPRS)的无线数据采集系统,对风光互补发电系统进行远程数据监测,并将数据传送到计算机网络服务器。文章介绍了无线GPRS监测系统的硬件和软件原理及设计方案。     

考虑需求响应的风光燃储集成虚拟电厂双层随机调度优化模型

为促进以风光为代表的分布式能源发电并网,文章集成了风电、光伏发电、燃气轮机、储能系统和激励型需求响应为虚拟电厂,引入两阶段优化理论克服风光不确定性,建立VPP双层随机调度优化模型。首先,介绍了VPP的基本结构,建立了电源出力模型和需求响应模型。然后,建立了虚拟电厂双层调度优化模型:在上层模型中,根据风电和光伏发电日前预测结果,制定日前调度计划;在下层模型中,根据WPP和PV的实际输出,修正日前调度计划,形成最终VPP调度方案。最后算例分析表明:所提模型能够衔接日前调度和时前调度,降低系统缺电惩罚成本,提升VPP运营收益。储能系统能够利用自身充放电特性配合VPP内部风电和光伏发电出力,有利于平缓系统净负荷曲线。PBDR能够引导用户侧配合VPP发电调度,削峰作用弱于IBDR,而填谷作用强于IBDR。同时引入ESSs和DR后,VPP运营收益达到最高,表明ESSs和DR具有协同优化效应。     

Design optimization for the hybrid power system of a green building

This paper proposes an optimal design procedure for a green building equipped with renewable energy, energy storages, and proton exchange membrane fuel cells (PEMFCs). First, we introduce the hybrid power system of the green building and construct a simulation model using Matlab/SimPowerSystem?. The model parameters are tuned so that the system responses can be estimated without extensive experiments in the optimization processes. Second, we define the cost and reliability indexes to optimize the system design using three steps: component selection, component sizing, and power management (PM) adjustment. We further define the safety index to evaluate the system's sustainability under extreme conditions when no renewable energy is available. Last, we apply the proposed procedures to the green building and demonstrate the benefits of the optimal design. The proposed method can be directly applied to develop customized hybrid power systems in the future.     

Joint market clearing in a stochastic framework considering power system security

This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.     

Improving coal-fired power generation by integrating PFBC and gas-fired combined cycles in a hybrid process

Two hybrid combined cycle electrical power generating schemes burning coal and natural gas were examined using the ECLIPSE chemical process simjlator. The results of these simulations are presented and analyzed. Comparison is made with conventional power generation technologies. Two additional pieces of work are included. The first examines the effect of the hot gas filtration operating conditions on the hybrid combined cycle efficiency and the second calculates the allowable increase in capital cost for a specified improvement in power plant efficiency while maintaining the same cost of electricity.     

Design and optimization of hybrid solar-hydrogen generation system using TRNSYS

Solar thermal systems are an efficient utilization of solar energy for hot water and space heating at domestic level. A Solar Water Heater (SWH) incorporating an Evacuated Glass Tube Collector (EGTC) is simulated using TRNSYS software. Efficiency parameters are pointed, and a parametric optimization method is adopted to design the system with maximum conceivable efficiency. In the first part, the selection of refrigerant for heat transportation in SWH loop is presented. A set of 15 working fluids are chosen, and their chemical properties are computed using NIST standard software (REFPROP). The selected working fluids are tested in the system under study and plots for energy gain and temperature are plotted using TRNSYS. Results showed that ammonia (NH₃) having specific heat 4.6kJ/kg-K and fluid thermal conductivity 2.12 kJ/hr-m supplies peak energy gain of 7500 kJ/h in winter and 8900 kJ/h in summer season along 120 °C temperature rise. On the other hand, R-123 having specific heat 0.65kJ/kg-K and fluid thermal conductivity 0.0293kJ/hr-m showed inferior performance during the simulation. A solar-hydrogen co-generation system is also designed and simulated under low solar insolation and warm climate regions to study annual hydrogen produced by the hybrid system. System comprises main components: a PV array, an electrolyzer, a fuel cell, a battery, a hydrogen storage unit and a controller in the complete loop. Results of Hydrogen cogeneration system provide 7.8% efficiency in the cold climate of Fargo North Dakota state due to lower solar insolation. While hot climate condition of Lahore weather provides efficiency of 11.8% which satisfy the statistics found in literature.     

Multi-objective planning framework for stochastic and controllable distributed energy resources

The amount of distributed energy resources (DER) in the grid is continually increasing, and the potential benefits and drawbacks are becoming clearer. However, there is still a lack of clarity in how these multiple effects interact and which trade-offs should be made in the integration of new DER. There is a clear need for appropriate DER planning tools in the current market environment, in which both DER operators and distribution system operators (DSOs) may have multiple, often conflicting objectives and where uncertainty remains present as to which targets can be reached with a high amount of DER in the grid. A novel multi-objective planning framework is presented for the integration of stochastic and controllable DER in the distribution grid. A case study that illustrates the proposed framework is presented. Active DER management in terms of curtailment as well as dispatch of units is studied using the proposed multi-objective approach. Additionally, the extent to which active DER can be used as an alternative for grid reinforcements is analysed. The results show that the proposed multi-objective approach permits a better evaluation of the potential of active DER to support system operation.     

A stochastic framework for clearing of reactive power market

This paper presents a new stochastic framework for clearing of day-ahead reactive power market. The uncertainty of generating units in the form of system contingencies are considered in the reactive power market-clearing procedure by the stochastic model in two steps. The Monte-Carlo Simulation (MCS) is first used to generate random scenarios. Then, in the second step, the stochastic market-clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. In each of these deterministic optimization problems, the objective function is total payment function (TPF) of generators which refers to the payment paid to the generators for their reactive power compensation. The effectiveness of the proposed model is examined based on the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS).