A study of robust generation mix under uncertain environments

In recent years, the trend has been toward factors (e.g., demand for load, energy cost) becoming more and more uncertain in power system expansion planning. Thus, it has become necessary to take this uncertainty into account in determining generation plans. In this paper, a robust generation plan is defined explicitly as a plan that ensures fixed performance (economy) despite deviations in these factors. In addition, a new formulation of the robust generation mix problem, whose purpose is to determine the most robust combination of plants in a target year, is proposed. It is generally difficult to solve the formulated problem with parameters. However, by analyzing a proposed formulation from the viewpoint of optimization, we show that the proposed formulation is transformed into the conventional optimization problem without parameters. The simulation results on a typical model show that the generation mix produced by the proposed formulation is more robust than that produced by the conventional formulation. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(1): 26–35, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.2009     

The right mix of drivers and power generation

This work presents an analytical methodology for selecting the most economical mix of drivers for large mechanical load and power generation for offshore production in the Gulf of Mexico. These method accounts for CAPEX, life-cycle costs and the effect of emissions and production availability in the selection of drivers and associated equipment. Result of driver studies have shown that all electric designs are viable on floating platforms in the Gulf of Mexico.     

Must-run generation: can we mix regulation and competition successfully?

In restructuring, certain generators will continue to be required to operate during critical times to assure local reliability. Regulation must allow these generators to participate in the competitive market, while assuming their availability to operate when needed and addressing concerns about their market power     

基于并联运行方式下的多变压器经济运行分析

通过分析电力变压器负荷与损耗的关系,从变压器并联运行的角度阐述变压器经济运行方式,从而找到变压器实际运行中的最佳经济运行方式。     

An evaluation of the optimal generation mix for controlling carbon dioxide emissions

This paper presents an efficient computational algorithm for selecting the optimal generation mix considering CO₂ emissions. To demonstrate the effectiveness and feasibility of the proposed method, a fundamental study of the evaluation of the optimal generation mix for controlling CO₂ emissions is indicated. Furthermore, by using a parametric analysis which considers load characteristics as parameters, a general trend for the optimal generation mix which is affected by controlling CO₂ can be derived. The proposed method is based on an optimization method known as simulated annealing. In the method, solutions in a generation mix problem are equivalent to state of a physical system, and the cost of a solution is equivalent to the energy of a state. The proposed method can easily accommodate not only CO₂ emissions but also many practical constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Case studies with various annual load patterns (combinations of annual load factors and the shapes of annual load duration curve) are presented and discussed. Consequently, a general trend for selecting generation technologies that should be added to a power system is derived, i.e., a useful guideline for studying generation expansion planning under controlling CO₂ emissions can be provided.     

含多种分布式电源的微网经济运行优化研究

微网能够有效地利用各种分布式电源,在解决能源短缺、环境保护和改善电能质量等方面表现出极大潜能。建立热电联产型微网系统经济调度模型,以一个包含风、光、储、微型燃气轮机、燃料电池、柴油发电机以及热电负荷的微网为例,总结了孤网运行方式下的调度策略,并运用改进遗传算法优化了各微源的出力,验证了所提模型、策略和算法的有效性。     

不确定性的电厂动力配煤优化模型

动力配煤是适合中国国情的一种洁净煤技术,可以降低燃煤电厂二氧化硫的排放,改善电厂的经济效益和燃烧性能。然而煤质参数是不确定的和可变的,电厂优化配煤决策必须处理这些不确定性。因此,建立了一个不确定性电厂优化配煤线性模型,处理以区间参数表征的不确定性。结果表明,可得到稳定和可行的区间解,决策者可根据实际情况,在解区间内调整决策变量值,从而得到多个决策替代方案。     

An efficient algorithm for optimal generation mix taking into account fuzziness of decision making and planning parameters

This paper presents an efficient computational algorithm for selecting the optimal generation mix under uncertain circumstances. Subjective, experiential or linguistic uncertainties are selected from among various uncertainties, i.e., we treat fuzziness in generation expansion planning. The fuzziness can be divided into: (1) the fuzziness of decision making; and (2) the fuzziness of some planning parameters, such as load growth, fuel price, and so on. Both classes of fuzziness are integrated into a fuzzy decision based on fuzzy sets theory, and then the optimal generation mix can be determined by the Fuzzy Dynamic Programming (FDP) technique. The proposed method, which is based on the dynamic programming technique, is extended by using the Bellman-Zadeh maximizing decision. In the method, each generation technology and generation capacity are selected as a stage and state, respectively. The proposed method can easily accommodate not only the fuzziness but also many constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Furthermore, the arbitrary shape of membership function can be used. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.     

Evaluation of a plug‐in hybrid electric vehicle considering power generation best mix

In the transport section, it is necessary to reduce the amount of CO₂ emissions and oil dependence. Bio fuels and fuel cell vehicle (FCV), electric vehicle (EV) and plug‐in hybrid electric vehicle (PHEV) are expected to reduce CO₂ emissions and oil dependence. We focus on PHEV. PHEV can reduce total energy consumption because of its high efficiency and can run with both oil and electricity. Introduction of PHEV reduces oil consumption, but it also increases electricity demands. Therefore, we must evaluate PHEV's CO₂ reduction potential, not only in the transport section but also in the power grid section. To take into account the distribution of the daily travel distance is also very important. All energy charged in the PHEV's battery cannot always be used. That influences the evaluation. We formulate the total model that combines passenger car model and power utility grid model, and we also consider the distribution of the daily travel distance. With this model, we show the battery cost per kWh at which PHEV begins to be introduced and oil dependence in the passenger car section is to be reduced to 80%. We also show PHEV's CO₂ reduction potentials and effects on the power supply system. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(2): 12–22, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20920     

挠性负载条件下步进电机特性测试

介绍了一种步进电动机在挠性负载条件下的特性测试装置,测得了步进电动机在挠性负载和刚性负载条件下的惯频特性和矩频特性曲线,结合测试结果分析了在设计此类系统时应注意的问题。     

Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller

This paper deals with automatic generation control (AGC) of a three unequal area hydrothermal system. Reheat turbines in thermal areas and electric governor in hydro area are considered. Appropriate generation rate constraints are considered in the areas. Bacterial foraging (BF) technique is used to simultaneously optimize the integral gains (K_Ii) and speed regulation parameter (R_i) keeping frequency bias fixed at frequency response characteristics. The integral controller in this case is termed as BFIC. The performance of a multilayer perception neural network (MLPNN) controller using reinforcement learning is evaluated for the system. In this reinforcement learning, the weights are dynamically adjusted online using backpropagation algorithm with error being the area control error (ACE). The performance of the MLPNN controller is compared with that of BFIC. Also, the performance of MLPNN controller over a wide range of system loading conditions and step load perturbations is compared with BFIC. Investigations clearly reveal the superior performance of MLPNN controller over BFIC. Sensitivity analysis subject to wide changes in system loading, inertia constant (H) and size and location of step load perturbation is carried out to investigate the robustness of the controller with the optimum K_Ii and R_i obtained at nominal condition.     

An interval full-infinite programming approach for energy systems planning under multiple uncertainties

An interval full-infinite programming regional energy model (IFIP-REM) is developed in this study for supporting energy systems management under uncertainty. IFIP-REM integrates full-infinite programming (FIP) into an interval linear programming (ILP) framework. The IFIP is capable of addressing multiple uncertainties existing in related costs, impact factors and system objectives (expressed as determinates, crisp interval values and functional intervals). The modeling approach inherits the advantages of ILP and FIP, and allows uncertainties and decision-makers’ aspirations to be directly communicated into the optimization process and resulting solutions. The developed method is applied to an energy planning system, where pollutant emissions are desired to be controlled. The results indicate that reasonable solutions can be generated and used to support the obtained interval solutions of IFIP-REM model, and the solutions can be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system constraints to coordinate the conflict interactions among economic cost, system efficiency, pollutant mitigation and energy-supply security.     

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

The optimum design of Hybrid Renewable Energy Systems (HRES) depends on different economical, environmental and performance related criteria which are often conflicting objectives. The Non-dominated Sorting Genetic Algorithm (NSGA-II) provides a decision support mechanism in solving multi-objective problems and providing a set of non-dominated solutions where finding an absolute optimum solution is not possible. The present study uses NSGA-II algorithm in the design of a standalone HRES comprising wind turbine, PV panel and battery bank with the (economic) objective of minimum system total cost and (performance) objective of maximum reliability. To address the uncertainties in renewable resources (wind speed and solar irradiance), an innovative method is proposed which is based on Chance Constrained Programming (CCP). A case study is used to validate the proposed method, where the results obtained are compared with the conventional method of incorporating uncertainties using Monte Carlo simulation.     

Planning for future uncertainties in electric power generation: ananalysis of transitional strategies for reduction of carbon and sulfuremissions

The authors discuss strategies for the US electric utility industry for reduction of both acid rain producing and global warming gases. The EPRI electric generation expansion analysis system (EGEAS) utility optimization/simulation modeling structure and the EPRI developed regional utilities were used. The focus is on the northeast and east central regions of the US. Strategies identified are fuel switching (predominantly between coal and natural gas), mandated emission limits, and a carbon tax. The overall conclusions are that conservation will always benefit carbon emissions but may not reduce acid rain emissions by the offsetting forces of improved performance of new plant as opposed to reduced overall consumption of final product. Results of the study are highly utility and regional demand specific     

Standby power generation under utility curtailment contractagreements

Many electric utilities in the United States offer large industrial and commercial customers power sales contracts which have attractive rates under a curtailment requirement. This curtailment requirement allows the utility to require the customer to reduce its power demand to a predetermined level within a specific time period. If the required curtailment is not achieved by the customer within the allocated time period, stiff financial penalties are usually enforced by the utility. The attractiveness of the contract rates usually is proportional to the amount of curtailment required. To take advantage of these attractive rates, a customer must be able to withstand the curtailment without supplemental generation or must add standby generation to meet its needs. Obviously, the cost of the curtailments to the customer should not exceed the economic benefits of reduced rates. This paper reviews the alternatives faced by a curtailment contract customer together with potential load-shedding and standby-generation system designs. An example of implementing a curtailment contract at an existing industrial facility is presented. The example facility. Boeing Helicopters, Philadelphia, PA, USA, required both load shedding and standby generation. The load-shedding scheme is fairly complex and is controlled by a programmable logic controller (PLC). The standby-generation and load-shedding systems for the example facility are examined in detail. Also, lessons learned from implementing the required modifications to the example facility are discussed     

Evaluation of the plug‐in hybrid electric vehicle considering learning curve on battery and power generation best mix

The plug‐in hybrid electric vehicle (PHEV) is a technology intended to reduce CO₂ emissions in the transport sector. This paper presents scenarios that show how widely used PHEVs will be in the future, how much CO₂ emissions will be reduced by the introduction of PHEVs, and whether there will be serious effects on the power supply system. PHEVs can run on both gasoline and electricity, and therefore we evaluate CO₂ emissions not only from gasoline consumption but also from electricity consumption. Consideration of the distribution of daily trip distances is important for evaluating the economical benefits and CO₂ emissions resulting from the introduction of PHEVs. Also, future battery costs are very important in constructing PHEV growth scenarios. The growth of the number of PHEVs will make battery costs lower. Thus, we formulate an overall model that combines the passenger car sector and power supply sector, taking account of the distribution of daily trip distances and incorporating a learning curve for battery costs. We use the iteration method to provide a learning curve that is nonlinear. Therefore, we set the battery cost only in the first year of the simulation: battery costs in the later years are calculated in the model. We focus on a 25‐year time period in Japan, starting from 2010, and divided into 5 parts (1st to 5th). The model selects the most economical combinations of car types and power sources. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 176(2): 31–40, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21098     

Thermodynamic analysis and multi objective optimization of performance of solar dish Stirling engine by the centrality of entransy and entropy generation

This paper makes attempts to perform multi-objective optimization on the solar-powered Stirling engine with high temperature differential. A new model was proposed based on the finite-time thermodynamic. Furthermore, the thermal efficiency of the solar Stirling system with a rate of finite heat transfer, regenerative heat loss, the output power, finite regeneration process time and conductive thermal bridging loss is specified. The thermal efficiency, entransy loss rate and power output have been maximized simultaneously for a dish-Stirling system and entropy generation’s rate in the engine minimized via using thermodynamic analysis and NSGA-II algorithm. To specify the optimum values of the above mentioned parameters three well known decision making methods have been employed. Finally, an error analysis was applied on the outputs gained from each decision makers.     

Hydropower generation management under uncertainty via scenarioanalysis and parallel computation

The authors present a modeling framework for the robust solution of hydroelectric power generation management problems with uncertainty in the values of the water inflows and outflows. A deterministic treatment of the problem provides unsatisfactory results, except for very short time horizons. They describe a model based on scenario analysis that allows a satisfactory treatment of uncertainty in the model data for medium and long-term planning problems. Their approach results in a huge model with a network submodel per scenario plus coupling constraints. The size of the problem and the structure of the constraints are adequate for the use of decomposition techniques and parallel computation tools. They present computational results for both sequential and parallel implementation versions of the codes, running on a cluster of workstations. The codes have been tested on data obtained from the reservoir network of Iberdrola, an electric utility owning 50% of the total installed hydroelectric capacity of Spain, and generating 40% of the total energy demand