An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

Technico-economic analysis of off grid solar PV/Fuel cell energy system for residential community in desert region

A technico-economic analysis based on integrated modeling, simulation, and optimization approach is used in this study to design an off grid hybrid solar PV/Fuel Cell power system. The main objective is to optimize the design and develop dispatch control strategies of the standalone hybrid renewable power system to meet the desired electric load of a residential community located in a desert region. The effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. The goal of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems. Simulation, modeling, optimization and dispatch control strategies were used in this study to determine the performance and the cost of the proposed hybrid renewable power system. The simulation results show that the distributed power generation using solar PV and Fuel Cell energy systems integrated with an electrolyzer for hydrogen production and using cycle charging dispatch control strategy (the fuel cell will operate to meet the AC primary load and the surplus of electrical power is used to run the electrolyzer) offers the best performance. The hybrid power system was designed to meet the energy demand of 4500 kWh/day of the residential community (150 houses). The total power production from the distributed hybrid energy system was 52% from the solar PV, and 48% from the fuel cell. From the total electricity generated from the photovoltaic hydrogen fuel cell hybrid system, 80.70% is used to meet all the AC load of the residential community with negligible unmet AC primary load (0.08%), 14.08% is the input DC power for the electrolyzer for hydrogen production, 3.30% are the losses in the DC/AC inverter, and 1.84% is the excess power (dumped energy). The proposed off-grid hybrid renewable power system has 40.2% renewable fraction, is economically viable with a levelized cost of energy of 145 $/MWh and is environmentally friendly (zero carbon dioxide emissions during the electricity generation from the solar PV and Fuel Cell hybrid power system).     

A green hydrogen energy storage concept based on parabolic trough collector and proton exchange membrane electrolyzer/fuel cell: Thermodynamic and exergoeconomic analyses with multi-objective optimization

With the continuous penetration of renewable energy plants into energy markets and their surplus power generation during off-peak periods, the need for utility-scale energy storage technologies is globally prioritized. Among the existing large-scale energy storage technologies, hydrogen storage has appeared as a powerful alternative due to its environmental benefits and the ability to store a large amount of energy for several hours to months. The major objective of the proposed research is to introduce a novel configuration of green hydrogen production for power generation during peak demand periods. In this regard, an innovative hybridization of a solar unit based on a parabolic trough collector with a proton-exchange membrane electrolyzer and a fuel cell is introduced and analyzed from the thermodynamic and exergoeconomic perspectives. Moreover, a sensitivity analysis and a multi-objective optimization based on the combination of neural network and grey wolf optimization algorithms are conducted to select the best working fluid of the solar unit and ideal operating conditions according to the minimum cost rate and the maximum exergy efficiency. The results indicate that Dowtherm? A synthetic oil is the best working fluid, and the proposed system can generate 9, 14.9, and 20.1 MW of power during off-, mid-, and on-peak times, respectively. The results also show that the proposed system operates with an exergy efficiency of 17.6% and a cost rate of 492.4 $/hr under the optimal conditions.     

An integrated scenario analysis for future zero‐carbon energy system

An integrated scenario analysis methodology has been proposed for zero‐carbon energy system in perspectives of social‐economy, environment and technology. By using the methodology, service demands in all sectors were estimated based on social‐economic data, and then the best technology and energy mixes were obtained to meet the service demands. The methodology was applied to Japan toward zero‐carbon energy system out to the year of 2100, and three different scenarios of nuclear power development are considered in light of the Fukushima accident: (i) no further introduction of nuclear, (ii) fixed portion and (iii) no limit of nuclear. The results show that, zero‐carbon energy scenario can be attained in the year 2100 when electricity will supply 75% of total energy consumption, and three power generation scenarios were proposed, 30% renewable and 70% gas‐carbon capture and storage (CCS) in Scenario 1, respective one‐third nuclear, renewable and gas‐CCS in Scenario 2, and 60% nuclear power, 20% renewable and 10% gas‐CCS in Scenario 3. Finally, Scenario 2 is rated as the most balanced scenario by putting emphasis on the availability of diversified power source, considering the inter‐comparison of the three scenarios from the four aspects of cost, CO₂ emission, risk and diversity. Copyright © 2015 John Wiley & Sons, Ltd.     

Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US?

An analytical job creation model for the US power sector from 2009 to 2030 is presented. The model synthesizes data from 15 job studies covering renewable energy (RE), energy efficiency (EE), carbon capture and storage (CCS) and nuclear power. The paper employs a consistent methodology of normalizing job data to average employment per unit energy produced over plant lifetime. Job losses in the coal and natural gas industry are modeled to project net employment impacts. Benefits and drawbacks of the methodology are assessed and the resulting model is used for job projections under various renewable portfolio standards (RPS), EE, and low carbon energy scenarios We find that all non-fossil fuel technologies (renewable energy, EE, low carbon) create more jobs per unit energy than coal and natural gas. Aggressive EE measures combined with a 30% RPS target in 2030 can generate over 4 million full-time-equivalent job-years by 2030 while increasing nuclear power to 25% and CCS to 10% of overall generation in 2030 can yield an additional 500,000 job-years.     

Carbon capture and storage potential in coal-fired plant in Malaysia—A review

Secure, reliable and affordable energy supplies are necessary for sustainable economic growth, but increases in associated carbon dioxide (CO₂) emissions, and the associated risk of climate change are a cause of major concern. Experts have projected that the CO₂ emissions related to the energy sector will increase 130% by 2050 in the absence of new policies or supply constraints as a result of increased fossil fuel usage. To address this issue will require an energy technology revolution involving greater energy efficiency, increased renewable energies and nuclear power, and the near-decarbonisation of fossil fuel-based power generation. Nonetheless, fossil fuel usage is expected to continue to dominate global energy supply. The only technology available to mitigate greenhouse gas (GHG) emissions from large-scale fossil fuel usage is carbon capture and storage (CCS), an essential part of the portfolio of technologies that is needed to achieve deep global emission reductions. However, CCS technology faces numerous issues and challenges before it can be successfully deployed. With Malaysia has recently pledged a 40% carbon reduction by 2020 in the Copenhagen 2009 Climate Summit, CCS technology is seen as a viable option in order to achieve its target. Thus, this paper studies the potential and feasibility of coal-fired power plant with CCS technology in Malaysia which includes the choices of coal plants and types of capture technologies possible for implementation.     

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.     

When intermittent power production serves transient loads

Renewable power generation exhibits notorious intermittence. The power load varies daily and also seasonally. The topic of renewable generation, storage and grid interfacing is complex in that it brings into one setting many diverse interests and technologies. Our long-term goal is to help define ways to profitably increase renewable generation. In this paper, we focus on normal day for a grid operator, PJM, (Pennsylvania, New Jersey and Maryland). The variability of wind and (and assumed) solar outputs require a certain capability for load following or storage. Using dynamic modelling, we estimate the variability of the wind output and we simulate a projected solar penetration of 3% of new capacity. To save for eventual use every unit of energy thus generated, a storage system must have the capability to levelize the supply of renewable power. The capacity requirements for storage and generation of such a system are mapped out in 1 min intervals, and are used to define the capacities and ramp rates for a hypothetical pumped storage plant. Knowledge of weather patterns may be helpful to plan dispatch and storage of renewable energy. The results of a brief excursion into the difficult topic of weather patterns are recorded here too.     

Assessment of oxyfuel power generation technologies

In this work, a cost–benefit analysis concerning the use of oxyfuel combustion technology is carried out. For the analysis, the IPP optimization software is used in which a decouple optimization method for power technology selection in competitive markets is employed and the electricity unit cost and the CO₂ avoidance cost are calculated. The results indicate that oxyfuel technology is a competitive CO₂ capture and storage (CCS) technology. In addition, the effect of varying loan interest rates was investigated in the economic performance of an oxyfuel combustion plant. This analysis, revealed that up to a value of loan interest of approximately 5.3% the oxyfuel plant retains the competitive electricity unit costs (compared to other CCS technologies). For higher interest rate levels, other CCS technologies become more economically attractive.     

兼顾公平性的多能源合作博弈优化调度

针对含高比例可再生能源的电力系统运行经济性和调度公平性问题,提出一种多种能源参与的双层优化调度模型。该模型考虑经济性的同时从两方面体现公平性：上层模型中首先将风电场、光伏电站、抽水蓄能电站和需求响应负荷聚合成虚拟电厂（VPP）,其次,将虚拟电厂作为大电网的一部分参与优化调度,采用基尼系数表征公平性并作为约束条件,以总成本最低为目标函数建立兼顾公平性的多能源经济优化调度模型,最后,将上层VPP的求解结果作为下层优化调度的输入变量;下层模型中基于合作博弈理论建立以VPP内部成员为博弈参与者,以各参与者收益最大为目标函数的合作博弈模型,采用均衡度对比不同联盟组合下各主体的收益分配公平性,对比均分法、按出力分配法及Shapely值法的收益分配均衡度。采用智能优化算法结合CPLEX求解器求解模型,应用实例验证模型的有效性和正确性。     

计及不确定性的多类型发电机组联合调度优化模型

为了促进可再生能源并网,解决可再生能源机组在内的多类型发电机组联合优化调度问题,引入全额收购可再生能源发电,由火电机组提供备用、允许适度弃风,由火电机组提供备用以及水火机组联合提供备用的可再生能源发电三种不同方案。首先构建了涵盖风、光伏、水力机组的可再生能源机组发电处理模型;再结合设定的三种方案组建了多类型发电机组节能调度优化模型;最后,基于基础数据,得出该节能调度优化模型的发电系统总煤耗、备用煤耗、系统发电成本、启停成本、弃风量及弃水量等关键指标的优化效果,对多类型发电机组联合调度优化具有一定的指导意义。     

Decarbonizing Europe's power sector by 2050 — Analyzing the economic implications of alternative decarbonization pathways

The European Union aims to reduce greenhouse gas emissions by 80–95% in 2050 compared to 1990 levels. The transition towards a low-carbon economy implies the almost complete decarbonization of Europe's power sector, which could be achieved along various pathways. In this paper, we evaluate the economic implications of alternative energy policies for Europe's power sector by applying a linear dynamic electricity system optimization model in over 36 scenarios. We find that the costs of decarbonizing Europe's power sector by 2050 vary between 139 and 633 bn €₂₀₁₀, which corresponds to an increase of between 11% and 44% compared to the total system costs when no CO₂ reduction targets are implemented. In line with economic theory, the decarbonization of Europe's power sector is achieved at minimal costs under a stand-alone CO₂ reduction target, which ensures competition between all low-carbon technologies. If, however, renewable energies are exempted from competition via supplementary renewable energy (RES-E) targets or if investments in new nuclear and CCS power plants are politically restricted, the costs of decarbonization significantly rise. Moreover, we find that the excess costs of supplementary RES-E targets depend on the acceptance of alternative low carbon technologies. For example, given a complete nuclear phase-out in Europe by 2050 and politically implemented restrictions on the application of CCS to conventional power plants, supplementary RES-E targets are redundant. While in such a scenario the overall costs of decarbonization are comparatively high, the excess costs of supplementary RES-E targets are close to zero.     

A novel methodology for scrutiny of autonomous hybrid renewable energy system

The optimal design of a hybrid system with different configurations including renewable generation is presented in this paper. A novel multi‐objective function consisting of 6 different objectives of hybrid system is reported using GA, PSO, and TLBO to decide the optimal configurations of parameters. The technical (loss of power supply probability, renewable factor), economical (cost of energy, penalty and fuel consumption), and social (job creation, human development index, and particular matter) features are investigated as objectives simultaneously for optimal design of hybrid system. The different objective indices namely cost of energy, loss of power supply probability, particular matter, human development index, job creation, and renewable factor indices are considered. The newly invented particular matter factor for design consideration of hybrid system directly shows the human health impacts, while pollutant emission is measured in the hybrid system design. The optimum values of objective indices are decided on the basis of the minimum value of multi‐objective function. The distinct cases from I to VI of hybrid system are examined for optimal configuration including different combinations of PV, wind, biomass, diesel generator, and battery bank. The resulting analysis of each case reveals that the performance of TLBO is better than PSO, and PSO is better than GA in all respect through new multi‐objective function and found case I is more efficient solution.     

火电机组间负荷优化分配算法比较与分析

负荷优化分配是电厂节能和运行优化的重要研究内容。在全厂机组组合方式一定的情况下,各台机组间合理的优化分配负荷可以提高整个电厂运行的经济性。为此,文中应用二次规划原理对某电厂4台300MW机组并列运行时的负荷分配进行优化。结果证明,它比线性规划分配负荷的调度方式所用煤耗明显降低。     

Global and regional potential for bioelectricity with carbon capture and storage

Among technological options to mitigate greenhouse gas (GHG) emissions, biomass energy with carbon capture and storage technology (BECCS) is gaining increasing attention. This alternative offers a unique opportunity for a net removal of atmospheric CO₂ while fulfilling energy needs. Empirical studies using bottom-up energy models show that BECCS has an important role to play in the future energy mix. Most of these studies focus on global BECCS potential, whereas it is of interest to understand where this mitigation option will be deployed. This key issue will strongly depend on regions’ biomass resources and possession of storage sites. The aim of this study is to assess the global and regional potential of BECCS up to 2050 in power generation. This analysis is conducted using the multiregional TIAM-FR optimization model. The climate policy scenarios investigated lead to a considerable expansion of renewable energy and CCS and BECCS technologies in the power sector. CCS from fossil fuel is mainly deployed in fast developing countries (India and China) and BECCS is highly distributed in developing countries, even though biomass resources are widely available in all regions.     

计及绿色电力证书与碳交易制度的“源-荷”协调优化调度

基于低碳电力和智能电网的背景,考虑现有的风电消纳困境,该文以绿色电力证书为基础,结合碳排放权的交易制度,同时引入需求侧高载能企业负荷的响应模型,并将虚拟电厂经济效益作为优化目标函数,建立以绿证交易为基础,结合碳交易制度和高载能需求侧响应的“源-荷”双侧互补协调优化调度模型。最后将某省份区域电网代入到该文所构建模型之中进行仿真,并采用自适应免疫疫苗算法对模型进行求解,结果表明所建立的计及绿证交易与碳交易的模型有利于促进风电消耗,降低单位发电量的碳排放。     

Development of an Energy‐Economic Model with Endogenous Technological Progress and Feasibility Study of CCS Systems

The main objective of this research is to develop a bottom‐up energy‐economic model considering endogenous technological development. The designed model analyzes the feasibility of CCS technologies in the Japanese electricity market to derive an optimum carbon reduction scenario. Two factors, a learning curve based on learning‐by‐doing and public R&D investment, precede technological progress. The analysis is calculated with a set of scenarios, which is based on alternative assumptions for technological characteristics: chemical and physical carbon absorptive technology. From modeling estimation, we conclude that technological progress reduces the generation costs of conversion technologies with CCS, as a CCS system acquires an additional unit of installation. Generation cost with chemical absorption remarkably reduces its marginal unit cost through a learning mechanism. The supply fraction from a gas‐fired power plant increases over the analytical time period. The introduction of CCS reduces carbon emission level 17% compared to the baseline scenario in 2050. Technological progress has little impact on the total system costs; however, learning‐by‐doing pushes the introduction of CCS into the market rather than into a R&D activity. Research and development efforts in the private sector or knowledge spillover are not modeled in the study; however, they have the potential to contribute to the mitigation of carbon emission as well. © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res, 43(4): 332–351, 2014; Published online 3 October 2013 in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21078     

考虑CVaR的机组组合和多场景备用决策联合优化

  目的  为提高可再生能源消纳水平以及衡量其出力不确定性带来的风险,该文以特性各异电源互补协调调度作为提升系统运行经济性和促进可再生能源消纳的手段,并针对系统内可再生出力和负荷的波动性,引入条件风险价值（conditional value-at-risk,CVaR）理论,量化其不确定性对系统调度运行时产生的风险。  方法  利用多场景技术模拟系统不确定性,建立考虑条件风险价值（CVaR）的机组组合和多场景备用决策联合优化模型,将风险水平限制在可接受的前提下,追求系统运行成本最小。最后在通用代数建模系统（the general algebraic modeling system, GAMS）平台上编程,并调用混合整数线性规划CPLEX求解器进行求解。  结果  仿真计算表明,不同类型电源的机组组合和备用决策能有效提高系统运行经济性、可靠性和可再生能源消纳水平。  结论  基于改进IEEE 24和某省实际电网的算例分析验证了所提模型和方法的合理性和有效性。     

Design of an energy hub based on natural gas and renewable energy sources

This paper presents a simulation model for an energy hub consisting of natural gas (NG) turbines as the main sources of energy (including both electricity and heat) and two renewable energy sources—wind turbines (WTs) and photovoltaic (PV) solar cells. The hub also includes water electrolyzers for hydrogen production. The hydrogen serves as an energy storage medium that can be used in some transportation applications, or it can be mixed with the NG feed stream to improve the emission profile of the gas‐turbine unit. The capacity of the designed hub is meant to simulate and replace the coal‐fired Nanticoke Generating Station with a NG‐fired power plant. Therefore, the aim of this work is to develop a simulated model that combines different energy generation technologies, which are evaluated in terms of the total energy produced, the cost per kWh of energy generated, and the amount of emissions produced. The proposed model investigates the benefits, both economic and environmental, the technological barriers, and the challenges of energy hubs by developing several scenarios. The simulation of these scenarios was done using General Algebraic Modeling System (GAMS®). Although the software is strongly known for its optimization capability, the mixed complementary problems solver makes it a strong tool for solving equilibrium problems. Excess energy produced during off‐peak demand by WTs and PV solar cells was used to feed the electrolyzer to produce H₂ and O₂. The proposed approach shows that a significant reduction in energy cost and greenhouse gas emissions were achieved, in addition to the increased overall efficiency of the energy hub. Out of the examined three scenarios, Scenario C appeared to be the most feasible option for a combination of renewable and non‐renewable technologies as it did not only produce hydrogen, but also provided electricity at relatively lower prices. Copyright © 2013 John Wiley & Sons, Ltd.     

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。