Modeling,evaluation, and optimization of gas-power and energy supply scenarios

Recently, renewable energy sources such as wind power and photovoltaic (PV) are receiving a wide acceptance because they are inexhaustible and nonpolluting. Renewable energy sources are intermittent ones because of climate changes in wind speed and solar irradiance. Due to the continuous demand growth and the necessity for efficient and reliable electricity supply, there is a real need to increase the penetration of gas technologies in power grids. The Canadian government and stakeholders are looking for ways to increase the reliability and sustainability of power grid, and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable generation technologies to provide a qualified, reliable, and environmentally friendly power system while satisfying regional electricity demands and reducing generation cost. Scenarios are evaluated using four key performance indicators (KPIs), economic, power quality, reliability, and environmental friendliness. Various scenarios outcomes are compared based on the defined performance indices. The proposed scenario analysis tool has three components, the geographic information system (GIS) for recording transmission and distribution lines and generation sites, the energy semantic network (ESN) knowledgebase to store information, and an algorithm created in Matlab/Simulink for evaluating scenarios. To interact with the scenario analysis tool, a graphical user interface (GUI) is used where users can define the desired geographic area, desired generation percentage via gas technology, and system parameters. To evaluate the effectiveness of the proposed method, the regional zone of the province of Ontario and Toronto are used as case studies.     

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.     

An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids

This paper presents an investigation of a hybrid DC/AC integration paradigm to establish microgrids (MGs) by using a conventional three-phase local power delivery system. This approach adds an additional DC power line to the local power distribution system in order to collect energy generated by distributed domestic renewable sources. The local renewable distributed generation (DG) works in conjunction with the conventional grid utility to reduce the power draw from the grid. Researchers designed an energy conversion station to mix energy from the local DGs with energy from the grid utility. This approach, therefore, uses a continuous energy mixing strategy for DC integration of local generation and grid energy to supply energy to MG consumers via the conventional three-phase power distribution system. Thus, local distributed renewable generators do not have to contend with AC integration problems, such as AC stability and line synchronization. This approach can facilitate the transformation of conventional local power distribution systems into reliable MGs in an affordable way for stakeholders and it is a step towards construction of future smart grids.     

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%.     

Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization).     

Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm

This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.     

Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO

This study presents an optimized design of microgrid (MG) in distribution systems with multiple distributed generation (DG) units under different market policies such as pool/hybrid electricity market.Proposed microgrid includes various energy sources such as photovoltaic array and wind turbine with energy storage devices such as battery bank.In this study, microgrid is considered as independent power producer company (IPP) in power system. Price of selling/buying power in on-peak or off-peak for MG, DG and upstream power system (DISCO) under pool/bilateral/hybrid electricity market are different. In this study, particle swarm optimization (PSO) algorithm has been implemented for the optimization of the microgrid cost. The costs include capital cost, replacement cost, operation and maintenance costs and production cost for microgrid and DGs. Then, an objective function to maximize total net present worth (NPW) is presented. PSO approach is employed to obtain the minimum cost of microgrid, during interconnected operation by optimizing the production of local DGs and power exchanges with the main distribution grid. The optimization algorithm is applied to a typical LV network operating under different market policies.     

Mini- and micro-gas turbines for combined heat and power

The use of mainframe gas turbines for power generation has increased in recent years and is likely to continue to increase. The proportion of power generation using combined heat and power is also growing mainly due to efficiency improvements and environmental benefits.

Mini- and micro-turbines offer a number of potential advantages compared to other technologies for small-scale power generation, particularly for distributed power generation, although there are some technical and non-technical barriers to the implementation of the technology. There is an uncertainty about their market potential but they could be used for power generation in the industrial, commercial and residential sectors. The market potential could increase substantially if the cost, efficiency, durability, reliability, and environmental emissions of the existing designs are improved.     

基于改进灰狼算法的独立微电网容量优化配置

为降低独立微电网的综合发电成本,提高供电可靠性,研究基于改进灰狼优化算法的独立微电网电源容量优化配置方法。针对基本灰狼算法在进化后期由于种群多样性的缺失而易出现局部收敛或算法早熟的问题,提出一种具有全局寻优性能的改进灰狼优化算法。改进算法首先利用Tent混沌序列产生初始种群,以增强种群的多样性;其次,通过对收敛因子设置非线性调整策略来调节算法的全局探索与局部开发之间的平衡,提高算法的收敛性;最后引入柯西变异算子有效降低算法出现早熟收敛的概率,从而全面提高算法的优化性能。在分析风力发电机-光伏组件-蓄电池/柴油发电机独立微电网各发电单元特性的基础上,建立以年均系统成本最小化为目标的独立微电网容量优化配置模型,并利用提出的改进灰狼优化算法对该模型进行优化求解,仿真结果证明该算法具有一定的实用性和鲁棒性。     

Reliability evaluation of power system incorporating wind farm for generation expansion planning based on ANLSA approach

Reliability evaluation of power‐generating systems gives a mechanism to guarantee proper system operations in the face of different uncertainties including equipment failures. It is regularly not attainable to identify all possible failure states to figure the reliability indices because of the large number of system states engaged with system operations. Therefore, a hybrid optimization technique is required to analyse the reliability of the power system. This paper proposes a hybrid optimization technique to evaluate the reliability of a power system for the generation expansion planning incorporating wind energy source. The proposed hybrid methodology is the joined execution of both ant lion optimization algorithm (ALO) and lightning search algorithm (LSA), and it is named as ANLSA. ALO searching behavior is enhanced by LSA. Through the inherent convergence mechanisms, ANLSA search the meaningful system states. The most probable failure states contribute reliability indices of power generating system including mean down time (MDT), loss of load probability (LOLP), loss of load expectation (LOLE), loss of load frequency (LOLF), and expected demand not supplied (EDNS). Furthermore, ANLSA is utilized to assess the reliability of system under normal condition, integration of wind farm with capacity of 150 MW, and considering spinning reserve requirement (SRR). By then, the proposed work is actualized in MATLAB/Simulink platform and it is demonstrated on IEEE reliability test system (IEEE RTS‐79). Furthermore, the statistical analysis of proposed and existing techniques such as Monte Carlo simulation (MCS) and discrete convolution (DC) is considered. The comparison results demonstrate that proposed approach confirms its ability for evaluating the power system reliability.     

Integrated model framework for the evaluation of an SOFC/GT system as a centralized power source

New power generation technologies are expected to reduce various environmental impacts of providing electricity to urban regions for some investment cost. Determining which power generation technologies are most suitable for meeting the demand of a particular region requires analysis of tradeoffs between costs and environmental impacts. Models simulating different power generation technologies can help quantify these tradeoffs. An Internet‐based modelling infrastructure called DOME (distributed object‐based modelling environment) provides a flexible mechanism to create integrated models from independent simulation models for different power generation technologies. As new technologies appear, corresponding simulation models can readily be added to the integrated model. DOME was used to combine a simulation model for hybrid SOFC (solid oxide fuel cell) and gas turbine system with a power generation capacity and dispatch optimization model. The integrated models were used to evaluate the effectiveness of the system as a centralized power source for meeting the power demand in Japan. Evaluation results indicate that a hybrid system using micro‐tube SOFC may reduce CO₂ emissions from power generation in Japan by about 50%. Copyright © 2004 John Wiley & Sons, Ltd.     

Study of MW-scale biogas-fed SOFC-WGS-TSA-PEMFC hybrid power technology as distributed energy system: Thermodynamic,exergetic and thermo-economic evaluation

Advanced biogas power generation technology has been attracting attentions, which contributes to the waste disposal and the mitigation of greenhouse gas emissions. This work proposes and models a novel biogas-fed hybrid power generation system consisting of solid oxide fuel cell, water gas shift reaction, thermal swing adsorption and proton exchange membrane fuel cell (SOFC-WGS-TSA-PEMFC). The thermodynamic, exergetic, and thermo-economic analyses of this hybrid system for power generation were conducted to comprehensively evaluate its performance. It was found that the novel biogas-fed hybrid system has a gross energy conversion efficiency of 68.63% and exergy efficiency of 65.36%, indicating high efficiency for this kind of hybrid power technology. The market sensitivity analysis showed that the hybrid system also has a low sensitivity to market price fluctuation. Under the current subsidy level for the distributed biogas power plant, the levelized cost of energy can be lowered to 0.02942 $/kWh for a 1 MW scale system. Accordingly, the payback period and annual return on investment can reach 1.4 year and about 20%, respectively. These results reveal that the proposed hybrid system is promising and economically feasible as a distributed power plant, especially for the small power scale (no more than 2 MW).     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm

Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study, an improved particle swarm optimization algorithm is developed to derive these nondominated solutions. Hybrid generation systems under different design scenarios are designed based on the proposed approach. First, a grid-linked hybrid system is designed without incoroprating system uncertainties. Then, adequacy evaluation is conducted based on probabilistic methods by accounting for equipment failures, time-dependent sources of energy, and stochastic generation/load variations. In particular, due to the unpredictability of wind speed and solar insolation as well as the random load variation, time-series models are adopted to reflect their stochastic characteristics. An adequacy evaluation procedure including time-dependent sources, is adopted. Sensitivity studies are also carried out to examine the impacts of different system parameters on the overall design performance.      

基于多因素分析的微网综合效益评价

为评价含有风电、光伏发电及微型抽水蓄能发电的微网系统的效益,采用多因素分析方法,通过电力不足概率和期望缺供电量两个指数检验微网供电可靠性,采用净现值、效益费用及回收期三个度量指标评价微网系统经济性,并通过计算安装微网系统后减少的碳排放量评估微网环境效益,最终得到微网的综合效益评价结果。结果表明,微网系统拥有较高的经济和环境效益,且运用蓄电池技术可显著提高微网的可靠性。     

Optimal economic operation of microgrids considering combined heat and power unit,reserve unit,and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.     

Survey on microgrids: Unplanned islanding and related inverter control techniques

Nowadays, the importance of electrical generation based on renewable energies is increasing, due to its low emissions of greenhouse gases. At the same time, Distributed Generation and Microgrids (MG) are becoming an important research line because of their peculiar characteristics. MGs are composed of small power sources which can be renewable, placed near customer sites. Moreover, they have the inherent property of islanding: the disconnection of either the MG from the main grid or a portion of a MG from the rest of the MG. There are two kinds of islanding: intentional or planned (for maintenance purposes), and unintentional or unplanned. The latter is mainly due to disturbances and it is used to avoid damages in sources and loads. It is the most critical case because it must be detected as soon as possible to activate all the control systems which allow continuing the energy production and distribution despite the disconnection. In islanding, it is crucial to ensure the power and the electrical signal quality. In grid-connected mode, the inverters use the electrical signal of the main grid as reference. Once in islanding, the main grid reference is lost and new control techniques for the inverters are needed in order to obtain the correct values of voltage magnitude and frequency in the MG.The main objective of this paper is to make a survey on MGs focussed on two important features: unplanned islanding and control of inverters in that scenario. The idea is to present the basic architectures and regulation techniques of MGs and to study the islanding behaviour, mainly the different detection techniques and the inverters’ control once islanded.     

计及天然气压力能的微电网储荷协调优化调度

为解决天然气输送过程中压力能的浪费问题,提高天然气能源利用率,提出将天然气压力能发电系统引入至微电网整体调度方案中。针对天然气压力能发电系统中前后端口的补热需要,将微电网中的风冷热泵补热系统与压力能发电系统进行耦合。考虑到由天然气管网中流量波动和环境因素造成的压力能出力波动问题,提出微电网储荷一体化协调优化方案,以确保系统高效稳定运行。基于上述内容,构建考虑可控电源出力成本、储能调度成本、微电网与配电网的交互成本和负荷调度成本的微电网优化调度模型,并采用Yalmip工具包编写优化调度程序。最后,通过对西南地区某调压站数据进行仿真,验证该方案的可行性与经济性。     

风/光/柴多能互补发电系统优化配置研究

为实现风/光/柴多能互补发电系统的经济运行,应用启发式算法以分布式发电投资、运行费用、向输电系统直接购电费用以及考虑环境因素费用总和最小为优化目标,分别对并网型和离网型两种情况进行规划和运行的综合分析,从而确定各DG类型的最优投入容量。研究结果表明:该模型可以全面评估分布式发电的经济性能,并对不同运行方式给出相应的最优配置方案,从而极大地提高系统的供电可靠性和经济可行性。     

实现电厂生产运行指标统计的新思路

发电企业迫切需要实用的信息手段,以提高发电运行管理水平。文中对生产指标特点进行研究,结合发电企业信息化建设及生产指标管理情况,基于先进的计算机技术,提出提高发电运行管理水平的新思路,并就其中的几个关键性问题进行研究解决。最后通过实践应用,证明该思路切实可行。