Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

Optimization of grid tied hybrid power system in smart premises

Tapping of renewable energy sources like solar and wind is given great priority by power producers all over the world. Technical problems of linking them to the grid are solved. The cost constraints of utilizing renewable energy at specific locations are to be determined. In this work, a model is developed for grid tied hybrid power system (HPS) consisting of photovoltaic (PV) module and wind mill at the roof top of smart premises. The grid is capable of delivering and receiving energy. Objective function is formed with constraints taking into account the cost of PV module, wind mill, and grid tied inverter with controller. The constraints are rating of HPS and energy that can be delivered to the grid. Using this model, case studies were conducted in three locations in India, each location having two different demands. The results are presented. With the optimal rating of HPS, results shows that, conventional energy cost is higher.     

Game theory and hybrid genetic algorithm for energy management and real-time pricing in smart grid: the Tunisian case

ABSTRACT

Microgrids are the key for integrating renewable energy from different sources into smart grid, that is why power grid evolves into a combination of interconnected microgrids. In fact, future power grids are undergoing this groundbreaking change that will help meet the increasing demand of electric power and reduce carbon emission. In this sense we study in this paper, based on measured data, a real case of energy management in the area of Beja located in Tunisia. Indeed, we propose a model for the power exchange which proves the potential of applying game theory in the development of both real-time pricing and energy management mechanism for an open electricity market. We also introduce a hybrid genetic algorithm to compute the Nash Equilibrium. Results show that the proposed smart energy management can decrease the real cost of power up to 20%, to divide the energy transmission losses by a factor of two and to reduce the carbon emission in the area of Beja.     

The Integration of a Genetic Programming-Based Feature Optimizer With Fisher Criterion and Pattern Recognition Techniques to Non-Intrusive Load Monitoring for Load Identification

Identification of electricity energy consumption on individual household appliances used in a smart house is the first important step for making the use and conservation of electricity energy more efficient. In the past, Non-Intrusive Load Monitoring (NILM) techniques, which are part of smart grid techniques realized to improve electricity energy usage efficiency, have been developed to identify individual appliances with avoiding installing many smart meters for appliances in a field. In this paper, a new NILM technique that integrates an efficient Genetic Programming (GP)-based feature optimizer with pattern recognition techniques is proposed to identify which appliance is being turned on or off. The proposed GP-based feature optimizer with Fisher criterion is used to generate a more efficient feature than original potential transient features extracted from captured transient response of household appliances through analysis of NILM. The new feature generated by GP is used by pattern recognition techniques as load identifiers for load identification. The load identifiers used and compared in this paper include k-Nearest-Neighbor Rule, Back-Propagation Artificial Neural Network, and Learning Vector Quantization. Experiments are conducted under different single-load and multiple-load operation circumstances at different actual experimental environments with small disturbances. As shown from the experimental results, the proposed is confirmed to be feasible and usable.     

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING1

ABSTRACT: Efficient operation of a city water supply system is an important goal of all municipalities. Efficient operation should result in minimum operation cost through reduction in total energy use and/ or reduction in on-peak energy consumption. An optimization model was designed for operating the water supply systems of cities using groundwater. The Newton-Raphson pipe network was used for network analysis and a dynamic programming optimization algorithm was used for determining a schedule for pump operation in the pipe network system. The model is most suitable for use in small cities with up to 45,000 in population, but with large-scale disintegration techniques may also be used for larger cities. The savings in operation costs are a function of energy cost and energy use pattern and water use pattern in the area.     

Triple-objective optimal sizing based on dynamic strategy for an islanded hybrid energy microgrid

A triple-objective optimal sizing method based on a dynamic strategy is presented for an islanded hybrid energy microgrid, consisting of wind turbine, solar photovoltaic, battery energy storage system and diesel generator. The dynamic strategy is given based on a dynamic complementary coordination between two different master-slave control modes for maximum renewable energy utilization. Combined with the proposed strategy, NSGA-II-based optimization program is applied to the sizing optimization problem with triple different objectives including the minimization of annualized system cost, the minimization of loss of power supply probability and the maximization of utilization ratio of renewable energy generation. The sizing results and the proposed strategy are both compared and analyzed to validate the proposed method in a real case of an islanded hybrid energy microgrid on Dong’ao Island, China.     

Optimal Design Methods for Hybrid Renewable Energy Systems

Renewable and hybrid energy systems (HESs) are expanding due to environmental concerns of climate change, air pollution, and depleting fossil fuels. Moreover, HESs can be cost effective in comparison with conventional power plants. This article reviews current methods for designing optimal HESs. The survey shows these systems are often developed on a medium scale in remote areas and stand-alone, but there is a global growing interest for larger scale deployments that are grid connected. Examples of HESs are PV–wind–battery and PV–diesel–battery. PV and wind energy sources are the most widely adopted. Diesel and batteries are often used but hydrogen is increasing as a clean energy carrier. The design of an efficient HES is challenging because HES models are nonlinear, non-convex, and composed of mixed-type variables that cannot be solved by traditional optimization methods. Alternatively, two types of approaches are typically used for designing optimal HESs: simulation-based optimization and metaheuristic optimization methods. Simulation-based optimization methods are limited in view of human intervention that makes them tedious, time consuming, and error prone. Metaheuristics are more efficient because they can handle automatically a range of complexities. In particular, multi-objective optimization (MOO) metaheuristics are the most appropriate for optimal HES because HES models involve multiple objectives at the same time such as cost, performance, supply/demand management, grid limitations, and so forth. This article shows that the energy research community has not fully utilized state-of-the-art MOO metaheuristics. More recent MOO metaheuristics could be used such as robust optimization and interactive optimization.     

PEAKING STORAGE TANKS FOR WATER SUPPLY SYSTEMS1

ABSTRACT: Construction of a “peaking storage tank” may reduce the operational cost of municipal water in the availability of a time-of-use energy rate. A peaking storage tank is used for storing water that is pumped from wells or other sources of supply during off-peak periods when energy costs are less for use during periods of on-peak water demand. The optimal size of a peaking storage tank is that which results in the minimum total cost, which includes both the storage construction cost and the cost of operation of the pumps. The operational cost for a given time-of-use rate is determined by help of a pipe network simulation model solved by the Newton-Raphson technique and a dynamic programming optimization model. A more simplified method is also introduced. Analyses show that low off-peak energy costs make the construction of peaking storage tanks economically attractive and reduce on-peak energy use, which results in electrical load leveling.     

Hybrid power supply solutions for off-grid green wireless networks

Increasing deployment of cellular networks across the globe is pushing the energy consumption in cellular networks at an exceptional rate. The integration of renewable energy (RE) harvesting technology into future mobile networks has the potential to positively cope with environmental contamination and ensure self-energy sustainability as a means to decrease fossil fuel consumption. Diesel generator (DG) in conjunction with on-site RE harvester has emerged as an economic and extent efficient option where commercial grid supply is not viable. This paper is focused on the cost aware energy management framework addressing to least net present cost (NPC) for the envisioned hybrid powered green cellular base stations (BSs) considering tempo-spatial traffic dynamics. In such wireless networks, solar photovoltaic modules are considered as a primary energy source, while the DG and energy storage device are kept as the standby supply in case of inadequate solar energy to ensure zero outage. A comprehensive simulation-based investigation is carried out in the context of downlink Long-Term Evolution (LTE) cellular networks for evaluating cost-efficiency and reliability performance under a wide range of network settings. Particularly, this paper examines the energy yield, greenhouse gas emissions, and cost analysis based on the optimal architecture of Remote Radio Head-enabled LTE BS. Moreover, wireless network performance in terms of throughput, energy efficiency gain, and radio efficiency is thoroughly investigated using Monte Carlo simulations. Numerical results demonstrate a substantial reduction of carbon footprints with minimum NPC while satisfying the quality of service requirements.     

基于智能能源网的资源开发与市场匹配模式研究——以煤基低碳能源转化为例

智能能源网是我国＂十二五＂期间完善资源开发及能源转化的重点科研攻关课题。以煤基低碳能源转化为例,提出基于智能能源网的资源开发模式。该模式可从根本上改善我国传统的对原生资源低效和掠夺式的开发方式,提高从原生资源到能源商品整个过程的资源转化效率,在极大程度上减少生产过程中对环境造成的碳排放污染。其市场匹配模式应以政府作为引导主体,按市场化所处的不同阶段积极施行经济和科技体制改革。     

Cost optimization and reliability assessment of a microgrid with large-scale plug-in electric vehicles participating in demand response programs

ABSTRACT

In this paper, an effective objective function is proposed to minimize the cost of operation of a microgrid with large-scale plug-in electric vehicles and renewable energy resources. The profit of consumers is taken into account by utilizing the incentives in the demand response programs, and vehicle-to-grid feature of the plug-in-electric vehicles integrated into the grid. The optimization is performed using genetic algorithms. Also, reliability indices of the economically optimized microgrid are computed for various operation configurations in both the grid-tied and islanded modes. Numerical studies are conducted on a microgrid testbed to validate the performance of the proposed strategy.     

Economic and environmental optimization of a multi-site utility network for an industrial complex

Most chemical companies consume a lot of steam, water and electrical resources in the production process. Given recent record fuel costs, utility networks must be optimized to reduce the overall cost of production. Environmental concerns must also be considered when preparing modifications to satisfy the requirements for industrial utilities, since wastes discharged from the utility networks are restricted by environmental regulations. Construction of Eco-Industrial Parks (EIPs) has drawn attention as a promising approach for retrofitting existing industrial parks to improve energy efficiency. The optimization of the utility network within an industrial complex is one of the most important undertakings to minimize energy consumption and waste loads in the EIP.In this work, a systematic approach to optimize the utility network of an industrial complex is presented. An important issue in the optimization of a utility network is the desire of the companies to achieve high profits while complying with the environmental regulations. Therefore, the proposed optimization was performed with consideration of both economic and environmental factors.The proposed approach consists of unit modeling using thermodynamic principles, mass and energy balances, development of a multi-period Mixed Integer Linear Programming (MILP) model for the integration of utility systems in an industrial complex, and an economic/environmental analysis of the results. This approach is applied to the Yeosu Industrial Complex, considering seasonal utility demands. The results show that both the total utility cost and waste load are reduced by optimizing the utility network of an industrial complex.     

WATERSHED OPTIMIZATION OF AGRICULTURAL BEST MANAGEMENT PRACTICES: CONTINUOUS SIMULATION VERSUS DESIGN STORMS1

ABSTRACT: Nonpoint source (NPS) models and expert opinions are often used to prescribe best management practices (BMPs) for controlling NPS pollution. An optimization algorithm (e.g., a genetic algorithm, or GA) linked with a NPS model (e.g., Annualized AGricultural Nonpoint Source pollution model, or AnnAGNPS), can be used to more objectively prescribe BMPs and to optimize NPS pollution control measures by maximizing pollutant reduction and net monetary return from a watershed. Pollutant loads from design storms and annual loads from a continuous simulation can both be used for optimizing BMP schemes. However, which strategy results in a better solution (in terms of providing water quality protection) for a watershed is not clear. The specific objective of the study was to determine the differences in watershed pollutant loads, in an experimental watershed in Pennsylvania, resulting from optimization analyses performed using pollutant loads from a series of five 2‐yr 24‐hr storm events, a series of five 5‐yr 24‐hr storm events, and cumulative pollutant loads from a continuous simulation of five years of weather data. For each of these three different event alternatives, 100 near optimal solutions (BMP schemes) were generated. Sediment (Sed), sediment nitrogen (SedN), dissolved N (SolN), sediment organic carbon (SedOC), and sediment phosphorus (SedP) loads from a different five‐year period (an evaluation period) suggest that the optimal BMP schemes resulting from the use of annual cumulative pollutant loads from a continuous simulation of five years of weather data provide smaller cumulative NPS pollutant loads at the watershed outlet.     

A review on optimization techniques for sizing of solar-wind hybrid energy systems

Solar and wind are inexhaustible, abundant, environmentally friendly and freely available renewable energy sources. Integration of these two sources has always been a complex optimization problem which requires efficient planning, designing and control strategies. Many researchers have designed cost effective and efficient hybrid solar-wind energy systems by using various available software tools and optimization algorithms. With the advancement in artificial intelligence methods, various new optimization techniques have been developed in the last few decades. This paper presents state of the art optimization methods applied to hybrid renewable based energy systems. A brief introduction of each technique is presented along with papers published in different reputed journals. This article also reviews different power management, control strategies and multi-objective optimization methods used for hybrid wind-solar systems. A case study is presented to demonstrate the efficacy of some of the algorithms.     

Energy-efficient HVAC system through chillers’ capacity sizing for a library building

Heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings consume the largest amount of energy. Recent surge in energy cost necessitates constant re-evaluation of HVAC system for most of the buildings. The objective of this study is to present the strategic approach on energy saving analysis of the HVAC system and chiller sizing optimization for a library building. Energy modeling code (eQUEST) for buildings simulation has been applied to verify and predict the long-term energy consumption of HVAC systems. To improve the accuracy of simulation results, the actual performance curves of the chillers and pumps were the inputs of curve fitting data from on-site field measurements data. Energy consumption data acquisition from the building energy management system (BEMS) for one year has been conducted comprehensively to calibrate energy modeling and to quantify energy saving results. The results revealed good agreement between energy modeling and BEMS data with the error of less than 10%. Besides, energy savings through the chillers’ sizing based on cooling load profile could be achieved satisfactorily by utilizing energy modeling by using the actual chiller performance curve. The energy saving for HVAC system can be obtained satisfactorily at the saving of 110,362 kWh per year. It is expected that the study will stimulate a more robust investigation of energy-efficient and cost-effective HVAC system specific for library buildings.     

A GIS/Simulation Framework for Assessing Change in Water Yield over Large Spatial Scales

Recent legislation to initiate vegetation management in the Central Sierra hydrologic region of California includes a focus on corresponding changes in water yield. This served as the impetus for developing a combined geographic information system (GIS) and simulation assessment framework. Using the existing vegetation density condition, together with proposed rules for thinning to reduce fire risk, a set of simulation model inputs were generated for examining the impact of the thinning scenario on water yield. The approach allows results to be expressed as the mean and standard deviation of change in water yield for each 1-km² map cell that is thinned. Values for groups of cells are aggregated for typical watershed units using area-weighted averaging. Wet, dry, and average precipitation years were simulated over a large region. Where snow plays an important role in hydrologic processes, the simulated change in water yield was less than 0.5% of expected annual runoff for a typical watershed. Such small changes would be undetectable in the field using conventional stream flow analysis. These results suggest that use of water yield increases to help justify forest-thinning activities or offset their cost will be difficult.     

An Exergy Aware Optimization and Control Algorithm for Sustainable Buildings

Abstract

Heating and air-conditioning systems have very low exergetic efficiency as they dissipate primary energy resources at low temperatures usually between 90 and 60°C. This compounds the problem that buildings spend approximately 30% of all the energy consumed in the U.S. for heating and air-conditioning. The overall result is a large entropy production and long-term environmental degradation that can be resolved only by substituting primary energy resources by low-temperature, waste, or alternative energy resources, usually available below 50°C. For such a replacement to be feasible the environmental cost of exergy production must be factored into calculations and compatible HVAC systems must be developed without any need for temperature peaking or equipment oversizing. This article addresses environmental and often-conflicting problems associated with exergy production by HVAC systems and presents an analytical optimization and control algorithm. Results indicate that when a careful design optimization is accompanied by a dynamic control of the split between radiant and convective means of satisfying thermal HVAC loads, exergy efficient sustainable buildings may be cost effective and environmentally benign.     

天然气处理装置节能降耗研究

吉拉克凝析气田是塔里木油田最复杂的大型凝析气田之一,其采用两套不同的凝析气处理方法,工艺较复杂,制约装置平稳生产的因素较多。针对这种情况,提出了应用HYSYS流程模拟技术,完成各单体设备处理流程模拟和天然气处理全流程模拟,提出了降低能耗和增大液化气收率的工艺生产全流程优化方案。     

资源、环境、生态的关系探讨及对自然资源管理的建议

资源、环境和生态是我国生态文明建设的三个关键词,也是地球系统科学的重要组成,但目前学术研究和政策制定上仍存在模棱两可的问题,给自然资源领域的科学发展和贯彻落实生态文明发展理念带来了潜在不利影响,明晰其各自概念边界具有重要理论价值和现实意义。本文在分析自然资源、自然环境和生态环境三者内涵的基础上,对比了国内外关注视角、问题和需求的差异,并根据各自特征属性尝试构建了三者关系及判别图解模型。主要结论为:(1)国内外对自然资源、自然环境和生态环境的关注视角、问题和需求存在显著差异,其关注视角分别为数量、质量和平衡状态,关注问题分别为枯竭、退化和破坏,而关注需求分别为节约、保护和恢复;(2)三个概念的相互关系可通过是否自然、是否有价值和是否生物相关三个属性进行判别,自然资源的本质为有价值的自然环境,与自然环境是包含与被包含的关系,生物相关的自然资源即是生态环境;(3)对自然资源管理提出了拓展调查研究内容、采用"资源环境"表述和对生态治理工程进行以生物多样性为重点的负面影响评价三方面的建议。