An integrated simulation tool proposed for modeling and optimization of CHP units

Master's thesis in Petroleum engineeringIn this project, a novel framework for CHP optimization is proposed. The objective of the study was to develop an automatic optimization tool based on the integration of IPSEpro simulation software and MATLAB programming environment. The data exchange between these components was organized via COM interface. An experimentally validated model of the commercial AET100 CHP unit was utilized. The CHP was considered as a part of a grid. Therefore electricity trading possibility was taken into account. The system was extended to polygeneration by implementing a solar panel as an additional power source. The objective was to minimize the cost function, which consists of operational and capital investments costs, under a set of constraints. For solving the problem, the Genetic Algorithm was applied. As an addition to the study, two other algorithms (Particle Swarm Optimization and Differential Evolution) were also tested. The applying a tool to real data was not considered in the project. However, an optimization was done for test data to show the performance of a developed framework. The test optimization was done for the 24-hours period in July and December, with different electricity and gas price profiles and various ambient conditions. The obtained results were analyzed in details. It was shown that the proposed optimization tool provides appropriate results. It is flexible and has a good potential to be further extended and developed

Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: a case study in Egypt

This paper investigates the design and operation management of VPPs in regulated markets. A new framework based on profit maximization objective function is presented in this study. The hypotheses of this research is that considering profit as an objective function would yield a more realistic and optimal sizes compared to Cost of Energy (COE) minimization approach adopted in literature. The analyzed VPP aggregates solar PV units, CCHP supplying power and thermal energy, Battery storage system and thermal energy storage system. The system is formulated in an optimization model fed by energy demand profile, prices and inputs for solar power (irradiance and weather data). The objective function is formulated based on maximization of profit of the VPP selling power to the grid by Power Purchase Agreement (PPA), selling power to consumers at the public electricity tariff, and selling thermal energy at an assumed constant tariff. CCHP non-linear part-load efficiency is also considered in the model, accordingly, Genetic Algorithm (GA) is employed to solve the optimization. Results of the optimally configured model achieved 36% improvement in COE compared to literature. Solar power contributed by 31% from the total produced energy without imbalance, grid power contributed by 4%, and CO2 emissions reduced by 47% compared to full dependency on the grid. Statistical relationships were drawn showing the relationship between profit, energy and exergy efficiencies versus different CCHP capacities. In addition, analysis is provided for the efficiencies’ relation with the dumped heat from the CCHP

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

Planning a Renewable Power System in Texas as an Introduction to Smart Power Grid

Design electrical systems from six renewable energy sources: photovoltaic, wind energy, geothermal, concentrated solar energy, biomass energy, and hydropower in addition to a storage system in the state of Texas, This power system converts the electric system in Texas into a 100 % renewable energy power system. Optimization technique has applied to the results to make the system economical and reduce the wasting resources, this system is considered as decentralized as well which is a great advantage for achieving the smart grid technology compared with the conventional plants where the generation parts are deposed in a small part of the grid, this design makes each part of the grid have two roles as a generator as well as load. The storage system relies on the heat storage of traditional batteries and concentrated solar power plants. Hence this power system could reduce the greenhouse gases by more than 90 %, the annual electricity bill in Texas could be decreased by amount form 10-20 billion dollars yearly, and finally, achieve a higher level of security and reliability of the system by applying the smart grid concept

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

The COVID-19 pandemic has had a significant impact on the supply chains of traditional fossil fuels. According to a report by the International Energy Agency (IEA) from 2020, oil-refining activity fell by more than the IEA had anticipated. It was also assumed that the demand in 2021 would likely be 2.6 million bpd below the 2019 levels. However, renewable markets have shown strong resilience during the crisis. It was determined that renewables are on track to meet 80% of the growth in electricity demand over the next 10 years and that sustainable energy will act as the primary source of electricity production instead of coal. On the other hand, the report also emphasized that measures for reducing environmental pollution and CO2 emissions are still insufficient and that significant current investments should be further expanded. The Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series is dedicated to the advancement and dissemination of knowledge on methods, policies and technologies for improving the sustainability of development by decoupling growth from the use of natural resources. The 15th SDEWES conference was held online from 1–5 September 2020; more than 300 reports with 7 special sections were organized on the virtual conference platform. This paper presents the major achievements of the recommended papers in the Special Issue of Energies. Additionally, related studies connected to the above papers published in the SDEWES series are also introduced, including the four main research fields of energy saving and emission reduction, renewable energy applications, the development of district heating systems, and the economic assessment of sustainable energy

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Enhancement of Industrial Energy Efficiency and Sustainability

Industrial energy efficiency has been recognized as a major contributor, in the broader set of industrial resources, to improved sustainability and circular economy. Nevertheless, the uptake of energy efficiency measures and practices is still quite low, due to the existence of several barriers. Research has broadly discussed them, together with their drivers. More recently, many researchers have highlighted the existence of several benefits, beyond mere energy savings, stemming from the adoption of such measures, for several stakeholders involved in the value chain of energy efficiency solutions. Nevertheless, a deep understanding of the relationships between the use of the energy resource and other resources in industry, together with the most important factors for the uptake of such measures—also in light of the implications on the industrial operations—is still lacking. However, such understanding could further stimulate the adoption of solutions for improved industrial energy efficiency and sustainability

Operation optimisation study for CCGT power plant.

A major concern for the power generation industry is to obtain a maximum economic benefit without over-consuming the remaining life of the gas turbine hot section. This study explored a methodology to support decision making for operational optimisation of a combined cycle gas turbine (CCGT) power plant. There is no published algorithm for modelling a parallel dual pressure, once-through steam generator (OTSG), nor any proposed method for OTSG degradation diagnosis and how the degradation affects OTSG performance. What is more, few publications were found for optimisation existing power plant operation considering gas turbine creep life. This study presents a new thermodynamic algorithm to simulate the thermodynamic performance of parallel dual pressure OTSG. In this study, a novel gas path diagnostic method for an OTSG based on the Newton-Raphson method was developed to predict the OTSG degradation caused by fouling. A daily operation decision support platform for this existing power plant is proposed that models CCGT performance, creep life, emissions, economics, and provides a basis for decision-making based optimised results. The OTSG performance model is applied to an OTSG operating in a CCGT power plant at Manx Utilities on the Isle of Man, United Kingdom to demonstrate the effectiveness of the simulation method. A comparison between predicted OTSG performance and OTSG field data showed that the proposed model offers good prediction accuracy when simulating OTSG performance for both design and off-design points. The OTSG diagnostic system was applied to a model OTSG to test its effectiveness. The impact of measurement noise on the diagnostic accuracy was also analysed and discussed. A comparison between predicted and implanted degradation of a model OTSG demonstrated that the results were satisfactory, and the method is promising. Moreover, the diagnostic analysis of an OTSG based on real measurement has further proved that the proposed diagnostic method works well. This simulation will recommend to the plant operator optimal operation schedules taking into consideration thermo-economics and lifing, under conditions of variation of power demand, electricity price, ambient conditions and gas turbine engine health states. It will suggest the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimisation framework will assist power plant operators to decide the total power output and power split between generators based on an optimisation system that considers both immediate economic benefit and life considerations. It will help existing power plant to adjust daily operation to achieve better thermoeconomic and lifing benefits. The outcome of this research will be useful for industrial CCGT power generation.PhD in Aerospac