Transactive energy integration in future smart rural network electrification

Environmental life cycle assessment (LCA) is today an important methodology to quantify the life cycle based environmental impacts of products, services or organisations. Since the very first LCA studies, the cumulative energy demand CED (also called ‘primary energy consumption’) has been one of the key indicators being addressed. Despite its popularity, there is no harmonised approach yet and the standards and guidelines define the cumulative energy demand differently. In this paper, an overview of existing and applied life cycle based energy indicators and a unifying approach to establish characterisation factors for the cumulative energy demand indicator are provided. The CED approaches are illustrated in a building’s LCA case study. The five approaches are classified into two main concepts, namely the energy harvested and the energy harvestable concepts. The two concepts differ by the conversion efficiency of the energy collecting facility. A unifying ‘energy harvested’ approach is proposed based on four theses, which ensure consistent accounting among renewable and non renewable energy resources. The indicator proposed is compared to four other CED indicators, differing in the characterisation factors of fossil and biomass resources (upper or lower heating value), the characterisation factor of uranium and the characterisation factors of renewable energy resources (amount harvested or amount harvestable). The comparison of the five approaches is based on the cumulative energy demand of a newly constructed building of the city of Zurich covering the whole life cycle, including manufacturing and construction, replacement and use phase, and end of life. The cumulative energy demand of the life cycle of the building differs between 336 MJ oil-eq/m2a (‘CED uranium low’) and 836 MJ oil-eq/m2a (‘CED energy statistics’). The main differences occur in the use phase. The main reason for the large differences in the results are the different concepts to determine the characterisation factors for renewable and nuclear energy resources. The energy harvested approach ‘CED standard’ is a consistent approach, which quantifies the energy content of all different (renewable and non-renewable) energy resources. The ‘CED standard’ approach and the impact category indicator results computed with this approach reflect the safeguard subject ‘energy resources’ but not (no other) environmental impacts. The energy harvested approach proposed in this paper can readily be implemented in different contexts and applied to various data sets.

At present, the power system is developing toward a fully renewable energy resources (RERs) equipped system due to significant challenges with the conventional units. This trend has led to remarkable new challenges for power system planners considering the stochastic nature of RERs, application of new emerging technologies, etc. In this article, a two-stage robust stochastic programming model for the optimal scheduling of commercial microgrids equipped with 100% RERs to handle the existing uncertainties is presented. In the day-ahead electricity market, microgrids maximize their expected profits by optimizing their bidding strategy, while minimizing the imbalance cost is targeted for microgrids by adjusting the distributed energy resources in the real-time balancing market. Transactive energy technology is effectively applied to manage the energy trading between microgrids with each other in the local energy transaction market and with the power grid. For demand-side management, the demand response program is posed considering the shiftable and interruptible features of the load. Simulation results on the IEEE 33-bus standard system integrated with microgrids verify that the proposed model could provide satisfactory profits for microgrids participated in the energy exchanging process based on the transactive energy architecture.

Rural energy development in Iran: Non-renewable and renewable resources

Renewable energy resources especially wind and solar energy are emerging as the modern power sources to electrify remote areas. The main reason behind their emergence is due to their environment-friendly behavior, unlimited availability and short period for replenishment over nonrenewable energy resources. Renewable energy resources are much better than nonrenewable energy resources, but the intermittency in renewable energy resources degrades the system performance. In order to overcome the intermittency, multiple hybrid system techniques were proposed in literature that can achieve suitable results but have disadvantages of complicated control structures and high implementation cost. Considering aforementioned shortcomings, a simple balancing approach is proposed to intermix solar and wind energy together so as to utilize the available energy from both sources at a given time. It is very common that solar farms are the dominating source of energy in daytime and summer, while wind farms are the dominating source of energy at night and in winter. The proposed approach delivers maximum possible power to the load by combining dominating and non-dominating resources all the time, hence mitigating the intermittency of individual resources. Compared with other approaches, the proposed approach offers key benefits with redundancy, simple design and low cost, which can be analyzed from simulation results.

Optimal scheduling strategy and benefit allocation of multiple virtual power plants based on general nash bargaining theory

Renewable energy resources like wind, sun, hydropower, geothermal, and biomass are better alternatives for conventional non-renewable energy resources such as fossil fuel reserves. Renewable energy resources are the better technological option to generate clean energy and overcome the depletion of non-renewable energy resources. This paper presents the complete system design of hybrid solar wind charger. The main contribution is to develop a compact system, which utilizes the eternal solar and wind power to solve the major crisis of pollution as well as the scarcity of fossil fuels. The functionality of the proposed system allows a reliable source of power generation for human beings in the energy crisis.

With the view to minimize the energy crisis due to the ever depleting non-renewable energy resources, this research aimed to apply nanotechnology to enhance the efficacy of solar energy power plant – a renewable energy resource. According to literature Concentrated Solar Power Plant system requires heat transfer fluid (HTF) for proficient working. This research reports the synthesis of novel, efficient heat transfer fluid doped with nano sized particle including carbon nanotubes and metal oxide i-e copper oxide. This nano heat transfer fluid (NHTF) has very low melting point in comparison to conventionally prepared molten salt mixture for heat transfer purpose which typically have high melting point characteristically 140°C or higher. Most prominently this NHTF has enhanced thermo-physical properties including high thermal stability and high specific heat capacity. These properties ensures effective increase in thermal energy storage (TES) which is a key factor in solar thermal power plant and make it broad range operating heat transfer fluid.

The transition towards a more affordable, reliable, and sustainable energy provision paradigm is one of the main 21st century challenges that humanity must overcome to protect the planet from the harmful effect caused by climate change. The concentration of CO2 in the atmosphere has been dramatically increasing since the pre-industrial era. If the increase of green-house gasses emissions continues unabated, this will bring dramatic consequences for planet Earth, compromising eventually the existence of many species, including the human race. To avoid a climate change catastrophe, the share of primary energy coming from renewable energy resources must increase from around 15% in 2015 to 65% in 2050. This energy transition can not rely solely on few successful technologies (i.e., solar photovoltaic, and wind energy), but it must count on a larger variety of technical solutions that are suitable for a wider range of renewable sources and diversity of circumstances. For instance, renewable thermal energy sources for power generation (i.e., geothermal reservoir, biomass fuel, and concentrated solar radiation), can provide a large portion of the world electricity demand in the future. However, the exploitation of a good portion of these sources strongly depends on the market success of technologies such as the Organic Rankine Cycle (ORC) power system. One of the key aspects to make ORC systems economically competitive, especially at the smaller sizes (⇡ 1 − 50 kW), is the realization of highly efficient turbomachinery components. The fluid-dynamic design of ORC turbomachinery significantly differs from the design of traditional machines (i.e., steam and gas turbines), and this is mainly due to the different thermo-physical properties and gas dynamic behavior of the organic working fluids. This means that design methods devised for standard steam and gas turbomachinery can not be used for turbomachinery operating in the Non-ideal compressible fluid dynamics (NICFD) region. Furthermore, no experimental campaigns have ever been carried out to create a body of empirical knowledge to support the design highly efficient ORC turbomachinery. As a consequence, the entire design process of ORC turbomachinery relies only on the use of advanced CFD software. The current trend is to couple CFD tools with numerical optimization techniques in order to automatically obtain optimal flow passage geometries. In particular, adjoint-based methods have clearly demonstrated to be the only optimization technique capable of tackling the multi-stage turbomachinery design problem, in which thousands of design variable must be concurrently optimized. Therefore, the research documented in this PhD dissertation aimed at extending the adjoint method in order to perform the fully-turbulent fluid-dynamic shape optimization of 3D multi-stage ORC turbomachinery. This document contains an extensive introduction, three main chapters, each documenting a building block towards the accomplishment of the main goal of this PhD project, and a final concluding chapter that summarizes all the research outcomes of this work and proposes future steps for research in this field. The first part of the thesis describes the extension of the RANS equations, the convective numerical schemes, and the viscous numerical schemes to the use of complex thermo-physical laws, so to simulate turbulent flows of components working in the NICFD thermodynamic region. The second part documents the derivation of the adjoint solver in order to resolve shape-optimization design problems for 2D single row of ORC turbomachinery. Finally, the last part reports the extension of the adjoint method to 3D multi-stage turbomachinery design.

Optimal energy management of a PV-WTG-BSS-DG microgrid system

A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost‐sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real‐time electricity rates to the grid frequency over a few seconds to provide demand‐side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.

This paper presents a process of electrical generator selection and operation for low speed renewable hydrokinetic energy resources. As more renewable energy resources enter the marketplace, generators customized towards unsteady mechanical input power and low speed operation become critical design points. Traditional power generation has relied upon steady operation to which the majority of generators are designed. The process of generator selection for low speed unsteady resources encompasses many design factors and tradeoffs. Generator selection is discussed and evaluated with respect to a low-speed vertical oscillator hydrokinetic energy resource. The complete generator selection entails selecting a generator type, modeling in simulation, and testing in a laboratory setting. This paper lays out the considerations that must be made in order to implement a successful design from the standpoint of power generation in many new and unproven renewable energy resources.

In recent year, alarming rate of natural disasters around the world have demanded the need for operative solution in field of power generation, to control polluted energy sources which are major cause of global warming. Microgrid facilitates penetration of renewable energy sources into the existing distribution systems to reduce the overall carbon footprint of the globe by reducing the dependency on the main grid. Efficient but linear microgrid resource scheduling algorithms are gaining interest in present time due to its simplicity and fast computation. This research paper aims to serve the purposes by designing a mixed integer linear programming based microgrid scheduling problem while various types of scenario, minimize the electricity cost for the utilities also maintain the generation and load balance. The strategy is implemented on a small microgrid to prove its efficacy .In this paper mainly optimal scheduling of microgrid has been done in various scenario, and obtained there global minimum electricity cost to the help of mixed integer linear programming (MILP) algorithm. Microgrid is a small scale type of power grid, which provides the energy locally, its offers integration between distributed energy resources and the locally connected loads. Microgrid able to be operated with the main grid and in standalone mode also ability to transitions between these two modes, the mode of operation of microgrid is depends on the system operating condition. The reliability of power grid is improving more when it’s integrated with the Microgrid and works together. Power exchange with the upper stream grid is done through the point of common coupling (PCC). Microgrid having renewable energy resources i.e. PV, Wind and non-renewable energy resources, DG, FC, and MC connected with the Battery storage type system and locally connected loads. So it is a very reliable scenario, main grid with the microgrid, it is more beneficial, economy and stable types of system. Keywords : Optimal Microgrid Scheduling, Linear Programming DOI : 10.7176/APTA/76-05 Publication date :March 31 st 2019

While microgrid distribution system technology is rapidly increasing, the importance of finding a way to evaluate the effect of adding renewable DGs on the overall grid reliability has become essential. Renewable energies resources are effective because they reduce environmental pollution and improve energy utilization efficiency and reliability, which makes them widely used in the network. In this paper, the impact of adding solar energy sources and energy storage to a microgrid distribution system will be investigated. Markov modeling is used to model the energy source and energy storage as well as to evaluate the overall reliability of the system. Roy Billinton Test System (RBTS) is used to evaluate different load and system indices, including System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration Index (CADI), Average System Unavailability Index (ASUI) and Energy Not Supplied (ENS).

Renewable energy resources are the major sources of energy for human being and current industrial applications. Exhausting resources such as petroleum, gas, coal and other non-renewable energy resources are depleting day by day and causing global warming. Due to this reason national and international trend of generating energy is moving to the renewable and clean energy resources. Energy from the wind is one of the prominent sources of production of electricity. Wind turbines are employed for electrical energy generation. Many types of AC generators have been employed for converting the rotational energy in form of mechanical energy from wind turbine into electricity. Doubly-fed induction generator is the prominent and escalating kind of generator employed in wind energy conversion system. Energy from the wind might be varied randomly therefore system reliability and stability is needed. The major purpose of this research work is to implement the grid connected wind turbine model and investigate the voltage and current profiles of system at normal condition and transient fault condition.

Sustainable thermal-based desalination with low-cost energy resources and low-carbon footprints