Search results

This study aims to exploit the abundance of solar energy resources for socioeconomic development in the semi -arid Northeastern Brazil as a potent adaptation tool to global climate change. It points out a set of conjuncture factors that allow us to foresee a new paradigm of sustainable development for the region by transforming the sun’s radiant energy into electricity through distributed photovoltaic generation. The new paradigm, as presented in this essay, has the transformative potential to free the region from past regional development dogma, which was dependent on the scarce water resource, and the marginal and predatory use of its Caatinga Biome.

The research uses a pre ante design, following the procedures of scenario building, as an adaptation mechanism to climate change in the sector of energy generation and socioeconomic inclusion.

The scenarios of socioeconomic resilience to climate change based on the abundance of solar radiation, rather than the scarcity of water, demonstrates its potential as a global adaptation paradigm to climate change.

The developments proposed are dependent on federal legislation changes, allowing the small producer to be remunerated by the energy produced.

The proposed smart grid photovoltaic generation program increases the country's resiliency to the effect of droughts and climate change.

As proposed, the program allows for the reversion of a pattern of long term poverty in semi-arid Northeast Brazil.

The exploitation of the characteristics of abundance of the semiarid climate, i.e. its very condition of semi-aridity with abundant solar radiation, is itself an advantage factor toward adaption to unforeseen drought events. Extensive previous research has focused on weighting and monitoring drought i.e. the paradigm of scarcity. The interplay between exploiting Northeast Brazil’s abundant factors and climate change adaptation, especially at the small farmer levels constitutes a discovery never before contemplated.

On‐site power has many potential benefits, from temporary relief to congested transmission and distribution systems, to alleviating problems created by incorrect load forecasts, all the way to meeting the load requirements of a facility. Many of these assets are being installed to take advantage of one or more of the above opportunities. This paper shows how effective utilisation of these assets not only increases the reliability of a facility, but also helps generate revenue from assets that would otherwise sit idle. It goes on to demonstrate to facility managers how they can successfully implement such a model through better understanding of their facilities’ needs combined with financial details of various programmes offered by local suppliers of electricity.

The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS).

This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied.

The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations.

The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system.

Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

The purpose of this paper is to estimate the locational marginal price (LMP) at each distributed generation (DG) bus based on DG unit contribution in loss reduction. This LMP value can be used by distribution company (DISCO) to control private DG owners and operate network optimally in terms of active power loss.

This paper proposes proportional nucleolus game theory (PNGT)-based iterative method to compute LMP at each DG unit. In this algorithm, PNGT has been used to identify the share of each DG unit in loss reduction. New mathematical modeling has been incorporated in the proposed algorithm to compute incentives being given to each DG owner.

The findings of this paper are that the LMP and reactive power price values for each DG unit were computed by the proposed method for the first time. Network can be operated with less loss and zero DISCO’s extra benefit, which is essential in deregulated environment. Fair competition has been maintained among private DG owners using the proposed method.

PNGT has been used for the first time for computation of LMP in distribution system based on loss reduction. Incentives to each DG unit has have been computed based on financial savings of DISCO due to loss reduction. Share of active and reactive power generation of each DG unit on change in active power loss of network due to that DG unit has been computed with new mathematical modeling. The proposed method provides LMP value to each DG unit in such a way that the network will be operated with less loss.

An electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.

This paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.

The proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.

Travelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

Gives a bibliographical review of the finite element meshing and remeshing from the theoretical as well as practical points of view. Topics such as adaptive techniques for meshing and remeshing, parallel processing in the finite element modelling, etc. are also included. The bibliography at the end of this paper contains 1,727 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1990 and 2001.

The platform economy reflects the business model of some of the largest and fastest-growing firms in the economy. Platform business models emerge and thrive because of the potential profit in taking advantage of transactions cost reductions to connect people for mutual benefit, and this value creation is best understood by thinking about the epistemology of decentralized market processes. Three essential aspects of knowledge are relevant to platform business models: (1) knowledge can be private and diffuse; (2) knowledge can be contextual; and (3) knowledge may not exist outside of the economic process. After defining and analyzing the technology, economic, and institutional aspects of platforms the author defines and applies market epistemology to explore how platforms harness technological and organizational features to create value-enhancing market platforms by exploiting the epistemic benefits of technology-enabled decentralized market processes. The author concludes by using this epistemic framework to propose an electricity distribution platform business model – the retail electricity industry is undergoing a process of technological dynamism, and as a regulated infrastructure industry, evolving into a decentralized market industry is presenting challenges to which this epistemic framework can bring increased understanding.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

The purpose of the paper is to find the best distributed generators (DGs) location for improving reliability and reducing power loss using distribution system reconfiguration. This is implemented in the presence of the tie-switches. It proposes a search-based algorithm for the reconfiguration problem. Individual DG placement is obtained for all system configurations, and analytical hierarchy process tool is used for finding the overall best location. This is carried out for various system loadings.

This paper proposes a knowledge-based search algorithm which needs the base conditions of the distribution system. A detailed analysis is carried out for finding the best DG locations from the obtained DG placements for various system configurations. Simulations are rigorously carried out with the help of programming. Results from these simulations are further given to analytical hierarchy tool for obtaining the DGs location.

The findings of the paper are the DG placement for various system loadings and various system configurations and to obtain the best DG location for any system configuration. A search-based algorithm is designed for accomplishing it.

The proposed method identifies the placement of distributed generation at distribution systems for reliability improvement and power loss reduction which is one of the present day needs for fulfilling the raising power consumers.

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.