Prosumer energy as a stimulator of micro- Smart Grids development - on the consumer side

Kucęba, Robert; Zawada, Marcin; Szajt, Marek; Kowalik, Jan

doi:10.1088/1755-1315/164/1/012003

Cited by 8 publications

(10 citation statements)

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“…In the first layer of Smart Grid one can find positioned sources of local electricity producers: distributed generation, small-scale RES, without neglecting the possibility to allocate in this layer prosumer micro-installations. In particular, indicating simultaneously empowered objects of Virtual Power Plant, the following can be allocated in the first layer of Smart Grid (Kuceba et al, 2018):…”

Section: Structure Of Vpp Embedded Into Layers Of Smart Gridmentioning

confidence: 99%

“…This is also an organisational networka set of integrated entities in the virtual management environment (Owoc and Sitarska, 2008). When discussing the issue of a virtual organisation in a purely subjective grasp, it should also be indicated that it also means inclusion of all or only selected persons from various organisations, and also economic entities, non-profit entities into a common market game (Kuceba et al, 2018;Kisielnicki, 2008). Obviously, it should be stressed that this is an organisation of a variable organisational structurethe structure that can be flexibly adjusted to the needs of the demand side (final customers).…”

Section: Introductionmentioning

confidence: 99%

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Dimensions and Factors that Determine Integration of Small-Scale Sources in the Structures of Virtual Power Plants

Kucęba

2022

Production Engineering Archives

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In the paper the author has attempted to achieve two convergent objectives: cognitive and empirical ones. The cognitive goal constituted an analysis of the definitions of virtual organi-sations and their adaptation while defining Virtual Power Plants (VPPs). When discussing the discourse in the area of virtual organisations, the author has attempted to justify the fact that the terminology pertaining to virtual organisations should constitute the foundations for defining Virtual Power Plants. With such an assumption, a vital importance has been assigned to co-sharing of “soft” resources – key competencies, and also organisational (managerial) integration. In the context of the adopted definitions, the distributed structure of virtual power plant has been em-bedded into four layers of Smart Grid: Customer Technology, Operational Technology, Smart Metering, Energy Management System. A measurable value of the conducted discourse has been aggregation of management functions of VPP, carried out in the four-layer structure of Smart Grid. In turn, the empirical objective was to determine and distinguish, based on the conducted expert research, the factors that determine the development of small-scale energy sector, including re-newable energy sources and prosumer installations – simultaneously determining the inclination of distributed electricity producers to mutual integration in the structures of virtual power plants. Assuming, in accordance with the definitions and discourse included in the first part of the paper, that the determined factors, among others, creating virtual power plants are not only of techno-logical nature, the author has developed four portfolios of these factors. They include the following ones: technological, economic (including micro- and macro-economic), environmental, and social. The experts participating in the research could select 5 factors from each of the developed portfolio which in their opinion determined the inclination of distributed electricity producers to integrate their sources in the structures of virtual power plants. A measurable value of the empirical part has been aggregating the determinants generated and distinguished in the research process.

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Section: Structure Of Vpp Embedded Into Layers Of Smart Gridmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dimensions and Factors that Determine Integration of Small-Scale Sources in the Structures of Virtual Power Plants

Kucęba

2022

Production Engineering Archives

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Section: Factors Stimulating Development and Competitiveness Of Projects And Implementations Of Photovoltaic Farmsmentioning

confidence: 99%

“…In the group of investigated enterprises, one can observe an interesting breakdown according to the activity classified in Polish Business Classification (PBC). Some of the enterprises do not limit their activity simply to one link of designing and constructing photovoltaic farms [23], which has been visualized in Figure 1. In reference to the primary subject matter of the paper as well as to the adopted research objective and problem, we summarized selected factors that stimulate the development and competitiveness of projects and implementations of photovoltaic farms.…”

Section: Factors Stimulating Development and Competitiveness Of Projects And Implementations Of Photovoltaic Farmsmentioning

confidence: 99%

Vital Factors Stimulating Development and Competitiveness of Design and Construction Enterprises of Photovoltaic Farms

Kucęba

Chmielarz

Soltysiak³

2021

Energies

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The underlying objective of the paper is to identify and evaluate in the research process vital factors that impact the development of design and construction enterprises of photovoltaic farms on the energy markets, in particular narrowed down to renewable energy sources (RES) markets, including energy coming from photovoltaic sources. The first part of the paper, the epistemological one, introduces a scientific discourse pertaining to conditionings and restrictions related to RES markets in the dimensions of sustainable development. This has been done with the use of literature query, legislative and administrative acts, and reports drawn up for the European Commission. In this reference, we summarized the EU’s essential measures aimed at promoting the idea of sustainability. The second part of the paper, the empirical one, presents a summary of the results obtained within the research pertaining to “opportunities and threats in managing projects of photovoltaic farms in production enterprises of energy appliances”. The research was conducted in a group of 24 design and construction enterprises of photovoltaic farms. The concentration of the research, in the context of the underlying objective of this paper, pertained to the aggregation and evaluation of selected factors that stimulate the development and competitiveness of construction projects and the implementation of photovoltaic farms. We considered the hierarchy of strategic objectives of the investigated enterprises and selected threats to their market position. Two primary criteria were adopted to aggregate and evaluate vital factors that impact the development of design and construction enterprises of PV farms. The first of them is the qualitative one—top scores, weighted averages of respondent evaluations on the five-point Likert scale. The second one is the quantitative criterion—number of indications N ≥ 22 of the respondents (over 90%). A measurable value of the paper is the scientific discourse pertaining to dimensions of competitiveness of the investigated and analyzed enterprises on the RES market, in particular narrowed down to photovoltaic farms.

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“…Congestion and reliability are the key concerns in the EMM presented in [3], [4]. An EMM with multiple layers, monitoring, communication, and smart metering was analyzed irrespective of the stochasticity of renewable energy sources [5]. A framework to enhance prosumer participation was deployed in [6] and [7] but the market for transactive environments poses challenges, such as finagling energy clearing prices and exchange of capital between prosumers; therefore, some decision restrictions are imposed on a prosumer operation that can degrade integrity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

et al. 2020

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The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

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Prosumer energy as a stimulator of micro- Smart Grids development - on the consumer side

Cited by 8 publications

References 0 publications

Dimensions and Factors that Determine Integration of Small-Scale Sources in the Structures of Virtual Power Plants

Dimensions and Factors that Determine Integration of Small-Scale Sources in the Structures of Virtual Power Plants

Vital Factors Stimulating Development and Competitiveness of Design and Construction Enterprises of Photovoltaic Farms

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

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